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 filed 07/22/2026 has been entered. Claims 1-18 remain pending in the application.
Claim Objections
Claims 1 and 10 are objected to because of the following informalities:
Claim 1, line 18 recites the phrase “multiple alerts; and” which should be “multiple alerts;”
For the informalities above and wherever else they may occur appropriate correction is required.
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 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 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim 1-2, 4-6, 8-11, 13-15 and 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over TIWARI et al. (US 20190379577 A1 hereinafter Tiwari) in view of Lukens et al. (US 20240137285 A1 hereinafter Lukens) and Chandrasekhar et al. (US 20200382361 A1 hereinafter Chandrasekhar)
As to independent claim 1, Tiwari teaches a method for predicting a fault condition in a wireless network, the method comprising: [predicts vulnerability ¶13 and monitors for fault ¶11]
receiving, at a machine-learning model from multiple subsystems of the wireless network, information associated with multiple alerts triggered across the multiple subsystems, each of the multiple alerts being indicative of a corresponding potential fault condition in one of the multiple subsystems;[receives faults, KPIs, alerts ¶4 " receive first network information associated with a first portion of a network, wherein the first network information may include information associated with faults detected in the first portion of the network, key performance indicators associated with the first portion of the network, or alerts received from the first portion of the network"]
receiving, at the machine-learning model from a network platform, information regarding resources of the wireless network; [receives inventory and topology information (resources) ¶3 "processing the alarm condition, network inventory information, network topology information, and network service information"]
training the machine-learning model using the multiple alerts, the observation information, and the information regarding the resources; [trains ML using inventory, topology, service, alarm conditions, historical customer information ¶30-31 " a training operation on the machine learning model with historical network inventory information, network topology information, network service information, and alarm conditions"]
Tiwari does not specifically teach receiving, at the machine-learning model from a plurality of external devices, observation information regarding the wireless network; training based on observation information, and further training the machine-learning model using the subset of alerts.
However, Lukens teaches receiving, at the machine-learning model from a plurality of external devices, observation information regarding the wireless network; [receives test data and reports (observation information) ¶17 "when a customer reports a network issue using the mobile application, the device can be prompted to perform a speed test. The results of the speed test are then used as inputs to the machine learning model"]
training based on observation information [trains based on collected reports ¶17]
further training the machine-learning model using the subset of alerts; [continuous updating and retraining [updating and retraining (further training) based on specific feedback on an issue ¶46, ¶52-53]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the alarm resolution by Tiwari by incorporating the receiving, at the machine-learning model from a plurality of external devices, observation information regarding the wireless network; training based on observation information, and further training the machine-learning model using the subset of alerts disclosed by Lukens because both techniques address the same field of alarm monitoring and by incorporating Lukens into Tiwari reduces downtime and improves network performance [Lukens ¶19]
Tiwari and Lukens do not specifically teach identifying, by the machine-learning model, a subset of alerts of the multiple alerts by identifying unusual patterns of behavior in the multiple alerts to correlate the subset of alerts, wherein the subset of alerts represents a set of one or more predicted fault conditions associated with the multiple alerts triggered across the multiple subsystems, a number of alerts in the subset of alerts being less than the number of the multiple alerts; identifying, by the machine-learning model, one or more remediating actions configured to address the one or more predicted fault conditions in at least one corresponding subsystem of the multiple subsystems; and generating, by the machine-learning model, one or more signals that initiate the one or more remediating actions in the at least one corresponding subsystem.
However, Chandrasekhar teaches identifying, by the machine-learning model, a subset of alerts of the multiple alerts by identifying unusual patterns of behavior in the multiple alerts to correlate the subset of alerts, wherein the subset of alerts represents a set of one or more predicted fault conditions associated with the multiple alerts triggered across the multiple subsystems, a number of alerts in the subset of alerts being less than the number of the multiple alerts; and [identifies anomalies in alarm data based on sample patterns ¶91 "The anomaly detector 412b looks at the real time data to identify patterns that correspond to operator network operations which are outside their normal range."; ¶102, ¶104 "machine learning training model 414 generates rules for identifying the detected anomalies"], [correlation for subsets of alarms ¶195-202 "correlations is presented in on a user interface, that depicts the distribution of the number of occurrences of alarms of different types in relation to each anomaly category"]
identifying, by the machine-learning model, one or more remediating actions configured to address the one or more predicted fault conditions in at least one corresponding subsystem of the multiple subsystems; and [determines remedial actions for detected anomaly ¶113-117 "RCA 420 determines the root causes for the detected anomalies, an explanation of the root cause as well as the remedial action 422 can be displayed, on a user interface. When the remedial action 422 is displayed it can include recommended action(s) to perform in order to restore the network to its normal functioning state."]
generating, by the machine-learning model, one or more signals that initiate the one or more remediating actions in the at least one corresponding subsystem. [generates and performs corrective actions ¶113-117, ¶236 "electronic device perform a corrective action to resolve the anomaly based on the first rule"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the alarm resolution by Tiwari and Lukens by incorporating the identifying, by the machine-learning model, a subset of alerts of the multiple alerts by identifying unusual patterns of behavior in the multiple alerts to correlate the subset of alerts, wherein the subset of alerts represents a set of one or more predicted fault conditions associated with the multiple alerts triggered across the multiple subsystems, a number of alerts in the subset of alerts being less than the number of the multiple alerts; identifying, by the machine-learning model, one or more remediating actions configured to address the one or more predicted fault conditions in at least one corresponding subsystem of the multiple subsystems; and generating, by the machine-learning model, one or more signals that initiate the one or more remediating actions in the at least one corresponding subsystem disclosed by Chandrasekhar because all techniques address the same field of alarm monitoring and by incorporating Chandrasekhar into Tiwari and Lukens reduces the time and effort needed to detect and fix network anomalies [Chandrasekhar ¶15].
As to dependent claim 2, the rejection of claim 1 is incorporated, Tiwari, Lukens and Chandrasekhar further teach wherein the one or more remediating actions include at least one of a microservice restart, an Elastic Kubernetes Service (EKS) pod restart, an application restart, a routing unit (RU) restart, traffic redirection, cell lock/unlock after call draining, and a graceful shutdown of a container network function/virtual network function (CNF/VNF). [Tiwari traffic redirection (divert) ¶35-42 "diverting traffic away from the failing interfaces"]
As to dependent claim 4, the rejection of claim 1 is incorporated, Tiwari, Lukens and Chandrasekhar further teach wherein the information regarding the resources include (i) a physical attribute, a logical attribute, a location, and status of the resources, (ii) new resources added to the wireless network, and (iii) a network topology. [Tiwari topology ¶3, base stations ¶14, interconnections, signal types, rates (physical) link location (location) ¶30, virtual machine, hypervisor (logic) KPI (status) ¶15, constant change (new resources) ¶11]
As to dependent claim 5, the rejection of claim 1 is incorporated, Tiwari, Lukens and Chandrasekhar further teach wherein the observation information includes information regarding customer experience of the wireless network. [Lukens customer services experience analysis ¶13, customer information ¶32]
As to dependent claim 6, the rejection of claim 1 is incorporated, Tiwari, Lukens and Chandrasekhar further teach wherein training the machine-learning model includes identifying and selecting features from the multiple alerts, [Tiwari minimum feature set is found ¶21, alerts ¶4]
the observation information, and [Lukens logs and diagnostics ¶47-48]
the information regarding the resources to be used in the machine-learning model. [Tiwari Inventory and topology ¶3]
As to dependent claim 8, the rejection of claim 1 is incorporated, Tiwari, Lukens and Chandrasekhar further teach wherein the machine-learning model is trained using supervised learning, unsupervised learning, or reinforcement learning. [Tiwari supervised and unsupervised ¶24], [Lukens reinforcement learning ¶44]
As to dependent claim 9, the rejection of claim 1 is incorporated, Tiwari, Lukens and Chandrasekhar further teach wherein the wireless network is configured to perform fifth generation (5G) cloud-native network operations. [Chandrasekhar 5g and cloud ¶44]
As to independent claim 10, Tiwari teaches a system for predicting a fault condition in a wireless network, the system comprising: [system predicts vulnerability ¶13 and monitors for fault ¶11]
multiple subsystems configured to monitor, measure, and analyze a performance of the wireless network; [monitors wireless networks ¶51, ¶44]
memory; and [¶78]
at least one processor, coupled to the memory and using a trained machine-learning model, the at least one processor configured to: [processors and model ¶78, ¶42]
receive, at a trained machine-learning model from multiple subsystems of the wireless network, information associated with multiple alerts triggered across the multiple subsystems, each of the multiple alerts being indicative of a corresponding potential fault condition in one of the multiple subsystems;[receives faults, KPIs, alerts ¶4 " receive first network information associated with a first portion of a network, wherein the first network information may include information associated with faults detected in the first portion of the network, key performance indicators associated with the first portion of the network, or alerts received from the first portion of the network"]
receive, at the machine-learning model from a network platform, information regarding resources of the wireless network; [receives inventory and topology information (resources) ¶3 "processing the alarm condition, network inventory information, network topology information, and network service information"]
train the machine-learning model using the multiple alerts, the observation information, and the information regarding the resources; [trains ML using inventory, topology, service, alarm conditions, historical customer information ¶30-31 " a training operation on the machine learning model with historical network inventory information, network topology information, network service information, and alarm conditions"]
Tiwari does not specifically teach receive, at the machine-learning model from a plurality of external devices, observation information regarding the wireless network; train based on observation information, and further train the machine-learning model using the subset of alerts.
However, Lukens teaches receive, at the machine-learning model from a plurality of external devices, observation information regarding the wireless network; [receives test data and reports (observation information) ¶17 "when a customer reports a network issue using the mobile application, the device can be prompted to perform a speed test. The results of the speed test are then used as inputs to the machine learning model"]
train based on observation information [trains based on collected reports ¶17]
further train the machine-learning model using the subset of alerts; [continuous updating and retraining [updating and retraining (further training) based on specific feedback on an issue ¶46, ¶52-53]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the alarm resolution by Tiwari by incorporating the receive, at the machine-learning model from a plurality of external devices, observation information regarding the wireless network; train based on observation information, and further train the machine-learning model using the subset of alerts disclosed by Lukens because both techniques address the same field of alarm monitoring and by incorporating Lukens into Tiwari reduces downtime and improves network performance [Lukens ¶19]
Tiwari and Lukens do not specifically teach identify, by the machine-learning model, a subset of alerts of the multiple alerts by identifying unusual patterns of behavior in the multiple alerts to correlate the subset of alerts, wherein the subset of alerts represents a set of one or more predicted fault conditions associated with the multiple alerts triggered across the multiple subsystems, a number of alerts in the subset of alerts being less than the number of the multiple alerts; identify, by the machine-learning model, one or more remediating actions configured to address the one or more predicted fault conditions in at least one corresponding subsystem of the multiple subsystems; and generate, by the machine-learning model, one or more signals that initiate the one or more remediating actions in the at least one corresponding subsystem.
However, Chandrasekhar teaches identify, by the machine-learning model, a subset of alerts of the multiple alerts by identifying unusual patterns of behavior in the multiple alerts to correlate the subset of alerts, wherein the subset of alerts represents a set of one or more predicted fault conditions associated with the multiple alerts triggered across the multiple subsystems, a number of alerts in the subset of alerts being less than the number of the multiple alerts; and [identifies anomalies in alarm data based on sample patterns ¶91 "The anomaly detector 412b looks at the real time data to identify patterns that correspond to operator network operations which are outside their normal range."; ¶102, ¶104 "machine learning training model 414 generates rules for identifying the detected anomalies"], [correlation for subsets of alarms ¶195-202 "correlations is presented in on a user interface, that depicts the distribution of the number of occurrences of alarms of different types in relation to each anomaly category"]
identify, by the machine-learning model, one or more remediating actions configured to address the one or more predicted fault conditions in at least one corresponding subsystem of the multiple subsystems; and [determines remedial actions for detected anomaly ¶113-117 "RCA 420 determines the root causes for the detected anomalies, an explanation of the root cause as well as the remedial action 422 can be displayed, on a user interface. When the remedial action 422 is displayed it can include recommended action(s) to perform in order to restore the network to its normal functioning state."]
generate, by the machine-learning model, one or more signals that initiate the one or more remediating actions in the at least one corresponding subsystem. [generates and performs corrective actions ¶113-117, ¶236 "electronic device perform a corrective action to resolve the anomaly based on the first rule"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the alarm resolution by Tiwari and Lukens by incorporating the identify, by the machine-learning model, a subset of alerts of the multiple alerts by identifying unusual patterns of behavior in the multiple alerts to correlate the subset of alerts, wherein the subset of alerts represents a set of one or more predicted fault conditions associated with the multiple alerts triggered across the multiple subsystems, a number of alerts in the subset of alerts being less than the number of the multiple alerts; identify, by the machine-learning model, one or more remediating actions configured to address the one or more predicted fault conditions in at least one corresponding subsystem of the multiple subsystems; and generate, by the machine-learning model, one or more signals that initiate the one or more remediating actions in the at least one corresponding subsystem disclosed by Chandrasekhar because all techniques address the same field of alarm monitoring and by incorporating Chandrasekhar into Tiwari and Lukens reduces the time and effort needed to detect and fix network anomalies [Chandrasekhar ¶15].
As to dependent claim 11, the rejection of claim 10 is incorporated, Tiwari, Lukens and Chandrasekhar further teach wherein the one or more remediating actions include at least one of a microservice restart, an Elastic Kubernetes Service (EKS) pod restart, an application restart, a routing unit (RU) restart, traffic redirection, cell lock/unlock after call draining, and a graceful shutdown of a container network function/virtual network function (CNF/VNF). [Tiwari traffic redirection (divert) ¶35-42 "diverting traffic away from the failing interfaces"]
As to dependent claim 13, the rejection of claim 10 is incorporated, Tiwari, Lukens and Chandrasekhar further teach wherein the information regarding the resources include (i) a physical attribute, a logical attribute, a location, and status of the resources, (ii) new resources added to the wireless network, and (iii) a network topology. [Tiwari topology ¶3, base stations ¶14, interconnections, signal types, rates (physical) link location (location) ¶30, virtual machine, hypervisor (logic) KPI (status) ¶15, constant change (new resources) ¶11]
As to dependent claim 14, the rejection of claim 10 is incorporated, Tiwari, Lukens and Chandrasekhar further teach wherein the observation information includes information regarding customer experience of the wireless network. [Lukens customer services experience analysis ¶13, customer information ¶32]
As to dependent claim 15, the rejection of claim 10 is incorporated, Tiwari, Lukens and Chandrasekhar further teach wherein training the machine-learning model includes identifying and selecting features from the multiple alerts, [Tiwari minimum feature set is found ¶21, alerts ¶4]
the observation information, and [Lukens logs and diagnostics ¶47-48]
the information regarding the resources to be used in the machine-learning model. [Tiwari Inventory and topology ¶3]
As to dependent claim 17, the rejection of claim 10 is incorporated, Tiwari, Lukens and Chandrasekhar further teach wherein the machine-learning model is trained using supervised learning, unsupervised learning, or reinforcement learning. [Tiwari supervised and unsupervised ¶24], [Lukens reinforcement learning ¶44]
As to dependent claim 18, the rejection of claim 10 is incorporated, Tiwari, Lukens and Chandrasekhar further teach wherein the wireless network is configured to perform fifth generation (5G) cloud-native network operations. [Chandrasekhar 5g and cloud ¶44]
Claims 3 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Tiwari in view of Lukens and Chandrasekhar, as applied in the rejection of claim 1 and 10 above, and further in view of SELOKAR et al. (US 20210273843 A1 hereinafter Selokar)
As to dependent claim 3, Tiwari, Lukens and Chandrasekhar teach the method of claim 1 above that is incorporated,
Tiwari, Lukens and Chandrasekhar do not specifically teach wherein the multiple alerts are provided in different formats from the multiple subsystems.
However, Selokar teaches wherein the multiple alerts are provided in different formats from the multiple subsystems. [alerts in different formats ¶20 "Such network devices 106, 108, 110, and 112 may belong to different vendors and may be configured to transmit network alarms in different formats"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the alert messaging disclosed by Tiwari, Lukens and Chandrasekhar by incorporating the wherein the multiple alerts are provided in different formats from the multiple subsystems disclosed by Selokar because all techniques address the same field of monitoring alerts and by incorporating Selokar into Tiwari, Lukens and Chandrasekhar helps better automate fault understanding reducing the need for time and human error in resolution [Selokar ¶12]
As to dependent claim 12, Tiwari, Lukens and Chandrasekhar teach the method of claim 1 above that is incorporated,
Tiwari, Lukens and Chandrasekhar do not specifically teach wherein the multiple alerts are provided in different formats from the multiple subsystems.
However, Selokar teaches wherein the multiple alerts are provided in different formats from the multiple subsystems. [alerts in different formats ¶20 "Such network devices 106, 108, 110, and 112 may belong to different vendors and may be configured to transmit network alarms in different formats"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the alert messaging disclosed by Tiwari, Lukens and Chandrasekhar by incorporating the wherein the multiple alerts are provided in different formats from the multiple subsystems disclosed by Selokar because all techniques address the same field of monitoring alerts and by incorporating Selokar into Tiwari, Lukens and Chandrasekhar helps better automate fault understanding reducing the need for time and human error in resolution [Selokar ¶12]
Claims 7 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Tiwari in view of Lukens and Chandrasekhar, as applied in the rejection of claim 1 and 10 above, and further in view of Noorhosseini et al. (US 6707795 B1 hereinafter Noorhosseini)
As to dependent claim 7, Tiwari, Lukens and Chandrasekhar teach the method of claim 1 above that is incorporated,
Tiwari, Lukens and Chandrasekhar further teach updating the machine-learning model based on a discrepancy between the subset of alerts and the [[expected alerts]]. [Lukens compares model output to desired output then changing weights of model (update) ¶46 "Output from the model can be compared to the desired output, and based on the comparison, the model can be modified, such as by changing weights"]
Tiwari, Lukens and Chandrasekhar do not specifically teach comparing the subset of alerts to expected alerts. [expected alarms examined vs alarm list Col. 8 ln 25-40 "determines the expected alarms on the TTP based on the StateText, CurrentTTP and Impact attributes of the correlation state and passes these to the TTP in the ExpectedAlarmList parameter of the correlation state. The TTP then examines its alarm list and adds any alarms found which are expected alarms to the associated problem object."]
However, Noorhosseini teaches comparing the subset of alerts to expected alerts,
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the alert messaging disclosed by Tiwari, Lukens and Chandrasekhar by incorporating the comparing the subset of alerts to expected alerts disclosed by Noorhosseini because all techniques address the same field of monitoring alerts and by incorporating Noorhosseini into Tiwari, Lukens and Chandrasekhar reduces the difficulty in analyzing results of network faults to better find root causes. [Noorhosseini Col. 1-2 ln. 24-12]
As to dependent claim 16, Tiwari, Lukens and Chandrasekhar teach the method of claim 1 above that is incorporated,
Tiwari, Lukens and Chandrasekhar further teach updating the machine-learning model based on a discrepancy between the subset of alerts and the [[expected alerts]]. [Lukens compares model output to desired output then changing weights of model (update) ¶46 "Output from the model can be compared to the desired output, and based on the comparison, the model can be modified, such as by changing weights"]
Tiwari, Lukens and Chandrasekhar do not specifically teach comparing the subset of alerts to expected alerts. [expected alarms examined vs alarm list Col. 8 ln 25-40 "determines the expected alarms on the TTP based on the StateText, CurrentTTP and Impact attributes of the correlation state and passes these to the TTP in the ExpectedAlarmList parameter of the correlation state. The TTP then examines its alarm list and adds any alarms found which are expected alarms to the associated problem object."]
However, Noorhosseini teaches comparing the subset of alerts to expected alerts,
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the alert messaging disclosed by Tiwari, Lukens and Chandrasekhar by incorporating the comparing the subset of alerts to expected alerts disclosed by Noorhosseini because all techniques address the same field of monitoring alerts and by incorporating Noorhosseini into Tiwari, Lukens and Chandrasekhar reduces the difficulty in analyzing results of network faults to better find root causes. [Noorhosseini Col. 1-2 ln. 24-12]
Response to Arguments
Applicant's arguments filed 07/22/2026, with respect to 112 and 101, these rejections have been withdrawn.
Applicant's arguments filed 07/22/2026. In the remark, applicant argues that:
(1) Tiwari, Lukens and Jayaram fail to teach "identifying, by the machine-learning model, one or more remediating actions configured to address the one or more predicted fault conditions in at least one corresponding subsystem of the multiple subsystems; and generating, by the machine-learning model, one or more signals that initiate the one or more remediating actions in the at least one corresponding subsystem." as recited by amended claim 1. See Tiwari ¶3, Lukens ¶17, and Jayaram ¶42.
As to point (1) Applicant’s arguments with respect to claims have been considered but are moot in view of a new ground of rejection made under 35 U.S.C. 103 as being unpatentable over Tiwari in view of Lukens and Chandrasekhar as set forth above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Applicant is required under 37 C.F.R. § 1.111(c) to consider these references fully when responding to this action.
O'Mahony et al. (US 20250077372 A1) teaches detecting faults and enabling remediation actions with avoidance (see ¶16-17)
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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/BEAU D SPRATT/Primary Examiner, Art Unit 2143
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