System and method for controlling and monitoring bathroom water flow

A system for training a neural network, the system comprising:

• a first processing device;
• a receiving device;
• and
• a transmitting device, wherein
• the receiving device provides to the first processing device a plurality of first reception signals received by a first occupancy sensor, the first reception signals corresponding to one or more features associated with one or more first sources of flowing water associated with a first property, wherein the one or more features includes a state of flowing water;
• the first processing device trains a neural network based on the one or more features and on the plurality of first reception signals, wherein the trained neural network is configured to provide one or more categorizations from a set of categorizations associated with reception signals;
• the first processing device optimizes the trained neural network for a lower-powered processing device to create an optimized neural network, wherein the first processing device is configured to operate at a higher power than the lower-powered processing device, wherein the trained neural network is configured to optimally operate at the higher power of the first processing device and the optimized neural network is configured to optimally operate at the lower power of the low-powered processing device;
• and
• the first processing device deploys, via the transmitting device, the optimized neural network on a remote device comprising a second processing device and a second occupancy sensor, wherein the deployed optimized neural network causes the remote device to determine, via the second processing device, a first categorization from the set of categorizations of second reception signals received by the second occupancy sensor from one or more second sources of flowing water associated with a second property.

A method for training a neural network, the method comprising:

• providing, to a first processor, a plurality of first reception signals received by a first occupancy sensor, the first reception signals corresponding to one or more features associated with one or more first sources of flowing water associated with a first property location, wherein the plurality of first reception signals comprises one or more first signals based on the one or more first sources of flowing water in at least one bath stall being occupied by at least a first bather at a first time and a second bather at a second time;
• training, by the first processor, a neural network based on the one or more features and on the plurality of first reception signals, wherein the trained neural network is configured to provide one or more categorizations from a set of categorizations associated with reception signals;
• optimizing, by the first processor, the trained neural network for a lower-powered processor to create an optimized neural network, wherein the first processor is configured to operate at a higher power than the lower-powered processor, wherein the trained neural network is configured to optimally operate at the higher power of the first processor and the optimized neural network is configured to optimally operate at the lower power of the low-powered processor;
• and
• deploying, by the first processor, the optimized neural network on a remote device comprising a second processor and a second occupancy sensor, wherein the deployed optimized neural network causes the remote device to minimize water usage associated with one or more second sources of flowing water based on a first categorization of whether a bath space is unoccupied or occupied-based on second reception signals received by the second occupancy sensor from one or more second sources of flowing water of the unoccupied or occupied bath space associated with a second property location.