Motion Inference System for Timed Message Delivery
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Solution Overview
Problem
Current technologies lack an effective method to determine when a user is likely to be available to receive information while traveling, particularly in high-congestion traffic situations, leading to undesirable interruptions or missed opportunities for message delivery.
Innovation Solution
A system that uses sensors to predict when a user will be stopped for a certain duration, generating probability distributions based on traffic data and contextual information, allowing for prioritization and timed delivery of messages to ensure optimal receipt.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If information is provided to the user continuously regardless of traffic conditions, then the user receives all information, but the user experiences unwanted interruptions during high-congestion travel
Solution Approach 1:
The system performs preliminary analysis of traffic conditions and user availability before delivering information. By predicting whether the user will be stopped for a threshold amount of time based on current traffic data and historical patterns, the system prepares and queues information delivery decisions in advance, delivering messages only when predicted availability exceeds the threshold.
2Object-affected harmful factors
If information is delayed until the user is stopped, then user interruption is reduced, but information delivery timing becomes uncertain and may be postponed too long
Solution Approach 1:
The system continuously monitors traffic conditions, user location, and movement status to dynamically adjust information delivery timing. By incorporating real-time feedback from sensors and traffic data sources, the system predicts upcoming stops and times information delivery to coincide with predicted availability, minimizing both interruption and delay.
3Measurement precision
If the system analyzes detailed traffic system representation to predict stop timing, then prediction accuracy improves, but system complexity increases
Solution Approach 1:
The system extracts only the essential features needed for prediction from the complex traffic system representation. Instead of analyzing complete traffic system models, the system focuses on key variables such as current traffic conditions, historical stop patterns, and user-specific behavior data to generate probability distributions for stop timing and duration.
Solution Approach 2:
The system uses lightweight, easily obtainable data sources such as sensor data from the user's device, public traffic data APIs, and simplified historical patterns rather than maintaining complex, resource-intensive traffic system models. This approach achieves sufficient prediction accuracy with minimal computational overhead.
Data Source
AI summary
An information delivery system comprises a receiver component that receives information about the movement, velocity, acceleration, and/or locations over time of a user. A computation component using a predictive model generates a probability distribution relating to one or more of when the user will next be stopped, how long the user will be stopped, how long a pattern of motion, such as walking, driving in stop and go traffic, and smooth highway motion will last, based at least in part upon signals about motion over time. The system can further comprise an alerting component that determines when to provide the user with information based at least in part upon the probability distribution over some aspect of motion or cessation of motion, and optionally the content, or tagged or inferred urgency or importance, of a message or communication.