Dynamic Communication Profiles for Mobile Collaboration
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing collaboration technologies fail to accurately determine a participant's communication availability across various modes (voice, video, messaging) and do not adapt to dynamic user preferences and behaviors, especially when participants use mobile devices in different activities.
Innovation Solution
A system that uses user motion data to select a dynamic communication profile based on user status, allowing settings for collaboration modes and features, and learns user preferences by tracking behavior to refine these profiles over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If presence detection is used to indicate participant availability, then collaboration session setup is simplified, but communication availability accuracy deteriorates
Solution Approach 1:
The system segments communication availability into multiple independent modes (voice, video, messaging) rather than treating presence as a single binary state. Each mode can be independently determined and configured, allowing accurate representation of partial availability (e.g., available for messaging but not voice calls).
Solution Approach 2:
The system dynamically determines communication availability based on real-time device state and user context rather than relying on static presence detection. Availability status can change independently for different communication modes based on current device capabilities and user preferences.
2Device complexity
If static communication profiles are used, then system complexity is reduced, but adaptability to user preferences deteriorates
Solution Approach 1:
The system pre-configures multiple communication profiles with different settings for various communication modes before runtime. These profiles are prepared in advance and can be quickly selected based on detected user context or device state, avoiding complex real-time configuration decisions.
Solution Approach 2:
The system monitors user responses to collaboration requests and uses this feedback to automatically refine and update communication profiles. User behavior patterns are learned and incorporated into profile selections, allowing the system to adapt to individual preferences over time without increasing operational complexity.
3Measurement precision
If motion data processing is added to determine user status, then communication profile accuracy is improved, but computational requirements increase
Solution Approach 1:
The system processes motion data locally on the user device using lightweight algorithms that analyze basic movement patterns without requiring complex cloud-based processing. Only essential motion characteristics are extracted and transmitted, minimizing computational energy consumption while maintaining sufficient accuracy for determining basic user states.
Solution Approach 2:
The system adjusts the level of motion data processing based on detected user context and device state. When battery level is low or device is in a power-saving mode, the system reduces motion analysis granularity and frequency, maintaining core functionality while minimizing energy consumption.
Data Source
AI summary
The disclosed technology addresses the need in the art for a solution that selects an appropriate communication profile for the user. A system is configured to receive motion data from a client device associated with a user, identify a communication profile for the user based on the motion data, administer collaboration requests for the user based on settings associated with the communication profile, track user actions based on the administration of the collaboration requests, and update settings for the active communication profile based on the user actions.