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 .
Response to Arguments
Applicant's arguments filed September 3, 2025 have been fully considered but they are not persuasive.
In re pages 6-7, Applicant argues that Schmidmer does not disclose “generating a prediction of a bit rate for encoding the video segment that satisfies the quality value,” as recited in claim 1.
The Examiner respectfully disagrees.
On page 6 of Applicant’s arguments, Applicant states:
In Applicant’s previous response, amendments were made to claim 1 including reciting “generating a prediction of a bit rate for encoding the video segment that satisfies the quality value” (emphasis added). For at least this reason claim 1 is patentably distinct over Schmidmer…
The previous version of claim 1 recited “determining… a predicted bit rate that satisfies the quality value” and “encoding, based on the predicted bit rate, the video segment.” It is the view of the Examiner that “generating a prediction of a bit rate for encoding the video segment that satisfies the quality value” is not substantially distinct from “determining… a predicted bit rate that satisfies the quality value.” Both versions of the claim state that the generating/determining is done “based on the data, based on a quality value, and using a machine learning model trained to correlate video segment characteristics with bit rates.” In other words, Applicant has not amended the claims to recite any new features.
Rather than articulate how the amended claim differs from previous versions and what new features are recited and allegedly not taught by the prior art, Applicant merely presents arguments already rejected by the Patent Trial and Appeal Board in their decision of January 21, 2025. The Examiner is not free to ignore their findings.
In re pages 8-9, Applicant argues that Otto does not teach “receiving data indicative of one ore more characteristics associated with one or more frames of a video segment; based on the data, based on a quality value… generating a prediction of a bit rate… and encoding based on the prediction of the bit rate,” as recited in claim 1.
In response, the Examiner respectfully disagrees.
Otto is directed to video streaming using predictive video encoders (Otto: paragraph [0001]). As taught by Otto, input video frames may be received for processing (Otto: paragraph [0035]). A set of training videos may be provided to a machine learning classifier to identify characteristics within the video data, characteristics “used to adjust one or more encoding parameters and/or patterns in video segments” (Otto: paragraph [0037]). Thus, Otto teaches receiving data indicative of one ore more characteristics associated with one or more frames of a video segment.
As further taught by Otto, based on these characteristics determined from the training set as well as on a quality measure, machine learning algorithms may predict characteristics about future video (Otto: paragraph [0037]). These predictions may be used to adjust encoding parameters—parameters such as a bitrate encoding parameter (Otto: paragraph [0051]). Thus Otto teaches based on the data, based on a quality value… generating a prediction of a bit rate.
Lastly, Otto teaches that the video segments may be encoded using the predicted parameters such as bit rate (Otto: Fig. 3, paragraphs [0053] – [0054]). Thus Otto teaches encoding based on the prediction of the bit rate.
In this manner, Otto discloses the features of claim 1.
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.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
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.
Claims 1-25 are rejected under 35 U.S.C. 103 as being unpatentable over Schmidmer
et al. (US 2016/0105728, referred to herein as “Schmidmer’’) in view of Su et al. (US
2017/0359580, already of record, referred to herein as “Su’’).
Regarding claim 1, Schmidmer discloses: A method comprising:
receiving data indicative of one or more characteristics associated with one or more frames of a video segment
(Schmidmer: Fig. 6, paragraph [0054], disclosing input video that is partitioned into segments of
video frames; Fig. 7, paragraph [0063], disclosing analysis of a data stream to determine various
characteristics of the video segments including parameter sets associated with the segments; Fig. 3,
paragraph [0041], disclosing that the parameter sets may be aggregated for associated with the video
segments and provided to other components);
based on the data, based on a quality value... generating a prediction of a bit rate for encoding the video
segment that satisfies the quality value (Schmidmer: Fig. 3, paragraph [0042], disclosing that the aggregated
parameter sets may be used to determine a quality value; paragraphs [0043] and [0054], disclosing
use of a bitrate associated with the determined quality; paragraph [0049], disclosing that the bitrate
may be used as a parameter during encoding); and
encoding, based on the prediction of the bit rate, the video segment (Schmidmer: paragraphs [0056] and
[0057], disclosing encoding of the video segments according to the quality-to-bitrate levels).
Schmidmer does not explicitly disclose: using a machine learning model trained to correlate video
segment characteristics with bit rates.
However, Su discloses: using a machine learning model trained to correlate video segment characteristics
with bit rates (Su: paragraphs [0025] through [0026] and [0044] through [0045], disclosing use of a
database of training videos in a machine learning model to generate correlations of video
parameters— including predicted bit rate—to various quality ratings; paragraph [0046], disclosing
use of the model to predict the relative quality of encoded video—e.g., to determine the encoding
parameters such as bit rate that will achieve the relative quality value).
At the time the application was effectively filed, it would have been obvious for a person
having ordinary skill in the art to use the machine learning of Su in the method of Schmidmer.
One would have been motivated to modify Schmidmer in this manner in order to more
optimally determine quality tiers for video coding and to better account for subjective visual quality
for such videos (Su: paragraphs [0002] through [0008]).
Regarding claim 2, Schmidmer and Su disclose: The method of claim 1, wherein the quality value
indicates at least one of a Mean Opinion Score (MOS), a peak signal-to-noise ratio (PSNR), or a structural
similarity index (SSIM) (Su: paragraph [0029], disclosing quality values involving peak signal to noise
ratio and a structural similarity index).
The motivation for combining Schmidmer and Su has been discussed in connection with
claim 1, above.
Regarding claim 3, Schmidmer and Su disclose: The method of claim 1, wherein the data
frame comprises a feature vector indicative of the one or more characteristics (Su: paragraph [0059],
disclosing that the features may be represented as a vector).
The motivation for combining Schmidmer and Su has been discussed in connection with
claim 1, above.
Regarding claim 4, Schmidmer and Su disclose: The method of claim 3, wherein the generating the
prediction of the bit rate comprises inputting the feature vector into the machine learning model (Su: paragraph
[0059], disclosing that the machine training model may include an input object such as a vector
representing features).
The motivation for combining Schmidmer and Su has been discussed in connection with
claim 1, above.
Regarding claim 5, Schmidmer and Su disclose: The method of claim 1, wherein the one or more
characteristics comprise at least one of a color profile, an edge histogram profile, scene cut information, a shot feature, a
spatial nature of the one or more frames, a temporal nature of the one or more frames, a chroma level, a luma level, a
brightness value, a contrast value, a sharpness value, a texture value, a motion factor, a color richness value, or a noise value (Schmidmer: paragraphs [0087] through [0101], disclosing parameters such as edge analysis,
chrominance analysis, time analysis, etc.; Su: paragraph [0026], disclosing feature characteristics
including spatial complexity, motion complexity, color richness, noise, sharpness, etc.).
The motivation for combining Schmidmer and Su has been discussed in connection with
claim 1, above.
Regarding claim 6, Schmidmer and Su disclose: The method of claim 1, wherein the data is based on
an aggregation comprising at least one of mean, standard deviation, count, or skew (Schmidmer: paragraph
[0078], disclosing averaging of determined parameter correlations; paragraph [0107], disclosing
statistical analysis of aggregated parameter sets).
Regarding claim 7, Schmidmer and Su disclose: The method of claim 1, wherein the generating the
prediction of the bit rate comprises correlating the one or more characteristics with an optimal bit rate for the one or
more characteristics (Schmidmer: Fig. 2, paragraphs [0033] and [0034], disclosing different quality-to-
bitrate levels and association of various video segments with the levels; Su: paragraph [0020],
disclosing classification of tiers associated with various bit rates; paragraph [0055], disclosing
determining whether extracted features correspond to a particular tier— e.g., whether the features
correlate with an optimal bit rate).
The motivation for combining Schmidmer and Su has been discussed in connection with
claim 1, above.
Regarding claim 8, Schmidmer and Su disclose: The method of claim 1, wherein training the
machine learning model comprises correlating a training video segment, encoded with a known bit rate, with one or
more characteristics extracted from the training video segment (Su: paragraph [0045], disclosing use of training video segments encoded at known bit rates to determine extracted features).
The motivation for combining Schmidmer and Su has been discussed in connection with
claim 1, above.
Regarding claim 9, Schmider and Su disclose: The method of claim 1, wherein the prediction of the
bit rate comprises an optimal number of bits per second allocated for the encoding (Su: paragraph [0020],
disclosing us of a target bits per second performance target).
The motivation for combining Schmidmer and Su has been discussed in connection with
claim 1, above.
Regarding claim 10, the claim recites analogous limitations to claim 1, above, and is
therefore rejected on the same premise.
Regarding claim 11, the claim recites analogous limitations to claim 9, above, and is
therefore rejected on the same premise.
Regarding claim 12, the claim recites analogous limitations to claim 2, above, and is
therefore rejected on the same basis.
Regarding claim 13, the claim recites analogous limitations to claim 5, above, and is
therefore rejected on the same premise.
Regarding claim 14, the claim recites analogous limitations to claim 6, above, and is
therefore rejected on the same premise.
Regarding claim 15, the claim recites analogous limitations to claim 4, above, and is
therefore rejected on the same premise.
Regarding claim 16, the claim recites analogous limitations to claim 8, above, and is
therefore rejected on the same premise.
Regarding claim 17, the claim recites analogous limitations to claim 1, above, and is
therefore rejected on the same premise.
Regarding claim 18, the claim recites analogous limitations to claim 5, above, and is
therefore rejected on the same premise.
Regarding claim 19, the claim recites analogous limitations to claim 6, above, and is
therefore rejected on the same premise.
Regarding claim 20, the claim recites analogous limitations to claim 4, above, and is
therefore rejected on the same premise.
Regarding claim 21, the claim recites analogous limitations to claim 2, above, and is
therefore rejected on the same basis.
Regarding claim 22, Schmidmer and Su disclose: The method of claim 17, further comprising:
sending, to a computing device, content comprising the encoded second video segment (Schmidmer: Fig. 11,
disclosing that the encoded video segments may be provided to a client device; Su: paragraph [0021]
and claim 5, disclosing transmission of encoded content to a terminal computer device for
playback).
The motivation for combining Schmidmer and Su has been discussed in connection with
claim 1, above.
Regarding claim 23, the claim recites analogous limitations to claim 22, above, and is
therefore rejected on the same basis.
Regarding claim 24, the claim recites analogous limitations to claim 22, above, and is
therefore rejected on the same basis.
Regarding claim 25, Schmidmer and Su disclose: The method of claim 1, wherein the quality value
is associated with a desired level of quality (Schmidmer: Fig. 2, paragraphs [0035] and [0036], disclosing
quality levels associated with different desired levels of quality; Su: paragraph [-0053], disclosing
encoding to a desired video quality).
The motivation for combining Schmidmer and Su has been discussed in connection with
claim 1, above.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (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.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 10 and 17 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by
Otto et al. (US 2019/0335192 A1, referred to herein as “Otto”).
Regarding claim 1, Otto discloses: A method comprising:
receiving data indicative of one or more characteristics associated with one or more frames of a video segment
(Otto: paragraph [0035], disclosing encoding of video segments with one or more frames; paragraph
[0037], disclosing use of a training set of videos provided to a machine learning classifier to identify
characteristics within the video data);
based on the data, based on a quality value, and using a machine learning model trained to correlate video
segment characteristics with bit rates, generating a prediction of a bit rate for encoding the video segment that satisfies
the quality value (Otto: paragraph [0037], disclosing machine learning algorithms to predict
characteristics about future video based on learning from the video training set as well as on a
quality measure; paragraph [0051], disclosing that encoding parameters can be adjusted based on
predictions from the machine learning classifier such as a bitrate encoding parameter); and
encoding, based on the prediction of the bit rate, the video segment (Otto: Fig. 3, paragraphs [0053] –
[0054], disclosing encoding the video segments according to the predicted parameters—e.g.,
including bitrate).
Regarding claim 10, the claim recites analogous limitations to claim 1, above, and is
therefore rejected on the same premise.
Regarding claim 17, the claim recites analogous limitations to claim 1, above, and is
therefore rejected on the same premise.
Conclusion
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 nonprovisional extension fee (37 CFR 1.17(a)) 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 Christopher Braniff whose telephone number is (571) 270-5009. The examiner can normally be reached M-F 7AM to 4PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Thai Tran can be reached at (571) 272-7382. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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CHRISTOPHER T. BRANIFF
Primary Examiner
Art Unit 2484
/CHRISTOPHER BRANIFF/Primary Examiner, Art Unit 2484
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