Remote Usage Diagnostics for Energy-Wasting Devices
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Solution Overview
Problem
Existing devices in smart homes and facilities often operate under suboptimal conditions due to undetected malfunctions, leading to energy waste and potential damage, as users rarely utilize available diagnostic tools.
Innovation Solution
A computing system analyzes energy or resource usage data, incorporating facility and environmental information, to identify inefficiencies and provide recommendations for repair, maintenance, or replacement, using machine learning models to distinguish efficient from inefficient conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If diagnostic tools are provided with devices, then device reliability can be improved through early detection of malfunctions, but device complexity increases due to additional diagnostic components and systems
Solution Approach 1:
The patent introduces a remote computing system as an intermediary that performs diagnostic analysis externally rather than embedding complex diagnostic hardware in each device. The computing system receives usage data from devices and performs sophisticated analysis remotely, providing diagnostic capabilities without adding complexity to the devices themselves.
Solution Approach 2:
The patent replaces physical/mechanical diagnostic tools with data-driven computational analysis. Instead of using sensors and mechanical diagnostic components in devices, the system uses machine learning models and algorithms to analyze usage data patterns and detect malfunctions, substituting physical diagnostic mechanisms with information processing.
2Loss of energy
If users manually monitor device performance, then energy efficiency can be improved by detecting malfunctions early, but loss of time increases due to the effort required for continuous monitoring
Solution Approach 1:
The patent implements self-service monitoring where devices automatically transmit usage data to the remote computing system, which then autonomously analyzes the data and generates diagnostic reports. This eliminates the need for users to manually monitor devices while still achieving early detection of malfunctions, reducing both energy waste and user time investment.
Solution Approach 2:
The system establishes continuous feedback loops where usage data is automatically collected, analyzed, and used to generate actionable insights. The feedback mechanism provides users with automated alerts and recommendations, enabling timely intervention without requiring continuous manual monitoring, thus reducing energy waste while minimizing user time commitment.
3Measurement precision
If sophisticated data analysis is performed locally on devices, then measurement precision of device performance can be improved, but device complexity and computational requirements increase
Solution Approach 1:
The patent segments the diagnostic system into two parts: a simple data collection component embedded in devices and a sophisticated analysis component hosted on remote computing systems. This segmentation allows high-precision analysis to be performed externally while keeping device complexity low, as devices only need to collect and transmit data rather than perform complex computations.
Data Source
AI summary
Systems and methods are provided to retrieve or analyze usage data collected from a device or a facility where the device, optionally with devices are located, and identify useful features for making a diagnosis of the device. The diagnosis can be made before a system failure to reduce down time and inefficient use of the device, or after the system failure to expedite and facilitate diagnosis and repair. In addition to the usage data, such as energy and resource consumption, the system can also obtain information relating to the facility and the device's external environment which can be used for normalizing the usage data. Further, based on the diagnosis, the system can make suitable recommendations for repair, replacement, maintenance and upgrade.