Machine-Learning Mobile Device State Recommendations for Telecom Support
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Solution Overview
Problem
Telecommunication service providers face challenges in efficiently and timely responding to subscriber inquiries about device and network conditions or states due to limited access to supplemental data and lack of specialized technical knowledge, leading to slow and incomplete responses.
Innovation Solution
A system that utilizes machine learning models to automatically retrieve subscriber data and generate recommendations for responding to device and network conditions or states, including modifying device settings, installing updates, or recommending new devices, based on interactions between agents and subscribers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If agents manually access supplemental data sources during interactions, then they can provide more complete responses to subscriber inquiries, but the response time increases and productivity decreases
Solution Approach 1:
The system performs preliminary actions by automatically retrieving and analyzing supplemental data from multiple sources before the agent begins the interaction with the subscriber. The data retrieval and analysis occur in advance, so when the agent needs to respond, the information is already prepared and ready for immediate use.
Solution Approach 2:
An automated system acts as an intermediary between the agent and the supplemental data sources. This intermediary automatically handles the complex tasks of data retrieval, analysis, and presentation, freeing the agent to focus on the subscriber interaction while still having access to complete information.
2Ease of operation
If agents rely on limited information available during interactions, then the interaction process remains simple, but the quality and accuracy of responses deteriorates
Solution Approach 1:
The system performs self-service by automatically retrieving, analyzing, and presenting supplemental data without requiring manual intervention from the agent. The automated system serves itself in gathering information from multiple sources and preparing it for the agent's use during the interaction.
Solution Approach 2:
The automated data retrieval and analysis system serves as an intermediary that bridges the gap between the agent and comprehensive data sources. It handles the complex data gathering and processing tasks, allowing the agent to maintain a simple interaction process while accessing accurate and complete information.
3Reliability
If the system automatically retrieves and analyzes supplemental data, then response accuracy and completeness improve, but device complexity increases
Solution Approach 1:
The automated system acts as an intermediary layer that manages the complexity of data retrieval and analysis from multiple sources. This intermediary handles the complex tasks of coordinating multiple data sources, analyzing the data, and presenting it in a usable format, thereby improving reliability without requiring the agent to directly manage the complexity.
Solution Approach 2:
The system segments the complex data retrieval and analysis function into separate automated modules that work independently. Each module handles specific aspects of data gathering or analysis, and their outputs are integrated to provide comprehensive recommendations, thereby managing system complexity through modular design.
Data Source
AI summary
Systems and methods for taking actions in response to a condition or state of a mobile device are disclosed. The system receives interactions between agents and subscribers of a telecommunications service provider relating to use of the mobile device to access a network. The system accesses subscriber data for the subscriber based on the interactions, the subscriber data including a coverage map. The system determines a usage pattern characterizing the use of the mobile device to access the network. And the system uses the interactions, the subscriber data, and the usage pattern to recommend an action to respond to a condition or state associated with the mobile device. The action can include updating the mobile device, changing a device state, or replacing the mobile device. In some implementations, the system trains and uses a machine learning model to recommend the action and/or to identify and access the subscriber data.