Automated Call Initiation via Environmental Availability Detection
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Solution Overview
Problem
Current conference systems often initiate calls unnecessarily, disturbing users who are not available due to interactions or other commitments, leading to wastage of resources and inefficiencies.
Innovation Solution
A conference system determines user availability based on physical environment data, such as audio/visual and sensor data from client devices, to automatically initiate calls only when the user is available, and uses a prediction model to prioritize meetings and manage conflicting schedules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If the conference system automatically initiates calls to all scheduled participants, then the meeting joining process is simplified and automated, but unnecessary calls are made to unavailable users, wasting resources and causing disturbances
Solution Approach 1:
The system performs preliminary availability assessment before initiating calls by analyzing environment data (audio, visual, sensor inputs) to determine user availability state. This preliminary action prevents unnecessary calls from being made to unavailable users, thereby reducing resource wastage while maintaining automated call initiation for available users.
2Measurement precision
If the conference system monitors user environment data to determine availability, then call initiation accuracy is improved, but system complexity and data processing requirements increase
Solution Approach 1:
The system utilizes multi-functional client devices that already possess audio, visual, and sensor capabilities for other purposes. By repurposing these existing components for availability determination, the system achieves accurate availability detection without adding significant complexity, as the same hardware serves multiple functions including meeting participation and availability monitoring.
3Measurement precision
If the system uses prediction models to prioritize conflicting meetings, then meeting selection accuracy is improved, but computational requirements and processing time increase
Solution Approach 1:
The prediction model is trained in advance using historical meeting data, user behavior patterns, and meeting importance metrics. This preliminary training enables the system to quickly determine meeting priorities during actual scheduling conflicts without requiring extensive real-time computation, thus reducing processing time while maintaining high accuracy in meeting selection.
Data Source
AI summary
In certain embodiments, an availability state of a user for joining a meeting is determined based on a physical environment of the user, and a call is initiated to the user based on the availability state indicating that the user is available for the meeting. Audio/visual data obtained from a client device associated with the user may be used to determine interaction data indicating an extent of interaction of the user with another individual. Interaction data indicating that the user is not interacting with the other individual either in person or on phone may be used to determine the availability state as being available for joining the meeting. Sensor data obtained from the client device, such as a rate of motion of the user, may also be used to determine the availability state. Meeting priority information may also be used to determine the availability state for joining the meeting.