DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
The amendment to the claims filed on 11/12/2025 complies with the requirements of 37 CFR 1.121(c) and has been entered. Objection to the Title of the application has been withdrawn.
Response to Arguments
Applicant's Arguments/Remarks filed 11/12/2025 (hereinafter Resp.) have been fully considered as follows.
First, Applicant argues that the AP-level settings in Shah et al., U.S. Patent Application Publication No. 2023/0126313 (hereinafter Shah) “do[] not contemplate a configuration associated with a client of a particular online application such that it is stored or scoped to a specific client/application pair” – See Resp., 9:¶2. To be sure, Shah’s subject matter is NMS operations based on client-side telemetry data providing wireless configurations pertaining to the aspects described in [¶¶0055-60]. Shah only discloses integration with third-party APM vendors for insights needed “when a user encounters a quality issue of an online application or service” – See [¶0061]. Therefore, Shah does not contemplate wireless configurations “scoped to a specific client/application pair.” However, narrowing the scope of the wireless configuration to a “specific client/application pair” is not clearly claimed in the present application, either; a person of ordinary skills in the art in at least one reasonable interpretation of the language “a wireless configuration associated with a client device of the particular online application for an access point of the wireless network,” as recited in the amended independent claims, would read the requirement as a wireless configuration for a AP to be used by the AP when communicating with a client device of the online application, regardless whether the device is running the application or not. Furthermore, the Specification discloses that the application related statistical data “might be important for radio optimization module 506 generating RRM optimization proposals” – See Spec., 21:7-8, whereby the optimizations, i.e., the claimed wireless configuration(s), apply to the client-side of the AP (increase RSSI, disable beamforming, roam to another AP) – See id., 22:10-28; the Specification is silent about “binding” the wireless configuration to an active client/application pair. There is no disclosure as to how to configure the AP, e.g., to increase the RSSI for the traffic between the online application and the client device but not for other traffic at the AP. Therefore, Applicant’s argument against Shah is weakened by the lack of support for a more granular wireless configuration for the AP in the present Specification, i.e., a configuration that applies to traffic between the online application and its client device, as disclosed, e.g., in Junkins et al., U.S. Patent Application Publication No. 2023/0081673 (hereinafter Junkins), infra, wherein the QoE monitoring system and method updates a wireless configuration with additional resources during the application-to-client session and the operator can charge for the use of additional radio resources to maintain the application QoE for a subscriber – See [¶0177] and Fig. 6B.
Second, Applicant makes the same argument against Wang et al., Patent Application Publication No. 2023/0231762 (hereinafter Wang), included by reference in Shah, for not “binding a configuration to a specific client/application tuple,” an unclaimed limitation, as explained supra. Examiner respectfully disagrees because Wang uses a temporal database graph specific to the application-to-client connectivity – See, e.g., [¶0130] (“NMS 300 generates an application session-specific topology for the application session based on the entity information and the connectivity information for the application session (912). The application session-specific topology may comprise a historical view of the subset of network devices and connections between the subset of network devices over the duration of the application session”). It is on this graph that Wang performs NMS operations, and those operations include corrective wireless configurations from SLE-related data received from the client device – See [¶0099] (“NMS 130 may determine one or more SLE metrics and store the SLE metrics as network data 137 (FIG. lA) based on the SLE-related data received from the UEs or client devices in the wireless network. NMS 130 may further update temporal graph database 138 (FIG. lA) of the network to include the telemetry data, or at least entity and connectivity information extracted from the telemetry data, received from the UEs or client devices in the wireless network over time”), e.g., using additional information and data provided by an NMS agent running on the client device – See [¶¶0100-101]. A person of ordinary skills in the art would then understand that when the RRM engine of the NMS “automatically change[s] or update[s] configurations of one or more APs 142 at a site 102 with an aim to improve the coverage and capacity SLE metrics and thus to provide an improved wireless experience for the user” – See [¶0072], the action may be taken by the NMS monitoring telemetry data of the temporal graph associated with the application session-specific topology, while the client device runs the online application. Therefore, for a person of ordinary skills in the art, the association between the wireless configuration generated by the NMS and the application/client device pair disclosed in Wang is stronger, based on the temporal database graph, than in the present disclosure.
Last, Applicant’s argument against Shah and Wang is also moot because of the new ground of rejection caused by the amendment and the matter specifically challenged in the argument. As further explained in this Office action, Junkins discloses real-time monitoring of client-side QoE with an application and real-time updates to the wireless configuration specifically applied to an application session with a client to maintain that QoE.
Therefore, Applicant' s arguments have been fully considered but they are either unpersuasive or moot.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-6, 10-16, and 20, as amended, are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al., Patent Application Publication No. 2023/0231762 (hereinafter Wang) and further in view of Junkins et al., U.S. Patent Application Publication No. 2023/0081673 (hereinafter Junkins).
Regarding Amended Claim 1, Wang teaches a method (“method that includes receiving, by a network management system, a query identifying an application session of an application running on a client device” – See [¶0010] and Fig. 1B showing multi-cloud apps 181 connected to user devices 148), comprising:
obtaining, by a device, telemetry data associated with a particular online application accessed via a wireless network (“During an application session of a cloud-based application, e.g., a VOIP or video conference call, a streaming video viewing session, or a gaming session, a client device running the application exchanges data through one or more access point (AP) devices, one or more switches at a wired network edge, and one or more network nodes, e.g., switches, routers, and/or gateway devices, to reach a cloud-based application server” – See [¶0004]; and the “NMS 130 monitors network data 137, e.g., one or more service level expectation (SLE) metrics, received from wireless networks 106A-106N at each site 102A-102N, respectively, and manages network resources, such as APs 142 at each site, to deliver a high quality wireless experience to end users” – See [¶0032] and Fig. 1A using an “application-session specific topology [that] is built based on telemetry data received from the network devices, e.g., client devices 148, AP devices 142, switches 146, and other network nodes such as routers 187 of FIG. 1B, over the duration of the particular application session” – See [0075] and NMS agents on network nodes “configured to collect logical path statistics via bidirectional forwarding detection (BFD) probing and data extracted from messages and/or counters at the logical path (e.g., peer path or tunnel) level” – See [¶0110] and Fig. 5, whereby “the package of statistical data may also include details about clients connected to network node 500 and the associated client sessions” – See [¶0111]);
computing, by the device, statistics regarding traffic associated with the particular online application in the wireless network (the “VNA/AI engine 350 includes application session troubleshooting engine 352 that builds an application-session specific topology for a particular application session based on data for the particular application session retrieved from temporal graph database 317” and “applies a ML model 380 to network data 316 and/or temporal graph database 317 to perform troubleshooting of the particular application session by identifying root causes of connectivity issues at one or more of the subset of network devices involved in the particular application session” – See [¶0070] and Fig. 3, while the “SLE module 322 further analyzes SLE-related data collected by APs, such as any of APs 142 from UEs in each wireless network 106A-106N . . . to determine one or more SLE metrics for each UE 148A-1 through 148A-N currently connected to wireless network 106A.” – See [¶0071] and Fig. 1A, whereby an “application session troubleshooting engine 352 analyzes network data 316 of the subset of network devices involved with the particular application session to identify root causes of the connectivity issues at one or more of the subset of network devices involved with the particular application session” – See [¶0080] and Fig. 3, i.e., the NMS computes statistics regarding traffic associated with the particular application sessions);
generating, by the device, a wireless configuration associated with a client device of the particular online application for an access point of the wireless network based on the telemetry data and the statistics (“Application session troubleshooting engine 352 may analyze AP health,
radio health, pre-connection issues, RF issues, and/or configuration issues to identify connectivity issues caused by an access point 142/200” – See [¶0081] and Figs. 1A and 3, and “generate data representative of a user interface to provide . . . visualization of the application session specific topology including color-coding, icons, or other indicia of connectivity issues within the topology over the duration of the particular application session” – See [¶0082], e.g., in “the illustrated example of FIG. 6B, as part of network health output 642, conversational assistant user interface 600 presents application session-specific topology 644 generated by conversational assistant engine 136, 356 for the selected application session 620” – See [¶0114] and Fig. 6B showing the subset of network devices used for “Teams call on client device A from 12:01 PM-1:03 PM on Oct 7th” including AP “BASEMENT” and, in Fig. 9, “a troubleshooting workflow for a group of application sessions for a particular AP device” – See [¶0125] and “a list of root causes of the connectivity issues at the AP device” – See [¶0127]; furthermore, an “RRM engine 360 may . . . make adjustments to the radio settings of the access points at each site to address the identified issues” – See [¶0072], i.e., generate a wireless configuration associated with a client device of the particular online application for an access point of the wireless network based on the telemetry data and the statistics); and
providing, by the device, the wireless configuration for selective use by the access point when communicating with the client device of the particular online application (the “RRM engine 360 may further automatically change or update configurations of one or more APs 142 at a site 102 with an aim to improve the coverage and capacity SLE metrics and thus to provide an improved wireless experience for the user” – See [¶0072] as applied to the “topology of the network devices and connections between the network devices that were involved in the particular application session over a duration of the particular application session” and identified/generated through the “[a]pplication session troubleshooting engine 352” – See [¶0075], i.e., the wireless configuration is provided to the access point of the client device when the client device next uses the Teams application).
In the alternative that Wang does not explicitly teach the “selective use” of the AP-level RRM driven optimization of the RF environment at each site, i.e., the application of the wireless configuration when the AP is communicating with the client device of the particular online application, Junkins specifically teaches this feature.
First, Junkins, like Wang, teaches “systems and methods . . . [to] monitor each session, from the application type, network slice loading, and RAN resource utilization and performance to provide true visibility into the QoE” – See [¶0040] wherein “QoE refers to a measure of the customer experience with a service, e.g., streaming video, gaming, phone call, TV broadcast” and “focuses on the entire service experience” – See [¶0037] and whereby “[t]he mobile device 104 may be operationally coupled to a wide area network (WAN) such as the Internet by being communicatively coupled to a Radio Access Network (RAN) associated with a service provider network” and “may also be communicatively coupled to the WAN via a Wi-Fi (or Bluetooth) access point (not shown) that is communicatively coupled to an illustrative modem (not shown), which is communicatively coupled to the WAN” – See [¶0054] and Fig. 1A.
Second, similar to Wang, Junkins further teaches “various methods for communicating application QoE requirements and the integration of the QoE requirements with applications” while the “application operates according to the terms of a service level agreement (SLA), which is a commitment between the CSP and the subscriber that relates to the expected QoE” – See [¶0136] whereby, for example, “the name of the application, the subscriber ID (IMSI, MSISDN), and the bandwidth requirements, packet loss rate needs, and latency,” i.e., QoE requirements, are registered or inferred by an “application QoE server . . . associated with the CSP network and may also be referred to as a QoE network appliance” – See [¶0138], e.g., “[t]he CSP determines the QoE requirements for the application with inferential systems, such as DPI, NWDAF network analytics, or other such analytics” – See [¶0141] and Fig. 6A, whereby “[t]he core compute and storage associated with the QoE network appliance support use cases across the whole network and gathers data from a plurality of edge locations” – See [¶0144].
Third, Junkins also teaches generating, by the device, a wireless configuration associated with a client device of the particular online application for an access point of the wireless network based on the telemetry data and the statistics (“the data sets collected by the core compute system is used to generate an ‘action’” that “may have an associated API that can be used to provide process automation or access to other APIs” – See [¶0145], e.g., the action may be to “modify the network resources to satisfy the interim QoE requirement” of a mobile application “while the mobile application is being executed on the mobile device” – See [¶0160] which inherently requires generating a wireless configuration associated with a client device of the particular online application for an access point of the wireless network based on the telemetry data and the statistics indicating insufficient resources; or, while “a measured QoE score is generated for the subscriber session . . .in real-time . . . generated with the RAN data set, the NWDAF data set and the CN data set” – See [¶0164], “[i]f a consistently low measured QoE score is identified . . . a network orchestration module adds or removes network resources. In operation, an illustration QoE message is communicated to the network orchestration module, which then adds or removes one or more network resources, which changes the measured QoE score” – See [¶0177] and Fig. 6B, whereby the QoE message would be the wireless configuration associated with a client device of the particular online application for an access point of the wireless network based on the telemetry data and the statistics indicating low QoE score).
Finally, Junkins teaches that the wireless configuration is for selective use by the access point when communicating with the client device of the particular online application (“the change in network may result in increasing the pricing to maintain the QoE” – See [¶0172], i.e., the wireless configuration adding network resources, e.g., increased AP bandwidth, is for selective use by the client device running the particular online application because only the client device using the respective application will be charged).
Thus, Wang and Junkins each teaches similar systems and methods for collecting data sets and computing statistics regarding traffic associated with a particular online application used by a mobile device in a wireless network and change or update configurations of one or more network resources to provide an improved wireless experience for the user. A person of ordinary skill in the art before the effective filing date of the claimed invention would have understood that generating the wireless configuration to add network resources, e.g., increased AP bandwidth, in order to maintain the measured QoE score for a particular subscriber of a particular online application, at a particular location, which is associated with a particular cell, and selectively using the wireless configuration by the access point when communicating with the client device of the particular online application, as taught by Junkins, could have been combined with the RRM engine that automatically changes or updates configurations of one or more APs, as taught by Wang, because they are both functions on a NMS/QoE appliance with visibility into application-session specific topology either directly or through APIs. Furthermore, a person of ordinary skill in the art would have been able to carry out the combination through techniques known in the art. Finally, the combination achieves the improvement of charging a specific subscriber of a particular application when the generated wireless configuration adds resources to maintain the QoE for that subscriber at that specific location.
Therefore, Amended Claim 1 is obvious over Wang in view of Junkins.
Regarding Claim 2, dependent from Amended Claim 1, Wang further teaches wherein the telemetry data is generated at least in part by the particular online application (“The application-session specific topology is built based on telemetry data received from the network devices, e.g., client devices 148, AP devices 142, switches 146, and other network nodes such as routers 187 of FIG. 1B, over the duration of the particular application session” – See [¶0075] which is data the NMS “collects from client devices 148, APs 142, switches 146, and/or other network nodes within network 134 at an application session-level granularity” and further “uses the connectivity and entity information at the application session-level to update temporal graph database 317, where the graph is representative of the network topology at the application session-level over the period of time” – See [¶0076], i.e., data is generated at least in part by the traffic of the particular online application traversing the network topology). Junkins further teaches a “QoE fingerprint that can be used to identify the mobile application” that “includes the RAN data set, the CN data set and NWDAF data set” – See [¶0162].
Therefore, Claim 2 is obvious over Wang in view of Junkins.
Regarding Claim 3, dependent from Amended Claim 1, Wang teaches wherein the statistics are indicative of when traffic associated with the particular online application is sent via the wireless network (“the package of statistical data may also include details about clients connected to network node 500 and the associated client sessions” – See [¶0111], e.g., the “network health output 642 for application session 620 (in this example ‘Teams call on client device A from 12:01 PM-1:03 PM on Oct 7th’) . . . presents application session-specific topology 644 generated by conversational assistant engine 136, 356 for the selected application session 620” – See [¶0114] and Fig. 6A, showing times when traffic associated with the particular online application is sent via the wireless network.
Therefore, Claim 3 is obvious over Wang in view of Junkins.
Regarding Amended Claim 4, dependent from Amended Claim 1, Wang further teaches wherein the telemetry data associated with the particular online application is indicative of a device type of the client device (a “conversational assistant user interface 700 presents output 714 for the identified application session (in this example ‘MS-Teams call from client user B-iphone’) . . . present[ing] application session-specific topology 716 generated by conversational assistant engine 136, 356 for the identified application session. Application session-specific topology 716 includes a client device ("iPhone") running the application, two AP devices ("Wireless") 718, a switch ("Wired"), a gateway ("WAN"), and the cloud-based application server ("Teams")” and “indicia of performance or connectivity health for each of the network devices”– See [¶0119], whereby application session-specific topology and indicia of performance or connectivity health are based on telemetry data as explained in Regarding Amended Claim 1, supra). Similarly, Junkins teaches “an analytics architecture that can be integrated with the services supported by the CSP network and the particular client devices using the CSP network . . . merg[ing] radio access network (RAN) performance data with slice-level telemetry from network data analytics function (NWDAF) and deep-packet inspection (DPI) network data” and “enabl[ing] a CSP to view the network resources utilized by subscribers based on the network slice architecture, a cell site, a device type, or a customer segment” – See [¶0034].
Therefore, Amended Claim 4 is obvious over Wang in view of Junkins.
Regarding Claim 5, dependent from Amended Claim 1, Wang further teaches wherein the telemetry data associated with the particular online application comprises user experience scores for the particular online application (“The current industry standard for high user sensitivity applications is to provide an overall score (i.e., a mean opinion score (MOS) having a scale of 1 to 5) of the quality of the voice or video sessions . . . typically presented on its own without additional analysis or troubleshooting to identify a root cause of a low quality score” – See [¶0074]). In addition, Junkins teaches that “a measured QoE score is generated for the subscriber session with the illustrative mobile application . . . in real-time in real-time . . . generated with the RAN data set, the NWDAF data set and the CN data set,” i.e., telemetry data – See [¶0164] and Fig. 6A.
Therefore, Claim 5 is obvious over Wang in view of Junkins.
Regarding Claim 6, dependent from Amended Claim 1, while Wang teaches the wireless configuration to affect the user experience score associated with the particular online application (to “improve the user experience at a flow session level for high user-sensitivity applications, e.g., VOIP applications, streaming video applications, gaming applications, or video conference applications” whereby MOS is indicative “of the quality of the voice or video sessions” – See [¶0074], the “VNA/AI engine 350 is configured to provide a granular troubleshooting workflow at the application session level using an application session-specific topology from client to cloud” – See [¶0075] and “may determine a recommended action based on the detected connectivity issue and/or a root cause determined for the detected connectivity issue” – See [¶0085]), Wang does not explicitly teach determining, by the device, whether the wireless configuration had an effect on that user experience score.
Junkins further teaches determining, by the device, whether the wireless configuration had an effect on a user experience score associated with the particular online application (“In FIG. 6A, the method 600 is initiated at block 602 where an illustrative mobile application is registered for QoE” – See [¶0149], wherein “[i]f a consistently low measured QoE score is identified at decision diamond 624, the method proceeds to block 626. At block 626, a network orchestration module adds or removes network resources” – See [¶0177] and “[i]f the consistently low measured QoE score does not improve, the method proceeds to block 628 in FIG. 6C where an anomaly detection step is performed” – See [¶0178], i.e., the QoE appliance determines if the wireless configuration adding network resources had an effect on the user experience score associated with the particular online application).
Therefore, Claim 6 is obvious over Wang in view of Junkins.
Regarding Claim 10, dependent from Amended Claim 1, Wang further teaches an RRM function as part of the NMS (the “RRM engine 360 monitors one or more metrics for each site 102A-102N in order to learn and optimize the RF environment at each site” and “make adjustments to the radio settings of the access points at each site to address the identified issues” or “automatically change or update configurations of one or more AP devices . . . with an aim to improve the coverage and capacity SLE metrics and thus to provide an improved wireless experience for the user,” i.e., reactive to the wireless configuration generated by the NMS – See [¶0072] and Fig. 3). In the alternative that Wang does not explicitly/sufficiently teach wherein the wireless configuration modifies a radio resource management (RRM) function in the wireless network, Junkins teaches such interaction in the context of maintaining the QoE for an online application.
First, Junkins, teaches a robotic process automation (RPA) similar to the NMS in Wang (a “RPA module 270, which can provide actionable data and/or intelligence for planning systems, operations systems, and predictive maintenance systems” – See [¶0108], whereby “the RPA module 270 receives the NWDAF data and identifies actionable events . . . used for network orchestration 274, RAN fine tuning 276” – See [¶0109], i.e., modifying RRM functions in the wireless network). Then, Junkins teaches the wireless configuration modifies a radio resource management (RRM) function in the wireless network (“The systems and methods described herein bring this disparate data together as an integrated data stream that is used as a single index to analyze call quality by determining the measured QoE score” – See [¶0196], and “an architecture that supports automated actions being performed the RPA. The RPA includes a set of test criteria for the action taken, in which the test criteria determine the success or failure for the action taken” – See [¶0194], i.e., “test whether the action taken produced the desired result based on a test criteria” – See [¶0195], e.g., test whether the QoE of the voice call has improved when applying the updated wireless configuration to fine tune the RAN).
Therefore, Claim 10 is obvious over Wang in view of Junkins.
Regarding Claims 11-16, as amended, Wang teaches in Fig. 3 an apparatus, i.e. the NMS 300, comprising: one or more network interfaces; a processor coupled to the one or more network interfaces and configured to execute one or more processes (“NMS 300 includes a communications interface 330, one or more processor(s) 306, a user interface 310, a memory 320, and a database 312” – See [¶0063]); and a memory configured to store a process that is executable by the processor, the process when executed configured to execute (“Processor(s) 306 execute software instructions, such as those used to define a software or computer program, stored to a computer-readable storage medium (such as memory 320) . . . that stores instructions to cause the one or more processors 306 to perform the techniques described herein” – See [¶0064]). Wang in view of Junkins further teaches that the apparatus executes the steps recited in Amended Claim 11 with the same language as in Amended Claim 1. Wang in view of Junkins further teaches all the limitations recited in Claims 12-16, respectively, as amended, as explained in Regarding Claims 2-6, respectively, as amended wherein the limitations are recited using the same language.
Therefore, Claims 11-16, as amended, are obvious over Wang in view of Junkins.
Regarding Amended Claim 20, Wang in view of Junkins further teaches a tangible, non-transitory, computer-readable medium storing program instructions that cause a device to execute a process comprising the steps of Amended Claim 1 (“software instructions, such as those used to define a software or computer program” are “stored to a computer-readable storage medium (such as memory 320), such as non-transitory computer-readable mediums . . .that stores instructions to cause the one or more processors 306 to perform the techniques described herein” – See Wang:[¶0065]).
Therefore, Amended Claim 20 is obvious over Wang in view of Junkins.
In sum, Claims 1-6, 10-16 and 20, as amended, are rejected under 35 U.S.C. 103 as obvious over Wang in view of Junkins.
Amended Claims 7-8 and 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Wang in view of Junkins as applied to Amended Claims 1 and 11 above, and further in view of Shah et al., U.S. Patent Application Publication No. 2023/0126313 (hereinafter Shah).
Regarding Amended Claim 7, dependent from Amended Claim 1, Wang in view of Junkins teaches wherein the wireless configuration controls RF optimization at the AP, including beamforming (“RRM engine 360 . . . optimize[s] the RF environment at each site . . . to make adjustments to the radio settings of the access points at each site to address the identified issues,” e.g., “events, power, channel, bandwidth, and number of clients connected to each AP” – See Wang:[¶0072]; “the RPA module 270 is used to manage and/or control operational tasks by communicating actionable events to various network functions or RAN systems that affect the QoE” including “RAN fine tuning” – See Junkins:[¶0109], wherein “the radio unit . . . handles the digital front end and the parts of the PHY layer and the digital beamforming functionality” – See id.:[¶0116]). However, Wang in view of Junkins does not teach the beamforming capability when communicating with the client device.
Shah, including by reference Wang and the NMS capabilities disclosed in Wang – See [¶0063], further teaches the method and systems discloses “may be used in conjunction with one or more types of wireless communication signals and/or systems” including “Multi-User MIMO (MU-MIMO)” – See [¶0143], so that for the RRM “to make adjustments to the radio settings of the access points at each site to address the identified issues” – See [¶0086], the wireless configuration must control whether the access point uses beamforming1 when communicating with the client). Because Shah disclosure includes Wang, a person of ordinary skill in the art would have been able to carry out the combination of Wang in view of Junkins and Shah through techniques known in the art motivated by the need of controlling with the corrective wireless configuration application-session specific SLEs, as taught by Wang in view of Junkins.
Therefore, Amended Claim 7 is obvious over Wang in view of Junkins and further in view of Shah.
Regarding Amended Claim 8, dependent from Amended Claim 1, while Wang in view of Junkins further teaches wherein the wireless configuration controls a transmit power level of the access point (“RRM engine 360 . . . optimize[s] the RF environment at each site . . . to make adjustments to the radio settings of the access points at each site to address the identified issues,” e.g., “events, power, channel, bandwidth, and number of clients connected to each AP” – See Wang:[¶0072]), Wang in view of Junkins does not teach the control of the transmit power level of the access point when communicating with the client device. Shah teaches that the methods disclosed can be used to “identifying asymmetries in the power level (or mismatch) between the client device and AP resulting in a poor connection,” whereby “an AP indicates the RSSI at which it hears a client, and the NMS client agent provides the other half of the conversation i.e., the RSSI at which the client hears the AP” – See [¶0057]. Because Wang in view of Junkins teaches corrective wireless configuration at application-session specific SLE/QoE, as explained in Regarding Amended Claim 1, supra, and Shah includes by reference Wang, a person of ordinary skill in the art would have been able to carry out the combination of Wang in view of Junkins and Shah through techniques known in the art motivated by the need of controlling with the corrective wireless configuration the transmit power level of the access point to maintain the application-session specific SLE/QoE, as taught by Wang in view of Junkins.
Therefore, Amended Claim 8 is obvious over Wang in view of Junkins and further in view of Shah.
Regarding Amended Claims 17-18, dependent from Amended Claim 11, the claim language merely recites the limitation of Amended Claims 7-8 as applied to the apparatus of Amended Claim 11. Because Amended Claim 11 is obvious over Wang in view of Junkins and Amended Claims 7-8 is obvious over Wang in view of Junkins and further in view of Shah, Amended Claims 17-18 are obvious over Wang in view of Junkins and further in view of Shah.
In sum, Amended Claims 7-8 and 17-18 are rejected under 35 U.S.C. 103 as obvious over Wang in view of Junkins and further in view of Shah.
Amended Claims 9 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Wang in view of Junkins as applied to Amended Claims 1 and 11 above, and further in view of Lin et al., U.S. Patent Application Publication No. 2023/0091127 (hereinafter Lin).
Regarding Amended Claim 9, dependent from Amended Claim 1, although Wang in view of Junkins teaches the wireless configuration controlling coverage and capacity at a site (the “RRM engine 360 may monitor the coverage and capacity SLE metrics for a wireless network 106 at a site 102 in order to . . . make adjustments to the radio settings of the access points at each site to address the identified issues” – See Wang:[¶0072]), Wang in view of Junkins does not explicitly teach wherein the wireless configuration controls whether the client device is to roam to a different access point.
Lin teaches a “network management system (NMS) [that] monitors and/or controls a wireless network based on one or more roaming quality assessments” by analyzing received “network data associated with a plurality of client devices associated with a wireless network” –See [¶0029], whereby “[t]he roaming quality assessments may include, for each roaming event and/or client device in the wireless network, a suboptimal roam score, a sticky client score, and/or an interband score. The NMS may further detect ping-pong and/or excessive roaming events for one or more client devices” – See [¶0030].
Lin further teaches wherein the wireless configuration controls whether the client is to roam to a different access point (e.g., when the NMS detects a severe sticky client issue, i.e., a “situation in which a client device has only one roaming option of the same AP on a different band” – See [¶0113], or a roaming between bands at the same AP issue, these are indication of “a coverage hole at the site, or a power imbalance between APs and/or bands at the site,” the NMS may (automatically) invoke “[r]emedial actions to address a potential coverage hole [to] include adding one or more additional APs to the wireless network and/or adjusting the transmit power balance among one or more of the APs in the wireless network” – See [¶0127], thereby controlling whether the client is to roam to a different access point because “the RRM engine 360 may further automatically change or update configurations of one or more APs 142” through the wireless configuration – See [¶0075]; see also [¶0076] (“invoking RRM 360 to reboot one or more APs and/or adjust/modify the transmit power of a specific radio in a specific AP”)).
Thus, Wang in view of Junkins and Lin each teaches a NMS containing an RRM to affect changes at one or more APs using a generated wireless configuration to improve wireless experience for the user/client device. A person of ordinary skill in the art before the effective filing date of the claimed invention would have understood the wireless configuration used by the RRM in Lin to configure APs so that the client is to roam to a different access point (or to another band of the same AP) could be substituted in for the step of generating, by the VNA/AI/RRM of the NMS in Wang in view of Junkins, a wireless configuration for an access point of the wireless network based on the telemetry data and the statistics because both procedures generate a wireless configuration for APs. Furthermore, a person of ordinary skill in the art would have been able to carry out the substitution through techniques known in the art. Finally, the substitution achieves the predictable result of solving worsening user experience because of a sticky client roaming issue.
Therefore, Amended Claim 9 is obvious over Wang in view of Junkins and further in view of Lin.
Regarding Amended Claim 19, dependent from Amended Claim 11, the claim language merely recites the limitation of Amended Claim 9 as applied to the apparatus of Amended Claim 11. Because Amended Claim 11 is obvious over Wang in view of Junkins and Amended Claim 9 is obvious over Wang in view of Junkins and further in view of Lin, Amended Claim 19 is obvious over Wang in view of Junkins and further in view of Lin.
In sum, Amended Claims 9 and 19 are rejected under 35 U.S.C. 103 as obvious over Wang in view of Junkins and further in view of Lin.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Halabian et al., U.S. Patent Application Publication No. 2019/0320325 discloses a centralized network management framework for optimizing WLAN networks using Self-Organizing Network (SON) managers to provide vendor agnostic Wi-Fi radio resource management services in the presence of a large and dynamically changing number of APs over various traffic;
Ratkovic et al., U.S. Patent No.10756983B2 teaches collecting traffic data to build time series of the application traffic as indicative of when traffic associated with the particular online application is sent via the wireless network;
Hariharan et al., U.S. Patent Application Publication No. 2020/0127901 discloses a network analysis platform using telemetry data and models to identify uplink quality degradation at a network cell, such as at a base station, whereby the user experience metric can be either downlink throughput or uplink voice quality;
Lunardi et al., U.S. Patent Application Publication No. 2024/0064072 discloses QoE measurement configuration of a client device related to an application and reporting of the measurements to a base station;
Yu et al., U.S. Patent Application Publication No. 2020/0304417 discloses using enforcement points to improve session/flow level QoS/QoE;
Henry et al., U.S. Patent Application Publication No. 2017/0245170 discloses dynamic application QoS profile provisioning;
Byrne, U.S. Patent Application Publication No. 2020/0177512 discloses control panel techniques for determining, based at least partially on the telemetry data and the one or more policies, whether a certain QoS level is provided to the user equipment; and performing one or more control plane functions to enforce the certain QoS level;
Kim et al., U.S. Patent Application Publication No. 2023/0370896 provides method and apparatus for end-to-end (E2E) quality of service (QoS) by performing scheduling regarding a UE so as to reflect a packet delay budget at an access network;
Thyagaturu et al., U.S. Patent Application Publication No. 2023/0006889 discloses mechanisms that expand existing end-to-end architectures in order to include quality of service and monitoring mechanisms that connect network slicing technologies with infrastructure and/or network data center quality of service provider domains;
Faustino et al., U.S. Patent Application Publication No. 2021/0337354 discloses a telemetry data computing engine;
Scully et al., U.S. Patent Application Publication No. 2016/0156520 discloses user experience measurements in the telecommunications network for user equipment and corresponding users in the network that use one or more service, whereby the methods and apparatus automatically changes certain parameters or adjusts the one or more network nodes in the network using one or more network performance mechanisms;
Liu et al., U.S. Patent Application Publication No. 2016/0346696 discloses using telemetry data (e.g., user specific telemetry data and/or aggregate telemetry data), service management data, and/or other types of data related to the online service, to generate new interactive experiences can by dynamically created for the users of the online service;
Safavi et al, U.S. Patent Application Publication No. 2025/0055772 (and incorporated patent applications) discloses a network assistant (VNA) of an NMS that implements an event processing platform for providing real-time insights and simplified troubleshooting using AI/ML;
Lin et al., U.S. Patent Application Publication No. 2024/0224075 (and incorporated patent applications) discloses techniques to automatically configure channel bandwidth settings of the wireless radios within the AP devices to optimize performance of wireless networks based on monitoring operational parameters of the wireless radios within the AP devices;
3GPP TS 26.247 V17.3.0 (2023-03), Title: “Technical Specification Group Services and System Aspects; Transparent end-to-end Packet-switched Streaming Service (PSS); Progressive Download and Dynamic Adaptive Streaming over HTTP (3GP-DASH) (Release 17),” discloses Server and Network Assisted DASH (SAND) for video-aware network resource management to provide consistent QoE/QoS for DASH clients using an out-of-band DASH-Aware Network Element;
3GPP TS 28.405 V18.2.0 (2023-03), Title: “Technical Specification Group Services and System Aspects; Telecommunication management; Quality of Experience (QoE) measurement collection; Control and configuration (Release 18)”;
3GPP TS 28.531 V17.7.0 (2023-03), Title: “Technical Specification Group Services and System Aspects; Management and orchestration; Provisioning; (Release 17)”;
3GPP TS 28.550 V18.1.0 (2023-03), Title: “Technical Specification Group Services and System Aspects; Management and orchestration; Performance assurance (Release 18)”;
3GPP TS 23.288 V17.8.0 (2023-03), Title: “Technical Specification Group Services and System Aspects; Architecture enhancements for 5G System (5GS) to support network data analytics services (Release 17)”;
3GPP TS 28.104 V17.3.0 (2023-03), Title: “Technical Specification Group Services and System Aspects; Management and orchestration; Management Data Analytics (MDA) (Release 17)”;
3GPP TSG SA5 Meeting #146-bis-e, S5-231043, Title: “Discussion paper on KQI, QoE,” Source: Nokia, January 2023.
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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/L.G.G./ Examiner, Art Unit 2478
/JOSEPH E AVELLINO/Supervisory Patent Examiner, Art Unit 2478
1 A person of ordinary skills in the art knowing that beamforming is used in wireless communication to enhance signal strength and quality by focusing radio waves in a specific direction, would know that in a network using MIMO devices, antenna arrays must be oriented to improve signal quality and performance through the process of beamforming.