Digital Content Delivery Based on Predicted User Behavior
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Solution Overview
Problem
Current mobile devices lack the ability to effectively anticipate and provide relevant digital content to users based on their predicted behavior and preferences, often requiring users to actively seek out information, which can be time-consuming and inefficient.
Innovation Solution
A system and method that analyzes user behavior patterns over time to identify time-dependent increases in activity and interest, correlating this data with external signals to provide relevant digital content, such as advertisements, promotions, or event information, at opportune times, without relying on explicit user preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If users actively seek out information on mobile devices, then they can obtain relevant content, but it consumes time and user effort
Solution Approach 1:
The system performs preliminary actions by analyzing user behavior patterns and predicting future information needs before users actively search. It proactively identifies and prepares relevant digital content based on observed usage patterns, device usage, and contextual signals, delivering information in advance of user requests.
Solution Approach 2:
The system implements self-service by automatically monitoring user behavior, analyzing patterns, and generating predictions without requiring explicit user input. The system serves itself by using its own observational data to improve predictions and deliver content autonomously, reducing the need for users to actively manage their information delivery preferences.
2Ease of operation
If the system provides more digital content proactively, then user experience improves, but device resources and energy consumption increase
Solution Approach 1:
The system applies partial action by selectively delivering only the most relevant digital content based on prediction confidence levels and user preferences. Rather than providing all possible content, it focuses resources on high-priority predictions where the user benefit is greatest, optimizing the balance between user experience and resource consumption.
Solution Approach 2:
The system uses periodic action by updating predictions and delivering content at strategically determined intervals rather than continuously. It monitors user behavior patterns over time and delivers content periodically based on predicted user needs, reducing constant resource consumption while maintaining relevant information delivery.
3Measurement precision
If the system analyzes detailed user behavior patterns, then prediction accuracy improves, but system complexity increases
Solution Approach 1:
The system segments the complex analysis task into distinct modules: behavior pattern observation, pattern analysis, prediction generation, and content delivery. Each module handles a specific aspect of the prediction process, making the overall system more manageable and maintainable while achieving high prediction accuracy through specialized processing in each segment.
Solution Approach 2:
The system introduces intermediary components that process and interpret raw user behavior data before generating predictions. These intermediaries include pattern recognition algorithms and contextual analysis layers that translate complex user actions into meaningful predictions, reducing the complexity of direct prediction generation from raw data.
Data Source
AI summary
In a computing system, information regarding a plurality of events that use a computing device is obtained, and a time-dependant increase in activity for each of at least some of the events is identified. An observed interest by a user in an event is correlated with an identified increase in activity for the event. Information about the activity at a time related to the event is provided for review by the user.