DETAILED ACTION
This application has been examined. Claims 1-20 are pending.
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 .
Making Final
Applicant's arguments filed 4/29/2026 have been fully considered but they are moot in view of the new grounds for rejection.
The claim amendments regarding -- ‘in view of a mean QoE value characterizing all of the one or more application traffic flows and deviation of an individual application's QoE value from the mean QoE value’ -- clearly change the literal scope of the independent and dependent claims and/or the range of equivalents for such claims. The said amendments alter the scope of the claims but do not overcome the disclosure by the prior art as shown below.
The Examiner is presenting new grounds for rejection as necessitated by the claim amendments and is thus making this action FINAL.
Response to Arguments
Applicant's arguments filed 4/29/2026 have been fully considered but they are moot in view of the new grounds for rejection.
While Garcarz-Smith substantially disclosed the claimed invention Garcarz-Smith does not disclose (re. Claim 1) adjusting configurations in view of a mean QoE value characterizing all of the one or more application traffic flows and deviation of an individual application's QoE value from the mean QoE value.
ElArabawy Paragraph 164 disclosed wherein Video Quality Metric VQM values are used to predict QoE for users of the video streams at their respective terminal nodes (UEs), and are used to drive the allocation of radio resources for the plurality of video streams in a manner that maximizes an overall QoE (VQM) metric.
ElArabawy Paragraph 196 disclosed an implementation of a QoE policy to maximize an overall (cell-wide across an access node) QoE metric, such as an overall VQM, for a plurality of video streams being delivered by the access node in a radio resource constrained wireless communication environment.
ElArabawy Paragraph 197 disclosed calculating the variance of VQM for a plurality of active video streams to minimize the variance of VQM across the plurality of video streams supported by the access node. This provides some fairness to video users (terminal nodes) based on VQM, which is a measure of the QoE perceived by the users.
ElArabawy disclosed (re. Claim 1) adjusting configurations in view of a mean QoE value (ElArabawy-Paragraph 198 , mean VQM which is a measure of the QoE perceived by the users ) characterizing all of the one or more application traffic flows and deviation of an individual application's QoE value from the mean QoE value.( ElArabawy-Paragraph 197,calculating the variance of VQM for a plurality of active video streams to minimize the variance of VQM across the plurality of video streams supported by the access node.This provides some fairness to video users (terminal nodes) based on VQM, which is a measure of the QoE perceived by the users.)
Garcarz, Smith and ElArabawy are analogous art because they present concepts and practices regarding optimization of QOS and QOE for WiFi network users. Before the time of the effective filing date of the claimed invention it would have been obvious to combine ElArabawy into Garcarz-Smith. The motivation for the said combination would have been to enable implementation of a QoE policy to maximize an overall (cell-wide across an access node) QoE metric while minimizing the variance of VQM across the plurality of video streams supported by the access node. This provides some fairness to video users (terminal nodes) based on VQM, which is a measure of the QoE perceived by the users.(ElArabawy-Paragraph 196-197)
Priority
The effective date of the claims described in this application is October 1, 2024.
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.
Claim(s) 1-6,8,10,13-15,17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Garcarz (USPGPUB 20240388954) further in view of Smith (USPGPUB 2025/0080469) further in view of ElArabawy (USPGPUB 2013/0298170)
Regarding Claim 1
Garcarz Paragraph 65 disclosed an RRM mechanism that makes use of application-level telemetry and inferred QoE scores (e.g., by using specially trained machine learning QoE models) to optimize the radio configurations in a wireless network, with the end goal of increasing end user satisfaction in the wireless network. In some aspects, such a cross-layer approach consisting in tuning the Wi-Fi configuration while optimizing the application experience.
Garcarz Paragraph 76 disclosed wherein telemetry collection module 502 may responsible for gathering various telemetry, either on a pull or push basis, such as all types of radio related telemetry gathered by existing RRM systems, from wireless controllers, from clients, and/or APs.
Garcarz disclosed (re. Claim 1) a method comprising: receive, from a network controller of a network, telemetry data from an access point (AP); (Garcarz-Paragraph 76-77, telemetry collection module 502 may responsible for gathering various telemetry, either on a pull or push basis, such as all types of radio related telemetry gathered by existing RRM systems, from wireless controllers, from clients, and/or APs.)
estimate based on the telemetry data, a quality of experience (QoE) value (Garcarz-Paragraph 65, RRM mechanism that makes use of application-level telemetry and inferred QoE scores (e.g., by using specially trained machine learning QoE models)) for individual application traffic flows of one or more application traffic flows passing through the AP; (Garcarz-Paragraph 82, telemetry collection module 502 may also obtain and associate the application QoE metrics)
While Garcarz substantially disclosed the claimed invention Garcarz does not disclose (re. Claim 1)
compute and assign, to the individual application traffic flows, an access category and traffic priority based on the estimated QoE value;
maximize overall QoE across the one or more application traffic flows and QoE parity as applied to the individual application traffic flows by adjusting configurations of the individual application traffic flows in accordance with their respective assigned access category and traffic priorities.
While Garcarz substantially disclosed the claimed invention Garcarz does not disclose (re. Claim 1) adjusting configurations in view of a mean QoE value characterizing all of the one or more application traffic flows and deviation of an individual application's QoE value from the mean QoE value.
Smith Paragraph 72 disclosed wherein proactive and continuous evaluation allows the WLC to adapt efficiently to changes in network conditions and traffic types. By not relying on SCS requests, the wireless local area network (LAN) controller (WLC or WCL) maintains a high level of network control and preemptively manages resources to prevent congestion.
Smith disclosed (re. Claim 1) compute and assign, to the individual application traffic flows, an access category and traffic priority (Smith-Paragraph 19, classification and prioritization of traffic based on application-specific profiles, allowing the applied QoS treatments to align with the specific needs of different applications)
maximize overall QoE across the one or more application traffic flows and QoE parity as applied to the individual application traffic flows (Smith-Paragraph 25, maintain optimal (or at least improved) performance and compliance across the network, Paragraph 66, WLC may reclassify the traffic to a lower priority to ensure fair bandwidth distribution and network efficiency) by adjusting configurations of the individual application traffic flows in accordance with their respective assigned access category and traffic priorities.(Smith-Paragraph 45, the SCS response 255 rejecting the original TCLAS (and/or QoS profile) treatments may be sent after the resource allocation process. The response may inform STA 210 that the UL/DL traffic has been processed according to a new priority level, with adjusted prioritization and resource settings.)
Garcarz and Smith are analogous art because they present concepts and practices regarding optimization of QOS and QOE for WiFi network users. Before the time of the effective filing date of the claimed invention it would have been obvious to combine Smith into Garcarz. The motivation for the said combination would have been to enable application-specific policy validation for requested data flows.(Smith-Paragraph 16)
Garcarz-Smith disclosed (re. Claim 1) compute and assign, to the individual application traffic flows, an access category and traffic priority (Smith-Paragraph 19, classification and prioritization of traffic based on application-specific profiles, allowing the applied QoS treatments to align with the specific needs of different applications) based on the estimated QoE value; (Garcarz-Paragraph 65, RRM mechanism that makes use of application-level telemetry and inferred QoE scores (e.g., by using specially trained machine learning QoE models))
maximize overall QoE across the one or more application traffic flows and QoE parity as applied to the individual application traffic flows (Smith- Paragraph 25, maintain optimal (or at least improved) performance and compliance across the network, Paragraph 66, WLC may reclassify the traffic to a lower priority to ensure fair bandwidth distribution and network efficiency) by adjusting configurations of the individual application traffic flows in accordance with their respective assigned access category and traffic priorities.(Smith-Paragraph 45, the SCS response 255 rejecting the original TCLAS (and/or QoS profile) treatments may be sent after the resource allocation process. The response may inform STA 210 that the UL/DL traffic has been processed according to a new priority level, with adjusted prioritization and resource settings.)
While Garcarz-Smith substantially disclosed the claimed invention Garcarz-Smith does not disclose (re. Claim 1) adjusting configurations in view of a mean QoE value characterizing all of the one or more application traffic flows and deviation of an individual application's QoE value from the mean QoE value.
ElArabawy Paragraph 164 disclosed wherein Video Quality Metric VQM values are used to predict QoE for users of the video streams at their respective terminal nodes (UEs), and are used to drive the allocation of radio resources for the plurality of video streams in a manner that maximizes an overall QoE (VQM) metric.
ElArabawy Paragraph 196 disclosed an implementation of a QoE policy to maximize an overall (cell-wide across an access node) QoE metric, such as an overall VQM, for a plurality of video streams being delivered by the access node in a radio resource constrained wireless communication environment.
ElArabawy Paragraph 197 disclosed calculating the variance of VQM for a plurality of active video streams to minimize the variance of VQM across the plurality of video streams supported by the access node. This provides some fairness to video users (terminal nodes) based on VQM, which is a measure of the QoE perceived by the users.
ElArabawy disclosed (re. Claim 1) adjusting configurations in view of a mean QoE value (ElArabawy-Paragraph 198 , mean VQM which is a measure of the QoE perceived by the users ) characterizing all of the one or more application traffic flows and deviation of an individual application's QoE value from the mean QoE value.( ElArabawy-Paragraph 197,calculating the variance of VQM for a plurality of active video streams to minimize the variance of VQM across the plurality of video streams supported by the access node.This provides some fairness to video users (terminal nodes) based on VQM, which is a measure of the QoE perceived by the users.)
Garcarz, Smith and ElArabawy are analogous art because they present concepts and practices regarding optimization of QOS and QOE for WiFi network users. Before the time of the effective filing date of the claimed invention it would have been obvious to combine ElArabawy into Garcarz-Smith. The motivation for the said combination would have been to enable implementation of a QoE policy to maximize an overall (cell-wide across an access node) QoE metric while minimizing the variance of VQM across the plurality of video streams supported by the access node. This provides some fairness to video users (terminal nodes) based on VQM, which is a measure of the QoE perceived by the users.(ElArabawy-Paragraph 196-197)
Regarding Claim 2
Garcarz-Smith-ElArabawy disclosed (re. Claim 2) wherein the received telemetry data comprises raw telemetry data including user metrics, network metrics of the network, and radio metrics of one or more radios operating in the AP. (Garcarz-Paragraph 76-77, telemetry collection module 502 may responsible for gathering various telemetry, either on a pull or push basis, such as all types of radio related telemetry gathered by existing RRM systems, from wireless controllers, from clients, and/or APs.)
Regarding Claim 3
Garcarz-Smith-ElArabawy disclosed (re. Claim 3) processing the raw telemetry data to extract QoE estimation-relevant telemetry data. (Garcarz-Paragraph 83,application statistics engine 504 may processes the telemetry gathered by telemetry collection module 502 and extract attributes and features which will be used by an RRM mechanism when making radio optimization decisions.)
Regarding Claim 4
Garcarz-Smith-ElArabawy disclosed (re. Claim 4) wherein the processing of the raw telemetry data comprises processing sequential telemetry data.(Garcarz-Paragraph 72, Long term recommendations from RRM based on historical application usage and experience (e.g., change AP location, buy new AP, buy new AP in 3 weeks from now, because based on current trends it will not be possible to accommodate current client growth without substantial app QoE degradation, Smith-Paragraph 38, the validation time may be defined to observe N period (e.g., where N is set to 10, resulting in a total of 200 ms). This period allows the AP/WLC 205 to capture enough data packets to make an informed decision regarding the traffic's nature and/or application classification).
Regarding Claim 5,15
Garcarz-Smith-ElArabawy disclosed (re. Claim 5,15) wherein the estimation of the QoE value comprises identifying an application class associated with packets of the one or more application traffic flows received by the network controller. (Smith-Paragraph 19, classification and prioritization of traffic based on application-specific profiles, allowing the applied QoS treatments to align with the specific needs of different applications, Paragraph 26, WLC 155 may implement packet analysis techniques, such as AVC, DPI, and NBAR2, and examine packet payloads to determine the actual service type or application classification of the UL/DL traffic.)
Regarding Claim 6,15
Garcarz-Smith-ElArabawy disclosed (re. Claim 6,15) using the identified application class to apply an application class-specific prediction model (Garcarz-Paragraph 33, application experience optimization process 248 may employ one or more supervised, unsupervised, or semi-supervised machine learning models, Paragraph 86, application statistics engine 504 may also normalize the telemetry from telemetry collection module 502) corresponding to the identified application class to estimate the QoE value in based on the telemetry data.(Garcarz-Paragraph 89, radio optimization module 506 estimates, based on historical data (and possibly across all customers and/or wireless clients), whether changing any of the RRM parameters generated by FRA, DCA, TPC, DBS would potentially improve the application user experience for each user.)
Regarding Claim 8,17
Garcarz-Smith-ElArabawy disclosed (re. Claim 8,17) training the application class-specific prediction model (Garcarz-Paragraph 33, training data may include sample telemetry that has been labeled as being indicative of an acceptable performance or unacceptable performance) using collected telemetry data and measured QoE under diverse network conditions including underloaded and overloaded network conditions.(Garcarz-Paragraph 73, Provide simulation capabilities that allow a network administrator to assess RRM settings based on application driven input and output. For example, to answer the question “what would be the RRM proposal if most of my users migrate from Zoom to Cisco WebEx?” In another example, to answer the question “what will my RRM proposal be if there will be additional 100 O365 users in my location (on average))
Regarding Claim 10
Garcarz-Smith-ElArabawy disclosed (re. Claim 10) wherein the computing and assignment, to the individual application traffic flows, of an access category and traffic priority based on the estimated QoE value, is performed by application class-specific policy agents.(Smith-Figure 6,Figure 7, stream classification service (SCS) management component 655,745)
Regarding Claim 13
Garcarz-Smith-ElArabawy disclosed (re. Claim 13) training the application class-specific policy agents in a simulation environment.(Garcarz-Paragraph 73, Provide simulation capabilities that allow a network administrator to assess RRM settings based on application driven input and output. For example, to answer the question “what would be the RRM proposal if most of my users migrate from Zoom to Cisco WebEx?” In another example, to answer the question “what will my RRM proposal be if there will be additional 100 O365 users in my location (on average))
Regarding Claim 14
Garcarz-Smith-ElArabawy disclosed (re. Claim 14) a system, comprising: a processor; and a memory unit including instructions that when executed, cause the processor to: receive telemetry data from an access point (AP); estimate based on the telemetry data, a quality of experience (QoE) value for each application traffic flow of a plurality of application traffic flows traversing the AP; (Garcarz-Paragraph 65, RRM mechanism that makes use of application-level telemetry and inferred QoE scores (e.g., by using specially trained machine learning QoE models)) compute, for each application traffic flow, an access category and a traffic priority, based on the estimated QoE value; (Smith-Paragraph 19, classification and prioritization of traffic based on application-specific profiles, allowing the applied QoS treatments to align with the specific needs of different applications) jointly assign the computed access category and traffic priority to each application flow; (Smith-Paragraph 19, classification and prioritization of traffic based on application-specific profiles, allowing the applied QoS treatments to align with the specific needs of different applications) and push a configuration comprising the jointly assigned access category and traffic priority to a network controller to be forwarded to the AP (Garcarz-Paragraph 97, device may provide the wireless configuration for use by the access point when communicating with a client of the particular online application. For instance, the device may provide the wireless configuration directly to the access point, to a controller for the access point) for reconfiguring the AP to process subsequent data packets belonging to each of the application traffic flows in accordance with the jointly assigned access category and traffic priority. (Smith-Paragraph 45, the SCS response 255 rejecting the original TCLAS (and/or QoS profile) treatments may be sent after the resource allocation process. The response may inform STA 210 that the UL/DL traffic has been processed according to a new priority level, with adjusted prioritization and resource settings.)
Claim(s) 7,9,11-12,16,20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Garcarz (USPGPUB 20240388954) further in view of Smith (USPGPUB 2025/0080469) further in view of ElArabawy (USPGPUB 2013/0298170) further in view of Orhan (USPGPUB 2022/0124543)
Regarding Claim 9
While Garcarz-Smith-ElArabawy substantially disclosed the claimed invention Garcarz-Smith-ElArabawy does not disclose (re. Claim 9) synchronizing the collected telemetry data with the measured QoE based on respective timestamps associated with the collected telemetry data and the measured QoE.
Orhan Paragraph 216 disclosed wherein Q-learning is a model-free RL algorithm that learns the value of an action in a particular state.
Orhan disclosed (re. Claim 9) synchronizing the collected telemetry data with the measured QoE based on respective timestamps associated with the collected telemetry data and the measured QoE.(Orhan-Paragraph 423, “time to live” (or “TTL”) or “hop limit” refers to a mechanism which limits the lifespan or lifetime of data in a computer or network. TTL may be implemented as a counter or timestamp attached to or embedded in the data.)
Garcarz and Orhan are analogous art because they present concepts and practices regarding optimization of QOS and QOE for WiFi network users. Before the time of the effective filing date of the claimed invention it would have been obvious to combine Orhan into Garcarz. The motivation for the said combination would have been to enable relevant features to be extracted from network logical entities using GNN tools such as graph convolutional neural network (CNN), spatial-temporal neural network, and/or the like. These tools can learn hidden spatial and temporal features of the network with different scales and configurations without significant performance losses.(Orhan-Paragraph 21)
Regarding Claim 11
While Garcarz-Smith substantially disclosed the claimed invention Garcarz-Smith does not disclose (re. Claim 11) wherein the application class-specific policy agents use a double deep Q-network (DDQN) reinforcement learning (RL) algorithm coupled with a feed-forward neural network.
Orhan Paragraph 216 disclosed wherein Q-learning is a model-free RL algorithm that learns the value of an action in a particular state.
Orhan disclosed (re. Claim 11) wherein the application class-specific policy agents use a double deep Q-network (DDQN) reinforcement learning (RL) algorithm coupled with a feed-forward neural network.(Orhan-Paragraph 216,Q-learning is a model-free RL algorithm that learns the value of an action in a particular state… value-based deep RL include Deep Q-Network (DQN), Double DQN, and Dueling DQN.)
Garcarz and Orhan are analogous art because they present concepts and practices regarding optimization of QOS and QOE for WiFi network users. Before the time of the effective filing date of the claimed invention it would have been obvious to combine Orhan into Garcarz. The motivation for the said combination would have been to enable relevant features to be extracted from network logical entities using GNN tools such as graph convolutional neural network (CNN), spatial-temporal neural network, and/or the like. These tools can learn hidden spatial and temporal features of the network with different scales and configurations without significant performance losses.(Orhan-Paragraph 21)
Regarding Claim 12
Garcarz-Smith-Orhan disclosed (re. Claim 12) wherein the computing and assignment, to the individual application traffic flows, of an access category and traffic priority based on the estimated QoE value, comprises assigning the access category and traffic priority jointly in accordance with an action space comprising possible access category and traffic priority combinations.(Smith-Paragraph 105, SCS management component 745 may also analyze the SCS responses from the WLC to determine appropriate actions or adjustments based on the network's feedback.)
Regarding Claim 20
Garcarz-Smith-ElArabawy disclosed (re. Claim 20) a system, comprising: a processor; and a memory unit including instructions that when executed, cause the processor to:; execute the applications at the client devices; collect telemetry data from the AP, and measure QoE, the telemetry data being associated with the execution of the applications; and train a QoE model with the telemetry data and the measured QoE to be operationalized for maximizing overall QoE across a plurality of traffic flows traversing the AP and QoE parity as applied to individual ones of the plurality of traffic flows, each of the plurality of traffic flows being associated with one of the number of applications executed at the client devices.(see rejection for Claim 1)
While Garcarz-Smith substantially disclosed the claimed invention Garcarz-Smith does not disclose (re. Claim 20) varying a number of applications to be concurrently run on client devices to force poor quality of experience (QoE) to be experienced by client devices served by an access point (AP) operative in a network
Orhan Figure 6,Paragraph 95 disclosed wherein for each conn-event 406, a loop of a total number of reshuffled UE iterations T is run. In each loop iteration, parallel processing of Q function computation depends on the overall conn-event 406 load and available cores in the CPU/processor. Simulation results show that 36 conn-event 406 can be served in parallel under 6 ms latency in which each conn-event 406 has at most T=30 reshuffled UEs in their local cell network. In each conn-event 406, the total number of GNN-RL inference scales with O(T.sup.2N.sub.meas).
Orhan disclosed (re. Claim 20) varying a number of applications to be concurrently run on client devices to force poor quality of experience (QoE) to be experienced by client devices served by an access point (AP) operative in a network (Orhan-Figure 6,Paragraph 95,for each conn-event 406, a loop of a total number of reshuffled UE iterations T is run. In each loop iteration, parallel processing of Q function computation depends on the overall conn-event 406 load and available cores in the CPU/processor. Simulation results show that 36 conn-event 406 can be served in parallel under 6 ms latency in which each conn-event 406 has at most T=30 reshuffled UEs in their local cell network. In each conn-event 406, the total number of GNN-RL inference scales with O(T.sup.2N.sub.meas). )
Garcarz and Orhan are analogous art because they present concepts and practices regarding optimization of QOS and QOE for WiFi network users. Before the time of the effective filing date of the claimed invention it would have been obvious to combine Orhan into Garcarz. The motivation for the said combination would have been to enable relevant features to be extracted from network logical entities using GNN tools such as graph convolutional neural network (CNN), spatial-temporal neural network, and/or the like. These tools can learn hidden spatial and temporal features of the network with different scales and configurations without significant performance losses.(Orhan-Paragraph 21)
Regarding Claim 7,16
Garcarz-Smith-Orhan disclosed (re. Claim 7,16) wherein the application class-specific prediction model comprises a long short-term memory (LSTM) neural network.(Orhan-Paragraph 222, the NN 1400 can be some other type of topology (or combination of topologies), such asLong Short Term Memory (LSTM) network)
Claim(s) 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Garcarz (USPGPUB 20240388954) further in view of Smith (USPGPUB 2025/0080469) further in view of ElArabawy (USPGPUB 2013/0298170) further in view of Gell (USPGPUB 20140153392)
Regarding Claim 18
While Garcarz-Smith-ElArabawy substantially disclosed the claimed invention Garcarz-Smith-ElArabawy does not disclose (re. Claim 18) policy agent instances specific to each identified application class
Gell Paragraph 70 disclosed wherein many combinations of applications and application agents and their related functions may also be used. There may be one application agent that communicates with all applications, one application agent for each particular application (e.g., a YouTube application agent, a Pandora application agent)
Gell disclosed (re. Claim 18) policy agent instances specific to each identified application class. (Gell-Paragraph 70,one application agent for each particular application (e.g., a YouTube application agent, a Pandora application agent) )
Garcarz and Gell are analogous art because they present concepts and practices regarding optimization of QOS and QOE for WiFi network users. Before the time of the effective filing date of the claimed invention it would have been obvious to combine Gell into Garcarz. The motivation for the said combination would have been to enable application agent to inform the application about the network conditions by communicating resource availability or by communicating new preferred or maximum data rates or resolutions for the video.(Gell-Paragraph 127)
Claim(s) 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Garcarz (USPGPUB 20240388954) further in view of Smith (USPGPUB 2025/0080469) further in view of ElArabawy (USPGPUB 2013/0298170) further in view of Gell (USPGPUB 20140153392) further in view of Orhan (USPGPUB 2022/0124543)
Regarding Claim 19
While Garcarz-Smith-ElArabawy-Gell substantially disclosed the claimed invention Garcarz-Smith-ElArabawy does not disclose (re. Claim 19) wherein the application class-specific policy agents use a double deep Q-network (DDQN) reinforcement learning (RL) algorithm coupled with a feed-forward neural network.
Orhan Paragraph 216 disclosed wherein Q-learning is a model-free RL algorithm that learns the value of an action in a particular state.
Orhan disclosed (re. Claim 19) wherein the application class-specific policy agents use a double deep Q-network (DDQN) reinforcement learning (RL) algorithm coupled with a feed-forward neural network.( Orhan-Paragraph 216,Q-learning is a model-free RL algorithm that learns the value of an action in a particular state… value-based deep RL include Deep Q-Network (DQN), Double DQN, and Dueling DQN.)
Garcarz,Gell and Orhan are analogous art because they present concepts and practices regarding optimization of QOS and QOE for WiFi network users. Before the time of the effective filing date of the claimed invention it would have been obvious to combine Orhan into Garcarz. The motivation for the said combination would have been to enable relevant features to be extracted from network logical entities using GNN tools such as graph convolutional neural network (CNN), spatial-temporal neural network, and/or the like. These tools can learn hidden spatial and temporal features of the network with different scales and configurations without significant performance losses.(Orhan-Paragraph 21)
Conclusion
Examiner’s Note: In the case of amending the claimed invention, Applicant is respectfully requested to indicate the portion(s) of the specification which dictate(s) the structure relied on for proper interpretation and also to verify and ascertain the metes and bounds of the claimed invention.
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 GREG C BENGZON whose telephone number is (571)272-3944. The examiner can normally be reached on Monday - Friday 8 AM - 4:30 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, John Follansbee can be reached on (571) 272-3964. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/GREG C BENGZON/ Primary Examiner, Art Unit 2444
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