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 with respect to claim(s) 1 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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.
Claim(s) 1 – 8 and 10 - 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Khsib et al (US 11,729,387, hereafter Khsib) in view of Tasinga et al (US 2022/0180178, hereafter Tasinga).
As per claim 1, Khsib discloses a method for managing encoding of multimedia content stored in a file, comprising:
determining, using a supervised learning algorithm, a prediction of processing resources required for encoding the multimedia content, based on one or more multimedia content characteristics of the multimedia content and on one or more multimedia content encoding parameters for encoding the multimedia content (column 3 lines 13 – 39); and
determining a processing configuration for encoding the multimedia content based on the prediction of processing resources (column 3 lines 13 - 16).
However, Khsib does not explicitly teach determining, using a neural network implementing a supervised learning algorithm, and wherein the predicted processing resources comprises one or more resources of one or more of: type of public cloud instance, CPU instances, RAM resources, storage type, public cloud provider, and time of day.
In the same field of endeavor, Tasinga teaches determining, using a neural network implementing a supervised learning algorithm (¶ 99), and wherein the predicted processing resources comprises one or more resources of one or more of: type of public cloud instance, CPU instances, RAM resources, storage type, public cloud provider, and time of day (¶ 100, 109, and 110).
Therefore, it would have been obvious for one of ordinary skill in the art before the time the invention was effectively filed to modify the invention of Khsib in view of Tasinga. The advantage would be optimizing video encoding and multimedia management.
As per claim 2, Khsib discloses the method according to claim 1, further comprising: encoding the multimedia content by a video encoder configured with the processing configuration (Figure 1 element 116; column 3 lines 15 – 19; an encoder 116 to encode a media file (e.g., “input video”) according to the one or more encoding settings and send the encoded media file to a viewer device 122 according to some embodiments).
As per claim 3, Khsib discloses the method according to claim 1, wherein the processing configuration comprises a configuration of a cloud instance, and wherein the encoding the multimedia content is performed by the cloud instance configured with the configuration of the cloud instance (column 10 lines 21 – 35; A provider network 500 (or, “cloud” provider network) provides users with the ability to utilize one or more of a variety of types of computing-related resources).
As per claim 4, Khsib discloses the method according to claim 1, further comprising a training phase for training a neural network implementing the supervised learning algorithm performed on a plurality of training multimedia content files, the training phase comprising, for a training multimedia content file of the plurality of training multimedia content files: determining, based on the training multimedia content file, a reference prediction of processing resources required for encoding a training multimedia content contained in the training multimedia content file, and performing training of the neural network based on input data comprising one or more multimedia content characteristics of the training multimedia content and on one or more multimedia content encoding parameters for encoding the training multimedia content, and based on the reference prediction of processing resources, to generate a prediction model for predicting a prediction of processing resources required for encoding multimedia content (column 7 lines 62 – column 8 lines 43).
As per claim 5, Khsib discloses the method according to claim 4, wherein the training phase further comprises, for the training multimedia content file: performing a plurality of encodings of the training multimedia content file using respective combinations of the one or more multimedia content encoding parameters; and determining, for each of the plurality of encodings, a respective result (column 8 lines 44 – 67).
As per claim 6, Khsib discloses the method according to claim 5, wherein one or more of the respective results comprise a respective combination of one or more performance metrics (column 8 lines 44 – 67).
As per claim 7, Khsib discloses the method according to claim 1, wherein the one or more multimedia content characteristics are one or more of: a type of the multimedia content, a duration of the multimedia content, a resolution of the multimedia content, one or more video characteristics of the multimedia content, and one or more audio characteristics of the multimedia content (column 8 lines 44 – 67).
As per claim 8, Khsib discloses the method according to claim 1, wherein the one or more multimedia content encoding parameters are one or more of: a video compression standard, a number of output streams and their corresponding resolution, bitrate and/or quality setting, pre-processing requirements, an audio compression standard, and a required turnaround time (column 3 lines 40 – 67).
As per claim 10, Khsib discloses the method according to claim 1, wherein the prediction of processing resources comprises a performance level associated with processing resources, and corresponding to one or more performance metrics, and wherein the processing configuration is determined based on the performance level of the associated processing resources (column 10 lines 21 – 39).
As per claim 11, Khsib discloses the method according to claim 6, wherein one or more of the one or more performance metrics are one or more of: time to encode, encoding speed versus real time, average CPU usage, peak CPU usage, average memory usage, peak memory usage, amount of storage usage, type of storage usage, visual quality of output stream, bit-rate of output stream (column 8 lines 27 – 39 and column 14 lines 65 - column 15 lines 10).
As per claim 12, Khsib discloses the method according to claim 1, further comprising: determining the one or more multimedia content characteristics based on the multimedia content, wherein the one or more multimedia content characteristics are of respective predetermined types of characteristic (column 11 lines 65 – column 12 lines 3).
As per claim 13, Khsib discloses the method according to claim 1, further comprising: obtaining one or more multimedia content classes, and selecting a multimedia content class among the one or more multimedia content classes based on the one or more multimedia content characteristics, wherein the prediction of processing resources is determined based on the selected multimedia content class (column 3 lines 33 – 39; Embodiments herein utilize one of more machine learning models 108 to adapt the (video) encoder settings 114 to content, e.g., especially the dynamic channels. For example, live broadcasts of a sport channels may contain segments of film trailers or computer-generated videos, and thus selecting a single tuning (e.g., single set of encoder settings) for “sports” may lead to non-optimal quality (e.g., for the film trailers or computer-generated videos) in these embodiments.; Khsib’s disclosure shows the different content classes as the type of videos being analyzed such as sports as a class).
Regarding claim 14, arguments analogous to those presented for claim 1 are applicable for claim 14.
Regarding claim 15, arguments analogous to those presented for claim 1 are applicable for claim 15.
Regarding claim 16, arguments analogous to those presented for claim 2 are applicable for claim 16.
Regarding claim 17, arguments analogous to those presented for claim 3 are applicable for claim 17.
Regarding claim 18, arguments analogous to those presented for claim 4 are applicable for claim 18.
Regarding claim 19, arguments analogous to those presented for claim 5 are applicable for claim 19.
Regarding claim 20, arguments analogous to those presented for claim 6 are applicable for claim 20.
As per claim 21, Khsib teaches the apparatus according to claim 14.
However, Khsib does not explicitly teach wherein the prediction of processing resource comprises a performance level associated with processing resources, and corresponding to one or more performance metrics, and wherein the processing configuration is determined based on the performance level of the associated processing resources.
In the same field of endeavor, Tasinga teaches wherein the prediction of processing resource comprises a performance level associated with processing resources, and corresponding to one or more performance metrics, and wherein the processing configuration is determined based on the performance level of the associated processing resources (¶ 110).
Therefore, it would have been obvious for one of ordinary skill in the art before the time the invention was effectively filed to modify the invention of Khsib in view of Tasinga. The advantage would be optimizing video encoding and multimedia management.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
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/CHIKAODILI E ANYIKIRE/Primary Examiner, Art Unit 2487