DETAILED ACTION
This action is responsive to the Amendments and Remarks received 06/03/2026 in which claims 6, 8, and 14 are cancelled, claims 1–4, 7, 9, and 10 are amended, and no claims are added as new claims.
Response to Arguments
Examiner incorporates herein previous Responses to Arguments.
The rejection of claim 8 under 35 U.S.C. 112(d) is obviated in view of its cancellation. Remarks, 6.
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 ‘135, nor the totality of the rationale for the rejection in view of its teachings, and therefore are unpersuasive. For example, Applicant suggests Examiner merely asserted that any number of samples is contemplated and otherwise concluded Laroche ‘135 does not teach the claimed 3 reference samples, rather than address the Laroche ‘135 publication’s teachings that any number is contemplated and that spatial diversity is a desirable feature for selecting appropriate reference samples. Applicant’s arguments seem to overlook Laroche ‘135 explicitly explains in at least paragraphs [0070] and [0225] that several other embodiments, in addition to those described in Fig. 10, 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 chosen reference samples 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 claimed by Applicant. In addition to the teachings of Laroche ‘135, which could reasonably be viewed as sufficient alone to teach or suggest to the skilled artisan using 3 samples, Sato’s teachings in combination with 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. A previous version of Applicant’s Remarks seemed to admit Sato’s Fig. 18A explicitly teaches 3 reference samples. It seems Applicant believes that, because Sato describes the number of samples can be decided based on the size of the block, the narrower teaching somehow fails to teach the broader feature of simply choosing 3. It is axiomatic that narrower teachings necessarily teach broader claim recitations. For example, a teaching of washing your car after it rains teaches the broader feature of washing your car. Rain is not a requirement and a reasonable definition of the skilled artisan would not interpret it as such. 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. Indeed, the claim may also be used according to block size (“comprises” language in a claim means it is open ended so as to include additional elements not recited). In addition, 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, especially when considering diversity, 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 (a.k.a. CCP or CCLM), see example after example of the use of 4 reference samples and conclude 3 would somehow be unworkable or otherwise non-obvious. To suggest otherwise is to unreasonably diminish the level of the skilled artisan in this field. Finally, it is noted that Applicant’s arguments are silent regarding the features taught in Ikai-1, which was also cited, in combination with the other references, for teaching or suggesting, in part, the averred feature. Ikai-1’s paragraphs [0168]–[0174] and Fig. 12 teach causal-neighborhood locations including AL (above-left), AR (above-right), and BL (below-left) as desirable reference locations such that, in combination with the other cited art teaching diversity and fewer total reference samples, teaches or suggests just using the positions of AL (above-left), AR (above-right), and BL (below-left) to achieve higher diversity and fewer total reference samples. For all the foregoing reasons, Examiner is unpersuaded of error.
On page 8 of the Remarks, Applicant contends Laroche requires 128 comparisons rather than Applicant’s claimed 2 comparisons. This argument is unreasonable. Anyone skilled in the art knows that finding the maximum or minimum of a set of three numbers requires 2 comparisons. The code is one of the most basic coding algorithms for any coder. Referencing the code examples provided, infra, notice the number of comparisons is (n - 1), chosen to be coded as a FOR loop (either starting at 2 and going through N; or starting at 1 and only progressing until i no longer satisfies the constraint that it be less than the length of the array). In other words, min and max inherently require (n - 1) comparisons. While much larger data sets can benefit from other algorithms, such as divide and conquer, Applicant’s argument asserts the most basic and fundamental of coding algorithms known to computer programmers. As Laroche ‘650 (cited under the Conclusion Section of this Office Action) explains in paragraph [0166], Laroche’s teaching of approximately 2N comparisons was calculated assuming a sorting algorithm, which is not necessary to find min or max. There is no need to sort the values to determine a minimum and maximum when a simpler pairwise comparison is a known basic algorithm in the art. As demonstrated, infra, the skilled artisan knows that one may find a min and/or max by simply stepping through an array executing pairwise comparisons to achieve n - 1 comparisons. That is, the sorting algorithm described in Laroche is unnecessary for finding min and max.
[ N numbers are stored in array X ]
M1 = X(1)
M2 = M1
FOR I = 2 TO N
IF X(I) < M1 THEN M1 = X(I)
IF X(I) > M2 THEN M2 = X(I)
NEXT I
PRINT “Minimum = “;M1
PRINT “Maximum = “;M2
END
OR
// precondition: arr.length > 0
public static int findMax(int[] arr)
{
int max = arr[0];
for(int i = 1; i < arr.length; i++)
if(arr[i] > max)
max = arr[i];
return max;
}
On page 8 of the Remarks, Applicant contends the claim requires adaptive precision based on the luma value and that the teachings of Laroche are insufficient. First, it is noted that Examiner interprets the averred subject matter to be that described by Applicant’s earliest priority date and the foreign filing’s pages 16–17, which describe an embodiment 4a. Notably, there is a discussion of a “precision” corresponding to the power-of-two division executed as a right-shift described in embodiment 4a. This “precision” is mathematically dictated by the value of the divisor and the approximation to a power-of-two (base-2) reciprocal. More on this, infra. Second, it is noted Applicant’s averred limitation also appears taught by the teachings of Laroche ‘650 (cited under the Conclusion Section of this Office Action) at para. [0168]–[0220], wherein it is explained that table size and content can be adapted to the necessary precision and that accuracy of the division is represented by the variable “k” (e.g. ¶¶ 0188–0189). Setting aside the teachings of Laroche ‘650, Applicant’s arguments are unreasonable. The skilled artisan knows that computers (compilers) replace computationally expensive division operations with adaptable precision table look ups, multiplications, and right shifts. Specifically, the skilled artisan knows the “magic number” (i.e. reciprocal of the divisor) that represents the multiplication can be pre-determined and stored in a lookup table for all possible divisors. The magic number is some base-2 fraction used as an approximation of and to replace the divisor wherein the numerator acts as the multiplication and the denominator is a power of 2 that is executed by bit-wise shifting. The “mathematical characteristics” of the divisor determines the precision necessary to achieve the division. Because Laroche, used in the rejection, explains the divisor is the difference between the luma_max and luma_min for the slope calculation (alpha), which is fed into the calculation for the offset calculation (beta), and because Laroche’s multiplication and shift (numerator/denominator) is the calculated approximation of the reciprocal of the divisor, the skilled artisan understands that Laroche’s explanation that, “of course the division is avoided and replaced by a multiplication and a shift” teaches the averred feature. By already meeting Examiner’s burden to demonstrate by a preponderance of the evidence that the claimed subject matter was already in possession of the skilled artisan in view of the teachings of the prior art and level of skill in the art, it should be unnecessary to further elaborate on how compilers achieve divisions of adaptable precision based on multiplication and shifts. Should more evidence be necessary than that already provided in the rejections under 35 U.S.C. 103, infra, then evidence supporting Examiner’s conclusions and findings of fact may be considered supplemented by the teachings of Laroche ‘650. Examiner notes the following helpful example: a divide by 3 for integer division can be approximated by (11/32), wherein it is noted the denominator is a power of 2 that can be executed using right shifts. So, dividing 14 by 3, would yield 4 (rounded down to nearest integer for integer math) if one were to go through the binary (1110 (i.e. 14) x 1011 (i.e. 11)) yields 1001 1010, then right shift 5 (corresponding to divide by 32) yields 100 (i.e. 4). It works. Applicant didn’t invent this. Any computer science major knows how a computer can achieve division this way. Accordingly, this feature is not differentiable over the prior art as averred.
Related to the argument above, Applicant contends Laroche’s teaching that shift S always has the same value is contrary to the claimed feature. Examiner disagrees. Laroche is describing the calculation of the predicted chroma value based on the linear model parameters when describing the shift S. This teaching is after Laroche’s teaching of how the model parameters of alpha and beta are calculated. Regarding the division operation for the alpha parameter, which is before calculating the predicted chroma component using the alpha parameter, Laroche explains the division is performed using a table, multiplication, and a shift. In other words, Applicant mischaracterizes Laroche’s teachings by referencing a later teaching of Laroche that is separate from the calculation of the alpha parameter itself. Furthermore, Laroche does not say precision must be configured to be static and non-adaptable. Examiner desires Applicant’s assistance in identifying where in the priority documents the features of adaptable precision can be found.
On pages 8–9 of the Remarks, Applicant contends the use of the teachings of Ikai-1 represents “a technical mischaracterization.” Examiner disagrees because Applicant’s arguments are treating the references individually rather than what the combination would teach or suggest to one of ordinary skill in the art. The explanation of how the combination of prior art, including the teachings of Ikai-1 are viewed to teach or suggest Applicant’s claimed features is explained, supra. Specifically, Ikai-1 demonstrates graphically how the skilled artisan would interpret a desire for diversity of reference sample positions within a causal neighborhood of a chroma block. The fact that Ikai-1 may utilize those positions for a difference purpose, i.e. to derive most-probable mode of intra prediction (i.e. MPM candidates), does not take away from the teaching that the skilled artisan would have known that these locations would be the most popular representation of diverse neighboring reference locations.
On page 9 of the Remarks, Applicant contends claim 7 “requires that the reference samples themselves are chosen to be spatially distant from one another to ensure representative boundary sampling for straight-line parameter calculation.” Examiner finds Applicant does not argue that which is claimed and that the teachings of Ikai-1 are sufficient under BRI. Furthermore, to the extent Applicant’s description in the arguments is accurate regarding what is meant by the spatial distant constraint, the other prior art of record regarding “diversity” of reference sample position would teach or suggest the averred feature. See, supra. Because Applicant does not argue that which is claimed and because the prior art’s teaching of diversity of reference samples teaches Applicant’s more narrowly argued feature, Examiner is unpersuaded of error.
On page 10 of the Remarks, Applicant contends claim 9 requires checking whether linear parameter derivation is mathematically stable before deciding to use a simplified model. Examiner finds Applicant does not argue that which is claimed and further finds Applicant’s arguments do not appear to particularly address the rationale for the rejection of claim 9 in view of the further teachings of Aono. Therefore, Applicant’s argument is unpersuasive of error.
On page 10 of the Remarks, Applicant contends claim 10 requires an additional dedicated term introduced into the formula or LUT to adjust the calculation of the scaling parameter to compensate for noise or division instability. Examiner finds Applicant does not argue that which is claimed and that Applicant’s arguments seem to assert that features from the Specification should be read into the claims contrary to the requirement that the claims be read with the broadest reasonable interpretation. Furthermore, to the extent Applicant’s description in the arguments is accurate regarding what is meant by the claimed features, Examiner notes Laroche ‘650 likely teaches those features commensurate with Applicant’s description of those features in the earliest priority document. Examiner requests here, and supra, Applicant’s assistance in identifying where in the earliest priority document support for the amendments can be found. Because Applicant does not argue that which is claimed and because a broadest reasonable interpretation is appropriate during prosecution, Examiner is unpersuaded of error.
Other claims are not argued separately. Remarks, 10.
Claim Rejections - 35 USC § 112(a)
The following is a quotation of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
Claim 10 is rejected under 35 U.S.C. 112(a) as failing to comply with the written description requirement. This is a new matter rejection. The amendment to claim 10 has not been shown to be supported by the earliest priority document. Examiner requests Applicant’s assistance in identifying the portion of Applicant’s earliest priority document Applicant believes supports the amendment to claim 10. “New or amended claims which introduce elements or limitations which are not supported by the as-filed disclosure violate the written description requirements.” MPEP 2163(I)(B). “[W]ith respect to newly added or amended claims, applicant should show support in the original disclosure for the new or amended claims.” MPEP 2163(II)(A). See also MPEP 2163(II)(A)(3)(b). Applicant’s assistance is kindly solicited.
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, 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. See Misra, cited under Conclusion Section of this Office Action, for an explanation on how LIC and CCLM are related.
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 among sample whilst keeping the total number of samples small; 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 described and claimed by Applicant? 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 the finding that the selected reference samples are obvious 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 causal-neighborhood locations including AL (above-left), AR (above-right), and BL (below-left)), wherein CCLM linear parameters derivation comprises 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 (scale and offset) that the parameters are based on a luma difference (L(n) is luma); Laroche, Section 1: teaches the division operation is replaced by a table, 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; To explain further, because Applicant’s arguments in the prosecution history seem to demonstrate confusion, the “magic number” (i.e. reciprocal of the divisor) that represents the multiplication is pre-determined and stored in a lookup table for all possible divisors; The magic number is some base-2 fraction used as an approximation of and to replace the divisor wherein the numerator acts as the multiplication and the denominator is a power of 2 that is executed by bit-wise shifting; The “mathematical characteristics” of the divisor determines the precision necessary to achieve the division; Because Laroche explains the divisor is the difference between the luma_max and luma_min for the slope calculation (alpha), which is fed into the calculation for the offset calculation (beta), and because Laroche’s multiplication and shift (numerator/denominator) is the calculated approximation of the reciprocal of the divisor, the skilled artisan understands that Laroche’s explanation that, “of course the division is avoided and replaced by a multiplication and a shift” teaches this claimed feature; It should be unnecessary to further explain how compilers achieve divisions of adaptable precision based multiplication and shifts); 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 reference samples of neighboring samples of the current block that have a spatial distance constraint (Ikai-1, ¶ 0287: teaches for CCLM, i.e. a linear prediction method, a distance constraint for picking the left and/or top reference samples).
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, the corrective parameter is used to correct the scaling parameter of the parametric model (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 linear model-based prediction is used if linear parameter derivation is well defined, and an alternate simplified model is used otherwise (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; 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).
Laroche (US 2020/0389650 A1) explains that table size and content can be adapted to the necessary precision (see ¶¶ 0168–0220) and that accuracy of the division is represented by the variable “k” (e.g. ¶¶ 0188–0189).
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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 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 571-273-8933.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/MICHAEL J HESS/Examiner, Art Unit 2481
Prosecution Insights