# US 2025/0097738 A1 · Wireless communication systems for identifying faults > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2025/0097738 A1 - Document ID: US20250097738A1 - Status: Published application - Kind: A1 - Application number: 18/370,757 - Filed: 2023-09-20 - Published: 2025-03-20 - Earliest claimed: 2023-09-20 - Assignees: DISH Wireless L.L.C. - Applicants: DISH Wireless L.L.C. - Inventors: Sumugam Balachandran - Web page: https://patentgrep.com/patent/US20250097738A1 ## CPC - H04W 24/04: Arrangements for maintaining operational condition - H04L 41/0631: using root cause analysis; using analysis of correlation between notifications, alarms or events based on decision criteria, e.g. hierarchy, tree or time analysis - H04L 41/064: {involving time analysis} - H04L 41/16: using machine learning or artificial intelligence - H04W 24/08: Testing, {supervising or monitoring} using real traffic - H04W: WIRELESS COMMUNICATION NETWORKS (broadcast communication H04H; communication systems using wireless links for non-selective communication, e.g. wireless extensions H04M1/72) - H04L: TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION (arrangements common to telegraphic and telephonic communication H04M) ## Abstract A method for automatically identifying a fault condition in a wireless network can include receiving, 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, identifying, by the machine-learning model, a subset of alerts of the multiple alerts, where the subset of alerts represents a set of one or more root fault conditions associated with the multiple alerts triggered across the multiple subsystems, and identifying, by the machine-learning model, one or more remediating actions configured to address the one or more root fault conditions in at least one corresponding subsystem of the multiple subsystems. The machine-learning model is trained using training data that identifies correlation between alerts generated in the multiple subsystems. ## Claims ### Claim 1 A method for automatically identifying a fault condition in a wireless network, the method comprising: receiving, 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; identifying, by the machine-learning model, a subset of alerts of the multiple alerts, wherein the subset of alerts represents a set of one or more root 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 identifying, by the machine-learning model, one or more remediating actions configured to address the one or more root fault conditions in at least one corresponding subsystem of the multiple subsystems, wherein the machine-learning model is trained using training data that identifies correlation between alerts generated in the multiple subsystems. ### Claim 2 (depends on 1) The method of claim 1, wherein the wireless network is configured to perform fifth generation (5G) cloud native network operations. ### Claim 3 (depends on 1) The method of claim 1, wherein the multiple subsystems include an external provider configured to provide a range of network testing, monitoring, and analytics solutions, a central component configured to perform collecting, storing, analyzing, and visualizing telemetry data, a radio access network (RAN) configured to provide wireless connectivity between user devices and the wireless network, and a cloud computing model configured to provide a platform for developing, deploying, and managing applications. ### Claim 4 (depends on 3) The method of claim 3, wherein each of the multiple subsystems is configured to monitor, measure, and analyze a performance of the wireless network. ### Claim 5 (depends on 1) The method of claim 1, further comprising generating a dashboard configured to provide real-time visibility and management of network operations of the wireless network in a single interface, wherein the dashboard is configured to display the subset of alerts. ### Claim 6 (depends on 1) The method of claim 1, wherein identifying the subset of alerts comprises identifying a timeline of events associated with the subset of alerts. ### Claim 7 (depends on 1) The method of claim 1, wherein identifying the subset of alerts of the multiple alerts includes identifying unusual patterns of behavior in the multiple alerts to correlate the subset of alerts associated with the identified unusual patterns of behavior. ### Claim 8 (depends on 1) The method of claim 1, wherein identifying the subset of alerts of the multiple alerts includes combining the multiple alerts into the subset of alerts. ### Claim 9 (depends on 1) The method of claim 1, wherein identifying the subset of alerts of the multiple alerts includes setting a threshold value that changes over time and identifying the subset of alerts of the multiple alerts, among the multiple alerts, associated with a value greater than the threshold value. ### Claim 10 (depends on 1) The method of claim 1, further comprising receiving, from a network platform, information regarding resources of the wireless network, wherein identifying the subset of alerts includes identifying the subset of alerts based on the information regarding resources, and wherein the information regarding the resources includes a topology of the wireless network. ### Claim 11 A system for automatically identifying a fault condition in a wireless network, the system comprising: multiple subsystems configured to monitor, measure, and analyze a performance of the wireless network; memory; and at least one processor, coupled to the memory and using a trained machine-learning model, the at least one processor configured to: receive, at the 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; identify, by the machine-learning model, a subset of alerts of the multiple alerts, wherein the subset of alerts represents a set of one or more root 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 identify, by the machine-learning model, one or more remediating actions configured to address the one or more root fault conditions in at least one corresponding subsystem of the multiple subsystems, wherein the machine-learning model is trained using training data that identifies correlation between alerts generated in the multiple subsystems. ### Claim 12 (depends on 11) The system of claim 11, wherein the wireless network is configured to perform fifth generation (5G) cloud native network operations. ### Claim 13 (depends on 11) The system of claim 11, wherein the multiple subsystems include an external provider configured to provide a range of network testing, monitoring, and analytics solutions, a central component configured to perform collecting, storing, analyzing, and visualizing telemetry data, a radio access network (RAN) configured to provide wireless connectivity between user devices and the wireless network, and a cloud computing model configured to provide a platform for developing, deploying, and managing applications. ### Claim 14 (depends on 11) The system of claim 11, wherein the at least one processor is further configured to generate a dashboard configured to provide real-time visibility and management of network operations of the wireless network in a single interface, wherein the dashboard is configured to display the subset of alerts. ### Claim 15 (depends on 11) The system of claim 11, wherein identifying the subset of alerts comprises identifying a timeline of events associated with the subset of alerts. ### Claim 16 (depends on 11) The system of claim 11, wherein identifying the subset of alerts of the multiple alerts includes identifying unusual patterns of behavior in the multiple alerts to correlate the subset of alerts associated with the identified unusual patterns of behavior. ### Claim 17 (depends on 11) The system of claim 11, wherein identifying the subset of alerts of the multiple alerts includes combining the multiple alerts into the subset of alerts. ### Claim 18 (depends on 11) The system of claim 11, wherein identifying the subset of alerts of the multiple alerts includes setting a threshold value that changes over time and identifying the subset of alerts of the multiple alerts, among the multiple alerts, associated with a value greater than the threshold value. ### Claim 19 (depends on 11) The system of claim 11, wherein the at least one processor is further configured to receive, from a network platform, information regarding resources of the wireless network, wherein identifying the subset of alerts includes identifying the subset of alerts based on the information regarding resources. ### Claim 20 (depends on 19) The system of claim 19, wherein the information regarding the resources includes a topology of the wireless network.