User Attention Estimation via Inactivity Segmentation
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Solution Overview
Problem
Current methods for estimating user attention on websites or applications struggle to accurately distinguish between active and passive presence, which is crucial for understanding user engagement and optimizing website design and operation.
Innovation Solution
A computer-implemented method that identifies user activity data, detects inactivity periods, and uses a trained model to predict passive presence based on contextual information and user responses to events such as dimming or confirmation requests, allowing for the estimation of total user attention time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional inactivity detection methods are used to estimate user attention, then the implementation is simple, but the measurement precision is low because they cannot accurately distinguish between active and passive presence
Solution Approach 1:
The patent segments user presence into distinct states (active presence, passive presence, and absence) by analyzing multiple signals including activity data, device orientation, and screen state. This segmentation enables precise measurement of different attention levels rather than treating all non-active periods as equal
Solution Approach 2:
The patent introduces intermediary sensors and data sources (accelerometer, gyroscope, screen state detectors) that mediate between raw user behavior and attention estimation. These intermediaries capture subtle cues about user engagement that simple inactivity detection misses
2Reliability
If simple inactivity timing is used to measure user engagement, then the ease of operation is high, but the reliability is low because it cannot distinguish passive from active presence
Solution Approach 1:
The patent implements dynamic classification of user presence by continuously monitoring multiple changing parameters (device orientation, screen state, activity patterns) rather than relying on static inactivity thresholds. This dynamic approach adapts to different usage scenarios and improves reliability
Solution Approach 2:
The patent changes the parameters used for measurement from simple time-based inactivity to multi-dimensional parameters including device orientation angles, screen state transitions, and activity pattern recognition. These parameter changes enable more reliable distinction between passive and active presence
3Measurement precision
If detailed activity monitoring is implemented to improve measurement precision, then the accuracy of user attention estimation improves, but the loss of time for data collection and processing increases
Solution Approach 1:
The patent performs preliminary classification of user presence states using readily available sensor data before initiating more intensive monitoring. This preliminary action filters out cases where detailed analysis is unnecessary, reducing overall processing time while maintaining precision for relevant cases
Solution Approach 2:
The patent applies partial monitoring strategies where full detailed analysis is performed only when needed (e.g., when passive presence is suspected), while using lighter-weight detection methods for routine monitoring. This selective approach balances precision requirements with time constraints
Data Source
AI summary
Techniques for estimating user attention on a website or application are provided. First activity data for a first user of a website or an application may be identified. The first activity data may indicate activities of the first user on the website or the application. A first predetermined period of inactivity may be detected in the first activity data. A response triggering event may be initiated after the first predetermined period of inactivity. An indication of a user response to the response triggering event may be monitored for. Whether the first user is passively present on the website or the application may be determined based on the monitoring for the indication of the user response.