Dynamic Interactive Content Customization via Behavioral Classification
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Solution Overview
Problem
Current interactive computing environments fail to tailor interactive content effectively to individual user characteristics and progress, leading to suboptimal user experience and engagement.
Innovation Solution
A machine-learning based approach that monitors user interactions to determine behavioral classes and customizes content presentation, including selecting appropriate content items and presentation methods based on user characteristics and progress, enhancing engagement and comprehension.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If simple metrics (progress rate or error rate) are used for customization, then the system complexity is reduced, but the user experience and engagement are insufficient
Solution Approach 1:
The patent changes the parameters used for customization from simple metrics (progress rate, error rate) to comprehensive user characteristics including capabilities, needs, personality, preferences, and engagement level. This allows the system to adapt content in multiple dimensions simultaneously, resolving the contradiction by enriching the customization parameters rather than reducing system complexity.
Solution Approach 2:
The patent introduces additional dimensions for user analysis beyond traditional progress tracking. By incorporating user capabilities, personality traits, preferences, and engagement levels as separate dimensional parameters, the system achieves comprehensive customization without being constrained by simple linear metrics, thus resolving the contradiction between complexity and adaptability.
2Measurement precision
If comprehensive user characteristics are monitored and analyzed, then the content customization accuracy is improved, but the data processing complexity increases
Solution Approach 1:
The patent segments user characteristics into distinct categories (capabilities, needs, personality, preferences, engagement level) that can be measured and processed independently. This segmentation allows the system to handle complex user data through modular processing steps, reducing overall system complexity while maintaining measurement precision for each characteristic type.
Solution Approach 2:
The patent introduces an intermediary processing layer that transforms raw user interaction data into structured user characteristic profiles. This intermediary layer simplifies the data processing pipeline by standardizing the transformation from diverse input data to organized user profiles, thereby reducing complexity while preserving measurement accuracy.
3Adaptability or versatility
If dynamic content selection is performed during session, then the user engagement is enhanced, but the content delivery time increases
Solution Approach 1:
The patent performs preliminary analysis of user characteristics at the beginning of the session or during idle periods, preparing user profiles before content delivery is needed. This preliminary action allows the system to have user customization parameters ready in advance, reducing the time required for dynamic content selection during actual content delivery while maintaining personalization quality.
Solution Approach 2:
The patent implements dynamic content selection that adapts to user characteristics in real-time during the session. By making the content delivery system dynamic and responsive to user state changes, the system can efficiently select personalized content without excessive delays, balancing adaptability with timely delivery through continuous optimization.
Data Source
AI summary
Various embodiments describe techniques for dynamically customizing structured interactive content for a particular user within a session of an interactive computing environment. Machine-learning techniques are used to establish a behavioral class of each individual user based on user interactions with a diagnostic set of interactive content items during the session. The identified behavioral class is used to customize interactive content presented later during the session using various machine-learning techniques. In some embodiments, the user progress during the session is determined based on user interactions with the customized interactive content, and a content customization is performed if the user progress is below a threshold value.