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 .
Applicant(s) Response to Official Action
The response filed on 02/03/2026 has been entered and made of record.
Response to Arguments/Amendments
Presented arguments have been fully considered, but are rendered moot in view of the new ground(s) of rejection necessitated by amendment(s) initiated by the applicant(s).
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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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 12, 14, 20, 22, 28 and 30 are rejected under 35 U.S.C. 103 as being unpatentable over Satoru Kobayashi [US 20080279286 A1: already of record] in view of Ahmed Toufique, [Predicting Video Conversion Time from Video Metadata and Conversion Parameters using Gradient Boosting Machine: already of record] and further in view of Minseok Song et al. [US 20210329279 A1].
Regarding claim 12, Satoru teaches:
12. (New) A method for video transcoding (i.e. An image-processing apparatus is configured to read encoded video data from a recording medium, decode the encoded video data, and re-encode the decoded video data- Abstract… The present invention relates to an image-processing apparatus and a method, and particularly relates to an apparatus and a method that are provided to transcode compressed and encoded video data- ¶0002), comprising:
obtaining a first video to be transcoded (i.e. read encoded video data from a recording medium- Abstract);
determining first video feature information corresponding to the first video (i.e. to record information about a viewing-operation-history relating to the encoded video data- Abstract);
determining a predicted play count of the first video (i.e. number of viewing operation- fig. 7) at each of bit rate levels that are currently not transcoded (i.e. FIG. 7 shows a second exemplary viewing-operation-history data and FIG. 8 shows an exemplary time variation in the bit rate determined by the target-bit-rate-determination unit 24, where the time-varying bit rate corresponds to the exemplary viewing-operation-history data shown in FIG. 7. In FIG. 8, the horizontal axis indicates the time and the vertical axis indicates the bit rate- ¶0050-52… FIG. 9 shows a third exemplary viewing-operation-history data and FIG. 10 shows an exemplary time variation in the bit rate determined by the target-bit-rate-determination unit 24, where the time-varying bit rate corresponds to the third exemplary viewing-operation-history data shown in FIG. 9. In FIG. 10, the horizontal axis indicates the time and the vertical axis indicates the bit rate- ¶0053-0056); and
determining a target bit rate level from the bit rate levels based on the predicted play count, and transcoding the first video based on the target bit rate level (i.e. The video-encoding unit 22 encodes video data output from the video-decoding unit 18 according to a target bit rate determined by a target-bit-rate-determination unit 24 (re-encoding processing)- ¶0031… FIG. 9 shows a third exemplary viewing-operation-history data and FIG. 10 shows an exemplary time variation in the bit rate determined by the target-bit-rate-determination unit 24, where the time-varying bit rate corresponds to the third exemplary viewing-operation-history data shown in FIG. 9. In FIG. 10, the horizontal axis indicates the time and the vertical axis indicates the bit rate- ¶0061-0064).
However, Satoru does not teach explicitly:
based on the first video feature information and a predetermined decision tree regression model.
In the same field of endeavor, Ahmed teaches:
based on the first video feature information and a predetermined decision tree regression model (i.e. gradient boosting machine (GBM)… The goal of this work is to predict the conversion time of videos using GBM algorithm in the regression setting. In training step of this method, heterogeneous set of video metadata and conversion features are fed to GBM to build the desired prediction model. The trained GBM model is then used in prediction step to carry out conversion time prediction task of the unseen videos. The rest of this section covers a brief description of the key idea, algorithmic description, and regularization strategies of GBM for regression- Section IV).
It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention, to modify the teachings of Satoru with the teachings of Ahmed to build a series of simple and probably inaccurate decision trees or weak models successively from the prediction residuals of the preceding decision trees and combine them to construct a final highly accurate prediction model (Ahmed- Section IV A).
However, Satoru and Ahmed do not teach explicitly:
wherein determining the target bit rate level from the bit rate levels comprises: ranking importance degrees of respective bit rate levels based on the predicted play count at respective bit rate level that is currently not transcoded, to determine the target bit rate level.
In the same field of endeavor, Minseok teaches:
wherein determining the target bit rate level from the bit rate levels comprises: ranking importance degrees (i.e. hot step when the video streaming is first released , a warm step in a middle level , and a cold step when a video is not exposed- ¶0034) of respective bit rate levels based on the predicted play count (i.e. An access number of τi is defined as Niaccess- ¶0034) at respective bit rate level (i.e. the bitrate of a transcoded version is called
B
i
,
j
,
k
- ¶0033) that is currently not transcoded, to determine the target bit rate level (i.e. When transcoding is completed, a video clip may be streamed to a user. The popularity of video streaming may be divided into three steps. For example, the popularity of video streaming may be divided into a hot step when the video streaming is first released, a warm step in a middle level, and a cold step when a video is not exposed. A total of access numbers for such popularity of video may be predicted using a machine learning technique. An access number of τi is defined as Niaccess- ¶0034… Assuming that video quality per bitrate of a transcoded version generated as the task τi is executed as the preset k at the node j by considering video popularity is called
G
i
,
j
,
k
=
Q
i
,
j
,
k
N
i
a
c
c
e
s
s
B
i
,
j
,
k
- ¶0035… Niaccesof each task τi may be derived based on the above settings- ¶0069-0070).
It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention, to modify the teachings of Satoru and Ahmed with the teachings of Minseok for maximizing video quality per bitrate in order to reduce a network bandwidth and also provide better video quality, while satisfying a transcoding deadline, in a server for transcoding a video encoded by a variable bitrate (VBR) technique (Minseok- ¶0011).
Regarding claim 14, Satoru, Ahmed and Minseok teach all the limitations of claim 12 and Satoru further teaches:
wherein determining the target bit rate level from the bit rate levels based on the predicted play count comprises:
comparing the predicted play counts corresponding to respective bit rate levels, and determining a bit rate level with the highest predicted play count as the target bit rate level; or obtaining at least one candidate bit rate level each with a predicted play count greater than or equal to a predetermined play count threshold by comparing the predetermined play count threshold with the predicted play count corresponding to each bit rate level, and determining a candidate bit rate level with the highest predicted play count as the target bit rate level (i.e. For the period where the number of the ordinary-playback operations is two, the period indicating a high degree of interest of the viewer, the target-bit-rate-determination unit 24 sets the rate of 7 Mbps, for example, which is higher than the reference bit rate. Further, for the period where the number of the ordinary-playback operations is four, the period indicating the high degree of interest of the viewer, the target-bit-rate-determination unit 24 sets, for example, the rate of 8 Mbps, which is still higher than the bit rate used in the period where the ordinary-playback operation is performed two times- ¶0052… Further, for the period where the playback operation is performed at twenty-five hundredths times ordinary speed (super slow speed), where the period indicates a still higher degree of interest of the viewer, the target-bit-rate-determination unit 24 sets, for example, the rate of 8 Mbps, which is higher than the bit rate used in the period where the playback operation is performed at the five-tenths times ordinary speed (slow speed). That is to say, the bit rate is increased with decreases in the playback speed- ¶0055… For the period where the video data is referred to a single time on the playlist, where the period indicates a high degree of interest of the viewer, the target-bit-rate-determination unit 24 sets, for example, the bit rate of 7 Mbps, which is higher than the reference bit rate. Further, for the period where the video data is referred to two times on the playlist, where the period indicates an even higher degree of interest of the viewer, the target-bit-rate-determination unit 24 sets, for example, the bit rate of 8 Mbps, which is even higher than the bit rate of the period where the video data is referred to a single time on the playlist. That is to say, the bit rate becomes higher than the reference bit rate with increases in the number of times the playlist is referred to- ¶0060).
Regarding claim 17, Satoru, Ahmed and Minseok teach all the limitations of claim 12 and Satoru further teaches:
wherein obtaining the first video to be transcoded comprises: obtaining a newly uploaded second video (i.e. read encoded video data from a recording medium- Abstract); determining second video feature information corresponding to the second video (i.e. to record information about a viewing-operation-history relating to the encoded video data- Abstract); determining a popularity prediction result corresponding to the second video (i.e. number of viewing operation- fig. 7) based on the second video feature information; and in response to the popularity prediction result indicating that the second video is popular, taking the second video as the first video to be transcoded(i.e. FIG. 7 shows a second exemplary viewing-operation-history data and FIG. 8 shows an exemplary time variation in the bit rate determined by the target-bit-rate-determination unit 24, where the time-varying bit rate corresponds to the exemplary viewing-operation-history data shown in FIG. 7. In FIG. 8, the horizontal axis indicates the time and the vertical axis indicates the bit rate- ¶0050-52… FIG. 9 shows a third exemplary viewing-operation-history data and FIG. 10 shows an exemplary time variation in the bit rate determined by the target-bit-rate-determination unit 24, where the time-varying bit rate corresponds to the third exemplary viewing-operation-history data shown in FIG. 9. In FIG. 10, the horizontal axis indicates the time and the vertical axis indicates the bit rate- ¶0053-0056).
However, Satoru does not teach explicitly:
a predetermined decision tree classification model.
In the same field of endeavor, Ahmed teaches:
a predetermined decision tree classification model (i.e. gradient boosting machine (GBM)… The goal of this work is to predict the conversion time of videos using GBM algorithm in the regression setting. In training step of this method, heterogeneous set of video metadata and conversion features are fed to GBM to build the desired prediction model. The trained GBM model is then used in prediction step to carry out conversion time prediction task of the unseen videos. The rest of this section covers a brief description of the key idea, algorithmic description, and regularization strategies of GBM for regression- Section IV).
It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention, to modify the teachings of Satoru with the teachings of Ahmed to build a series of simple and probably inaccurate decision trees or weak models successively from the prediction residuals of the preceding decision trees and combine them to construct a final highly accurate prediction model (Ahmed- Section IV A).
Regarding claim 20, apparatus claim 20 is drawn to the apparatus using/performing the same method as claimed in claim 12. Therefore, apparatus claim 20 corresponds to method claim 12, and is rejected for the same reasons of obviousness as used above.
Regarding claim 22, apparatus claim 22 is drawn to the apparatus using/performing the same method as claimed in claim 14. Therefore, apparatus claim 22 corresponds to method claim 14 and is rejected for the same reasons of obviousness as used above.
Regarding claim 25, apparatus claim 25 is drawn to the apparatus using/performing the same method as claimed in claim 17. Therefore, apparatus claim 25 corresponds to method claim 17 and is rejected for the same reasons of obviousness as used above.
Regarding claim 28, computer-readable medium storing instructions claim 28 corresponds to the same method as claimed in claim 12, and therefore is also rejected for the same reasons of obviousness as listed above.
Regarding claim 30, computer-readable medium storing instructions claim 30 corresponds to the same method as claimed in claim 14, and therefore is also rejected for the same reasons of obviousness as listed above.
Claims 13, 18, 21, 26 and 29 are rejected under 35 U.S.C. 103 as being unpatentable over Satoru Kobayashi [US 20080279286 A1: already of record] in view of Ahmed Toufique, [Predicting Video Conversion Time from Video Metadata and Conversion Parameters using Gradient Boosting Machine: already of record] further in view of Minseok Song et al. [US 20210329279 A1] and even further in view of Shengxiang Feng [US 20220286718 A1: already of record].
Regarding claim 13, Satoru, Ahmed and Minseok teach all the limitations of claim 12 and Satoru further teaches:
wherein the first video feature information comprises:
video information (i.e. number of viewing operations- ¶0046),
information about a current video play count (number of viewing operations- ¶0045), and
information about a current video play growth rate corresponding to the first video (i.e. If part of the video data is slowly played back at a low multiple (100% or less) of ordinary speed, it means that the part is deliberately viewed by a viewer. Therefore, it can be considered that the part is of a high degree of interest to the viewer. Further, as the number of viewing operations grew, the degree of the user's interest increases- ¶0045); and
However, Satoru does not teach explicitly:
the predetermined decision tree regression model is a gradient boosting based decision tree regression model.
In the same field of endeavor, Ahmed teaches:
the predetermined decision tree regression model is a gradient boosting based decision tree regression model (i.e. gradient boosting machine (GBM)… The goal of this work is to predict the conversion time of videos using GBM algorithm in the regression setting. In training step of this method, heterogeneous set of video metadata and conversion features are fed to GBM to build the desired prediction model. The trained GBM model is then used in prediction step to carry out conversion time prediction task of the unseen videos. The rest of this section covers a brief description of the key idea, algorithmic description, and regularization strategies of GBM for regression- Section IV).
It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention, to modify the teachings of Satoru with the teachings of Ahmed to build a series of simple and probably inaccurate decision trees or weak models successively from the prediction residuals of the preceding decision trees and combine them to construct a final highly accurate prediction model (Ahmed- Section IV A).
However, Satoru, Ahmed and Minseok do not teach explicitly:
uploader information, and information about the number of current video viewers.
In the same field of endeavor, Feng teaches:
uploader information (i.e. attention degree of the category to which the source live-streaming video belongs (for example, in a case that the source live-streaming video is an apple-related video (Chinese pronunciation of apple has a meaning of peace and safe)- ¶0088), and information about the number of current video viewers (i.e. play attention degree of the source live-streaming video during the live streaming (the play attention degree is determined based on the number of viewers during the live streaming and indicates popularity)- ¶0088).
It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention, to modify the teachings of Satoru, Ahmed and Minseok with the teachings of Feng to improve the response speed and hit rate of user access (Feng- ¶0064).
Regarding claim 18, Satoru, Ahmed and Minseok teach all the limitations of claim 17 and Satoru further teaches:
wherein the second video feature information comprises:
video information (i.e. number of viewing operations- ¶0046),
information about a current video play count corresponding to the second video (i.e. number of viewing operations- ¶0046… viewing-operation-history indicates at least one of a number of times a playback operation is accepted and a specified playback speed- claim 4); and.
However, Satoru does not teach explicitly:
the predetermined decision tree classification model is a gradient boosting based decision tree classification model
In the same field of endeavor, Ahmed teaches:
the predetermined decision tree classification model is a gradient boosting based decision tree classification model (i.e. gradient boosting machine (GBM)… The goal of this work is to predict the conversion time of videos using GBM algorithm in the regression setting. In training step of this method, heterogeneous set of video metadata and conversion features are fed to GBM to build the desired prediction model. The trained GBM model is then used in prediction step to carry out conversion time prediction task of the unseen videos. The rest of this section covers a brief description of the key idea, algorithmic description, and regularization strategies of GBM for regression- Section IV).
It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention, to modify the teachings of Satoru with the teachings of Ahmed to build a series of simple and probably inaccurate decision trees or weak models successively from the prediction residuals of the preceding decision trees and combine them to construct a final highly accurate prediction model (Ahmed- Section IV A).
However, Satoru, Ahmed and Minseok do not teach explicitly:
uploader information, information about an upload end hardware.
In the same field of endeavor, Feng teaches:
uploader information (i.e. attention degree of the category to which the source live-streaming video belongs (for example, in a case that the source live-streaming video is an apple-related video (Chinese pronunciation of apple has a meaning of peace and safe)- ¶0088), and information about an upload end hardware (i.e. play attention degree of the source live-streaming video during the live streaming (the play attention degree is determined based on the number of viewers during the live streaming and indicates popularity)- ¶0088).
It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention, to modify the teachings of Satoru, Ahmed and Minseok with the teachings of Feng to improve the response speed and hit rate of user access (Feng- ¶0064).
Regarding claim 21, apparatus claim 21 is drawn to the apparatus using/performing the same method as claimed in claim 13. Therefore, apparatus claim 21 corresponds to method claim 13, and is rejected for the same reasons of obviousness as used above.
Regarding claim 26, apparatus claim 26 is drawn to the apparatus using/performing the same method as claimed in claim 18. Therefore, apparatus claim 26 corresponds to method claim 18, and is rejected for the same reasons of obviousness as used above.
Regarding claim 29, computer-readable medium storing instructions claim 29 corresponds to the same method as claimed in claim 12, and therefore is also rejected for the same reasons of obviousness as listed above.
Claims 15, 23 and 31 are rejected under 35 U.S.C. 103 as being unpatentable over Satoru Kobayashi [US 20080279286 A1: already of record] in view of Ahmed Toufique, [Predicting Video Conversion Time from Video Metadata and Conversion Parameters using Gradient Boosting Machine: already of record] further in view of Minseok Song et al. [US 20210329279 A1] and even further in view of Hongshun Zhang et al. [US 20200382803 A1: already of record].
Regarding claim 15, Satoru, Ahmed and Minseok teach all the limitations of claim 21:
However, Satoru, Ahmed and Minseok do not teach explicitly:
further comprising:
after transcoding the first video based on the target bit rate level, in response to detecting that at least two untranscoded bit rate levels currently exist for the first video, returning, in response to a predetermined transcoding trigger condition, to perform an operation of determining the first video feature information corresponding to the first video.
In the same field of endeavor, Zhang teaches:
further comprising:
after transcoding the first video based on the target bit rate level, in response to detecting that at least two untranscoded bit rate levels currently exist for the first video, returning, in response to a predetermined transcoding trigger condition, to perform an operation of determining the first video feature information corresponding to the first video (i.e. Based on the description of this embodiment, the main transcoder cooperates with the backup transcoder. Because a certain amount of un-transcoded data exists when the main transcoder transcodes input video data, the backup transcoder picks up and continuously transcodes the un-transcoded data in a case that the main transcoder is down. Seamless switchover between the main transcoder and the backup transcoder may be implemented- ¶0168).
It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention, to modify the teachings of Satoru, Ahmed and Minseok with the teachings of Zhang to improve the whole system’s reliability (Zhang- ¶0048).
Regarding claim 23, apparatus claim 23 is drawn to the apparatus using/performing the same method as claimed in claim 15. Therefore, apparatus claim 23 corresponds to method claim 15, and is rejected for the same reasons of obviousness as used above.
Regarding claim 31, computer-readable medium storing instructions claim 31 corresponds to the same method as claimed in claim 15, and therefore is also rejected for the same reasons of obviousness as listed above.
Claims 16 and 24 are rejected under 35 U.S.C. 103 as being unpatentable over Satoru Kobayashi [US 20080279286 A1: already of record] in view of Ahmed Toufique, [Predicting Video Conversion Time from Video Metadata and Conversion Parameters using Gradient Boosting Machine: already of record] further in view of Minseok Song et al. [US 20210329279 A1] and even further in view of Otto K. Sievert et al. [US 20150281710 A1: already of record].
Regarding claim 16, Satoru, Ahmed and Minseok teach all the limitations of claim 12:
However, Satoru and Ahmed do not teach explicitly:
further comprising:
after transcoding the first video based on the target bit rate level, in response to detecting that at least one untranscoded bit rate level currently exists for the first video, deleting the currently existing at least one untranscoded bit rate level.
In the same field of endeavor, Otto teaches:
further comprising:
after transcoding the first video based on the target bit rate level, in response to detecting that at least one untranscoded bit rate level currently exists for the first video, deleting the currently existing at least one untranscoded bit rate level (i.e. The video transcoder 371 obtains transcoding instructions and outputs transcoded media. Transcoding (or performing a transcoding operation) refers to converting the encoding of media from one format to another. Transcoding instructions identify the media to be transcoded and properties of the transcoded video (e.g., file format, resolution, frame rate). The transcoding instructions may be generated by a user (e.g., through the video editing interface 360) or automatically (e.g., as part of a video upload instructed by the media server 130). The video transcoder 371 can perform transcoding operations such as adding or removing frames from an HDHF video (to modify the frame rate), reducing the resolution of all or part of the HDHF video, changing the format of the HDHF video into a different video format using one or more encoding operations (e.g., converting an HDHF video from a raw data format to an LD video in H.264), or performing any other transcoding operation. The video transcoder 371 may transcode media using hardware, software, or a combination of the two. For example, the client device is a docking station 120 that transcodes the HDHF video using a specialized processing chip such as an integrated ISP (image signal processor). As another example, the client device is a user device 140 that transcodes the HDHF video using a CPU or GPU- ¶0048).
It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention, to modify the teachings of Satoru, Ahmed and Minseok with the teachings of Otto to smooth surges in demand to upload videos and improve flexibility to allocate upload bandwidth among different client devices (Otto- ¶0090).
Regarding claim 24, apparatus claim 24 is drawn to the apparatus using/performing the same method as claimed in claim 16. Therefore, apparatus claim 24 corresponds to method claim 16, and is rejected for the same reasons of obviousness as used above.
Claims 19 and 27 are rejected under 35 U.S.C. 103 as being unpatentable over Satoru Kobayashi [US 20080279286 A1: already of record] in view of Ahmed Toufique, [Predicting Video Conversion Time from Video Metadata and Conversion Parameters using Gradient Boosting Machine: already of record] further in view of Minseok Song et al. [US 20210329279 A1] and even further in view of Anjie Liu [US 20210344937 A1: already of record].
Regarding claim 19, Satoru, Ahmed and Minseok teach all the limitations of claim 17:
However, Satoru, Ahmed and Minseok do not teach explicitly:
further comprising: in response to the popularity prediction result indicating that the second video is not popular, returning, in response to a predetermined popularity prediction trigger condition, toperform an operation of determining the second video feature information corresponding to the second video.
In the same field of endeavor, Anjie teaches:
further comprising: in response to the popularity prediction result indicating that the second video is not popular, returning, in response to a predetermined popularity prediction trigger condition, toperform an operation of determining the second video feature information corresponding to the second video (i.e. In implementation, the background server may periodically detect the current device performance load and line bandwidth load, and adjust the transcoding processing of each video to be transcoded on a current device according to a detection result. Specifically, the background server may first determine one or more videos to be transcoded for which transcoding processing needs to be adjusted according to the attribute information of all videos to be transcoded. After that, the background server may adjust the transcoding area corresponding to the transcoding rate of each level for each video to be transcoded according to the attribute information of the video to be transcoded and the above detection result. For example, if both the current device performance load and the line bandwidth load are relatively low, a video to be transcoded with higher popularity may be selected and the transcoding area corresponding to a high transcoding rate may be increased. And if both the current device performance load and the line bandwidth load are relatively high, the video to be transcoded with lower popularity may be selected and the transcoding area corresponding to the high transcoding rate may be reduced. Of course, in addition to the video popularity, the background server may also select the video to be transcoded that needs to be adjusted according to multi-dimensional attribute information such as a video owner, posting time, video type and video duration- ¶0075).
It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention, to modify the teachings of Satoru, Ahmed and Minseok with the teachings of Anjie to reduce consumption of the bandwidth resources during the video transmission. (Anjie- ¶0005).
Regarding claim 27, apparatus claim 27 is drawn to the apparatus using/performing the same method as claimed in claim 19. Therefore, apparatus claim 27 corresponds to method claim 19, and is rejected for the same reasons of obviousness as used above.
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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CLIFFORD HILAIRE
Primary Examiner
Art Unit 2488
/CLIFFORD HILAIRE/Primary Examiner, Art Unit 2488