DETAILED ACTION
Claims 1-20 are pending in this application.
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 .
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, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 9 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Pub. No. 20160248834 A1 to Richards et al. in view of C.N. No. 104813674 B to Vuskovic et al. and further in view of U.S Pub. No. 2015/0213389 A1 to Modarresi.
As to claim 1, Richards teaches a parameter configuration method, wherein the method comprising:
receiving, a configuration parameter (Descriptive metadata…Such metadata can include, for each of the plurality of shots, data describing a plurality of frames within the respective shot) of a service system and a service objective (Algorithms 230/235/240/245) of the service system through a standard interface (“…Descriptive metadata can be obtained through a number of different procedures, including manual, semi-automatic and fully automatic procedures. Automatic metadata extraction algorithms are typically complex algorithms which usually contain several intermediate media processing algorithms. As such, these metadata extraction algorithms are usually computational expensive operations to perform…Accordingly, embodiments provide techniques for extracting and using descriptive metadata as part of a video transcoding operation. Embodiments receive an instance of video content for processing and determine a plurality of shots within the instance of video content. Generally, each shot includes a sequence of frames within the video content and every frame within the video content is included within one of the plurality of shots. Embodiments analyze the instance of video content to generate metadata describing the media content. Such metadata can include, for each of the plurality of shots, data describing a plurality of frames within the respective shot. Embodiments then determine an optimized transcoding schedule for transcoding the instance of video content from a first video encoding format to a second video encoding format, based on the generated metadata. Embodiments then transcode the instance of video content according to the optimized transcoding schedule…Block 640…” paragraphs 0025/0026/0068);
performing parameter optimization on the configuration parameter to obtain a recommended parameter value (generate an optimized transcoding schedule… Such a schedule can include, for instance, a group of pictures (GOP) size, particular frames to use as reference frames within a GOP, a GOP pattern for use in transcoding the instance of video content, encoding bitrates for particular frames/ Node Management Controller 535) that meets the service objective (“…The metadata-based transcoding component 115 may then generate an optimized transcoding schedule for transcoding the instance of video content from a first video encoding format to a second video encoding format, based on the generated metadata. Generally, the transcoding schedule describes an optimized transcoding operation for the instance of video content. Such a schedule can include, for instance, a group of pictures (GOP) size, particular frames to use as reference frames within a GOP, a GOP pattern for use in transcoding the instance of video content, encoding bitrates for particular frames within the instance of video content, and so on…The user then inputs to the media processing API 530 the details of the transcoding operation to be performed (block 640). Such details may input, e.g., identifying information for the instance of media content to be transcoded, details of the transcoding operation to be performed such as the encoding format to be used, the encoding bitrate to be used, a total file size for the resulting transcoded media content, and so on. The media processing API 530 accepts the new transcoding job and forwards information describing the transcoding job to the node management controller 535 (block 645). The node management controller 535 calculates estimated processing metrics for performing the requested transcoding operation (block 650). Such metrics can include an estimated completion time for the transcoding operation. Additionally, the node management controller 535 could perform a final optimization of the transcoding schedule. For example, the node management controller 535 could determine that the instance of media content specified by the user is significantly larger in size than a typical instance of media content and thus could determine that a greater number of worker VMs should be used to process the media content…” paragraphs 0031/0040-0042/0048/0068); and
outputting the recommended parameter value to configure the configuration parameter (“…The node management controller 535 returns the determined transcoding details and estimated metrics to the media processing API 530 (block 655), which in turn outputs the transcoding details and estimated metrics for display to the user (block 660) and the method 600 ends. Doing so provides an optimized technique for transcoding media content…” paragraph 0069).
Richards does not explicitly teach receiving, through a standard interface a configuration parameter and wherein the service objective is a requirement of at least one key performance indicator (KPI) based on requirements of the service system.
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Vuskovic teaches receiving, through a standard interface a configuration parameter (“…in some embodiments, the video optimizer 122 can provide the client machine 102A-102N and/or a third party server machine 118 accesses an application programming interface (API). using a video optimizer 122 API, the client machine 102A-102N and/or a third party server machine 118 can select the video (or a single video) and starting analyzing, recommending and/or optimization of the video… wherein analyzing the video includes metadata analysis associated with the video, and analyzed using the metadata to identify the one or more optimization…” claim 6).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claim invention to modify the system of Richards with the teaching of Vuskovic because the teaching of Vuskovic would improve the system of Richards by providing application programming interface for optimizing video distribution.
Modarresi teaches wherein the service objective is a requirement of at least one key performance indicator (KPI) based on requirements (KPIs) of the service system (multimedia assets) (“…Embodiments disclosed herein provide automated and semi-automated methods and systems for determining KPIs associated with user interactions with online content. The online content can include multimedia assets such as video content and advertisements (i.e., ads) included within the video content hosted on a website. In the context of online video content, exemplary methods and systems can determine a KPI for video advertisement views (i.e., ad views). Such a KPI can be analyzed to determine if ad views are down significantly because users watch video content on a website but fail to watch enough video to generate more ad views. Embodiments track outputs and metrics related to monetization, such as, but not limited to, presentations of and interactions with linear advertisements, overlay advertisements, and other types of advertisements in online content being viewed. Although exemplary computer-implemented methods and systems are described herein in the context of websites, it is to be understood that the systems and methods can be applied to multimedia assets, such as, but not limited to, web applications (web apps), interactive video on demand (VOD) assets (i.e., pay-per-view movies and rental assets), subscription video on demand (SVOD) assets, and software programs such as video games…” paragraphs 0037).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claim invention to modify the system of Richards and Vuskovic with the teaching of Modarresi because the teaching of Modarresi would improve the system of Richards and Vuskovic by providing KPI calculator to obtain performance comparisons across the portfolios or across domains to optimally perform multimedia assets functions.
As to claims 9 and 17, see the rejection of claim 1 above.
Claims 1, 3, 9 11, 17 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Pub. No. 20160248834 A1 to Richards et al. in view of C.N. No. 104813674 B to Vuskovic et al. and further in view of U.S Pub. No. 2018/0143891 A1 to Polisetty et al.
As to claim 1, Richards teaches a parameter configuration method, wherein the method comprising:
receiving, a configuration parameter (Descriptive metadata…Such metadata can include, for each of the plurality of shots, data describing a plurality of frames within the respective shot) of a service system and a service objective (Algorithms 230/235/240/245) of the service system through a standard interface (“…Descriptive metadata can be obtained through a number of different procedures, including manual, semi-automatic and fully automatic procedures. Automatic metadata extraction algorithms are typically complex algorithms which usually contain several intermediate media processing algorithms. As such, these metadata extraction algorithms are usually computational expensive operations to perform…Accordingly, embodiments provide techniques for extracting and using descriptive metadata as part of a video transcoding operation. Embodiments receive an instance of video content for processing and determine a plurality of shots within the instance of video content. Generally, each shot includes a sequence of frames within the video content and every frame within the video content is included within one of the plurality of shots. Embodiments analyze the instance of video content to generate metadata describing the media content. Such metadata can include, for each of the plurality of shots, data describing a plurality of frames within the respective shot. Embodiments then determine an optimized transcoding schedule for transcoding the instance of video content from a first video encoding format to a second video encoding format, based on the generated metadata. Embodiments then transcode the instance of video content according to the optimized transcoding schedule…Block 640…” paragraphs 0025/0026/0068);
performing parameter optimization on the configuration parameter to obtain a recommended parameter value (generate an optimized transcoding schedule… Such a schedule can include, for instance, a group of pictures (GOP) size, particular frames to use as reference frames within a GOP, a GOP pattern for use in transcoding the instance of video content, encoding bitrates for particular frames/ Node Management Controller 535) that meets the service objective (“…The metadata-based transcoding component 115 may then generate an optimized transcoding schedule for transcoding the instance of video content from a first video encoding format to a second video encoding format, based on the generated metadata. Generally, the transcoding schedule describes an optimized transcoding operation for the instance of video content. Such a schedule can include, for instance, a group of pictures (GOP) size, particular frames to use as reference frames within a GOP, a GOP pattern for use in transcoding the instance of video content, encoding bitrates for particular frames within the instance of video content, and so on…The user then inputs to the media processing API 530 the details of the transcoding operation to be performed (block 640). Such details may input, e.g., identifying information for the instance of media content to be transcoded, details of the transcoding operation to be performed such as the encoding format to be used, the encoding bitrate to be used, a total file size for the resulting transcoded media content, and so on. The media processing API 530 accepts the new transcoding job and forwards information describing the transcoding job to the node management controller 535 (block 645). The node management controller 535 calculates estimated processing metrics for performing the requested transcoding operation (block 650). Such metrics can include an estimated completion time for the transcoding operation. Additionally, the node management controller 535 could perform a final optimization of the transcoding schedule. For example, the node management controller 535 could determine that the instance of media content specified by the user is significantly larger in size than a typical instance of media content and thus could determine that a greater number of worker VMs should be used to process the media content…” paragraphs 0031/0040-0042/0048/0068); and
outputting the recommended parameter value to configure the configuration parameter (“…The node management controller 535 returns the determined transcoding details and estimated metrics to the media processing API 530 (block 655), which in turn outputs the transcoding details and estimated metrics for display to the user (block 660) and the method 600 ends. Doing so provides an optimized technique for transcoding media content…” paragraph 0069).
Richards does not explicitly teach receiving, through a standard interface a configuration parameter and wherein the service objective is a requirement of at least one key performance indicator (KPI) based on requirements of the service system.
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Vuskovic teaches receiving, through a standard interface a configuration parameter (“…in some embodiments, the video optimizer 122 can provide the client machine 102A-102N and/or a third party server machine 118 accesses an application programming interface (API). using a video optimizer 122 API, the client machine 102A-102N and/or a third party server machine 118 can select the video (or a single video) and starting analyzing, recommending and/or optimization of the video… wherein analyzing the video includes metadata analysis associated with the video, and analyzed using the metadata to identify the one or more optimization…” claim 6).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claim invention to modify the system of Richards with the teaching of Vuskovic because the teaching of Vuskovic would improve the system of Richards by providing application programming interface for optimizing video distribution.
Polisetty teaches wherein the service objective is a requirement of at least one key performance indicator (KPI) (KPI Values 250) based on requirements (KPI Thresholds 460/462) of the service system (mobile application portfolio management system 100) (“…A KPI calculator 240 calculates various KPI values 250 from the performance data obtained from the various data sources 202-212. As mentioned herein, KPIs across an organization or enterprise-wide KPIs obtained for the mobile applications sharing the same ORG ID, portfolio-wide KPIs, KPIs for particular domains and application-level KPIs may be calculated by the KPI calculator 240. The KPI calculator 240 can also obtain performance comparisons across the portfolios or across domains. The KPI values 250 thus obtained may be stored to the local data storage 152 for use by other elements of the mobile application portfolio management system 100 in downstream processes for generating alerts or updating dashboards. Based on an analysis of the KPI behavior various adjustments and tweaks as outlined herein can be made to the mobile applications within the mobile application portfolios 110, 112 thereby improving their performances. In some examples the table 214 and the KPI values 250 may be stored in external databases that may be remote from the mobile application portfolio management system 100… FIG. 4 shows a block diagram of the alert generator 166 in accordance with an example. The alert generator 166 is configured with a rules engine 402 that analyzes the KPI values 250, the summarized KPIs 310, the KPI trends 330 to identify KPIs that indicate subpar performance by the mobile application, organization or a category on one or more metrics. The subpar performance can be identified by comparing one or more of the KPI values 250, summarized KPIs 310 or KPI trends 330 with respective KPI thresholds 462. The respective KPI thresholds 462 can be determined for each of a plurality of KPIs, summarized KPIs or KPI trends 330 based on rules 450. For each mobile application associated with a unique APP ID within the portfolio manager, a table 480 including the KPIs, rules 450, thresholds 460 which include a plurality of KPI thresholds 462 and a plurality of element thresholds 464 and the corrective actions 470 can be stored in the local data storage 152 wherein each of the corrective actions 470 may be mapped to specific rules associated with a given KPI…The portfolio level view of mobile app performance may be used for diagnostics, such as to identify causes of mobile app crashes. For example, a user may be responsible for different versions of a video on demand (VoD) mobile app that are deployed in different geographic regions. The mobile application portfolio management system 100 may capture performance metrics of the VoD mobile apps, and from the portfolio view, the user can ascertain that the mobile app is performing worse in a particular region and/or on a particular device when compared to other regions or devices. If it is a region-specific problem, then the crashes may be caused by region-specific features of the mobile app, such as language, or other region-specific features that are causing failures. An example of a region-specific cause of failure for the VoD mobile app is bandwidth. From the portfolio view, it may be determined that regions with less bandwidth are having worse performance, and a corrective action to modify the VoD mobile app for those regions to highlight low-resolution video options may be suggested…” paragraphs 0037/0041/0055).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claim invention to modify the system of Richards and Vuskovic with the teaching of Polisetty because the teaching of Polisetty would improve the system of Richards and Vuskovic by providing KPI calculator to obtain performance comparisons across the portfolios or across domains to optimally perform VoD mobile apps functions.
As to claim 3, Vuskovic teaches the parameter configuration method according of claim 1, wherein the standard interface comprises an application programming interface (API) (“…in some embodiments, the video optimizer 122 can provide the client machine 102A-102N and/or a third party server machine 118 accesses an application programming interface (API). using a video optimizer 122 API, the client machine 102A-102N and/or a third party server machine 118 can select the video (or a single video) and starting analyzing, recommending and/or optimization of the video… wherein analyzing the video includes metadata analysis associated with the video, and analyzed using the metadata to identify the one or more optimization…” claim 6).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claim invention to modify the system of Richards and Polisetty with the teaching of Vuskovic because the teaching of Vuskovic would improve the system of Richards and Polisetty by providing a performance measurement or metrics to evaluate a success of a particular activity (such as projects, programs, products and other initiatives) in which encourages a focus for strategic and operational improvement.
As to claims 9 and 17, see the rejection of claim 1 above.
As to claims 11 and 18, see the rejection of claim 3 above.
Claims 2 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Pub. No. 20160248834 A1 to Richards et al. in view of C.N. No. 104813674 B to Vuskovic et al. and further in view of U.S Pub. No. 2018/0143891 A1 to Polisetty et al. as applied to claims 1 and 9 above, and further in view of U.S. Pat. No. 7,446,780 B1 issued to Everitt et al.
As to claim 2, Richards as modified by Vuskovic and Polisetty teaches the parameter configuration method of comprising however it is silent with reference to wherein the method further receiving, through the standard interface, at least one of a load or a constraint condition wherein performing the parameter optimization comprises performing the parameter optimization on the configuration parameter based on the at least one of the load or the constraint condition.
Everitt teaches wherein the method further receiving, through the standard interface (application program interface (API)), at least one of a load or a constraint condition wherein performing the parameter optimization comprises performing the parameter optimization on the configuration parameter based on the at least one of the load or the constraint condition (“…APPLICATION PROGRAM INTERFACE (API)…The embodiments of the invention described above provide temporal multisampling of pixel data within the graphics driver and/or graphics processing subsystem, thereby making temporal multisampling largely transparent to the application. To enable temporal multisampling, applications provide data indicating the motion of a primitive, as described above, but applications are not required to specify multisampling parameters, generate intermediate primitives, or otherwise control the temporal multisampling process…In some embodiments, an application program interface (API) can be provided in the graphics driver, thereby enabling applications to exert control over various features and options of the temporal multisampling process. As is generally known, an API provides a set of function calls that an application programmer can invoke in order to interact with various features of a platform on which the application is running, e.g., by adjusting graphics processing parameters to optimize the application's performance. In an embodiment of the present invention, the API of the graphics driver can include function calls that enable or disable temporal multisampling, select the number of sample locations and/or time points to use, select a sampling pattern to be used by the rasterizer, select among options for computing a shading value to be shared among sample locations for a pixel, and so on. In addition, as described above, multidimensional multisampling can be used for any number of dimensions corresponding to various properties of an image, such as motion and/or shadowing. The API can include function calls to select the dimensions and/or properties to be multisampled…Providing an API is not required, but allowing developers to control trade offs between rendering speed and image quality increases flexibility of the system and the ability to deliver optimum performance for a variety of applications…” Col. 13 Ln. 1-24).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claim invention to modify the system of Richards, Vuskovic and Polisetty with the teaching of Everitt because the teaching of Everitt would improve the system of Richards, Vuskovic and Polisetty by providing an application programming interface that allow developers to control trade offs between rendering speed and image quality increases flexibility of the system and the ability to deliver optimum performance for a variety of applications (Everitt Col. 13 Ln. 21-24).
As to claim 10, see the rejection of claim 3 above.
Claims 5, 6, 13, 14, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Pub. No. 20160248834 A1 to Richards et al. in view of C.N. No. 104813674 B to Vuskovic et al. and further in view of U.S Pub. No. 2018/0143891 A1 to Polisetty et al. as applied to claims 1, 9, and 17 above, and further in view of C.N. No. 106648654 A to Luo et al.
As to claim 5, Richards as modified by Vuskovic and Polisetty teaches the parameter configuration method according of claim 1, however it is silent with reference to wherein the method further comprises:
determining, using condition-based determining control logic, a parameter optimization algorithm corresponding to the configuration parameter.
Luo teaches wherein the method further comprises:
determining, using condition-based determining control logic (decision tree), a parameter optimization algorithm corresponding to the configuration parameter (“…Specifically, the random forest algorithm for modelling specifically comprises the training set from a given training set through multiple times of repeated random sampling to obtain multiple bootstrap data set for each bootstrap data set building a decision tree. constructed by iteration of the data point to the left and right two in the subset, the partitioning process is the distribution parameter space of one search function to find the optimal parameter maximum information gain meaning, centralized histogram empirical classification tag reaches the leaf node of the statistical training position in each leaf node estimation on the leaf node; iterative training process is performed to the maximum tree depth or until can not be set by the user by continuously obtaining a greater information gain in the random forest algorithm, execution time as the dependent variable, input set and configuration parameter as the independent variable, it also needs to determine the ntree and mtry values, ntree value is established in random forest decision tree number, mtry value is number of sample predictor at the each one split node of the decision tree…” claim 4).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claim invention to modify the system of Richards, Vuskovic and Polisetty with the teaching of Luo because the teaching of Luo would improve the system of Richards, Vuskovic and Polisetty by providing a decision support recursive partitioning structure that uses a tree-like model of decisions and their possible consequences.
As to claim 6, Richards as modified by Vuskovic and Polisetty teaches the parameter configuration method according of claim 5, however it is silent with reference to wherein determining the parameter optimization algorithm comprises determining the parameter optimization algorithm using a condition-based determining decision tree.
Luo teaches wherein determining the parameter optimization algorithm comprises determining the parameter optimization algorithm using a condition-based determining decision tree (decision tree) (“…Specifically, the random forest algorithm for modelling specifically comprises the training set from a given training set through multiple times of repeated random sampling to obtain multiple bootstrap data set for each bootstrap data set building a decision tree. constructed by iteration of the data point to the left and right two in the subset, the partitioning process is the distribution parameter space of one search function to find the optimal parameter maximum information gain meaning, centralized histogram empirical classification tag reaches the leaf node of the statistical training position in each leaf node estimation on the leaf node; iterative training process is performed to the maximum tree depth or until can not be set by the user by continuously obtaining a greater information gain in the random forest algorithm, execution time as the dependent variable, input set and configuration parameter as the independent variable, it also needs to determine the ntree and mtry values, ntree value is established in random forest decision tree number, mtry value is number of sample predictor at the each one split node of the decision tree…” claim 4).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claim invention to modify the system of Richards, Vuskovic and Polisetty with the teaching of Luo because the teaching of Luo would improve the system of Richards, Vuskovic and Polisetty by providing a decision support recursive partitioning structure that uses a tree-like model of decisions and their possible consequences.
As to claims 13 and 20, see the rejection of claim 5 above.
As to claim 14, see the rejection of claim 6 above.
Claims 8 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Pub. No. 20160248834 A1 to Richards et al. in view of C.N. No. 104813674 B to Vuskovic et al. and further in view of U.S Pub. No. 2018/0143891 A1 to Polisetty et al. as applied to claims 1 and 9 above, and further in view of U.S. Pub. No. 2010/0091888 A1 to Nemiroff.
As to claim 8, Richards as modified by Vuskovic and Polisetty teaches the parameter configuration method according of claim 1, however it is silent with reference to wherein the method further preprocessing the configuration parameter.
Nemiroff teaches wherein the method further preprocessing the configuration parameter (“…Preprocessing is used to accomplish rate control in the embodiment of FIG. 1C. In this regard, the GOP coding modules 150A through 150N comprise preprocessing. Essentially preprocessing involves analyzing unencoded GOPs before encoding. For instance, the content of a picture can be analyzed for complexity and variance of complexity within a picture. Here, the purpose of preprocessing is to better estimate a bit budget of encoded GOP bits. In this regard, the closer the number of bits budgeted to the actual number of encoded GOP bits, the closer the match of GOPs generated from GOP coding modules 150A through 150N with regard to the ratio of number of encoded GOP bits divided by the bit-rate of the encoder…” paragraph 0068).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claim invention to modify the system of Richards, Vuskovic and Polisetty with the teaching of Nemiroff because the teaching of Nemiroff would improve the system of Richards, Vuskovic and Polisetty by providing a technique for analyzing unencoded GOPs before encoding in order to make sure of optimal final product.
As to claim 16, see rejection of claim 8 above.
Allowable Subject Matter
Claims 4, 7, 12, 15 and 19 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Reasons for Allowance
The following is an examiner’s statement of reasons for allowance:
The closest prior art of records, (U.S. Pub. No. 20160248834 A1 to Richards et al. and C.N. No. 104813674 B to Vuskovic et al.), taken alone or in combination do not specifically disclose or suggest the claimed recitations (claims 4, 7, 12, 15 and 19), when taken in the context of claims as a whole.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Response to Arguments
Applicant’s arguments with respect to claims 1-20 have been considered but are moot because the new ground of rejection relies on additional references not applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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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/CHARLES E ANYA/Primary Examiner, Art Unit 2194