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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Response to Amendment
Applicant filed a Preliminary Amendment on February 14th, 2025.
Applicant amended the Drawings, Abstract, and Specification which are considered. Objections are made to the clean copy of the Specification amended filed.
Applicant amended claims 1 – 16.
The pending clams are 1 – 16.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on March 14th, 2025 was filed before the mailing date of the First Action on the Merits (this Office Action). The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the Examiner.
The listing of references in the specification is not a proper information disclosure statement. 37 CFR 1.98(b) requires a list of all patents, publications, or other information submitted for consideration by the Office, and MPEP § 609.04(a) states, "the list may not be incorporated into the specification but must be submitted in a separate paper." Therefore, unless the references have been cited by the examiner on form PTO-892, they have not been considered.
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they include the following reference character(s) not mentioned in the description: “1030” [on Page 15], “S1310”, “S1320”, “S1330”, and “S1340” [Figure 13]; and “2210” [on Page 15].
Corrected drawing sheets in compliance with 37 CFR 1.121(d), or amendment to the specification to add the reference character(s) in the description in compliance with 37 CFR 1.121(b) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
The disclosure is objected to because of the following informalities:
On Page 15 line 27, reference character “1130” should read as --1030-- for clarity and consistency with Figure 10.
Appropriate correction is required.
Claim Interpretation – Functional Analysis
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that use the word “means” or “step” or a generic placeholder but are nonetheless not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph because the claim limitation(s) recite(s) sufficient structure, materials, or acts to entirely perform the recited function.
Such claim limitation(s) is/are: “processor configured to …” in claim 15.
The Examiner affords the claimed “processor” connotes sufficient structure to one of ordinary skill in the art. While the “processor” is the only structure claimed, it is not functionally claimed thus single-means analysis and related Rejection is moot.
Because this/these claim limitation(s) is/are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are not being interpreted to cover only the corresponding structure, material, or acts described in the specification as performing the claimed function, and equivalents thereof.
If applicant intends to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to remove the structure, materials, or acts that performs the claimed function; or (2) present a sufficient showing that the claim limitation(s) does/do not recite sufficient structure, materials, or acts to perform the claimed function.
Claim Rejections - 35 USC § 112
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.
Claims 1 – 16 are 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 applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claim 1, the claimed “selecting” limitation has Indefinite metes and bounds as circularly defining the selection of points while being a subset form the second set of points as being a function of the subset of selected points even though the selection has not been made clearly or a final selection made.
Regarding claims 14 and 15, see claim 1 which is the method implemented by the claimed program and apparatus, respectively, and thus similarly Rejected.
Regarding claims 2 – 13 and 16, the dependent claims do not cure the deficiencies of their respective independent claims and thus are similarly Rejected.
Claim 4 recites the limitation "a quality of a representation" in lines 4 and 6. There is insufficient antecedent basis for this limitation in the claim. Additionally the claim has Indefinite metes and bounds regarding which conditions need to be met (e.g. one or both) and should corrections / processing occur if indeed the conditions are possible to meet.
Claim 8 recites the limitation "the value range" in line 4. There is insufficient antecedent basis for this limitation in the claim.
Claim 8 recites the limitation "the predicted qualities" in line 4. There is insufficient antecedent basis for this limitation in the claim.
Regarding claim 8, the claimed “monotonicity conditions” has Indefinite metes and bounds in view of the Specification as the term has not definition or example or description to properly afford one of ordinary skill in the art of the scope of the claimed subject matter.
Claim 9 recites the limitation "a bit rate" in lines 6 and 8. There is insufficient antecedent basis for this limitation in the claim.
Claim 9 recites the limitation "the associated predicted quality" in line 8. There is insufficient antecedent basis for this limitation in the claim.
Claim 11 recites the limitation "a representation" in line 11. There is insufficient antecedent basis for this limitation in the claim.
Regarding claim 12, the claimed “comprising the representation” in the first “if” conditional of the claim has Indefinite metes and bounds as to inclusion of the point / representation in the claimed bit rate ladder.
Claims 13 and 16 recites the limitation "a representation" in line 5 (claim 13) or line 6 (Claim 16). There is insufficient antecedent basis for this limitation in the claim.
Claim limitations
“method for …” [Claim 1]; and
“device for …” [Claim 15]
have been evaluated under the three-prong test set forth in MPEP § 2181, subsection I, but the result is inconclusive. Thus, it is unclear whether this limitation should be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because regarding claim 1, the “for” invocation is similar to a “step for” invocation of Functional Analysis and additionally the preamble is ordinarily not afforded patentable weight (as an intended use may be recited) thus the preamble in claim 1 has Indefinite metes and bounds regarding weight and invocation of Functional Analysis; Regarding claim 15, the “device” if indeed intended to be functionally claimed would be a single-means claim (only one structure performing the function) and not a combination of elements as 112(f) Functional Analysis requires and additionally the preamble is ordinarily not afforded patentable weight (as an intended use may be recited) thus the preamble in claim 1 has Indefinite metes and bounds regarding weight and invocation of Functional Analysis. The boundaries of this claim limitation are ambiguous; therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
In response to this rejection, applicant must clarify whether this limitation should be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. Mere assertion regarding applicant’s intent to invoke or not invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph is insufficient. Applicant may:
(a) Amend the claim to clearly invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, by reciting “means” or a generic placeholder for means, or by reciting “step.” The “means,” generic placeholder, or “step” must be modified by functional language, and must not be modified by sufficient structure, material, or acts for performing the claimed function;
(b) Present a sufficient showing that 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, should apply because the claim limitation recites a function to be performed and does not recite sufficient structure, material, or acts to perform that function;
(c) Amend the claim to clearly avoid invoking 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, by deleting the function or by reciting sufficient structure, material or acts to perform the recited function; or
(d) Present a sufficient showing that 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, does not apply because the limitation does not recite a function or does recite a function along with sufficient structure, material or acts to perform that function.
Regarding claims 2 – 13 and 16, the dependent claims do not cure the deficiencies of their respective independent claims and thus are similarly Rejected.
For purposes of Examination (e.g. 102 / 103 Rejections), the preambles are not being afforded patentable weight.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
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) 1 – 7 and 9 – 16 are rejected under 35 U.S.C. 103 as being unpatentable over Chadwick, et al. (US PG PUB 2022/0030229 A1 referred to as “Chadwick” throughout) [Cited in Applicant’s March 14th, 2025 IDS], and further in view of Lin, et al (US Patent #11,871,061 B1 referred to as “Lin” throughout) and Reznik, et al (US PG PUB 2018/0160161 A1 referred to as “Reznik” throughout).
Regarding claim 1, see claim 15 which is the apparatus performing the steps of the claimed method.
Regarding claim 14, see claim 15 which is the apparatus implementing the steps of the claimed program.
Regarding claim 15, Chadwick teaches forming bit ladders including using neural networks to form the convex hull / surface for selection of the bit ladder. Lin teaches selections of points to form the bit ladder and selection criteria in levels / granularity of the points used. Reznik teaches having a monotonic / monic condition and interpolation among points / data in forming a bit rate ladder for encoding / decoding.
It would have been obvious to one of ordinary skill art before the effective filing date of the claimed invention to modify the teachings of Chadwick to including point selection techniques as taught by Lin and to enforce criteria / requirements in forming the convex hull for the ladder; and Reznik to further include conditions on forming the hull / bit rate ladder (e.g. monotonic / monic conditions) and interpolating between levels in the ladder. The combination teaches
at least one processor configured to [Chadwick Paragraphs 95 (processor based implementation of forming bit rate ladder of previously disclosed methods)]:
determining a first set of reference points, wherein a reference point indicates a quality of a representation based on a bit rate and a resolution and the quality is based on a comparison with an original representation [Chadwick Figures 1 – 3 (see at least reference characters S102, S112, and S120) as well as Paragraphs 15 (various quality metrics including VMAF), 38 – 40 and 46 – 52 (initial / first resolution and bitrate for the current frames / selected frames compared to an initial / training set), 70 – 76 (initial / first points with bitrate / quality and resolution)];
create a second set of reference points based on the first set of reference points [See next limitation additionally and Chadwick Figures 1 – 4 (see at least reference characters S120, S130, and S170) as well as Paragraphs 38 (second input video to generate second / additional ladder information), 40 – 43 and 48 – 52 (subsets of prior points or additional points generated / studied / used in forming the bit rate ladder based on prior bitrate / quality determinations to generate the convex hull and interpolation between previous points based on training video, VMAF, and audience considerations rendering obvious more points than the first number of points)], wherein the second set comprises more reference points than the first set [See previous citation and additionally Lin Figures 5 – 8 and 11 – 13 (see at least the multiplex stages (Figures 6 – 7) and the grid in Figure 5 (more points / points for hull formation)) as well as Column 5 lines 24 – 59 (refined sets of data used – more points used for mesh / convex hull generate to select the ladder) and Column 14 lines 5 – 20 (more intermediate points generated for the convex hull than the initial points / points initially used)],
select a subset of reference points of the second set while taking quality requirements into account for creating the bit rate ladder based on the subset of reference points [Chadwick Figures 1 – 4 (final ladder determination) as well as Paragraphs 39 – 48 (subset of points / values tested to generate points for the ladder using bitrate-resolution pairs which had the best quality – to combine with Reznik and obvious to optimize in view of Lin Figures 5 – 8 (see at least reference characters 706 and 708) as well as Column 4 line 26 – Column 5 line 10 (obvious to optimize) and Column 13 line 33 – Column 14 line 20 (quality considered for the resolution / bit rate of the current condition))), 58, 63, and 78 (subset of computed points selected for ABR ladder) and 48 – 52 (subsets of prior points used in forming the bit rate ladder based on prior bitrate / quality determinations to generate the convex hull (e.g. Lin Figure 5) based on quality considerations such as VMAF or audience considerations); Reznik Figures 3 and 7 – 8 as well as Paragraphs 88 – 94 (selection of probe encodings to form the ladder based on quality / bitrate considerations at resolutions), 160 – 164 (ladder formation) and 174].
The motivation to combine Lin with Chadwick is to combine features in the same / related field of invention of generating ABR (adaptive bit rate ladders) [Lin Column 1 lines 40 – 56] in order to improve coding performance by creating more dynamic ladders / making content based adjustments [Lin Column 4 lines 22 – 58 where the Examiner observes at least KSR Rationales (D) or (F) are also applicable].
The motivation to combine Reznik with Lin and Chadwick is to combine features in the same / related field of invention of generating ABR ladders [Reznik Paragraphs 2 – 4] in order to improve dynamic performance to change the ladders and more efficient / closer to optimal encoding [Reznik Paragraphs 5 – 7 where the Examiner observes KSR Rationales (D) or (F) are also applicable].
This is the motivation to combine Chadwick, Lin, and Reznik which will be used throughout the Rejection.
Regarding claim 16, Chadwick teaches forming bit ladders including using neural networks to form the convex hull / surface for selection of the bit ladder. Lin teaches selections of points to form the bit ladder and selection criteria in levels / granularity of the points used. Reznik teaches having a monotonic / monic condition and interpolation among points / data in forming a bit rate ladder for encoding / decoding.
It would have been obvious to one of ordinary skill art before the effective filing date of the claimed invention to modify the teachings of Chadwick to including point selection techniques as taught by Lin and to enforce criteria / requirements in forming the convex hull for the ladder; and Reznik to further include conditions on forming the hull / bit rate ladder (e.g. monotonic / monic conditions) and interpolating between levels in the ladder. The combination teaches
create for each of the quality levels of the bit rate ladder, a representation comprising encoding the video section according to the respective quality level [Chadwick Figures 1 – 4 (see the ladder in Figure 4 formed by the points / selections in Figures 2 – 3) as well as Paragraphs 23, 48 – 52 and 56 – 60 (the encoded frame / rendition (obvious variants of the claimed “representation” claimed to one of ordinary skill in the art represents the output for the conditions in the ladder), and 79 – 83 (specific ladder using based on desire quality, resolution, and bitrate available)].
See claim 15 for the motivation to combine Chadwick, Lin, and Reznik.
Regarding claim 2, Chadwick teaches forming bit ladders including using neural networks to form the convex hull / surface for selection of the bit ladder. Lin teaches selections of points to form the bit ladder and selection criteria in levels / granularity of the points used. Reznik teaches having a monotonic / monic condition and interpolation among points / data in forming a bit rate ladder for encoding / decoding.
It would have been obvious to one of ordinary skill art before the effective filing date of the claimed invention to modify the teachings of Chadwick to including point selection techniques as taught by Lin and to enforce criteria / requirements in forming the convex hull for the ladder; and Reznik to further include conditions on forming the hull / bit rate ladder (e.g. monotonic / monic conditions) and interpolating between levels in the ladder. The combination teaches
selecting a first grid of pairs of values in a bit rate resolution space, and determining qualities of representations at the pairs of values of the first grid to obtain a first set of reference points [Chadwick Figures 1 – 3 (see at least reference characters S102, S112, and S120) as well as Paragraphs 15 (various quality metrics including VMAF), 38 – 40 and 46 – 52 (initial / first resolution and bitrate for the current frames / selected frames compared to an initial / training set), 70 – 76 (initial / first points with bitrate / quality and resolution)].
See claim 1 for the motivation to combine Chadwick, Lin, and Reznik.
Regarding claim 3, Chadwick teaches forming bit ladders including using neural networks to form the convex hull / surface for selection of the bit ladder. Lin teaches selections of points to form the bit ladder and selection criteria in levels / granularity of the points used. Reznik teaches having a monotonic / monic condition and interpolation among points / data in forming a bit rate ladder for encoding / decoding.
It would have been obvious to one of ordinary skill art before the effective filing date of the claimed invention to modify the teachings of Chadwick to including point selection techniques as taught by Lin and to enforce criteria / requirements in forming the convex hull for the ladder; and Reznik to further include conditions on forming the hull / bit rate ladder (e.g. monotonic / monic conditions) and interpolating between levels in the ladder. The combination teaches
the first grid comprises at least the predetermined pairs of values
maximum bit rate, maximum resolution, [Chadwick Figures 2 – 4 as well as Paragraphs 50, 60, and 66 (maximum bitrate and maximum resolution to process determined or alternatively for a given maximum bitrate determining maximum resolution supported)] and
minimum bit rate, minimum resolution [Chadwick Figures 2 – 4 as well as Paragraphs 68 – 73 (minimum bitrate and resolution pairs in the ladder)],
wherein
the minimum bit rate for the minimum resolution is determined taking quality requirements into account [Chadwick Figures 2 – 4 (see at least reference character S140) as well as Paragraphs 50 and 66 – 76 (quality considered in determination of the minimum / bottom bitrate-resolution pair where Paragraph 15 suggests VMAF as the quality metric (shown in the Figures as well) with viewer considerations in Paragraphs 74 – 76)],
the maximum bit rate for the maximum resolution is determined taking quality requirements into account [Chadwick Figures 2 – 4 (see at least reference character S140) as well as Paragraphs 50 and 66 – 76 (quality considered in determination of the maximum / top bitrate-resolution pair where Paragraph 15 suggests VMAF as the quality metric (shown in the Figures as well) with viewer considerations in Paragraphs 74 – 76)],
the maximum resolution corresponds to a resolution of the original representation [Chadwick Figures 2 – 4 (see at least reference character S140) as well as Paragraphs 48 – 52 (initial (obvious variants of the claimed “original” to one of ordinary skill in the art) renditions / representations maybe at high / top bit rate and resolution or “highest supported”)], and
the minimum resolution corresponds to a predetermined resolution that is lower than the resolution of the original representation [Chadwick Paragraphs 66 – 73 (determination of bottom / minimum bitrate / resolution pairs being lower than top pairs from initial / training video combinable with Lin Figures 6 – 8 as well as Column 6 line 44 – Column 7 line 36 (including table 1 with resolutions / bit rates possible and ranges of max / min bitrates and resolutions to test) and 93 – 95 (bottom / minimum done after initial / highest supported bitrate / resolution))].
See claim 1 for the motivation to combine Chadwick, Lin, and Reznik.
Regarding claim 4, Chadwick teaches forming bit ladders including using neural networks to form the convex hull / surface for selection of the bit ladder. Lin teaches selections of points to form the bit ladder and selection criteria in levels / granularity of the points used. Reznik teaches having a monotonic / monic condition and interpolation among points / data in forming a bit rate ladder for encoding / decoding.
It would have been obvious to one of ordinary skill art before the effective filing date of the claimed invention to modify the teachings of Chadwick to including point selection techniques as taught by Lin and to enforce criteria / requirements in forming the convex hull for the ladder; and Reznik to further include conditions on forming the hull / bit rate ladder (e.g. monotonic / monic conditions) and interpolating between levels in the ladder. The combination teaches
the quality requirements comprise at least two target quality levels that correspond to a minimum target quality and a maximum target quality [Chadwick Figures 2 – 4 as well as Paragraphs 15, 43, 66 – 73 (exemplary minimum VMAF score given) and 85 – 92 (maximum quality scores from trials / iterations for scenarios to set quality requirements – combinable with Lin Column 10 line 1 – Column 11 line 40 (various quality metrics / scores to consider in which the target quality is an obvious variant of the maximum quality target based on bitrate and resolution considerations); Reznik Figure 3 (see at least reference characters 330 and 340) as well as Paragraphs 64 – 66 (minimum quality value for a threshold the target point needs to be greater than) and 126 – 132 (maximum or a merit function as an obvious variant of the quality score)],
a quality of a representation that is created based on the minimum bit rate and the minimum resolution falls below the minimum target quality [Chadwick Figures 2 – 4 as well as Paragraphs 15, 43, 66 – 73 (exemplary minimum VMAF score given) and 85 – 92 (maximum quality scores from trials / iterations for scenarios to set quality requirements – combinable with Lin Column 10 line 1 – Column 11 line 40 (various quality metrics / scores to consider in which the target quality is an obvious variant of the maximum quality target based on bitrate and resolution considerations); Reznik Figure 3 (see at least reference characters 330 and 340) as well as Paragraphs Reznik Paragraphs 55 – 63 (worst case analysis of the targets / bit rate / resolutions) and 64 – 66 (minimum quality value for a threshold the target point needs to be greater than) and 126 – 132 (maximum or a merit function as an obvious variant of the quality score)] and
a quality of a representation that is created based on the maximum bit rate and the maximum resolution exceeds the maximum target quality [Chadwick Figures 2 – 4 as well as Paragraphs 15, 43, 66 – 74 (inclusion of points in the convex hull based on quality / representation of the points), 78 – 81 (inclusion of point / representation in the encoding / bit rate ladder) and 85 – 92 (maximum quality scores from trials / iterations for scenarios to set quality requirements – combinable with Lin Column 10 line 1 – Column 11 line 40 (various quality metrics / scores to consider in which the target quality is an obvious variant of the maximum quality target based on bitrate and resolution considerations); Reznik Figure 3 (see at least reference characters 330 and 340) as well as Paragraphs 55 – 63 (worst case analysis of the targets / bit rate / resolutions) and 64 – 66 (minimum quality value for a threshold the target point needs to be greater than) and 126 – 132 (maximum or a merit function as an obvious variant of the quality score)].
See claim 1 for the motivation to combine Chadwick, Lin, and Reznik.
Regarding claim 5, Chadwick teaches forming bit ladders including using neural networks to form the convex hull / surface for selection of the bit ladder. Lin teaches selections of points to form the bit ladder and selection criteria in levels / granularity of the points used. Reznik teaches having a monotonic / monic condition and interpolation among points / data in forming a bit rate ladder for encoding / decoding.
It would have been obvious to one of ordinary skill art before the effective filing date of the claimed invention to modify the teachings of Chadwick to including point selection techniques as taught by Lin and to enforce criteria / requirements in forming the convex hull for the ladder; and Reznik to further include conditions on forming the hull / bit rate ladder (e.g. monotonic / monic conditions) and interpolating between levels in the ladder. The combination teaches
creating a second grid of pairs of values in a bit rate resolution space that comprises pairs of values of the first set and creating qualities for the pairs of values of the second set based on the reference points of the first set [Chadwick Figures 1 – 4 (see at least reference characters S120, S130, and S170) as well as Paragraphs 38 (second input video to generate second / additional ladder information with quality metrics / considerations also in Paragraph 15 (VMAF)), 40 – 43 and 48 – 52 (additional points generated / interpolated (Paragraph 50) / used in forming the bit rate ladder based on prior bitrate / quality determinations to generate the convex hull and interpolation between previous points based on training video, VMAF, and audience considerations); Lin Figures 5 – 8 and 11 – 13 (see at least the multiplex stages (Figures 6 – 7) and the grid in Figure 5 (more points / points for hull formation)) as well as Column 5 lines 24 – 59 (refined sets of data used – more points used for mesh / convex hull generate to select the ladder), Column 14 lines 5 – 20 (more intermediate points generated for the convex hull than the initial points / points initially used), and Column 15 lines 29 – 40 (interpolating to generate more points to construct the convex hull – combinable with Reznik Figures 3 – 6, 9 (see at least reference character 960) and 11 – 13 as well as Paragraphs 64 – 70 and 98 – 108 (second probe points with quality scores related to the first probe points collected)].
See claim 1 for the motivation to combine Chadwick, Lin, and Reznik.
Regarding claim 6, Chadwick teaches forming bit ladders including using neural networks to form the convex hull / surface for selection of the bit ladder. Lin teaches selections of points to form the bit ladder and selection criteria in levels / granularity of the points used. Reznik teaches having a monotonic / monic condition and interpolation among points / data in forming a bit rate ladder for encoding / decoding.
It would have been obvious to one of ordinary skill art before the effective filing date of the claimed invention to modify the teachings of Chadwick to including point selection techniques as taught by Lin and to enforce criteria / requirements in forming the convex hull for the ladder; and Reznik to further include conditions on forming the hull / bit rate ladder (e.g. monotonic / monic conditions) and interpolating between levels in the ladder. The combination teaches
creating qualities for the pairs of values of the second set comprises at least one of the following [See limitations below for citations]:
interpolation of the reference points [Chadwick Figures 2 – 4 as well as Paragraph 50 (interpolation between training / initial points to form convex hull such as in Lin Figure 5 as well as Column 15 lines 29 – 40))], and/or
processing by a neural network [Chadwick Figure 1 (see NN implemented at top of figure as well as reference characters S110 and S120) as well as Paragraphs 16 – 18 (ANN / NN implementation to form convex hull), 34 (filtering in NNs), 39 – 40 (NNs to generate convex hull), and 55], and/or
a combination thereof [See above to limitation for citations].
See claim 1 for the motivation to combine Chadwick, Lin, and Reznik.
Regarding claim 7, Chadwick teaches forming bit ladders including using neural networks to form the convex hull / surface for selection of the bit ladder. Lin teaches selections of points to form the bit ladder and selection criteria in levels / granularity of the points used. Reznik teaches having a monotonic / monic condition and interpolation among points / data in forming a bit rate ladder for encoding / decoding.
It would have been obvious to one of ordinary skill art before the effective filing date of the claimed invention to modify the teachings of Chadwick to including point selection techniques as taught by Lin and to enforce criteria / requirements in forming the convex hull for the ladder; and Reznik to further include conditions on forming the hull / bit rate ladder (e.g. monotonic / monic conditions) and interpolating between levels in the ladder. The combination teaches
obtaining reference points of the first set or an interpolation of reference points of the first set as input data [Chadwick Figure 1 (see NN implemented at top of figure as well as reference characters S110 and S120) as well as Paragraphs 16 – 18 (ANN / NN implementation to form convex hull), 34 – 37 (filtering in NNs done by layers in Figure 1), 39 – 40 (NNs to generate convex hull using initial renditions as input), and 50 – 55 (interpolation done by NN / interpolated data given as NN input)], and
creating output data comprising processing the input data by one or more layers of the neural network [Chadwick Figure 1 (see NN implemented at top of figure as well as reference characters S110 and S120 with input layers and outputting convex hull (e.g. Lin Figure 5)) as well as Paragraphs 16 – 18 (ANN / NN implementation to form convex hull), 34 – 40 (layers for filtering / video processing used to generate convex hull), and 50 – 55 (interpolation done by NN / interpolated data given as NN input)].
See claim 1 for the motivation to combine Chadwick, Lin, and Reznik.
Regarding claim 9, Chadwick teaches forming bit ladders including using neural networks to form the convex hull / surface for selection of the bit ladder. Lin teaches selections of points to form the bit ladder and selection criteria in levels / granularity of the points used. Reznik teaches having a monotonic / monic condition and interpolation among points / data in forming a bit rate ladder for encoding / decoding.
It would have been obvious to one of ordinary skill art before the effective filing date of the claimed invention to modify the teachings of Chadwick to including point selection techniques as taught by Lin and to enforce criteria / requirements in forming the convex hull for the ladder; and Reznik to further include conditions on forming the hull / bit rate ladder (e.g. monotonic / monic conditions) and interpolating between levels in the ladder. The combination teaches
the quality requirements comprise at least two target quality levels that correspond to a minimum target quality and a maximum target quality [Chadwick Figures 2 – 4 as well as Paragraphs 15, 43, 66 – 73 (exemplary minimum VMAF score given) and 85 – 92 (maximum quality scores from trials / iterations for scenarios to set quality requirements – combinable with Lin Column 10 line 1 – Column 11 line 40 (various quality metrics / scores to consider in which the target quality is an obvious variant of the maximum quality target based on bitrate and resolution considerations); Reznik Figure 3 (see at least reference characters 330 and 340) as well as Paragraphs 64 – 66 (minimum quality value for a threshold the target point needs to be greater than) and 126 – 132 (maximum or a merit function as an obvious variant of the quality score)] and
selecting the subset of reference points comprising [See next limitation for citations and claim 1 last limitation for selection of the “subset” claimed]:
determining a bit rate for a bit rate specification of an encoder for each target quality level from the quality requirements, comprising determining a bit rate for each resolution, the associated predicted quality of which meets the quality requirements for the respective target quality level, and selecting the minimum bit rate from the determined bit rates as the bit rate specification [Chadwick Figures 2 – 4 (setting bit rate / resolution for various quality levels / quality for bitrate / resolution pair as in Figure 4 forming the ladder) as well as Paragraphs 15, 43, 66 – 73 (exemplary minimum VMAF score given and selection of top (maximum) / bottom (minimum) bitrate-resolution pairs in which the bit rate specifications are set) and 85 – 92 (maximum quality scores from trials / iterations for scenarios to set quality requirements – combinable with Lin Column 10 line 1 – Column 11 line 40 (various quality metrics / scores to consider in which the target quality is an obvious variant of the maximum quality target based on bitrate and resolution considerations); Reznik Figure 3 (see at least reference characters 330 and 340) as well as Paragraphs Reznik Paragraphs 55 – 63 (worst case analysis of the targets / bit rate / resolutions) and 64 – 66 (minimum quality value for a threshold the target point needs to be greater than) and 126 – 132 (maximum or a merit function as an obvious variant of the quality score)].
See claim 1 for the motivation to combine Chadwick, Lin, and Reznik.
Regarding claim 10, Chadwick teaches forming bit ladders including using neural networks to form the convex hull / surface for selection of the bit ladder. Lin teaches selections of points to form the bit ladder and selection criteria in levels / granularity of the points used. Reznik teaches having a monotonic / monic condition and interpolation among points / data in forming a bit rate ladder for encoding / decoding.
It would have been obvious to one of ordinary skill art before the effective filing date of the claimed invention to modify the teachings of Chadwick to including point selection techniques as taught by Lin and to enforce criteria / requirements in forming the convex hull for the ladder; and Reznik to further include conditions on forming the hull / bit rate ladder (e.g. monotonic / monic conditions) and interpolating between levels in the ladder. The combination teaches
determining the bit rate for the bit rate specification comprises an interpolation based on the reference points of the second set [Chadwick Figures 1 – 4 (see at least reference characters S120, S130, and S170) as well as Paragraphs 38 (second input video to generate second / additional ladder information with quality metrics / considerations also in Paragraph 15 (VMAF)), 40 – 43 and 48 – 52 (additional points generated / interpolated (Paragraph 50) / used in forming the bit rate ladder based on prior bitrate / quality determinations to generate the convex hull and interpolation between previous points based on training video, VMAF, and audience considerations); Lin Figures 5 – 8 and 11 – 13 (see at least the multiplex stages (Figures 6 – 7) and the grid in Figure 5 (more points / points for hull formation)) as well as Column 5 lines 24 – 59 (refined sets of data used – more points used for mesh / convex hull generate to select the ladder), Column 14 lines 5 – 20 (more intermediate points generated for the convex hull than the initial points / points initially used), and Column 15 lines 29 – 40 (interpolating to generate more points to construct the convex hull – combinable with Reznik Figures 3 – 6, 9 (see at least reference character 960) and 11 – 13 as well as Paragraphs 64 – 70 and 98 – 108 (second probe points with quality scores related to the first probe points collected)].
See claim 1 for the motivation to combine Chadwick, Lin, and Reznik.
Regarding claim 11, Chadwick teaches forming bit ladders including using neural networks to form the convex hull / surface for selection of the bit ladder. Lin teaches selections of points to form the bit ladder and selection criteria in levels / granularity of the points used. Reznik teaches having a monotonic / monic condition and interpolation among points / data in forming a bit rate ladder for encoding / decoding.
It would have been obvious to one of ordinary skill art before the effective filing date of the claimed invention to modify the teachings of Chadwick to including point selection techniques as taught by Lin and to enforce criteria / requirements in forming the convex hull for the ladder; and Reznik to further include conditions on forming the hull / bit rate ladder (e.g. monotonic / monic conditions) and interpolating between levels in the ladder. The combination teaches
creating a representation comprising encoding the video section with the respective bit rate specification for each target quality level from the quality requirements [Chadwick Figures 1 – 4 (final ladder determination) as well as Paragraphs 39 – 48 (subset of points / values tested to generate points for the ladder using bitrate-resolution pairs which had the best quality – to combine with Reznik and obvious to optimize in view of Lin Figures 5 – 8 (see at least reference characters 706 and 708) as well as Column 4 line 26 – Column 5 line 10 (obvious to optimize) and Column 13 line 33 – Column 14 line 20 (quality considered for the resolution / bit rate of the current condition))), 58, 63, and 78 (subset of computed points selected for ABR ladder) and 48 – 52 (subsets of prior points used in forming the bit rate ladder based on prior bitrate / quality determinations to generate the convex hull (e.g. Lin Figure 5) based on quality considerations such as VMAF or audience considerations); Reznik Figures 3 and 7 – 8 as well as Paragraphs 88 – 95 (selection of probe encodings to form the ladder based on quality / bitrate targets computer rendering obvious the claimed specification), 160 – 164 (ladder formation) and 174].
See claim 1 for the motivation to combine Chadwick, Lin, and Reznik.
Regarding claim 12, Chadwick teaches forming bit ladders including using neural networks to form the convex hull / surface for selection of the bit ladder. Lin teaches selections of points to form the bit ladder and selection criteria in levels / granularity of the points used. Reznik teaches having a monotonic / monic condition and interpolation among points / data in forming a bit rate ladder for encoding / decoding.
It would have been obvious to one of ordinary skill art before the effective filing date of the claimed invention to modify the teachings of Chadwick to including point selection techniques as taught by Lin and to enforce criteria / requirements in forming the convex hull for the ladder; and Reznik to further include conditions on forming the hull / bit rate ladder (e.g. monotonic / monic conditions) and interpolating between levels in the ladder. The combination teaches
determining a quality of the representation created [Chadwick Figures 1 – 3 (see at least reference characters S102, S112, and S120) as well as Paragraphs 15 (various quality metrics including VMAF), 38 – 40 and 46 – 52 (initial / first resolution and bitrate for the current frames / selected frames compared to an initial / training set), 70 – 76 (initial / first points with bitrate / quality and resolution); Reznik Figures 3 and 7 – 8 as well as Paragraphs 88 – 95 (selection of probe encodings to form the ladder based on quality / bitrate targets computer rendering obvious the claimed specification), 160 – 164 (ladder formation) and 174], and
comparing the quality determined with the quality requirements [Chadwick Figures 2 – 4 as well as Paragraphs 15, 43, 66 – 73 (exemplary minimum VMAF score given) and 85 – 92 (maximum quality scores from trials / iterations for scenarios to set quality requirements – combinable with Lin Column 10 line 1 – Column 11 line 40 (various quality metrics / scores to consider in which the target quality is an obvious variant of the maximum quality target based on bitrate and resolution considerations); Reznik Figure 3 (see at least reference characters 330 and 340) as well as Paragraphs Reznik Paragraphs 55 – 63 (worst case analysis of the targets / bit rate / resolutions) and 64 – 66 (minimum quality value for a threshold the target point needs to be greater than) and 126 – 132 (maximum or a merit function as an obvious variant of the quality score],
if the quality determined meets the quality requirements: comprising the representation in the bit rate ladder [Chadwick Figures 2 – 4 as well as Paragraphs 15, 43, 66 – 74 (inclusion of points in the convex hull based on quality / representation of the points), 78 – 81 (inclusion of point / representation in the encoding / bit rate ladder) and 85 – 92 (maximum quality scores from trials / iterations for scenarios to set quality requirements – combinable with Lin Column 10 line 1 – Column 11 line 40 (various quality metrics / scores to consider in which the target quality is an obvious variant of the maximum quality target based on bitrate and resolution considerations); Reznik Figure 3 (see at least reference characters 330 and 340) as well as Paragraphs 55 – 63 (worst case analysis of the targets / bit rate / resolutions) and 64 – 66 (minimum quality value for a threshold the target point needs to be greater than) and 126 – 132 (maximum or a merit function as an obvious variant of the quality score)];
if the quality determined does not meet the quality requirements: determining a new representation based on a new bit rate specification [Chadwick Figures 2 – 4 as well as Paragraphs 15, 43, 66 – 74 (inclusion of points in the convex hull based on quality / representation of the points or not including the points if quality isn’t met (alternatively bottom pairs of bit rate / resolution not usable)), 78 – 81 (inclusion of point / representation in the encoding / bit rate ladder) and 85 – 92 (maximum quality scores from trials / iterations for scenarios to set quality requirements – combinable with Lin Column 10 line 1 – Column 11 line 40 (various quality metrics / scores to consider in which the target quality is an obvious variant of the maximum quality target based on bitrate and resolution considerations); Reznik Figure 3 (see at least reference characters 330 and 340) as well as Paragraphs 55 – 63 (worst case analysis of the targets / bit rate / resolutions) and 64 – 68 (refining requirements based on current bit rate / resolutions used), 126 – 132 (maximum or a merit function as an obvious variant of the quality score), and 156 – 162 (refinement of representation based on quality computed)].
See claim 1 for the motivation to combine Chadwick, Lin, and Reznik.
Regarding claim 13, Chadwick teaches forming bit ladders including using neural networks to form the convex hull / surface for selection of the bit ladder. Lin teaches selections of points to form the bit ladder and selection criteria in levels / granularity of the points used. Reznik teaches having a monotonic / monic condition and interpolation among points / data in forming a bit rate ladder for encoding / decoding.
It would have been obvious to one of ordinary skill art before the effective filing date of the claimed invention to modify the teachings of Chadwick to including point selection techniques as taught by Lin and to enforce criteria / requirements in forming the convex hull for the ladder; and Reznik to further include conditions on forming the hull / bit rate ladder (e.g. monotonic / monic conditions) and interpolating between levels in the ladder. The combination teaches
wherein the bit rate ladder comprises two or more quality levels [Chadwick Figure 4 (see the different quality levels in the final ladder) as well as Paragraphs 15 and 47 – 52 (multiple quality levels (e.g. using VMAF scores) / selection of quality level based on testing of resolution of video for a desired bit rate including comparison of second / third representation for similar / same encoder settings)];
for each of the quality levels of the bit rate ladder, a representation comprising encoding the video section according to the respective quality level [Chadwick Figures 1 – 4 (see the ladder in Figure 4 formed by the points / selections in Figures 2 – 3) as well as Paragraphs 23, 48 – 52 and 56 – 60 (the encoded frame / rendition (obvious variants of the claimed “representation” claimed to one of ordinary skill in the art represents the output for the conditions in the ladder), and 79 – 83 (specific ladder using based on desire quality, resolution, and bitrate available)].
See claim 1 for the motivation to combine Chadwick, Lin, and Reznik.
Claim(s) 8 is rejected under 35 U.S.C. 103 as being unpatentable over Chadwick, Lin, Reznik, as applied to claim 6 above, and further in view of Moorthy, et al. (US PG PUB 2022/0256168 A1 referred to as “Moorthy” throughout).
Regarding claim 8, Chadwick teaches forming bit ladders including using neural networks to form the convex hull / surface for selection of the bit ladder. Lin teaches selections of points to form the bit ladder and selection criteria in levels / granularity of the points used. Reznik teaches having a monotonic / monic condition and interpolation among points / data in forming a bit rate ladder for encoding / decoding.
It would have been obvious to one of ordinary skill art before the effective filing date of the claimed invention to modify the teachings of Chadwick to including point selection techniques as taught by Lin and to enforce criteria / requirements in forming the convex hull for the ladder; and Reznik to further include conditions on forming the hull / bit rate ladder (e.g. monotonic / monic conditions) and interpolating between levels in the ladder. The combination teaches
wherein output data of the neural network is processed by [See next two limitation for citations]
filtering the output data to comply with monotonicity conditions [Chadwick Figure 1 (see NN implemented at top of figure as well as reference characters S110 and S120) as well as Paragraphs 16 – 18 (ANN / NN implementation to form convex hull), 34 – 37 (filtering in NNs done by layers in Figure 1), 39 – 40 (NNs to generate convex hull using initial renditions as input) in which Reznik Paragraph 55 or 140 – 144 (rate limits on quality moving around the convex hull suggested) and Moorthy Paragraphs 62 and 110 enforce monotonic conditions on the bitrate / resolution / quality relationship for the filtering in the NN taught by Chadwick to obey / perform], and/or
limiting the value range of the predicted qualities [Chadwick Figure 1 (see NN implemented at top of figure as well as reference characters S110 and S120) as well as Paragraphs 16 – 18 (ANN / NN implementation to form convex hull), 34 – 37 (filtering in NNs done by layers in Figure 1), 39 – 40 (NNs to generate convex hull using initial renditions as input) in which Reznik Paragraphs 140 – 144 render obvious rate changes / limits for quality steps in the convex hull].
See claim 6 for the motivation to combine Chadwick, Lin, and Reznik.
The motivation to combine Moorhty with Reznik, Lin, and Chadwick is to combine features in the same / related field of invention of generating encoding ladders [Moorthy Paragraph 1] in order to avoid / limit saturations in quality / performance of the ladder [Moorthy Paragraphs 6 – 7 where the Examiner observes at least KSR Rationales (D) or (F) are also applicable].
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Menon, et al. (US PG PUB 2023/0388511 A1 referred to as “Menon” throughout) teaches a NN implementation to form the claimed convex hull.
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/TYLER W. SULLIVAN/ Primary Examiner, Art Unit 2487
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