Predictive Model for User Availability and Receptiveness
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Solution Overview
Problem
Existing systems fail to determine opportune times for user interactions, such as notifications or messages, as they do not account for a user's availability or receptiveness, leading to missed opportunities for engagement when the user is not free or able to participate.
Innovation Solution
A method and system that utilize predictive models to assess a user's contextual state based on device usage data, including availability and receptiveness, to determine opportune times for interactions, with feedback loops to update and refine these models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If notifications are pushed to users continuously, then information delivery coverage is improved, but user engagement quality deteriorates due to sending notifications at inappropriate times
Solution Approach 1:
The system performs preliminary analysis of user context (current activity, historical patterns, device state) before delivering notifications. The predictive model evaluates multiple features including app usage, location, and temporal patterns in advance to determine optimal delivery timing, ensuring notifications are sent when users are most receptive rather than continuously.
2Measurement precision
If the system monitors detailed usage information to improve prediction accuracy, then user engagement optimization is improved, but system complexity and data processing requirements worsen
Solution Approach 1:
The system segments the prediction task into distinct components: feature extraction (collecting usage data), predictive modeling (analyzing patterns), and decision making (determining notification timing). Each component processes specific types of information independently, allowing the system to handle detailed usage information without overwhelming complexity by organizing processing into modular stages.
Solution Approach 2:
The predictive model acts as an intermediary layer between raw usage data and notification delivery decisions. It processes detailed usage information through learned patterns and relationships, transforming complex multi-dimensional data into simplified predictions about user receptiveness, thereby reducing the complexity burden on the overall system.
3Measurement precision
If the system collects and processes extensive usage data to refine predictive models, then prediction accuracy is improved, but data processing time and computational resources worsen
Solution Approach 1:
The system performs preliminary data processing by continuously collecting and storing usage information in structured formats as it becomes available. Feature extraction and data preprocessing occur in advance during normal device operation, so that when prediction is needed, the processed data is already ready for rapid analysis by the predictive model, minimizing real-time processing delays.
Data Source
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AI summary
Various embodiments described herein relate to a method, system, and non- transitory machine-readable storage medium for determining an opportune time for user interaction including one or more of the following: the method comprising: receiving a request from a client application for an indication of whether a user is open to participate in a user interaction; obtaining usage information regarding the user's recent activity on a user device; applying at least one trained predictive model to the usage information to identify the user's current contextual state, wherein the current contextual state includes at least one of: an availability measure representative of the user's current ability to perform a physical action associated with the user interaction, and a receptiveness measure representative of the user's current ability to pay attention to the user interaction; determining an opportunity indication based on the user's contextual state; and providing the opportunity indication to the client application.