Context-Aware Activity Suggestion System
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Solution Overview
Problem
Current mobile devices lack an effective method to suggest activities to users based on their real-time state, which includes biometric and environmental data, limiting personalized and context-aware recommendations.
Innovation Solution
A computing device receives inputs from sensors and user interfaces to determine a collective user state, evaluates possible activities and outcomes using behavior models, and selects activities for display, incorporating preferences and influencing values to provide tailored suggestions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If mobile devices collect and process multiple sensor inputs and user inputs to determine collective user state, then personalization and context-awareness of activity suggestions are improved, but device complexity and computational requirements increase
Solution Approach 1:
The system segments the complex task of activity suggestion into distinct functional modules: a state analyzer that processes sensor inputs to determine user state, an activity system that generates possible activities based on user state and preferences, and a behavior system that evaluates activities using multiple independent behaviors. Each module operates with specific inputs and outputs, reducing overall system complexity while enabling comprehensive personalization.
Solution Approach 2:
The computing device integrates multiple functions into a unified system: it collects biometric data, environmental data, and user inputs; determines collective user state; generates activity suggestions; and evaluates activities using multiple behaviors. This multi-functional approach allows the device to provide personalized recommendations without requiring separate systems for each function.
2Measurement precision
If the system evaluates multiple possible activities with multiple behaviors modeling human behavior, then accuracy and relevance of activity recommendations are improved, but processing time and computational resources increase
Solution Approach 1:
The system dynamically adjusts the evaluation process by allowing behaviors to be selectively activated based on the determined user state. The behavior system evaluates activities using multiple behaviors that model different aspects of human behavior, but only when relevant to the current context. This dynamic activation reduces processing time while maintaining evaluation accuracy.
Solution Approach 2:
The system incorporates feedback mechanisms where the state analyzer continuously monitors user state changes and adjusts activity suggestions accordingly. The behavior system evaluates activities based on user preferences and collective user state, providing feedback that refines recommendations over time. This feedback loop improves accuracy without requiring complete re-evaluation of all activities.
3Adaptability or versatility
If the system integrates biometric data, environmental data, and user preferences to determine collective user state, then relevance of activity suggestions is improved, but data processing complexity and privacy requirements increase
Solution Approach 1:
The system segments data processing into distinct categories: biometric data processing, environmental data processing, and user preference processing. The state analyzer receives inputs from multiple sensors including biometric sensors and environmental sensors, processing each type of data separately before integrating them to determine collective user state. This segmentation reduces processing complexity while enabling comprehensive context-awareness.
Solution Approach 2:
The collective user state acts as an intermediary that integrates multiple data types (biometric, environmental, preferences) into a unified representation. This intermediary structure simplifies subsequent activity generation and evaluation processes by providing a consolidated view of user context, reducing the complexity of handling multiple separate data streams.
Data Source
AI summary
A method includes receiving inputs indicative of a user state of each user of a group of users. The inputs include sensor inputs from one or more and/or user inputs received from a graphical user interface. The method includes determining a collective user state for each user based on the received inputs and determining one or more possible activities for the group of users and one or more predicted outcomes for each activity based on the collective user states. The method includes executing one or more behaviors that evaluate the one or more possible activities and/or the corresponding one or more predicted outcomes, selecting one or more activities based on the evaluations of the one or more possible activities and/or the corresponding one or more predicted outcomes, and sending results including the selected one or more activities from the computing device to one or more screens.