Probabilistic Content Targeting via Pause Detection
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Solution Overview
Problem
Current content targeting methods in physical spaces, such as retail stores and airports, are ineffective as they fail to ensure that users actually see or engage with content relevant to their interests, as they rely solely on location-based targeting without considering user attention or interest levels.
Innovation Solution
A system and method for content targeting with probabilistic presentation time determination, using Bluetooth beacons and accelerometer data to detect user pauses and predict hangout patterns, allowing for the selection and transmission of content at optimal times when user interaction is most likely, employing machine-learning models to refine predictions and adjust content delivery based on historical data and user interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If content is transmitted to user's device based on location, then content targeting is enabled, but user engagement probability is low
Solution Approach 1:
The system transmits content to the user's device in advance during the pause period before the user is expected to move. This preliminary action ensures the content is already available on the device when the user becomes interested, increasing engagement probability while maintaining location-based targeting capability.
Solution Approach 2:
The system dynamically adjusts content transmission timing based on real-time movement detection. By monitoring user motion and identifying pause periods, the system adapts when to transmit content, making the targeting both location-aware and behaviorally responsive to maximize engagement.
2Quantity of substance
If content is transmitted continuously to all devices in space, then all users receive content, but content relevance to individual users decreases
Solution Approach 1:
The system applies different content transmission strategies to different devices based on their individual characteristics and current states. Instead of uniform broadcasting, each device receives content tailored to its user's location, movement pattern, and pause behavior, making the coverage broad but the content individually relevant.
Solution Approach 2:
The system segments the user base into different groups based on their movement patterns and pause behaviors. By analyzing individual device trajectories and identifying pause periods, the system creates segmented targeting groups that receive customized content, maintaining overall coverage while improving individual relevance.
3Device complexity
If content transmission timing is based only on location, then transmission is simple, but transmission occurs at suboptimal times
Solution Approach 1:
The system uses real-time feedback from movement sensors to detect pause periods and adjust content transmission timing accordingly. This feedback loop allows the system to automatically identify optimal transmission moments without complex manual programming, improving timing while keeping the control mechanism relatively simple through automated sensor-based decision making.
Data Source
AI summary
For targeted presentation of information on a mobile device, a presence of the device is detected at a given time at in a zone. A pause is detected in a movement of the device in the zone. A hangout pattern of the device is predicted. The hangout pattern includes an expected pause duration of the pause. Using the hangout pattern and the detected pause, a time is computed to present a content on the device. The content presented at the time is expected to have a higher than a threshold probability of receiving an input at the device. The content is selected according to the probability of receiving the input. The content is transmitted to the device such that the content is available for presenting at the device at the computed time.