Ubiquitous Computing Recommendation Engine Using Neural Network Data Merging
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Solution Overview
Problem
Current ubiquitous computing technologies struggle to provide comprehensive interaction recommendations that accurately reflect user preferences and intentions, as they often rely on singular data channels and lack contextual understanding, limiting their ability to anticipate and adjust to changing circumstances.
Innovation Solution
A system that tracks activity from multiple ubicomp devices, converts data into standardized formats, and uses an inference recommendation engine to generate personalized interaction recommendations based on analyzed behavioral and activity data, incorporating real-time insights from various sensors and devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple ubicomp devices and sensors are deployed to collect comprehensive data, then measurement precision and reliability of user preference detection are improved, but device complexity and data processing requirements increase
Solution Approach 1:
The patent combines data from multiple ubicomp devices (smart meters, thermostats, sensors, cameras, wearables) into a unified data stream that is processed by a single neural network application. This merging approach maintains comprehensive measurement capability while reducing the complexity of managing and processing data from individual devices by consolidating it into one coordinated system.
Solution Approach 2:
The neural network application serves multiple functions simultaneously: it collects data from various device types, processes the data, detects user preferences, and generates recommendations. This multi-functional design reduces overall system complexity by eliminating the need for separate specialized systems for each function while maintaining comprehensive capabilities.
2Adaptability or versatility
If real-time data collection from multiple devices is implemented, then adaptability to changing user circumstances is improved, but loss of time in data processing and analysis increases
Solution Approach 1:
The system performs preliminary data processing and preference detection continuously in the background as data streams in from multiple devices. By maintaining an ongoing analysis process rather than waiting for complete data sets, the system achieves real-time adaptability without significant processing delays, as the neural network is already active and ready to process new data inputs immediately.
3Measurement precision
If comprehensive data analysis is performed to extract user insights, then measurement precision of user behavior prediction is improved, but loss of time in analysis and processing increases
Solution Approach 1:
The neural network application performs self-service processing by automatically detecting user preferences and generating recommendations without requiring manual analysis or external processing. The system uses its own built-in algorithms to process data and produce insights in real-time, eliminating the need for separate analysis stages and reducing overall processing time while maintaining high prediction accuracy.
Data Source
AI summary
Systems and methods for creating an ad hoc pervasive computing environment comprised of an inference recommendation engine coupled to commodity devices and sensors that passively collect human activity and behavioral data. Methods include machine learning and deep learning applications that analyze data to generate preference based recommendations to assist, inform, and guide subjects interacting with a connected living space and their connected social network.