Rules Engine for Dynamic User Interaction Timing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing human-machine interaction technologies lack customizable systems and methods to effectively track and understand user mindsets, leading to intrusive interactions that may deter users and result in losses for content hosts.
Innovation Solution
A system and method utilizing a rules engine that associates end-user computing devices with configurable rules to monitor and stimulate user actions, determining device and server states to execute rules that tailor interactions, such as displaying content based on user behavior and preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a universal interaction solution is used (e.g., popping up a window after a fixed time), then the system is simple to implement, but the user experience becomes intrusive and unpleasing
Solution Approach 1:
The system changes interaction parameters dynamically based on user behavior. Instead of fixed timing, the popup trigger is adjusted according to user engagement metrics such as time spent on page, scroll depth, and interaction history, transforming a static universal solution into a dynamic personalized one
Solution Approach 2:
The system implements feedback loops by monitoring user responses to previous interactions and using this information to adjust future interaction timing and content. User behavior data feeds back into the decision engine to optimize when and what popups are displayed, reducing intrusion while maintaining engagement
2Object-affected harmful factors
If personalized interactions are implemented based on user behavior tracking, then user experience is improved, but system complexity increases
Solution Approach 1:
The system introduces an intermediary layer (the rules engine and decision engine) that sits between raw user behavior data and interaction execution. This intermediary processes and interprets user actions, translating complex behavior patterns into simple decision rules that trigger appropriate responses without requiring complex embedded logic in the core system
Solution Approach 2:
The system segments the interaction logic into separate configurable rules that can be independently managed and applied. Each rule represents a discrete condition-action pair that can be configured without affecting other rules, allowing complex personalized behavior to be built from simple, manageable segments
3Loss of information
If user behavior monitoring is enhanced to understand user mindsets, then content relevance is improved, but data processing requirements increase
Solution Approach 1:
The system applies partial monitoring by selectively tracking only the most relevant user behaviors that indicate intent or engagement, rather than comprehensively monitoring all user actions. This focused approach captures sufficient information to understand user mindset while minimizing data processing requirements
Data Source
AI summary
A system for monitoring and stimulating user actions to contents retrieved from a network is disclosed. The system may comprise a set of configurable rules, a rules engine for applying the rules, and a server coupled to an end-user computing device and the network. The server may be configured to associate the end-user computing device to the rules engine and to at least one of the rules, determine at least one of a state of the server, an input at the server, a state of the end-user computing device, or an input at the end-user computing device each associated with at least a portion of the retrieved contents, and execute the associated at least one rule utilizing the associated rules engine to stimulate a user action from the end-user computing device.