DETAILED ACTION
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 01/07/2026 has been entered.
Response to Arguments
On page 7 of the Remarks, Applicant contends the combination of Laroche ‘135 and Sato fails to teach or suggest using three reference samples. First, Applicant’s arguments against Laroche ‘135 and Sato individually, rather than what their combination would teach or suggest one of ordinary skill in the art is unpersuasive of error. MPEP 2145(IV). Applicant’s arguments against Laroche ‘135 do not fairly or accurately represent the teachings of Laroche and therefore are unpersuasive. For example, Applicant suggests Examiner merely asserted that any number of samples is contemplated, rather than address the Laroche ‘135 publication’s teachings that any number is contemplated. Applicant erroneously asserts Laroche ‘135 teaches the use of specific sets of reference samples limited only to those illustrated in Laroche ‘135’s Fig. 10 despite Laroche ‘135 explicitly explaining in at least paragraphs [0070] and [0225] that several other embodiments are contemplated including samples only along one of the top or left and “a different number of samples” other than 4 samples and representing increased diversity (i.e. spread out). Indeed, Laroche ‘135 exhaustively utilizes the term, “exemplary,” to go out of its way to explain the reference sample sets are not constrained to those depicted or described. Applicant addresses none of these teachings in any meaningful way in the Remarks and overlooks that the skilled artisan would consider Laroche ‘135’s teachings to teach or suggest other numbers of samples than the preferred embodiment’s number of 4, for example 3, as Applicant claims. In addition to the teachings of Laroche ‘135, which would have alone taught or suggested to the skilled artisan using 3 samples, Sato’s teachings in combination of Laroche ‘135’s further bolsters the finding of obviousness in view of the prior art. Sato explicitly teaches using a reduced number of reference samples (characterizing the reduction in terms of a ratio, which is not germane) and explicitly reducing the number of reference samples to three. Applicant’s Remarks seem to admit Sato’s Fig. 18A explicitly teaches 3 reference samples, but takes issue with the fact that the number of samples was decided based on the size of the block. Examiner is unclear why, in the opinion of Applicant’s representative, the size of the block being a consideration takes away from teaching an element in a claim. MPEP 2144(IV) explains that under an obviousness rejection it is permissible that a reference teach or “suggest what the inventor has done, but for a different purpose or to solve a different problem.” Therefore, in combination, Laroche ‘135 and Sato unequivocally teaches or suggests to one of ordinary skill using 3 reference samples wherein Laroche ‘135 explicitly explains the publication is not constrained to only picking 4 samples and explicitly explains other numbers of samples also qualify as alternative embodiments and Sato explicitly teaches Applicant’s 3 reference samples. One of ordinary skill in the art is not going to read the large number of prior art references drawn to cross-component prediction, see example after example of the use of 4 reference samples and think 3 would be non-obvious. For all the foregoing reasons, Examiner is unpersuaded of error.
On pages 8–9 of the Remarks, Applicant contends the combination of prior art used to reject claim 1 does not teach or suggest the three reference samples coming from a right most reference sample of a top line, a bottom most reference sample from a left column and a reference sample at the intersection of the top row and left column. As explained in the Response, supra, and the rejection, infra, the combination of Laroche ‘135 and Sato teach or suggest any number of samples are contemplated, including three reference samples. Given such explicit teachings, the skilled artisan is then left to decide which samples to include in the set of three. The most obvious scenario is the one Applicant claims, especially in view of the cited prior art. Laroche ‘135 teaches that the set of reference samples can benefit from being diverse and then depicts, like in Fig. 10, Elements 1006, 1009, and 1015, that the right most top row samples could represent good candidates, the bottommost left column samples could represent good candidates, and the top-left intersection point between the top neighboring row and left neighboring column could represent good candidates. Viewed differently, what other samples would the skilled artisan choose if limited to three samples other than those claimed by Applicant? As explained in the rejection, infra, Examiner interprets this limitation consistent with Applicant’s Fig. 12 and as an attempt to claim the pixel positions depicted therein. Because Ikai-1’s ¶ 0116 and Figs. 18 and 19 teach top, left, and above-left (intersection) samples used for linear prediction models and Sato’s col. 21, ll. 26–34 teaches the number of reference pixels and their locations (mapping) can be signaled to the decoder, those teachings, when combined with Laroche ‘135’s Fig. 10 illustrating examples of what Laroche means when teaching diversity of reference samples, would teach or suggest Applicant’s reference sample locations, especially when viewed in light of the teachings of Ikai’-1 regarding linear model intra prediction utilizing reference samples from top and left neighboring blocks like those identified in Ikai’s Fig. 12. See rejection, infra.
Other claims are not argued separately. Remarks, 9.
Claim Rejections - 35 USC § 112(d)
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claim 8 is rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends. Specifically, claim 8 appears to claim the same feature that is already present in independent claim 3 regarding the locations of the reference samples being the rightmost, bottommost, and intersection samples. Thus, Claim 8 appears to not further limit the subject matter of claim 3, the claim upon which it depends. Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
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 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 1–5, 7, 8, 10, 12, 13, and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Sato (US 9,153,040 B2), Laroche et al., “Non-CE3: On cross-component linear model simplification,” JVET-K0204-v3, 11th Meeting: Ljubljana, SI, July 2018 (herein “Laroche”), Laroche (US 2020/0288135 A1) (herein “Laroche ‘135”), and Ikai (US 2019/0327466 A1) (herein “Ikai-1”).
Examiner notes the rejections, infra, at times refer to LIC mode because Applicant’s claims shifted during prosecution between referencing terms applicable to CCLM mode and LIC mode.
Regarding claim 1, the combination of Sato, Laroche, Laroche ‘135, and Ikai-1 teaches or suggests a method, comprising: determining a CCLM intra prediction of a sample in a current block from at least one of neighboring samples in the current block and from a parametric model computed from neighboring samples of the current block (Laroche ‘135, ¶ 0139: teaches CCLM), wherein three reference samples are used for said prediction (Laroche ‘135, ¶¶ 0155, 0224, and 0232: teach single line per boundary reference samples and reduced samples per line, wherein it is preferable to have both top and left boundaries represented in the reference sample set and wherein it is envisaged that other variations are possible to desirably increase the diversity between sample sets; While Laroche ‘135, ¶¶ 0070 and 0225 suggests any number of samples are likely contemplated, Laroche does not explicitly teach using three reference samples; Sato, Fig. 18A and col. 14, ln. 64–col. 15, ln. 4: teaches using three reference pixels and controlling the number of reference pixels so that processing burden remains small; Sato, col. 21, ll. 26–34: teaches the number of reference pixels and their locations (mapping) can be signaled to the decoder), comprised of a rightmost reference sample from a top neighboring line of reference samples and from a bottommost reference sample from a left neighboring column of reference samples, and a reference sample from an intersection of the top neighboring line of reference samples and the left neighboring column of reference samples (Examiner interprets this limitation consistent with Applicant’s Fig. 12 and as an attempt to claim the pixel positions depicted therein; Ikai-1, ¶¶ 0116–0118 and Figs. 18 and 19: teach top, left, and above-left (intersection) samples used for linear prediction models; Laroche ‘135, Fig. 10, Elements 1006, 1009, and 1015: teach that the right most top row samples could represent good candidates, the bottommost left column samples could represent good candidates, and the top-left intersection point between the top neighboring row and left neighboring column could represent good candidates; Sato, col. 21, ll. 26–34: teaches the number of reference pixels and their locations (mapping) can be signaled to the decoder; In view of the teaching or suggestion of using 3 reference samples, as taught by the combination of Laroche ‘135 and Sato (see rejection and Response to Arguments, supra); the teachings of Laroche ‘135 would teach or suggest to the skilled artisan the use of outer reference samples in the top or left lines to add diversity to the reference samples and the use of the intersection sample to reduce complexity; Rhetorically, given the teaching of the use of 3 reference samples, what other possibility is more obvious than the one Applicant claims? Specifically, Laroche ‘135’s Fig. 10 illustrates examples of what Laroche means when teaching diversity of reference samples, such as those depicted in Fig. 10, Elements 1006, 1009, or 1015; Sato’s teachings bolster this finding by explaining the explicitly chosen locations of the reference samples can be transmitted to the decoder using a mapping such a teaching suggesting any number of possible locations is contemplated in the art such that specified locations would be necessary; Finally, the most prevalent reference locations for prediction are depicted and described in Ikai-1 such that skilled artisan would find Applicant’s locations obvious; Thus the combination of Laroche ‘135, Sato, and Ikai-1 teaches or suggests Applicant’s reference sample locations; see also Ikai-1, ¶¶ 0168–0174 and Fig. 12: teaching CCLM utilizing AL, AR, and BL), wherein CCLM linear parameters derivation comprise two comparisons to determine minimum and maximum luminance values from the three reference samples (Consistent with Applicant’s published paragraph [0091], Examiner interprets this feature as Applicant-Admitted Prior Art (AAPA) and equivalent to Laroche’s use of the minimum and maximum luma values as described in JVET-L0191; The same relevant teachings of Laroche’s JVET-L0191 cited by Applicant can likewise be found in Laroche’s JVET-K0204-v3; Laroche, Section 2 and Fig. 1: teaches the linear model parameters are derived from the reference sample luma minimum and maximum values), and wherein an index to a single lookup table implements a division for derivation of parameters of the parametric model (Laroche, Section 1: teaches the alpha and beta parameters are calculated using a division operation and explains, “the division operation is of course implemented thanks to a table, a multiplication and a shift.” (emphasis added)) based on a luma difference between the minimum and the maximum luminance values, the precision of the derived parameters is adaptable based on the luma difference (Laroche, Section 1: demonstrates in the equations for the alpha and beta parameters that the parameters are based on a luma difference (L(n) is luma); Laroche, Section 1: teaches the division operation is replaced by a multiplication and a shift wherein the precision is controlled by the particular calculation driven by the actual values and recognizing the shifts represents power-of-two division); and, encoding said sample in the current block based on said prediction (Laroche ‘135, ¶ 0094: teaches encoding using techniques such as CCLM; see Misra, cited under Conclusion Section of this Office Action, for an explanation on how LIC and CCLM are related).
One of ordinary skill in the art, before the effective filing date of the claimed invention, would have been motivated to combine the elements taught by Sato, with those of Laroche, because both references are drawn to the same field of endeavor such that one wishing to practice a state-of-the-art video coding tool like CCLM would be led to their relevant teachings, and because, as Laroche explains, the skilled artisan understands that computationally expensive division operations can “of course” be achieved using computationally cheaper indexing into a lookup table. This rationale applies to all combinations of Sato and Laroche used in this Office Action unless otherwise noted.
One of ordinary skill in the art, before the effective filing date of the claimed invention, would have been motivated to combine the elements taught by Sato and Laroche, with those of Laroche ‘135, because all three references are drawn to the same field of endeavor such that one wishing to practice a state-of-the-art video coding tool like CCLM would be led to their relevant teachings and because, as Laroche ‘135 and Sato explain, different numbers of reference samples can be configured to balance computational complexity with diversity and number of reference samples. This rationale applies to all combinations of Sato, Laroche, and Laroche ‘135 used in this Office Action unless otherwise noted.
One of ordinary skill in the art, before the effective filing date of the claimed invention, would have been motivated to combine the elements taught by Sato, Laroche, and Laroche ‘135, with those of Ikai-1, because all four references are drawn to the same field of endeavor such that one wishing to practice a state-of-the-art video coding tool like CCLM would be led to their relevant teachings and because, as Ikai-1 explains, the skilled artisan understands the farther in distance from a predicted sample the prediction is, the less relevant (less correlated) the sample typically is. In other words, the skilled artisan knows well in this art that the closest samples to the current sample are most often the closest match. Furthermore, Ikai-1 merely teaches what was already well-known in the art regarding the most prevalently selected reference sample locations. Therefore, combining Ikai-1’s teachings with those of the other prior art amounts to a mere combination of prior art elements, according to known methods, to yield a predictable result. This rationale applies to all combinations of Sato, Laroche, Laroche ‘135, and Ikai-1 used in this Office Action unless otherwise noted.
Claim 2 lists the same elements as claim 1, but is drawn to an apparatus rather than a method. Therefore, the rationale for the rejection of claim 1 applies to the instant claim.
Claim 3 lists the same elements as claim 1, but is drawn to the corresponding decoding method rather than the encoding method. Therefore, the rationale for the rejection of claim 1 applies to the instant claim.
Claim 4 lists the same elements as claim 1, but is drawn to the corresponding decoding apparatus rather than the encoding method. Therefore, the rationale for the rejection of claim 1 applies to the instant claim.
Regarding claim 5, the combination of Sato, Laroche, Laroche ‘135, and Ikai-1 teaches or suggests the method of claim 3, wherein said parametric model is derived from a linear model (Examiner notes at least Laroche also teaches CCLM is a linear model).
Regarding claim 7, the combination of Sato, Laroche, Laroche ‘135, and Ikai-1 teaches or suggests the method of claim 3, wherein said parameters of said parametric model are derived from at least two samples of neighboring samples that have a spatial distance constraint (Ikai-1, ¶ 0287: teaches for CCLM, a linear prediction method, a distance constraint for picking the left and/or top reference samples).
Regarding claim 8, the combination of Sato, Laroche, Laroche ‘135, and Ikai-1 teaches or suggests the method of claim 3, wherein said parameters of said parametric model are derived from at least three neighboring samples, wherein said three samples are located at rightmost top row of neighboring samples above the block, bottom of left column of neighboring samples, and at an intersection of top reference row and left reference column, respectively (Ikai-1, ¶ 0116 and Figs. 18 and 19: teach top, left, and above-left (intersection) samples used for linear prediction models).
Regarding claim 10, the combination of Sato, Laroche, Laroche ‘135, and Ikai-1 teaches or suggests the method of 3, wherein a derivation of parameters of said parametric model comprises a corrective parameter (Examiner notes that the offset parameter b (beta) is a corrective parameter; see e.g. Laroche, Section 1: teaching the scaling factor and offset (alpha and beta) for the linear model used to best fit (“least means square algorithm”) the relationship between predictor and predicted).
Claim 13 lists the same elements as claim 1, but is drawn to the product-by-process result of the claimed method. Therefore, the rationale for the rejection of claim 1 applies to the instant claim.
Claim 15 lists the same elements as claim 1, but is drawn to a CRM rather than a method. Therefore, the rationale for the rejection of claim 1 applies to the instant claim.
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Sato, Laroche, Laroche ‘135, Ikai-1, and Aono (US 2020/0177911 A1).
Regarding claim 9, the combination of Sato, Laroche, Ikai-1, and Aono teaches or suggests the method of claim 3, wherein a liner model-based prediction is used if linear parameter derivation is well defined, and an alternate mode is used otherwise (Aono, ¶¶ 0222 and 0227: teach using either CCLM or MMLM linear models depending on whether one or the other is better suited for the application; This teaching demonstrates the skilled artisan is aware that use of certain linear models which are not indicated by the circumstances should be turned off in favor of better alternatives).
One of ordinary skill in the art, before the effective filing date of the claimed invention, would have been motivated to combine the elements taught by Sato, Laroche, Laroche ‘135, and Ikai-1, with those of Aono, because all five references are drawn to the same field of endeavor such that one wishing to practice a state-of-the-art video coding tool like CCLM would be led to their relevant teachings and because, as Aono explains, the skilled artisan understands that use of certain linear models which are not indicated by the circumstances should be turned off in favor of a better alternative. This rationale applies to all combinations of Sato, Laroche, Laroche ‘135, Ikai-1, and Aono used in this Office Action unless otherwise noted.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Sato, Laroche, Laroche ‘135, Ikai-1, and Ikai (US 2020/0195970 A1) (herein Ikai-2).
Regarding claim 11, the combination of Sato, Laroche, Laroche ‘135, Ikai-1, and Ikai-2 teaches or suggests the method of 3, wherein a cross component linear model is enabled for predicting a chroma component of an intercoded block (Ikai-2, ¶ 0238: teaches combining CCLM with inter-prediction).
One of ordinary skill in the art, before the effective filing date of the claimed invention, would have been motivated to combine the elements taught by Sato, Laroche, Laroche ‘135, and Ikai-1, with those of Ikai-2, because all five references are drawn to the same field of endeavor such that one wishing to practice a state-of-the-art video coding tool like CCLM would be led to their relevant teachings and because, as Ikai-2 explains, the skilled artisan understands one can combine CCLM with inter-predicted neighbors. This rationale applies to all combinations of Sato, Laroche, Laroche ‘135, Ikai-1, and Ikai-2 used in this Office Action unless otherwise noted.
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Sato, Laroche, Laroche ‘135, Ikai-1, and Zhang (US 2018/0077426 A1).
Regarding claim 12, the combination of Sato, Laroche, Laroche ‘135, Ikai-1, and Zhang teaches or suggests a device comprising: an apparatus according to claim 4; and at least one of (i) an antenna configured to receive a signal, the signal including the video block, (ii) a band limiter configured to limit the received signal to a band of frequencies that includes the video block, and (iii) a display configured to display an output representative of a video block (Zhang, ¶¶ 0003, 0047, and 0048: teaches the encoder can be attached to a set-top box or display device utilizing wired or wireless communication).
One of ordinary skill in the art, before the effective filing date of the claimed invention, would have been motivated to combine the elements taught by Sato, Laroche, Laroche ‘135, and Ikai-1, with those of Zhang, because all five references are drawn to the same field of endeavor such that one wishing to practice a state-of-the-art video coding tool like CCLM would be led to their relevant teachings and because Zhang teaches it is obvious to use available reference samples for CCLM and because Zhang is merely relied upon for teaching that video decoding technology is useful for set-top boxes and display devices utilizing either wired or wireless communication. This rationale applies to all combinations of Sato, Laroche, Laroche ‘135, Ikai-1, and Zhang used in this Office Action unless otherwise noted.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Laroche et al., “CE3-5.1: On cross-component linear model simplification,” JVET-L0191, 12th Meeting: Macao, NC Oct. 2018. Section 2 teaches using 2-point (min and max) to derive linear model parameters.
Misra (US 2021/0092372 A1) teaches, for prediction techniques using linear models such as CCLM and LIC, using reference samples that are a power of 2 so that when computing averages it can be accomplished using simple bit-shift operations rather than computationally expensive division operations, which can be accomplished with ease when the blocks are square blocks (¶ 0221).
Aono (US 2021/0136407 A1) teaches that a number of reference samples can be 4 (see the threshold) that are located at the upper and left sides of a square block (¶ 0235) but not in a rectangular slice.
Zhang (US 10,334,248 B2) teaches the benefits of using lookup tables for parameters is the reduction in computational load, which is beneficial for certain low-power devices (Zhang, col. 15, ll. 44–46).
Hu (US 2018/0063531 A1). Examiner notes Hu was used for certain dependent claims to teach basic features in the art and was also used importantly for teaching aspects of LIC. During prosecution Applicant’s claims straddled between claiming CCLM and LIC but later focused on CCLM, getting rid of references to LIC features. Therefore, Hu became less necessary in the rejections over time. According to Applicant’s published para. [0047], the claimed prediction process using a parametric model refers to prediction modes such as CCLM and LIC. According to Applicant’s published para. [0074], the claimed use of reference samples from a reference frame appears to be referring to extending CCLM to mixed intra-inter coded blocks. However, the claim merely requires one of the prediction techniques, which could simply mean CCLM or LIC wherein conventional CCLM mode uses neighboring samples in the current block and conventional LIC uses reference samples in a reference frame. Hu, ¶ 0052: explains LIC uses neighboring samples of the current block and corresponding pixels in the reference picture; Hu, ¶ 0012: teaches encoding using techniques such as LIC and CCLM. One of ordinary skill in the art, before the effective filing date of the claimed invention, would have been motivated to combine the elements taught by Hu, with those of Lee, because both references are drawn to the same field of endeavor such that one endeavoring to implement the state-of-the-art CCLM technique or LIC technique would have been led to their teachings and because Lee is merely describing the current state of the art in terms of reference sample selection from the top and left neighboring rows for linear parameter derivations such that the combination with Hu represents a mere combination of prior art elements, according to known methods, to yield a predictable result. Hu, ¶ 0050: teaches LIC is based on a linear model. Hu, ¶¶ 0023–0024: teaches the encoder can be attached to a set-top box or display device utilizing wired or wireless communication.
Lee et al., “Intra Prediction Mode Dependent Reference Sample Selection Method for Cross-Component Linear Model,” Proceedings of the Korean Society of Broadcast Engineers Conference, June 20, 2018 (herein “Lee”). This publication was cited for a previous version of claim 1. See prosecution history.
Panusopone (US 2018/0288425 A1) teaches reference samples starting at the R(0,0) position and LIC mode (e.g. ¶ 0051 and Fig. 7C).
Laroche (US 2020/0288135 A1) teaches much of what other Laroche publications teach regarding CCLM as well as many approaches to defining the reference sample set including using outer neighboring samples (e.g. Fig. 10 and Abstract).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Michael J Hess whose telephone number is (571)270-7933. The examiner can normally be reached on Mon - Fri 9:00am-5:30pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, William Vaughn can be reached on (571)272-3922. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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MICHAEL J. HESS
Primary Examiner
Art Unit 2481
/MICHAEL J HESS/Primary Examiner, Art Unit 2481