POLICY DRIVEN INTELLIGENT TELEMETRY CONTROLLER

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 . Examiner notes the entry of the following papers: Amended claims filed 8/6/2026. Applicant arguments/remarks made in amendment filed 8/6/2026. Claims 1, 8, and 15 are amended. Claims 1-20 are presented for examination. Response to Arguments Applicant presents arguments. Each is addressed. Applicant argues “Amended claim 1 is allowable over Yermakov for at least the reasons discussed above.” (Remarks, page 11, paragraph 3, line 11.) The argument is moot in view of new areas of the prior art found that teaches the amended limitations. Applicant argues “Amended claim 1 is allowable over Wang for at least this reason.” (Remarks, page 12, paragraph 2, line 8.) The argument is moot in view of new areas of the prior art found that teaches the amended limitations. Applicant argues “Amended claim 1 is allowable over Wang and Jain for at least this reason.” (Remarks, page 13, paragraph 2, line 9.) The argument is moot in view of new areas of the prior art found that teaches the amended limitations. Applicant argues “The combination still would not teach or suggest the amended claim features tying the historical utilization, business policy, predicted demand, and transfer use to data sets of application data and entity data and to business data set/telemetry data transfer. The Office Action's stated motivations for improving traffic flow classification and predicting bandwidth demand for prioritization do not explain why one of ordinary skill would have modified Yermakov's customer/circuit aberrance detector with Wang's SDN traffic-flow QoS classifier and Jain's KPI/QoS analytics to arrive at Applicant's claimed business/telemetry dataset demand prediction and data-set transfer control. The cited rationales therefore do not provide the necessary link between the factual findings and the claimed invention as amended.” (Remarks, page 13, paragraph 3, line 1.) The argument is moot in view of new motivation used for the combination to teach the amended claims. Applicant argues “The alleged combination of Wang and Jain with Yermakov suggested by the Office Action, even if proper, which Applicant does not concede, does not teach or suggest every element of amended claim 1.” (Remarks, page 14, paragraph 2, line 1.) The argument is moot in view of new areas of the prior art found that teach the amended claim. Applicant argues “Amended claims 8 and 15 include features similar as those of amended claim 1 identified herein as allowable over Yermakov, Wang, and Jain. Applicant respectfully requests that the § 103 rejections of independent claims 1, 8, and 15 be withdrawn.” (Remarks, page 14, paragraph 3, line 1.) However, claim 1 remains rejected. Claims 8 and 15 remain rejected over new areas of the prior art found that teach the amended claims. Applicant argues “Applicant submits that these dependent claims are allowable by reason of depending from an allowable claim, as well as for adding further elements. Applicant respectfully requests allowance of all pending claims.” (Remarks, page 15, paragraph 1, line 3.) However, the independent claims remain rejected. The dependent claims remain rejected at least for depending from rejected base claims. No “further elements” of the dependent claims were identified, so no rebuttal is made. 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. Claims 1, 4-5, 7-8, 11-12, 14-15, and 18-19 are rejected under 35 U.S.C. § 103 as being unpatentable over Yermakov, (US 2018/0123901 A1, Distributed Calculation of Customer Bandwidth Utilization Models, herein Yermakov), Wang, et al (A Framework for QoS-aware Traffic Classification Using Semi-supervised Machine Learning in SDNs, herein Wang), and Jain, et al (Applying Big Data Technologies to Manage QoS in an SDN, herein Jain). Regarding claim 1, Yermakov teaches an Information Handling System (IHS), comprising: a processor; and a memory coupled to the processor, wherein the memory comprises program instructions stored thereon that, upon execution by the processor (Yermakov, paragraph [0004], line 1 “In an embodiment, a method is disclosed for predicting bandwidth utilization for a customer of a connectivity service provider. In the exemplary embodiment, a connectivity service provider network provides internet and other network connectivity services to a client network via network interface devices.” And, paragraph [0032], line 7 “These two modules can be implemented in software on a single NID device, which generally will contain surplus memory for storage and computing resources in the form of processors for some general uses.” Examiner notes the specification of the instant application recites “This disclosure relates generally to Information Handling Systems (IHSs), and, more specifically, to a policy driven intelligent telemetry controller.” (Specification, paragraph [0001], line 1.) And, “An IHS generally processes, compiles, stores, and/or communicates information or data for business, personal, or other purposes thereby allowing users to take advantage of the value of the information” (Specification, paragraph [0002], line 1.) Therefore, Examiner is interpreting IHS as any system or method that processes, compiles, stores, and/or communicates information or data. In other words, method for predicting bandwidth utilization is IHS, processors is processor, and memory is memory for storing program instructions to be executed by a processor.), cause the IHS to: [use a support vector machine (SVM) classification model] applied to historical network bandwidth utilization data for application data and entity data (Yermakov, paragraph [0043], line 2 “The aggregation server 220 serves as the central repository for historical bandwidth utilization data for each client node and the most updated bandwidth utilization models for each client node circuit.” In other words, historical bandwidth utilization data is historical network bandwidth utilization data for application data and entity data.) , the historical network bandwidth utilization data including past network bandwidth utilization by data set of the application data and entity data over different timeframes (Yermakov, paragraph [0024], line 1 “Given the periodical nature of bandwidth utilization that is typical of many client networks, historical bandwidth utilization data for a customer is collected and analyzed to learn the bandwidth utilization pattern of a customer during an interval of the periodicity (e.g., day, week, or month) by a connectivity service provider (CSP).” And, paragraph [0075], line 1 “The foregoing description of the specific embodiments will so fully reveal the general nature of the invention that others can, by applying knowledge within the skill of the art, readily modify and/or adapt for various applications such specific embodiments, without undue experimentation, without departing from the general concept of the present invention.” In other words, historical bandwidth utilization data is historical bandwidth utilization data, interval of the periodicity (e.g., day, week, or month) is over different timeframes, customer is entity, and adapt for various applications is dataset of the application data.) and a policy configuration of data criticality (Yermakov, paragraph [0073], line 1 “Following this step, a policy can be applied in step 625 as warranted by the connectivity service provider.” In other words, a policy can be applied is a policy configuration of data criticality.) comprising a business policy set (Yermakov, paragraph [0073], line 1 “Following this step, a policy can be applied in step 625 as warranted by the connectivity service provider. For example, the connectivity service provider may notify the customer by flagging the customer account and reflecting this flag in the customer's portal page.” Examiner notes that it is unclear what “business policy set” means. The specification of the instant application recites “Embodiments of the present policy driven intelligent telemetry controller are implemented as a software component to predict network bandwidth demand for a business data set and telemetry data transfer, based on set business policy and past network bandwidth utilization by the data set, over a different timeframe, for example time of a day and/or month.” (Specification, paragraph [0034], line 4.) This indicates that the business contemplated is the customer that is using the network service (“predict network bandwidth demand for a business data set” ). There is no description or patentable distinction between business data as compared to customer data, or any other data, provided in the specification. Therefore, Examiner is interpreting that customer is business and customer data is business data. In other words, policy is policy, customer is business, and from previous mapping, utilization data is data set.), to [classify and create a cluster for each critical data set of the application data and entity data]; [use linear regression to predict future network bandwidth demand variations for each cluster, across a plurality of time frames], wherein the future network bandwidth demand variations comprise bandwidth demand for a business data set and telemetry data transfer based on the business policy set and the past network bandwidth utilization by the data sets (Yermakov, claim 1, line 1 “A method for calculating a bandwidth utilization model at a network interface device (ND) of a customer service provider (CSP) network, comprising: collecting, at the ND, bandwidth utilization data for a circuit associated with a customer network, the bandwidth utilization data comprising utilization metrics for time slices within a time period…” and, claim 1, line 20 “…the updated bandwidth model forecasts bandwidth utilization at time slices during a future time period according to the utilization of respective time slices during the time period.” Examiner notes the specification of the instant application recites “Transfer of telemetry data (e.g., usage utilization, performance metrics, security configuration, business data, etc.) to cloud (a) server(s) is one of the key 5 functions that consumes significant amount of network bandwidth, and on many occasions, utilization of network bandwidth causes significant performance impact in business function.” (Specification, paragraph [0031], line 3 .) Based on this, Examiner is interpreting telemetry data is utilization metrics comprising bandwidth utilization, and performance. In other words, forecast bandwidth utilization...during a future time period is the future network bandwidth demand, from prior mapping, policy is policy, customer is business, historical bandwidth utilization data is past network bandwidth utilization by the data sets, and utilization metrics for time slices within a time period from a network device (ND) is telemetry data transfer.) ; and use a resulting predicted network bandwidth for the business data set and telemetry data transfer to transfer critical data sets during a specific time window (Yermakov, paragraph [0072], line 1 “In step 624, the utilization data received by the NID is then compared to the predicted utilization obtained at step 622 to determine if the utilization of the circuit is aberrant compared to its predicted value. Several criteria can be applied to determine this aberrance, such as simple thresholds applied to the difference between the predicted utilization and the actual utilization represented by the utilization data. If the difference is larger than a threshold, such that the actual utilization is higher than or lower than the predicted utilization by some unacceptable amount, this circuit can be flagged in this time slice.” And, paragraph [0073], line 1 “Following this step, a policy can be applied in step 625 as warranted by the connectivity service provider. The policy itself could take many forms.” In other words, predicted utilization is predicted network bandwidth for transferring critical dataset, and from prior mapping, customer data is business data, and utilization metrics for time slices within a time period from a network device (ND) is telemetry data transfer during a specific time window.) Thus far, Yermakov does not explicitly teach use a support vector machine (SVM) classification model. Wang teaches use a support vector machine (SVM) classification model (Wang, page 762, column 1, paragraph 2, line 2 “QoS-aware traffic classification: The QoS classifier exploits the semi-supervised ML algorithm, e.g., Laplacian SVM which is hosted inside the centralized SDN controller.” In other words, Laplacian SVM is support vector machine, and classification is classification.) Wang teaches classify and create a cluster for each critical data set of the application data and entity data (Wang, page 761, column 1, paragraph 3, line 1 “Our proposed framework aims to classify a traffic flow into a QoS category in a real-time and adaptive fashion without the need of identifying the exact application from which the traffic flow is generated. On one hand, real-time traffic classification is of critical important, which, however, has not been properly explored. Many existing algorithms use flow-level features for traffic classification, which is ”a posteriori” method and may generate classification results when the flow ends. On the other hand, an adaptive traffic classification system is required, which should be adapted to the everchanging network applications by periodically re-training the classifiers.” And, page 762, column 1, paragraph 3, line 3 “It is assumed that applications that require the same QoS, tend to exhibit similar statistical properties. This is a typical semi-supervised learning assumption [16], called the cluster assumption.” In other words, classify is classify, cluster assumption is cluster, and QoS(Quality of Service)-aware is classifying based on criticality of data set.) Both Yermakov and Wang are directed to monitoring and classifying network traffic using machine learning, among other things. Yermakov teaches an Information Handling System (IHS), comprising: a processor; and a memory coupled to the processor, wherein the memory comprises program instructions for prediction of bandwidth utilization; but does not explicitly teach using a support vector machine (SVM) classification model for classification. Wang teaches using a support vector machine (SVM) classification model for classification. In view of the teaching of Yermakov, it would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Wang into Yermakov. This would result in an Information Handling System (IHS), comprising: a processor; and a memory coupled to the processor, wherein the memory comprises program instructions for prediction of bandwidth utilization using a support vector machine (SVM) classification model for classification. One of ordinary skill in the art would be motivated to do this in order to improve traffic flow classification in real-time without specifically identifying the application generating the flow. (Wang, page 760, column 1, paragraph 2, line 1 “Our proposed framework aims to classify a traffic flow into a QoS category in a real-time and adaptive fashion without the need of identifying the exact application from which the traffic flow is generated.”) Thus far, the combination of Yermakov and Wang does not explicitly teach use linear regression to predict future network bandwidth demand variations for each cluster, across a plurality of time frames. Jain teaches use linear regression to predict future network bandwidth demand variations for each cluster, across a plurality of time frames (Jain, page 303, column 2, paragraph 4, line 1 “We also used a linear regression machine learning algorithm, M5Rules that is pre-integrated with Weka. We chose M5Rules as it combines decision trees and linear regression for prediction.” And, abstract, line 6 “This work shows how big data analytics were used to improve the management of QoS in an SDN by performing multi-dimensional analysis of Key Performance Indicators (KPIs) and applying machine learning algorithms to discover new correlations, perform root cause analysis and predict traffic congestion.” In other words, used a linear regression machine learning algorithm…for prediction is use linear regression to predict, and predict traffic congestion is predict future network bandwidth. Examiner notes that time frames/window is previously mapped to Yermakov, and cluster is previously mapped to Wang.) Both Jain and the combination of Yermakov and Wang are directed to predicting bandwidth usage of network traffic using machine learning, among other things. The combination of Yermakov and Wang teach an Information Handling System (IHS), comprising: a processor; and a memory coupled to the processor, wherein the memory comprises program instructions stored thereon that, upon execution by the processor, cause the IHS to: use a support vector machine (SVM) classification model applied to historical network bandwidth utilization data for application data and entity data, the historical network bandwidth utilization data including past network bandwidth utilization by data set of the application data and entity data over different timeframes and a policy configuration of data criticality comprising a business policy set, to classify and create a cluster for each critical data set of the application data and entity data; but does not explicitly teach using linear regression to predict future network bandwidth demand variations for each cluster, across a plurality of time frames. Jain teaches using linear regression to predict future network bandwidth demand variations for each cluster, across a plurality of time frames. In view of the teaching of the combination of Yermakov and Wang, it would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Jain into the combination of Yermakov and Wang. This would result in an Information Handling System (IHS), comprising: a processor; and a memory coupled to the processor, wherein the memory comprises program instructions stored thereon that, upon execution by the processor, cause the IHS to: use a support vector machine (SVM) classification model applied to historical network bandwidth utilization data for application data and entity data, the historical network bandwidth utilization data including past network bandwidth utilization by data set of the application data and entity data over different timeframes and a policy configuration of data criticality comprising a business policy set, to classify and create a cluster for each critical data set of the application data and entity data, and using linear regression to predict future network bandwidth demand variations for each cluster, across a plurality of time frames. One of ordinary skill in the art would be motivated to do this because effectively managing large volumes of performance data across a network requires effective prediction of bandwidth demand for prioritization. (Jain, page 302, column 1, paragraph 2, line 1 “Effective QoS management requires accurate and timely collection of large volumes of performance data across the network. KPIs [1, 2] such as delay, round trip time, jitter, dropped packets and utilization need to be analyzed to determine where there are QoS degradations and how they can be best resolved e.g. through network expansion, changing traffic prioritization or applying more restrictive controls.”)8. Regarding claim 4, The combination of Yermakov, Wang and Jain teaches the IHS of claim 1, wherein, upon execution, by the processor, the program instructions further cause the IHS to collect network utilization for one or more applications, processes and/or subnetworks, at predetermined time intervals, to provide the historical network bandwidth utilization data for application data and entity data (Yermakov, paragraph [0024], line 1 “Given the periodical nature of bandwidth utilization that is typical of many client networks, historical bandwidth utilization data for a customer is collected and analyzed to learn the bandwidth utilization pattern of a customer during an interval of the periodicity (e.g., day, week, or month) by a connectivity service provider (CSP). To learn the bandwidth utilization pattern of a customer during a periodicity interval, machine learning techniques such as regression and kernel methods may be applied within the connectivity service provider network, and using this technique, the bandwidth utilization may be forecasted. In the current invention, the machine learning technique is applied for each client network, indexed by a "circuit" and its associated "circuit network identifier." In other words, utilization data… is collected is collect network utilization data, for a customer is for one or more applications, periodicity (e.g., day, week, or month) is predetermined time intervals, and historical bandwidth utilization data for a customer is historical network bandwidth utilization data for application data and entity data.) Regarding claim 5, The combination of Yermakov, Wang, and Jain teaches the IHS of claim 4, wherein the network utilization for each application data or entity data set comprises a historical transfer time and bandwidth allocation (Wang, Fig. 3, and page 763, paragraph 2, line 4 “The flow information is stored in a .info file with a format shown in Fig. 3. After going through all the flows, we found that more than half of the flows were torrent traffic, so that from the perspective of building a more diverse data set, 440000 torrent flows were removed which finally produced the 59GB .csv file.” PNG media_image1.png 218 444 media_image1.png Greyscale In other words, flow_id start_time minus end_time is historical transfer time. Examiner notes that historical bandwidth utilization is previously mapped to Yermakov.). Regarding claim 7, The combination of Yermakov, Wang, and Jain teaches the IHS of claim 1, wherein the IHS is an edge gateway (Yermakov, paragraph [0024], line 14 “The processes applying the machine learning techniques occur at a network interface device that serves as an edge, or "gateway," routers into the connectivity service provider network.” In other words, machine learning techniques occur at a network interface device that serves as an edge, or “gateway” is the IHS is an edge gateway.) . Claims 8, 11-12, and 14 are non-transitory computer-readable storage media claims that correspond to information handling system claims 1, 4-5, and 7, respectively. Otherwise, they are not patentably distinct. The combination of Yermakov, Wang, and Jain teaches a non-transitory computer-readable storage media (Yermakov, claim 18 “A program storage device tangibly embodying a program of instructions executable by at least one machine to perform a method for providing a predicting bandwidth utilization for a customer of a connectivity service provider (CSP) network…” In other words, a program storage device is a non-transitory, computer-readable storage medium.). Therefore, claims 8, 11-12, and 14 are rejected for the same reasons as claims 1, 4-5, and 7, respectively. Claims 15, and 18-19, are method claims corresponding to information handling system claims 1, and 4-5, respectively. Otherwise, they are not patentably distinct. The combination of Yermakov, Wang, and Jain teaches a method (Yermakov, claim 1 “A method for calculating a bandwidth utilization model at a network interface device (ND) of a customer service provider (CSP) network…” In other words, a method is a method.). Therefore, claims 15, and 18-19 are rejected for the same reasons as claims 1, and 4-5, respectively. 13. Claims 2-3, 6, 9-10, 13, 16-17, and 20 are rejected under 35 U.S.C § 103 over Yermakov, Wang, Jain, and Isolani, et al (SD-RAN Interactive Management Using In-band Network Telemetry in IEEE 802.11 Networks, herein Isolani). 14. Regarding claim 2, The combination of Yermakov, Wang, and Jain teaches the IHS of claim 1, wherein, upon execution, by the processor, the program instructions further cause the IHS to Thus far, the combination of Yermakov, Wang and Jain does not explicitly teach dynamically schedule instructions for telemetry transfer of application data and entity data, based, at least in part, on the resulting predicted network bandwidth to use the resulting predicted network bandwidth to transfer critical data sets during the specific time window. Isolani teaches dynamically schedule instructions for telemetry transfer of application data and entity data, based, at least in part, on the resulting predicted network bandwidth to use the resulting predicted network bandwidth to transfer critical data sets during the specific time window (Isolani, Fig. 3, and, page 7, paragraph 2, line 1 “Two components define the programmable agent that runs at each AP: an Open vSwitch4 instance that operates under the supervision of the OpenFlow-enabled SDN controller and a Click modular router [30] instance implementing the IEEE 802.11 data-path with a hypervisor.” And, page 8, paragraph 2, line 1 “Slices are therefore mapped by the hypervisor according to the Service Set Identifier (SSID) and the Differentiated Services Code Point (DSCP). The SSID is the name of an IEEE 802.11 network and the DSCP determines the priority of each IP packet. The hypervisor is in charge of creating, monitoring, and managing network slices according to traffic rules, ensuring performance isolation and efficient resource utilization. Each traffic rule contains multiple aggregation buffers, one for each user in the slice and these aggregation buffers are scheduled using the Round Robin policy.” And, page 9, paragraph 1, line 1 “To gather application QoS (Quality of Service) requirements and the INT (Inband Network Telemetry) measurements over time, we introduced a programmable data plane…We use a Message Queuing Telemetry Transport (MQTT) broker5 where monitoring data is exchanged via the publish-subscribe messaging pattern. Moreover, we use a PostgreSQL database6 to store such monitoring data.” In other words, aggregation buffers are scheduled is dynamically schedule, network telemetry is telemetry transfer of application and entity data, QoS is critical data sets, and slice is during a specific time window.) Both Isolani and the combination of Yermakov, Wang and Jain are directed to network data transfer using machine learning, among other things. The combination of Yermakov, Wang and Jain teach the system of claim 1, but does not explicitly teach dynamically scheduling instructions for telemetry transfer of application data and entity data, based, at least in part, on the resulting predicted network bandwidth to use the resulting predicted network bandwidth to transfer critical data sets during the specific time window. Isolani teaches dynamically scheduling instructions for telemetry transfer of application data and entity data, based, at least in part, on the resulting predicted network bandwidth to use the resulting predicted network bandwidth to transfer critical data sets during the specific time window. In view of the teaching of the combination of Yermakov, Wang, and Jain, it would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Isolani into the combination of Yermakov, Wang, and Jain. This would result in the system of claim 1, and dynamically scheduling instructions for telemetry transfer of application data and entity data, based, at least in part, on the resulting predicted network bandwidth to use the resulting predicted network bandwidth to transfer critical data sets during the specific time window. One of ordinary skill in the art would be motivated to do this for better reliability and lower latency in network traffic. (Isolani, page 2, paragraph 1, line 1 “Despite the 5G ever-increasing demand for mobile services, services and applications are expected to run with better reliability, massive connectivity, and low latency [1]. In the context of future digital factories, IEEE 802.11 networks provide flexibility. However, while 5G advances, such networks must also provide robustness and Quality of Service (QoS) support. Moreover, in this context, applications often end up sharing the wireless infrastructure and Industrial, Scientific, and Medical (ISM) radio bands. Therefore, making it difficult to meet diverging Service Level Agreements (SLAs).”) Regarding claim 3, The combination of Yermakov, Wang, Jain, and Isolani teaches the IHS of claim 2, wherein, upon execution, by the processor, the program instructions further cause the IHS to queue the application data and entity data for transfer, according to the resulting schedule instructions (Jain, page 304, column 1, paragraph 2, line 4 “Queues were installed on ports that had hosts connected to them so that the flows from these hosts could be provided with different Quality of Service (QoS) values.” In other words, queues were installed on ports is queue the application data and the entity data for transfer according to the schedule, and QoS values is schedule instructions.) . Regarding claim 6, The combination of Yermakov, Wang, Jain, and Isolani teaches the IHS of claim 1, wherein, upon execution, by the processor, the program instructions further cause the IHS to implement a policy configuration for telemetry transfer control, to define business critical, and/or time sensitive, application data and entity data to define the policy configuration of data criticality (Wang, Table 1, and, page 761, column 2, paragraph 4, line 2 “All the flows went through a DPI module which outputs the label of each flow. The labeled flows were then categorized into 4 QoS classes including voice/video conference, interactive data, streaming, and bulk data transfer. Examples of the mapping policy between the application and the QoS class is shown in Table 1.” PNG media_image2.png 210 700 media_image2.png Greyscale In other words, mapping policy between application and QoS class is a policy configuration to define business critical and/or time sensitive application data and entity data to data criticality.) Claims 9-10, and 13 are non-transitory computer readable storage media claims that correspond to information handling system claims 2-3, and 6, respectively. Otherwise, they are not patentably distinct. Therefore, claims 9-10 and 13 are rejected for the same reasons as claims 2-3 and 6, respectively. Claim 16 is a method claim that has the same substantial limitations as information handling system claim 2, but in reverse order. Otherwise, they are not patentably distinct. Therefore, claim 16 is rejected for the same reasons as claim 2. Claims 17 and 20 are method claims that correspond to information handling system claims 3 and 6, respectively. Otherwise, they are not patentably distinct. Therefore, claims 17 and 20 are rejected for the same reasons as claims 3 and 6, respectively. 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. Any inquiry concerning this communication or earlier communications from the examiner should be directed to BART RYLANDER whose telephone number is (571)272-8359. The examiner can normally be reached Monday - Thursday 8:00 to 5:30. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. 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If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /Bart I Rylander/Examiner, Art Unit 2124