AI-Powered User Complaint Prediction and Preventive Automated Network Configuration Method

Abstract

The invention relates to an AI-based user complaint prediction and preventive automated network configuration method that predicts the likelihood of end-users making customer service complaints in subscriber facility equipment (1) using AI methods and enables proactive reconfiguration of network parameters to reduce this likelihood.

Description

1 TARIFF AI-powered automated network for predicting and preventing user complaints. Configuration Method Technical Area The invention is a CPE (Customer Premises 5) device used in home, small office and corporate environments. Equipment / Subscriber Facility Equipment) in modem / router devices, latest artificial intelligence methods to increase the likelihood of users filing complaints with customer service. predicting and proactively adjusting network parameters to reduce this possibility. enabling its restructuring, and predicting user complaints with artificial intelligence. It relates to the preventive automatic network configuration method. 10 State of the Art Currently, existing modem and network management systems include:  User experience measurements are mostly reactive in nature,  A direct correlation between network performance data and user complaint behavior. not established, 15  User dissatisfaction is not treated as a predictable event on the network side,  Operators can take action after receiving a complaint from a user, and,  Modem devices cannot detect that the user experience is deteriorating, and It cannot produce preventive action. These situations mean that users report performance problems to customer service within 20 days. this leads to reflection and high operational costs for operators. is happening. In conclusion, due to the negative aspects described above and the current solutions being the subject of discussion... Due to its shortcomings, an improvement is needed in the relevant technical field. It has been made. 25 Purpose of the Invention 2 The invention was created by drawing inspiration from existing situations and overcoming the aforementioned drawbacks. It aims to solve the problem. The invention is intended to integrate DSL (Digital Subscriber Line), fiber, cable, and LTE. (Long-Term Evolution (4G)) and 5G FWA (5G Fixed Wireless Access Modem / router that works with 5G (Fixed Wireless Access) access technologies 5 applicable to devices, on the Wi-Fi (2.4 GHz, 5 GHz, 6 GHz) and WAN side. AI-powered user complaint prediction, encompassing performance parameters, and The aim is to establish a preventive automated network configuration method. Another aim of the invention is to predict user complaints before they occur. Another aim of the invention is to proactively modify network configurations for user 10 The goal is to reduce dissatisfaction. Another aim of the invention is to reduce the number of calls to customer service. Another objective of the invention is to reduce maintenance and support costs for operators. Another aim of the invention is to improve the perceived quality of service on the same infrastructure. Another aim of the invention is to improve upon the classic QoE (Quality of Experience) 15 Unlike monitoring or speed optimization solutions, it analyzes user complaint behavior. The goal is to offer a predictive, AI-based decision-making mechanism. The structural and characteristic features and all the advantages of the invention are given in the figures below. This becomes clearer thanks to the detailed explanation written with references to these figures. This will be understood, and therefore the evaluation should also take these forms and detailed explanations into account. 20 It needs to be done by taking precautions. Figures that will help understand the invention. Figure 1 shows the artificial intelligence-based automated prediction and prevention of user complaints that is the subject of the invention. This is a representative illustration of the elements that enable the network configuration method to be implemented. Description of Part References 25 1. Subscriber facility equipment 2. Network behavior telemetry collection module 3 3. User experience disruption detection module 4. AI complaint prediction engine 5. Complaint risk scoring unit 6. Preventive configuration decision module 7. Proactive network configuration management unit 5 8. Learning database 9. TR-069 / USP telemetry agent 10. Feedback and model update module 11. Operator management system 12. End-user devices 10 Detailed Description of the Invention In this detailed explanation, the preferred configurations of the method in question are described only. This is explained to facilitate a better understanding of the subject. The elements that enable the realization of the method described in the invention are as follows: - subscriber facility equipment (1), 15 - Network behavior telemetry collection module (2), - User experience degradation detection module (3), - AI complaint prediction engine (4), - Complaint Risk Scoring Unit (5), - preventive configuration decision module (6), 20 - Proactive Network Settings Management Unit (7), - learning database (8), - TR-069 / USP telemetry agent (9), - feedback and model update module (10), - operator management system (11), 25 - end user devices (12). 4 Subscriber facility equipment (1), DSL (Digital Subscriber Line) belonging to the end user. Subscriber Line), fiber, cable, LTE (Long-Term Evolution (4G)) and 5G With FWA (5G Fixed Wireless Access) access technologies It is the end device that operates. All analysis, forecasting, and preventive configuration processes are performed on this device. It happens. 5 Network behavior telemetry collection module (2), latency, jitter (delay variation), packet retransmission, throughput (data transmission speed) fluctuations, connection drops, and speed testing. It is an element that collects network behavior data regarding post-performance changes. User experience degradation detection module (3) from the collected network behavior data, abnormal patterns and indicators of deterioration that point to user dissatisfaction 10 It detects. AI complaint prediction engine (4) analyzes user experience deterioration patterns. By doing so, it estimates the likelihood of a user filing a complaint. Complaint risk scoring unit (5), artificial intelligence prediction output a numerical complaint risk It provides input to decision-making mechanisms by converting it into a score. 15 The preventive configuration decision module (6) makes decisions on the network side depending on the complaint risk score. It identifies the preventive actions to be taken. Proactive network configuration management (7), channel structure in line with determined preventive decisions, QoS (Quality of Service) profiles, WAN (Wide Area Network) Automatically allocate bandwidth and buffer management settings for the Area Network (ANP). It implements as follows. Learning database (8) analyzes network behaviors before and after complaint prediction and By storing the actions taken, the system learns over time. TR-069 / USP (User Services Platform) telemetry agent (9), Complaint risk information and related telemetry data generated on subscriber allocation equipment 25 the operator's management system, ACS (Auto-Configuration Server / Automatic (Configuration Server) transmits. Feedback and model update module (10) assesses the impact of implemented preventive actions. By analyzing the data, it improves the accuracy and prediction performance of the artificial intelligence model. The operator management system (11) enables the operator to monitor complaint risk information and It ensures that policy or model updates are communicated when needed. End-user devices (12) represent the end devices of the user that connect to the network and the network It refers to the points where behavioral data is generated. The steps in the preferred configuration of the method described in this invention are as follows: 5 - Network behavior telemetry collection module (2) by end-user devices (12) resulting in delay, delay variation, packet retransmission rates, Connection drops, data transmission speed, and performance after speed testing. changes in internet traffic flow through subscriber facility equipment (1) real-time monitoring, 10 - the network behavior telemetry collection module (2) monitors the data to the user transferring the experience to the disruption detection module (3), - collected data to the user experience degradation detection module (3) Behavior that indicates user dissatisfaction, as analyzed by [the organization / institution]. Identifying patterns, 15 - patterns detected by the artificial intelligence complaint prediction engine (4) Evaluation and generation of a complaint risk value for each user, - complaint risk value is a numerical value through the complaint risk scoring unit (5) converting it into a risk score, - If the complaint risk score exceeds the defined threshold values, 20 The preventive configuration decision module (6) will be implemented on the network side determining the actions, - determined actions channel by proactive network settings management unit (7) structure, quality of service profiles, wide area network bandwidth allocation and buffering Applications on memory management. 25 In a preferred configuration of the invention, application results and user Changes in behavior are recorded in the learning database (8) and the system accuracy feedback and model update module (10) over time is being increased. 6 In another preferred configuration of the invention, complaint risk information and related telemetry are included. data to be shared with operator systems TR-069 / USP telemetry agent (9) It can be transmitted via. The method described in the invention involves user, modem / router device and operator management. It has a closed-loop structure located among the systems. The system requires 5 from the user. By analyzing incoming network behavior, preventive measures can be taken without requiring operator intervention. It generates actions. The method described in this invention can be used in the following areas:  Mesh network Wi-Fi systems,  small office networks, 10  IoT (Internet of Things) intensive home environments,  5G FWA (5G Fixed Wireless Access) modem and network passages.

Claims

7 REQUESTS 1. Complaints of end users to customer service regarding subscriber installation equipment (1). predicting the probability of its occurrence using artificial intelligence methods and this probability proactive restructuring of network parameters to reduce providing AI-powered user complaint prediction and prevention automated network 5 It is a configuration method and its characteristic is; - Network behavior telemetry collection module (2) by end-user devices (12) resulting in delay, delay variation, packet retransmission rates, Connection drops, data transmission speed, and performance after speed testing. changes in internet traffic flow via subscriber facility equipment (1) 10 real-time monitoring - the network behavior telemetry collection module (2) monitors the data to the user transferring the experience to the disruption detection module (3), - collected data to the user experience degradation detection module (3) Behavior that indicates user dissatisfaction, as analyzed by [the organization / institution], 15 Identifying patterns, - patterns detected by the artificial intelligence complaint prediction engine (4) Evaluation and generation of a complaint risk value for each user, - complaint risk value is a numerical value through the complaint risk scoring unit (5) conversion to risk score, 20 - if the complaint risk score exceeds the defined threshold values, The preventive configuration decision module (6) will be implemented on the network side determining the actions, - determined actions channel by proactive network settings management unit (7) structure, quality of service profiles, wide area network bandwidth allocation and buffer 25 Applications on memory management It includes the steps of the process.

2. This method complies with Claim 1 and its characteristic is that it reflects application results and user behavior. recording the changes in the learning database (8) and back up the accuracy of the system through the feed and model update module (10) over time 30 It is to increase it. 8 3. The method is compliant with Claim 1 or 2 and its characteristics include complaint risk information and related telemetry. data to be shared with operator systems TR-069 / USP telemetry agent (9) It is transmitted through [the device / instrument].

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