18372372. DETERMINING OPTIMAL WATER SENSOR PLACEMENT USING A MACHINE LEARNING CHATBOT simplified abstract (STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANY)
Contents
DETERMINING OPTIMAL WATER SENSOR PLACEMENT USING A MACHINE LEARNING CHATBOT
Organization Name
STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANY
Inventor(s)
Sean Kingsbury of Bloomington IL (US)
DETERMINING OPTIMAL WATER SENSOR PLACEMENT USING A MACHINE LEARNING CHATBOT - A simplified explanation of the abstract
This abstract first appeared for US patent application 18372372 titled 'DETERMINING OPTIMAL WATER SENSOR PLACEMENT USING A MACHINE LEARNING CHATBOT
Simplified Explanation: The patent application discusses using a machine learning chatbot to determine the best location for water sensors near a structure.
Key Features and Innovation:
- Utilizes a machine learning chatbot to identify optimal placement locations for water sensors near a structure.
- Provides structure information to a trained ML model to generate the best sensor placement location.
- Delivers the indication of the optimal sensor placement location to a user device for implementation.
Potential Applications: This technology can be applied in various industries such as construction, infrastructure monitoring, and environmental protection.
Problems Solved: Addresses the challenge of determining the most effective placement of water sensors near structures to enhance monitoring and early detection of water-related issues.
Benefits:
- Improves the efficiency and accuracy of water sensor placement.
- Enhances the overall safety and security of structures.
- Facilitates proactive maintenance and risk mitigation strategies.
Commercial Applications: Optimal for companies involved in building management, smart infrastructure development, and environmental monitoring services.
Prior Art: Readers can explore prior research on machine learning applications in sensor placement optimization and structural monitoring systems.
Frequently Updated Research: Stay informed on the latest advancements in machine learning algorithms for sensor placement optimization and structural monitoring.
Questions about Water Sensor Placement Optimization: 1. How does the machine learning chatbot determine the optimal placement location for water sensors? 2. What are the potential cost savings associated with implementing this technology in structural monitoring systems?
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Original Abstract Submitted
Systems and methods disclosed herein relate to determining an optimal placement location of one or more water sensors proximate a structure using a machine learning (ML) chatbot. The ML chatbot may detect a request to identify the optimal placement location of the water sensors. In response to the request, structure information is provided to a trained ML model to generate an indication of the optimal placement location of the water sensors. The ML chatbot may detect the indication of the optimal placement location of the water sensors. The indication of the optimal placement location of the water sensors is provided to a user device.
