Call Progress Status Updates via Machine Learning
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Solution Overview
Problem
Current digital communication platforms inadequately address the issue of premature call terminations during call setup, leading to negative experiences for both callers and callees, as they fail to differentiate between calls likely to connect and those unlikely to connect, and do not provide customized progress status updates based on the likelihood of caller termination.
Innovation Solution
The implementation of a communication platform that uses machine-learned models, such as boosted decision trees, to analyze conditions associated with both callers and callees, providing tailored call progress status updates and effects, such as animations, sounds, and haptic feedback, to reduce premature call terminations by indicating call progress and likelihood of connection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the communication platform provides detailed call progress status updates, then user experience is improved, but network data usage increases
Solution Approach 1:
The system provides differentiated call progress updates based on the specific situation and user characteristics. Machine learning models analyze caller behavior patterns, call conditions, and user preferences to deliver customized status information only when and where it is most valuable, avoiding uniform detailed updates for all calls.
Solution Approach 2:
The system dynamically adjusts the level of detail in call progress updates based on multiple parameters including call duration, user behavior patterns, network conditions, and predicted user intent. This allows the platform to optimize between providing helpful information and conserving network resources.
2Reliability
If the platform sends frequent call progress updates, then caller reassurance is improved, but network data usage increases
Solution Approach 1:
The frequency and content of call progress updates are dynamically adjusted based on real-time analysis of call conditions, user behavior patterns, and predicted likelihood of premature termination. The system intensifies updates when callers show signs of impatience or when call conditions suggest potential connection issues, and reduces updates when the call is progressing smoothly.
Solution Approach 2:
The system implements feedback loops where machine learning models continuously analyze caller responses to progress updates and adjust future update strategies. By monitoring whether callers remain on the line or terminate calls, the system learns to optimize update frequency and content to maximize reassurance while minimizing data usage.
3Reliability
If the platform uses machine-learned models to analyze caller conditions, then premature terminations are reduced, but device complexity increases
Solution Approach 1:
The system introduces machine learning models as intermediary components that analyze complex caller conditions and translate them into actionable insights. These models process multiple input factors including user behavior patterns, call conditions, and device states to generate predictions about premature termination risk, which then guide the call management system's responses.
Solution Approach 2:
The platform implements self-service mechanisms where the system automatically adjusts call management strategies based on machine learning predictions without requiring manual intervention. The system autonomously modifies progress update frequency, contacts alternative devices, or provides targeted information to callers based on predicted risks, reducing the need for complex manual configuration.
Data Source
AI summary
Techniques are described that update a caller on call progress status using various effects based on a likelihood that the call will connect with the callee, and/or a likelihood that the caller will prematurely terminate the call. In some examples, a machine-learned model may determine a likelihood that a call will connect based on conditions associated with the callee. In some cases, a machine-learned model may determine a likelihood that a call will be prematurely terminated by the caller based on conditions associated with the caller. Animations, haptic outputs, sounds, and/or other content may be used to indicate likelihood of the call connecting to the caller.