Real-Time Activity Recommendation Engine Using Sensor Detection
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Solution Overview
Problem
Existing decision support systems provide outdated and irrelevant recommendations to users, as they are based solely on past actions, failing to account for real-time activities and personal links, leading to suboptimal decision-making.
Innovation Solution
A method and device that detect ongoing activities in remote locations using sensors and generate real-time, personalized activity recommendations based on the detected activities and user links, utilizing a neural network for optimization and adaptation to user contexts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If recommendations are generated based solely on past actions, then the system can provide personalized recommendations, but the recommendations become obsolete and irrelevant to current situations
Solution Approach 1:
The system transitions from static recommendation generation based on historical data to dynamic recommendation generation that continuously adapts to current ongoing activities. Sensors detect real-time activities and the recommendation engine dynamically updates suggestions based on current context rather than relying solely on past patterns.
Solution Approach 2:
The system implements a feedback loop where sensor data about ongoing activities continuously feeds back into the recommendation engine. This real-time feedback allows the system to adjust recommendations based on current user context and environmental conditions, ensuring recommendations remain relevant and timely.
2Loss of information
If information about remote activities is broadcast to all users, then users receive comprehensive information, but users must manually analyze each activity to determine necessary actions
Solution Approach 1:
The system introduces an intermediary recommendation engine that processes raw activity information and transforms it into actionable recommendations. Instead of presenting users with raw activity data requiring manual analysis, the intermediary automatically interprets the data and generates specific action suggestions, reducing cognitive load while maintaining information completeness.
Solution Approach 2:
The system enables automatic decision support by having the recommendation engine autonomously analyze activities and generate recommendations without requiring user intervention. The system serves itself by automatically processing sensor data, identifying relevant activities, and presenting tailored recommendations, freeing users from manual analysis tasks.
3Productivity
If the system detects and processes ongoing activities in real-time, then recommendations become timely and relevant, but the system complexity increases
Solution Approach 1:
The system is segmented into distinct functional modules: sensor modules for detecting specific types of activities, a processing module for analyzing detected activities, and a recommendation module for generating suggestions. This segmentation allows real-time processing to be distributed across specialized components, managing complexity through modular architecture while maintaining timely recommendation generation.
Data Source
AI summary
A method and a device for recommending activities to at least one user are described. A detection of at least one first ongoing activity in a first location based on data measured by at least one sensor triggers the sending, to at least one terminal of said at least one user located in a second location, of at least one activity recommendation. The at least one activity recommendation is generated as a function of the at least one first activity, and at least one link between the at least one user and the at least one first activity.