AI Wearable Device Recommendation by Sensor Capability Matching
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Solution Overview
Problem
Wearable smart devices lack an efficient method to recommend the optimal device for a user's upcoming activity based on their location and historical data, leading to suboptimal sensor measurement and communication requirements.
Innovation Solution
A computer-implemented method and system that utilizes artificial intelligence to analyze location data, compare it with historical activity data, and recommend wearable devices by matching their sensor capabilities to the predicted activity, ensuring optimal device selection and communication mechanisms for the user.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If wearable devices collect and store extensive historical activity data and location data for analysis, then the system can provide more accurate activity prediction and device recommendations, but the data processing complexity and computational resources required increase
Solution Approach 1:
The system performs preliminary actions by continuously collecting and storing historical activity data and location data in advance. This pre-collected data is then analyzed using AI algorithms to predict upcoming activities and recommend appropriate wearable devices, thereby improving prediction accuracy without requiring complex real-time processing
Solution Approach 2:
The patent introduces an intermediary AI-based analysis system that acts as a mediator between raw data collection and device recommendation. This intermediary layer processes historical data and location data to generate activity predictions, simplifying the overall system architecture while maintaining high prediction accuracy
2Reliability
If the system provides real-time wearable device recommendations based on AI analysis of location and historical data, then user experience and sensor measurement accuracy are improved, but the computational processing time and energy consumption increase
Solution Approach 1:
The system performs preliminary data collection and AI analysis in advance to predict upcoming activities before they occur. By preparing recommendations beforehand based on historical patterns and location data, the system reduces the need for intensive real-time computational processing, thereby lowering energy consumption while maintaining reliable sensor measurement accuracy
Solution Approach 2:
The wearable device system operates autonomously by automatically collecting data, analyzing patterns, and generating device recommendations without requiring intensive user interaction or external processing. This self-service approach optimizes energy usage by performing computations efficiently in the background while ensuring accurate sensor measurements
Data Source
AI summary
Artificial intelligence (AI) is used to generate contextual wearable device recommendations to a user. Data is received at a computer that characterizes a user's environment, wherein the user environment includes location data. A prediction is made of user activity. A prediction is made, with a computer, of user activity from the location data. Predicting the user activity includes artificial intelligence analyzing the location data for comparison with activities from historical activity data for the user. A list of wearable devices that are present on the user are characterized by sensors for capability. At least one of the wearable devices is matched to the user activity. By employing artificial intelligence the computer wearable devices are matched by capability of their sensor to the user activity