Adaptive Engagement Rules for Dynamic User Behavior Profiles
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Solution Overview
Problem
Existing systems for assessing user behavior are ineffective due to the use of static rules, which fail to account for the complexity and dynamic changes in user behaviors over time.
Innovation Solution
An adaptive system that processes user-specific compressed multidimensional data profiles to dynamically select and adapt engagement rules based on confidence levels, ensuring that stimuli are displayed according to the most relevant rules, thereby aligning with individual mindset and preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If static rules are used to assess user behavior, then the system is simple to implement, but the system effectiveness deteriorates due to inability to account for complex and dynamic user behaviors
Solution Approach 1:
The patent implements dynamic engagement rules that automatically adapt to changing user behaviors over time. The system transitions from static rules to dynamic rules that can modify their parameters based on observed user actions, thereby improving reliability in assessing complex behaviors while managing system complexity through automated adaptation mechanisms
Solution Approach 2:
The system incorporates feedback loops where user behavior data is continuously collected and used to refine engagement rules. This feedback mechanism allows the system to learn from user interactions and improve its effectiveness over time, resolving the contradiction between simple implementation and effective assessment of dynamic behaviors
2Adaptability or versatility
If a priori rules are applied to user behavior, then the system is easy to operate, but the system adaptability deteriorates due to inability to handle complex and changing user behaviors
Solution Approach 1:
The system implements self-service capabilities where engagement rules automatically adjust themselves based on user behavior patterns. The rules autonomously adapt to new behaviors without requiring manual reconfiguration, thereby improving adaptability while maintaining ease of operation through automated self-optimization
Solution Approach 2:
The patent employs parameter changes in engagement rules to adapt to varying user behaviors. The system dynamically modifies rule parameters such as thresholds, weights, and conditions based on observed user patterns, enabling high adaptability while keeping the underlying rule structure simple and easy to operate
3Measurement precision
If static engagement rules are used, then the system is simple to implement, but the measurement precision deteriorates due to inability to capture complex user behaviors
Solution Approach 1:
The system uses dynamic engagement rules that continuously adapt their parameters based on user behavior observations. This dynamic approach enables precise measurement of complex behaviors by adjusting assessment criteria in real-time, improving measurement precision while managing complexity through automated adaptation
Solution Approach 2:
The system incorporates feedback mechanisms where assessment results and user responses are used to refine measurement precision. The feedback loops enable the system to learn from measurement outcomes and improve the accuracy of behavior assessment over time, resolving the contradiction between precision and complexity
Data Source
AI summary
Some embodiments relate generally to the processing of compressed multidimensional data and selection of engagement rules based on the compressed multidimensional data. In some embodiments, a method includes retrieving, via a processor, a multidimensional data profile that includes a set of first inclination distributions, each associated with a data dimension. The processor matches a first set of engagement rules to the multidimensional data profile to define a matched set, each engagement rule of the first set of engagement rules having a corresponding confidence level and a corresponding set of second inclination distributions. The processor selects an engagement rule from the matched set that has a corresponding confidence level no less than a corresponding confidence level for each remaining engagement rule from the matched set, and sends a signal causing display of a stimulus to a user according to the selected engagement rule and not according to the remaining engagement rules.