User Customizable Call Handling via Behavioral Learning
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Solution Overview
Problem
Current user equipment (UE) is unable to handle multiple incoming calls simultaneously without limiting user options, often forcing users to choose between dropping an active or on-hold call when a third call arrives, and system-generated messages lack personal touch, leading to poor customer service experiences.
Innovation Solution
Implementing a user-customizable call handling system that learns from past user behavior to automatically handle incoming calls, suggest personalized messages, and present tailored options, using a machine learning model to infer user preferences and streamline call management in multi-call scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If the UE provides limited options for handling the third incoming call (reject or answer with drop), then the device complexity is reduced, but the adaptability deteriorates
Solution Approach 1:
The patent implements dynamic call handling where the available options and system behavior adapt based on the current call state (active call duration, on-hold call presence, user preferences). The system transitions from static limited options to dynamic contextual options, allowing the UE to provide appropriate handling methods based on real-time call scenarios while maintaining manageable complexity through automated decision-making.
2Ease of operation
If the UE automatically rejects the third incoming call, then the ease of operation is improved, but the reliability deteriorates
Solution Approach 1:
The patent enables the system to serve itself by automatically analyzing call scenarios, determining appropriate handling actions, and executing decisions without requiring user intervention. The UE learns from past user decisions and autonomously manages call routing, message generation, and call state transitions, improving ease of operation while maintaining reliability through intelligent automation rather than simple rejection.
Solution Approach 2:
The system incorporates feedback mechanisms where user responses to automated decisions are learned and stored, continuously improving the system's decision-making accuracy. The feedback loop ensures that automation becomes increasingly reliable over time by adjusting behavior based on actual user preferences and outcomes, resolving the contradiction between automation ease and service reliability.
3Adaptability or versatility
If the UE presents multiple customized options for handling the incoming call, then the adaptability is improved, but the device complexity increases
Solution Approach 1:
The patent implements preliminary action by pre-configuring user preferences, call handling rules, and message templates before call scenarios occur. The system prepares multiple customized options in advance based on user profiles and historical behavior, so that when calls arrive, the UE can quickly present appropriate options without complex real-time processing, balancing adaptability with manageable device complexity.
4Productivity
If the system sends preconfigured text messages to rejected callers, then the productivity is improved, but the adaptability deteriorates
Solution Approach 1:
The patent applies local quality by customizing message content based on the specific call scenario, caller type, and user preferences rather than using uniform preconfigured messages. The system generates context-appropriate messages (formal for business, casual for personal) while maintaining productivity through automated generation, resolving the contradiction between efficiency and personalization by applying different message qualities locally to different situations.
Data Source
AI summary
In a multi-call scenario, a UE can receive an incoming call while an active call is already established on the UE. In response to receiving the incoming call, a process for handling the incoming call in a user-customizable manner can include accessing a history of user input received by the UE in context of handling multiple contemporaneous calls on the UE, and handling the incoming call based at least in part on the history of the user input. By the UE considering past user input received by the UE in context of handling multiple contemporaneous calls, the UE is configured to learn from past user behavior during multi-call scenarios, and adjust its call handling approach for the incoming call based on the past user behavior.