Adaptive Call Handling via User Behavior Learning
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Solution Overview
Problem
Existing mobile phone systems lack effective mechanisms to automatically manage unsolicited calls, particularly advertisements and unknown numbers, without blocking important calls or requiring constant user intervention.
Innovation Solution
A method that dynamically determines actions based on user behavior and contextual data to automatically handle incoming calls by analyzing caller identity and user interactions, allowing for self-learning and adaptive call management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the user manually decides to answer or reject each call, then the user can control which calls are answered, but the user must constantly intervene and press buttons
Solution Approach 1:
The system automatically learns from user actions and makes decisions about call handling without requiring user intervention. The processing unit analyzes user behavior patterns and autonomously determines whether to answer or reject incoming calls, allowing the system to serve itself rather than requiring constant user input
Solution Approach 2:
The system incorporates feedback loops where user actions (manual answer/reject decisions) are continuously monitored and used to refine the learning model. This feedback mechanism enables the system to improve its call handling decisions over time by adapting to user preferences and behavioral patterns
2Object-affected harmful factors
If the user blocks unknown numbers or uses Do Not Disturb mode, then unsolicited calls are automatically rejected, but important calls may also be blocked
Solution Approach 1:
The system transitions from static blocking rules to dynamic, adaptive decision-making. The processing unit continuously learns from user behavior and adjusts call handling decisions in real-time, allowing the system to differentiate between unsolicited and important calls based on contextual patterns rather than rigid predefined rules
Solution Approach 2:
The system changes the parameters of call handling from binary block/answer decisions to a spectrum of probabilistic decisions based on learned user behavior patterns. By analyzing multiple parameters (calling time, caller identity, user response patterns), the system dynamically adjusts its decision threshold to balance filtering unsolicited calls while preserving important ones
3Extent of automation
If the system automatically handles calls based on predefined rules, then call management is simplified, but the system cannot adapt to individual user behavior patterns
Solution Approach 1:
The system performs self-learning by automatically analyzing user actions and updating its behavioral model without external programming. The processing unit autonomously identifies patterns in user call handling preferences and adapts its automatic decision-making accordingly, enabling the system to improve its performance over time through self-service learning
Solution Approach 2:
The system implements continuous feedback loops where automated call handling decisions are monitored and used to refine the learning algorithm. User corrections or manual interventions provide feedback signals that adjust the system's understanding of user preferences, enabling adaptive automation that evolves with user behavior
Data Source
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AI summary
Handle a received a phone call from a calling party on a device. Determine an action to be performed by the device as a function of identity information about the calling party, such as rejecting the call, forward it to voicemail, answer the call, ring the device. Then action is determined based on an actions previously taken by the user concerning this caller. Learn past user behaviour to automatically screen the call and perform the most appropriate action.