Multi-dimensional User State Content Suggestion
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Solution Overview
Problem
Existing methods for customizing user content on user interfaces fail to account for how user states evolve over time and in response to various influences, using a static view to assess dynamically changing user conditions.
Innovation Solution
A system that generates a multi-dimensional user state by combining physiological, location, and historical data, with at least one dimension being time, to rank and suggest relevant content based on past reactions and desired user states, using a server to process data from sensors, computing devices, and databases to provide personalized content suggestions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If content customization is based on current user status only, then content suggestion speed is improved, but content relevance to user evolution is worsened
Solution Approach 1:
The system performs preliminary actions by collecting and storing historical user data, physiological data, and location data in advance. This preparation enables the multi-dimensional user state model to be constructed quickly when content suggestions are needed, resolving the contradiction between fast suggestion speed and comprehensive user state tracking.
Solution Approach 2:
The patent introduces a multi-dimensional user state model that adds temporal and contextual dimensions to traditional user status assessment. By incorporating time-based evolution, physiological state, and location context as separate dimensions, the system comprehensively captures user state changes without sacrificing suggestion speed.
2Device complexity
If static user status assessment is used, then system complexity is reduced, but adaptability to dynamic user conditions is worsened
Solution Approach 1:
The system implements dynamics by continuously updating the user state model as new physiological, location, and historical data becomes available. The multi-dimensional user state is not static but evolves with the user, allowing the system to adapt to changing conditions while maintaining manageable complexity through modular data processing.
Solution Approach 2:
The patent creates a universal user state model that can adapt to various user conditions and contexts through its multi-dimensional structure. The same framework handles different types of user data (physiological, location, historical) and can be applied across different content suggestion scenarios, providing versatility without requiring separate complex systems for each condition.
3Device complexity
If only current physiological data is used, then data processing complexity is reduced, but content personalization accuracy is worsened
Solution Approach 1:
The system merges multiple data sources including current physiological data, historical user data, and location data into a unified multi-dimensional user state model. This combination provides comprehensive personalization accuracy while managing complexity through integrated processing that treats diverse data types within a single framework.
Solution Approach 2:
Historical user data and location data are collected and stored in advance as preliminary actions. When content suggestions are generated, this pre-collected data is readily available for integration with current physiological data, enabling accurate personalization without the complexity of real-time collection and processing of all data types.
Data Source
AI summary
Systems, methods, and computer-readable storage media for how to select, suggest, and modify content, which is relevant to the user, on a user interface. The system does this by combining physiological data, location data, and historical data to create a multi-dimensional user state of the user, where at least one dimension is time. The system also identifies available content, ranks the available content based on the multi-dimensional user state, and transmits a user interface a suggestion for a top-ranked item within the ranked list of available content.