US-20260129481-A1 - CELLULAR NETWORK ARTIFICIAL INTELLIGENCE / MACHINE LEARNING-ASSISTED USER INTERACTION WORKFLOW ENHANCEMENT

US20260129481A1US 20260129481 A1US20260129481 A1US 20260129481A1US-20260129481-A1

Abstract

Aspects of the subject disclosure may include, for example, retrieving network performance data for a cellular network, wherein the retrieving network performance data comprises retrieving cell key performance indicator (KPI) data for selected cells of the cellular network and user equipment (UE) KPI data for a user device associated with a service issue in the cellular network, inferring, by a machine learning (ML) model, a category for a cause of the service issue, wherein the ML model receives at least a portion of the cell KPI data and the UE KPI data as input, and providing information about the category for the cause of the service issue to a service agent for resolution of the service issue before connecting a care call with a customer associated with the user device. Other embodiments are disclosed.

Inventors

Xiaofeng Shi
Jia Wang
Amit Kumar SHEORAN
Mukesh Mantan

Assignees

AT&T INTELLECTUAL PROPERTY I, L.P.

Dates

Publication Date: 20260507
Application Date: 20241105

Claims (20)

1 . A device, comprising: a processing system including a processor; and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations, the operations comprising: receiving a care call from a user reporting a service issue in a cellular network, the cellular network including cells for providing communication services to user equipment; retrieving network performance data based on a user identification of the user; identifying network performance anomalies based on the network performance data; identifying service degradation for a user device of the user based on the network performance data; correlating, in an artificial intelligence/machine learning (AI/ML) process, the network performance anomalies and the service degradation for the user; identifying a service issue category for the service issue based the AI/ML process; and reporting the service issue category to a care agent prior to connecting the care call between the user and the care agent.
2 . The device of claim 1 , wherein the retrieving network performance data comprises: retrieving user key performance indicator data for the user device; and retrieving network key performance indicator data for cells of the cellular network.
3 . The device of claim 2 , wherein the operations further comprise: identifying critical serving cells of the cells of the cellular network, wherein the critical serving cells includes cells of the cellular network to which the user device of the user attached for a time exceeding a time threshold.
4 . The device of claim 3 , wherein the identifying critical serving cells of the cellular network comprises: identifying, based on the network performance data, visited cells of the cellular network, wherein the visited cells include cells of the cellular network to which the user device of the user attached during a predetermined time period; identifying, based on the network performance data, a utilization ratio for each cell of the visited cells; and ranking the visited cells based on the utilization ratio.
5 . The device of claim 1 , wherein the identifying network performance anomalies comprises: identifying critical serving cells of the cells of the cellular network; identifying missing data for the critical serving cells among the network performance data; identifying chronic abnormal data values for the critical serving cells among the network performance data; and identifying temporary traffic variations for the critical serving cells among the network performance data.
6 . The device of claim 5 , wherein the retrieving network performance data comprises: identifying a service outage time corresponding to the service issue; and retrieving network key performance indicator data for the critical serving cells at the service outage time.
7 . The device of claim 1 , wherein the identifying the service degradation for the user device of the user comprises: computing a signal and channel quality value for the user device of the user, wherein the signal and channel quality value is based on the network performance data; computing a traffic value for the user device of the user, wherein the traffic value is based on the network performance data; and computing a radio resource control (RRC) message rate for the user device of the user, wherein the RRC message rate is based on the network performance data.
8 . The device of claim 1 , wherein the operations further comprise: identifying critical serving cells of the cells of the cellular network; combining information about the network performance anomalies for the critical serving cells and information about the service degradation for a user device of the user to form a cell profile for each cell of the critical serving cells; concatenating the cell profile for each cell of the critical serving cells, forming a case profile; providing the case profile as an input to the AI/ML process; and identifying a most likely root cause for the service issue based on an output from the AI/ML process.
9 . The device of claim 1 , wherein the operations further comprise: identifying critical serving cells of the cells of the cellular network; combining information about the network performance anomalies for the critical serving cells and information about the service degradation for a user device of the user to form a cell profile for each cell of the critical serving cells; providing the cell profile for each respective cell of the critical serving cells as respective inputs to the AI/ML process; and identifying the service issue category for the service issue based on an output from the AI/ML process.
10 . The device of claim 1 , wherein the operations further comprise: identifying critical serving cells of the cells of the cellular network; combining information about the network performance anomalies for the critical serving cells and information about the service degradation for a user device of the user to form a cell profile for each cell of the critical serving cells; defining analysis time periods for the cell profile, each analysis time period including selected cell profile information for a selected time period; providing respective cell profile information for each respective cell for respective selected time periods of the critical serving cells as respective inputs to the AI/ML process; and identifying the service issue category for the service issue based on an output from the AI/ML process.
11 . A non-transitory machine-readable medium, comprising executable instructions that, when executed by a processing system including a processor, facilitate performance of operations, the operations comprising: retrieving network performance data for a cellular network, wherein the retrieving the network performance data is responsive to receipt of user identification of a user associated with a user device, the user device associated with a service issue in the cellular network; identifying critical serving cells of the cellular network for the user device; identifying, for the critical serving cells, network performance anomalies, wherein the identifying the network performance anomalies is based on the network performance data; identifying, for the user device of the user, service degradation, wherein the identifying the service degradation is based on the network performance data; combining anomaly information based on the network performance anomalies and service degradation information based on the service degradation, forming a cell profile; providing the cell profile as an input to a machine learning model; receiving, from the machine learning model, a service issue category for the service issue; and providing information about an identification of the service issue category to a care agent for resolution of the service issue.
12 . The non-transitory machine-readable medium of claim 11 , wherein the operations further comprise: receiving, from the user associated with the user device, a care call regarding the service issue; determining user identification information for the user based on the care call; and connecting the care call between the user associated with the user device and the care agent after the providing the information about identification of the service issue category to the care agent.
13 . The non-transitory machine-readable medium of claim 11 , wherein the identifying network performance anomalies comprises: identifying missing data for the critical serving cells among the network performance data; identifying chronic abnormal data values for the critical serving cells among the network performance data; and identifying temporary traffic variations for the critical serving cells among the network performance data.
14 . The non-transitory machine-readable medium of claim 11 , wherein the identifying the service degradation comprises: retrieving user key performance indicator (KPI) data for the user device; computing, based on the KPI data, a signal and channel quality value for the user device; computing, based on the KPI data, a traffic value for the user device of the user; and computing, based on the KPI data, a radio resource control (RRC) message rate for the user device of the user.
15 . The non-transitory machine-readable medium of claim 11 , wherein the operations further comprise: retrieving device provisioning information for a subscription for cellular services associated with the user of the user device; comparing the device provisioning information for the subscription for cellular services and current provisioning of the user device; and identifying a provisioning issue as the service issue category for the service issue, wherein the identifying is based on the comparing.
16 . The non-transitory machine-readable medium of claim 15 , wherein the receiving a service issue category for the service issue comprise: receiving, from the machine learning model, information identifying the service issue as a network outage, acute temporary congestion in the cellular network, a chronic problem in the cellular network or a coverage issue in the cellular network.
17 . A method, comprising: retrieving, by a processing system including a processor, network performance data for a cellular network, wherein the retrieving network performance data comprises retrieving cell key performance indicator (KPI) data for selected cells of the cellular network and user equipment (UE) KPI data for a user device associated with a service issue in the cellular network; inferring, by a machine learning (ML) model implemented by the processing system, a category for a cause of the service issue, wherein the ML model receives at least a portion of the cell KPI data and the UE KPI data as input; and providing, by the processing system, information about the category for the cause of the service issue to a service agent for resolution of the service issue.
18 . The method of claim 17 , comprising: grouping, by the processing system, portions of the cell KPI data and portions of the UE KPI data to form a cell profile; and providing the cell profile as an input to the ML model.
19 . The method of claim 18 , wherein the grouping the portions of the cell KPI data and the portions of the UE KPI data comprises: grouping, by the processing system, critical serving cell KPI data associated with critical serving cells of the cellular network and the UE KPI data to form the cell profile.
20 . The method of claim 18 , wherein the grouping the portions of the cell KPI data and the portions of the UE KPI data comprises: identifying, by the processing system, critical serving cells of the cellular network for the user device; selecting, by the processing system, a target cell of the critical serving cells of the cellular network; and grouping, by the processing system, critical serving cell KPI data associated with the target cell and the UE KPI data to form the cell profile.

Description

FIELD OF THE DISCLOSURE The subject disclosure relates to a machine learning-assisted service diagnosis and system and method for a next-generation cellular network. BACKGROUND As the primary channel for users to report and resolve service issues, customer care has historically been a critical and resource-intensive aspect of operating cellular networks. A customer care agent of the network operator interfaces directly with a customer experiencing an issue. Inherent complexity in correlating network events with the service performance experienced by individual users has heretofore limited available solutions. BRIEF DESCRIPTION OF THE DRAWINGS Reference will now be made to the accompanying drawings, which are not necessarily drawn to scale, and wherein: FIG. 1 is a block diagram illustrating an exemplary, non-limiting embodiment of a communications network in accordance with various aspects described herein. FIG. 2A is a block diagram illustrating an example, non-limiting embodiment of a system functioning within the communication network of FIG. 1 in accordance with various aspects described herein. FIG. 2B. illustrates a conventional troubleshooting and resolution workflow for resolving a customer service issue in a cellular network such as the cellular network of FIG. 2A. FIG. 2C is a functional block diagram illustrating an exemplary pre-diagnosis module for operation with an improved troubleshooting and resolution workflow for resolving customer service issues. FIG. 2D depicts a block diagram of a machine learning module (ML module) for use in conjunction with the pre-diagnosis module of FIG. 2C, in accordance with various aspects described herein. FIG. 2E illustrates candidate feature profiling methods for use with a machine learning model to profile a service quality history of a user equipment and its critical serving cells. FIG. 2F illustrates root cause determination for service issues in a cellular network in accordance with various aspects described herein. FIG. 2G depicts an illustrative embodiment of a method in accordance with various aspects described herein. FIG. 3 is a block diagram illustrating an example, non-limiting embodiment of a virtualized communication network in accordance with various aspects described herein. FIG. 4 is a block diagram of an example, non-limiting embodiment of a computing environment in accordance with various aspects described herein. FIG. 5 is a block diagram of an example, non-limiting embodiment of a mobile network platform in accordance with various aspects described herein. FIG. 6 is a block diagram of an example, non-limiting embodiment of a communication device in accordance with various aspects described herein. DETAILED DESCRIPTION The subject disclosure describes, among other things, illustrative embodiments for inferring causes of customer-reported service issues in a cellular network based on network data and providing a recommended resolution accordingly. Other embodiments are described in the subject disclosure. One or more aspects of the subject disclosure include receiving a care call from a user reporting a service issue in a cellular network, the cellular network including cells for providing communication services to user equipment, retrieving network performance data based on a user identification of the user, identifying network performance anomalies based on the network performance data, and identifying service degradation for a user device of the user based on the network performance data. Aspects of the subject disclosure further include correlating, in an artificial intelligence/machine learning (AI/ML) process, the network performance anomalies and the service degradation for the user, identifying a service issue category for the service issue based the AI/ML process, and reporting the service issue category to a care agent prior to connecting the care call between the user and the care agent. One or more aspects of the subject disclosure include retrieving network performance data for a cellular network, wherein the retrieving the network performance data is responsive to receipt of user identification of a user associated with a user device, the user device associated with a service issue in the cellular network, identifying critical serving cells of the cellular network for the user device, identifying, for the critical serving cells, network performance anomalies, wherein the identifying the network performance anomalies is based on the network performance data, and identifying, for the user device of the user, service degradation, wherein the identifying the service degradation is based on the network performance data. Aspects of the subject disclosure further include combining anomaly information based on the network performance anomalies and service degradation information based on the service degradation, forming a cell profile, providing the cell profile as an input to a machine learning model, receiving, from the machine learn