Adaptive Recombinant System for Dynamic User Preference Inference
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Solution Overview
Problem
Current computer-based information management approaches, such as flat files and relational database management systems, are brittle and limited in their ability to adapt to changing circumstances and user requirements without human intervention, leading to software bottlenecks.
Innovation Solution
An adaptive recombinant system that tracks and infers user preferences and behaviors to dynamically adapt its structure and content, enabling evolution and extensibility by integrating users and usage behaviors into the system architecture, using a fuzzy network or fuzzy content network structure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional information management approaches (flat files, RDBMS) are used, then system structure is simple and easy to implement, but adaptability to changing circumstances and user requirements deteriorates
Solution Approach 1:
The patent implements dynamic adaptation by enabling the system to automatically modify its structure and content based on observed usage behaviors. The system transitions from a static architecture to a dynamic one where components can be automatically reconfigured, added, or removed based on inferred user preferences and usage patterns, resolving the contradiction between adaptability and complexity.
Solution Approach 2:
The system performs self-adaptation through automated inference engines that observe usage behaviors and automatically modify system structure without human intervention. This self-service capability allows the system to improve its own adaptability while managing complexity internally, eliminating the need for manual reprogramming or content management.
2Adaptability or versatility
If manual intervention is used to adapt systems, then adaptability improves, but productivity and automation deteriorate
Solution Approach 1:
The system automatically observes usage behaviors, infers user preferences, and modifies its own structure and content without requiring manual intervention. This self-service mechanism maintains high adaptability while eliminating the productivity loss associated with manual system adaptation, as the system performs these tasks autonomously.
Solution Approach 2:
The system implements continuous feedback loops where usage behaviors are monitored, analyzed through inference engines, and used to automatically trigger system adaptations. This feedback mechanism enables the system to maintain adaptability while improving productivity by eliminating manual intervention cycles and enabling continuous automated optimization.
3Adaptability or versatility
If monolithic application architecture is used, then system structure is simple, but extensibility and ability to recombine components deteriorates
Solution Approach 1:
The patent applies segmentation by breaking down the monolithic application into independent, reusable components that can be dynamically assembled and reconfigured. This component-based architecture enables extensibility while managing complexity through modular design, allowing system subsets to be separated and recombined based on inferred usage patterns.
Solution Approach 2:
The system implements dynamic component assembly where the architecture transitions from static monolithic structure to dynamic composable components. Usage behaviors trigger automatic recombination of system subsets, enabling extensibility while the system manages architectural complexity through automated component orchestration rather than manual integration.
4Measurement precision
If system adaptation requires human programming, then precision of adaptation improves, but loss of time and automation deteriorates
Solution Approach 1:
The system performs self-adaptation by automatically observing usage behaviors, inferring preferences, and modifying its structure without human programming intervention. This maintains adaptation precision through sophisticated inference engines while eliminating the time loss associated with manual programming and content management.
Solution Approach 2:
The system performs preliminary adaptation actions by continuously monitoring usage behaviors and proactively modifying structure before users explicitly request changes. This preliminary action maintains precision through automated inference while eliminating time loss by preventing the need for subsequent manual programming interventions.
Data Source
AI summary
An adaptive recommendation system and a mobile adaptive recommendation system are disclosed. The adaptive recommendation system and the mobile adaptive recommendation system include algorithms for monitoring user usage behaviors across a plurality of usage behavior categories associated with a computer-based system, and generating recommendations based on inferences on user preferences and interests. Privacy control functions and compensatory functions related to insincere usage behaviors can be applied. Adaptive recommendation delivery can take the form of visual-based or audio-based formats.