Messaging Delivery for Non-Stationary Mobile Users
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Solution Overview
Problem
Existing messaging systems face challenges in accurately detecting when a non-stationary mobile user is engaged in another activity, making it difficult to determine the appropriate time to deliver messages, as mere velocity does not indicate engagement level, and existing methods lack robustness in assessing activity levels on mobile devices.
Innovation Solution
A messaging system that uses a combination of velocity detection and activity level assessment based on machine-learning models, accessing location and application usage data to determine if a user is actively engaged, and sending confirmation prompts to the sender before delivering messages, with options to hold messages, convert to audio, or prompt a call, tailored for specific user profiles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If messages are delivered immediately to non-stationary mobile users, then message delivery speed is improved, but user distraction and safety are compromised
Solution Approach 1:
The system performs preliminary detection of user activity level and engagement status before delivering messages. It assesses whether the user is engaged in another activity (such as driving or walking) and only delivers messages when the user is determined to be available, thereby preventing distraction-related harm while maintaining efficient delivery.
Solution Approach 2:
The system introduces an intermediary assessment layer between the message sender and the mobile user. This intermediary evaluates user engagement status and activity level, acting as a mediator that decides whether message delivery should proceed, thus resolving the conflict between delivery speed and user safety.
2Device complexity
If velocity-based detection is used alone to determine user availability, then detection simplicity is improved, but measurement precision deteriorates
Solution Approach 1:
The system merges multiple detection approaches by combining velocity-based detection with activity-level assessment. It integrates data from mobile device sensors (accelerometers, gyroscopes) with application usage information to create a comprehensive view of user availability, thereby improving measurement precision without excessive complexity.
Solution Approach 2:
The system changes the parameters used for detection from simple velocity metrics to a multi-parameter assessment including activity level, application usage status, and engagement indicators. This parameter transformation enables more precise determination of user availability while maintaining manageable system complexity.
Data Source
AI summary
In one embodiment, a method includes managing delivery of messages to non-stationary mobile clients. A computing system receives a request from a first user to send a message to a second user. The computing system then accesses the location information of the second user which includes the velocity of the mobile client of the second user. The computing system further accesses the activity information of the second user which indicates if the second user is currently active on a social-networking application. If the velocity indicates that the second user is non-stationary, the computing system then determines an activity-level of the second user. If the activity-level is below a threshold activity-level, the computing system confirms with the first user whether the message to the second user should be sent. If the activity-level is above a threshold activity-level, the computing system sends the message to the receiver.