Server Predicting Mobility Device VOC Issues
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Solution Overview
Problem
Existing personal mobility sharing services face challenges in efficiently addressing user needs and issues in real-time, leading to potential delays and inefficiencies in problem resolution.
Innovation Solution
A server-based system that collects device state and user state information to predict potential Voice of Customer (VOC) issues and device defects. The system transmits guide information to users and managers, including self-diagnosis instructions and remote diagnosis connectivity, to facilitate timely issue resolution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If real-time monitoring and prediction of user issues is implemented, then service efficiency is improved, but system complexity increases
Solution Approach 1:
The system performs preliminary actions by collecting device state information and user state information before issues actually occur, analyzing this data to predict potential problems, and providing guide information in advance. This proactive approach enables early intervention, resolving issues before they affect service delivery, thereby improving service efficiency without requiring complex real-time intervention systems.
Solution Approach 2:
The system implements self-service by enabling automated collection and analysis of device and user state data, automatic prediction of potential issues using the prediction model, and automated generation and transmission of guide information to users. This reduces the need for manual monitoring and centralized support intervention, improving service efficiency while the automation handles the complexity internally.
2Measurement precision
If comprehensive device and user state information is collected and analyzed, then issue prediction accuracy is improved, but information processing requirements increase
Solution Approach 1:
The system extracts only the essential and relevant features from the collected device state information and user state information that are most indicative of potential issues. By focusing on key parameters rather than processing all raw data, the system achieves high prediction accuracy while minimizing information processing requirements and avoiding information overload.
3Productivity
If guide information is transmitted to users for self-diagnosis, then centralized support burden is reduced, but user operation complexity increases
Solution Approach 1:
The system introduces an intermediary layer in the form of automated guide information that mediates between the complex prediction analysis system and the user. This guide information translates complex technical predictions into simple, actionable instructions for users, reducing the burden on centralized support while maintaining ease of user operation through clear, step-by-step guidance.
Data Source
AI summary
A personal mobility sharing service providing method is implemented by a server for executing the method. The server includes: a communication device configured to communicate with device terminals mounted on personal mobility devices managed by the server and user terminals of users using the personal mobility sharing services; and a control device configured to acquire device state information on the personal mobility devices through the device terminals, acquire use state information of respective users on the personal mobility devices through the user terminals, and when generation of at least one voice of customer (VOC) to a first user is predicted based on the use state information and the device state information, transmit first guide information on the at least one VOC to a first user terminal of the first user.