Communication Session Mute Control Using Interaction Intent Signals
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Solution Overview
Problem
Users often forget to mute or unmute their microphones during communication sessions, leading to disruptions and hindered communication.
Innovation Solution
An interaction intent metric is generated based on user factors such as speech tone, gaze direction, and historical characteristics to determine if a user intends to interact with a communication session, and actions are taken to align the user's intent with their mute status.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If users manually manage their mute status during communication sessions, then they have control over their participation, but they may inadvertently forget to mute or unmute, leading to communication disruptions
Solution Approach 1:
The system automatically detects user intent to participate in communication sessions and adjusts mute status without requiring manual user action. The computing device monitors user behavior patterns, speech characteristics, and contextual signals to determine when the user intends to engage, then automatically unmutes or mutes the user's audio feed, making the system self-regulating rather than requiring continuous manual control
Solution Approach 2:
The system continuously monitors multiple signals including user speech patterns, gaze direction, and interaction cues to detect intent to participate. This feedback loop allows the system to adaptively adjust mute status in real-time based on detected user intent, creating a closed-loop control system that responds to user behavior rather than requiring explicit manual commands
2Reliability
If the system automatically detects user intent to participate, then communication effectiveness is improved, but the device complexity increases due to multiple monitoring factors
Solution Approach 1:
The system leverages existing multi-functional components already present in modern computing devices - cameras for gaze detection, microphones for speech analysis, and processors for pattern recognition. By making these existing components serve the additional function of intent detection for communication management, the system avoids adding dedicated hardware while achieving accurate participant engagement detection
Solution Approach 2:
The system introduces a software-based intermediary layer that processes signals from existing hardware components (camera, microphone, keyboard) to detect user intent. This software mediator translates raw sensor data into actionable insights about user participation intent, avoiding the need for complex dedicated hardware while maintaining detection accuracy through intelligent signal processing
Data Source
AI summary
Aspects of the present disclosure relate to engagement-based communication session management. In examples, an interaction intent metric is generated for a user according to a variety of factors relating to the user (e.g., the semantic content and tone of the user's speech, a direction of the user's gaze, and historical user characteristics) and the meeting (e.g., whether the user's name was mentioned by another communication participant or whether another communication participant is soliciting input), among other examples. Accordingly, if a positive interaction intent is identified and the user is currently muted, an action can be recommended to address the mismatch between the positive interaction intent and the muted status of the user. Similarly, if a negative interaction intent is identified and the user is currently unmuted, an action can be performed to address the mismatch between the negative interaction intent and the unmuted status of the user.