Multi-Sensor AI Alert Filtering for Context-Aware Smart Sensors
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Solution Overview
Problem
Existing sensors often provide binary alerts without considering contextual factors, leading to unnecessary notifications and resource wastage.
Innovation Solution
A system that combines data from multiple sensors and an artificial intelligence model to determine whether and how to present alerts based on contextual data, such as camera images and sensor data, to make nuanced decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If binary alert system is used, then simplicity and speed of notification are improved, but false positives increase and resources are wasted
Solution Approach 1:
The system segments the alert generation process into multiple independent evaluation stages. Instead of a single binary decision, the system divides the detection process into event detection, contextual analysis, and final alert determination phases, allowing each stage to contribute to the overall accuracy while maintaining operational speed
Solution Approach 2:
The system introduces an intermediary AI model that acts as a mediator between sensor detection and alert generation. This intermediary layer analyzes contextual data from multiple sensors and determines whether an alert should be generated, thereby reducing false positives while maintaining the speed of the original binary system
2Reliability
If contextual analysis is added, then alert accuracy is improved, but system complexity increases
Solution Approach 1:
The system implements a multi-functional AI model that performs multiple tasks: detecting events, analyzing contextual data from different sensor types, determining alert necessity, and generating notifications. This universal approach improves accuracy without proportionally increasing complexity, as a single intelligent system handles multiple functions
Solution Approach 2:
The system changes the parameters of the detection system by introducing contextual parameters from multiple sensor types (camera images, environmental sensors, motion detectors) rather than relying on a single detection parameter. This allows the system to maintain simplicity while improving accuracy through multi-parameter analysis
3Loss of information
If multiple sensors are integrated, then situational awareness is improved, but resource consumption increases
Solution Approach 1:
The system applies partial action by selectively activating and analyzing data from multiple sensors only when an event is detected. Instead of continuously monitoring all sensors at full capacity, the system triggers contextual analysis only when necessary, reducing overall resource consumption while maintaining information completeness when needed
Solution Approach 2:
The system performs preliminary event detection using primary sensors before activating secondary contextual sensors. This preliminary action allows the system to filter out non-critical events early, consuming minimal resources, and only engages multiple sensors when a potential threat is identified, optimizing the balance between information completeness and resource usage
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for enhanced sensors. One of the methods includes accessing i) data for a detected event that was detected using first sensor data captured by a first sensor at a property and ii) second sensor data captured by a second sensor for the property, the first sensor having a different type than the second sensor; providing, to an artificial intelligence model trained to determine whether to provide a notification about the detected event, the data for the detected event and the second sensor data to cause the artificial intelligence model to generate output; receiving, from the artificial intelligence model, the output that indicates whether to provide a notification about the detected event; and performing one or more actions using the output that indicates whether to provide a notification about the detected event.