Adaptive Template Generation via User Behavior Clustering
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Solution Overview
Problem
Conventional templates are pre-designed and lack insight into their intended use, making them inflexible and unable to adapt to changing user preferences, limiting their applicability.
Innovation Solution
A computer-implemented method that extracts text content from user information to generate feature vectors, forms clusters of existing user editions, creates tree structures, and merges them to generate customized templates tailored to individual user preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If conventional pre-designed templates are used, then template generation is simplified and resources can be spent elsewhere, but the templates are fixed and unable to adapt to changing user preferences
Solution Approach 1:
The patent transforms static, fixed templates into dynamic, adaptive templates that automatically evolve based on user interactions. The system continuously monitors user behavior patterns and modifies template structures in real-time, enabling templates to adapt to changing user preferences while maintaining ease of use through automated adjustments.
Solution Approach 2:
The patent implements a feedback mechanism where user interactions with templates are continuously tracked and analyzed. This feedback loop allows the system to learn from user behavior patterns and automatically adjust template configurations, ensuring templates remain aligned with user preferences without requiring manual redesign.
2Device complexity
If conventional fixed templates are used, then template design process is simple, but they lack insight into intended use and cannot evolve with user preferences
Solution Approach 1:
The patent enables templates to serve themselves by automatically analyzing user behavior data and performing self-adjustment. The system extracts insights from user interactions and autonomously modifies template structures, eliminating the need for continuous manual redesign while preserving and utilizing user preference information.
Solution Approach 2:
The patent performs preliminary analysis of user behavior patterns and pre-adapts templates before users fully utilize them. By anticipating user needs through pattern recognition, the system proactively adjusts template configurations to align with emerging user preferences, preventing information loss about user intentions.
3Adaptability or versatility
If personalized custom templates are generated for each user, then template applicability and retention increase, but the complexity of template generation and processing increases
Solution Approach 1:
The patent merges multiple user behavior data sources and template modification operations into a unified processing framework. By combining cluster analysis, pattern recognition, and template adaptation into an integrated system, the patent reduces overall complexity while achieving personalized template generation through synergistic interaction of components.
Solution Approach 2:
The patent creates a universal template adaptation system that handles multiple user preferences and template types through a single multi-functional framework. The system uses generalizable algorithms for behavior analysis and template modification that can be applied across different contexts, reducing complexity through reuse of core functionality.
Data Source
AI summary
A computer-implemented method, according to one embodiment, is used to creating customized templates and template recommendations. The computer-implemented method includes: extracting text content from user information which corresponds to a user, and using the text content extracted from the user information to generate feature vectors. The feature vectors are further used to form one or more clusters of existing user editions. For each of the one or more formed clusters, a tree structure is generated for each user edition in the cluster. Moreover, the tree structures in the cluster are combined into a merged tree structure. A customized template is generated for each of the one or more formed clusters using the respective merged tree structure, and at least one of the customized templates is recommended to the user based on the user information.