Patent Publication Number: US-2020300664-A1

Title: Method and system for monitoring water flow

Description:
BACKGROUND 
     Humans use water for a large variety of reasons. Each individual person requires water for drinking each day. Farmers require large amounts of water to grow and harvest crops that represent the primary food supplies of the world. People use water each day for cooking, cleaning, showering, and disposing of human waste. Furthermore, individuals also typically use water for watering lawns, gardens, and decorative vegetation. 
     As the human population of the Earth increases, conservation of water becomes increasingly important. Supplies of fresh water are not limitless. In some areas, fresh water reservoirs have been entirely depleted due to overuse, requiring emergency measures by local governments and populations in order to meet the most basic water needs of individuals. 
     In addition to rising human populations, climate change also plays an increasingly large role in water conservation. Due to the changing climate, some areas may suffer reduced yearly precipitation. Others may face increasing temperatures, causing a corresponding increase in the loss of water in reservoirs and rivers from evaporation. 
     Accordingly, there is a clear need for more effective water conservation methods in order to preserve water supplies. Water conservation is important in both large-scale agricultural operations, and in the daily routines of individuals in their homes. 
     Some water conservation systems have sought to reduce water use by implementing more efficient water usage devices. For example, many jurisdictions now require that toilets use reduced amounts of water per flush. Some jurisdictions have restricted the total amount of water that a user can user. Toilet manufacturers have developed toilets that enable separate flushing functions for entirely liquid and solid waste, so that less water can be used to flush liquids. Other conservation systems track overall water usage in a home. In this way, people can see their overall water usage and possibly attempt to decrease it. 
     However, in spite of these advancements, traditional water conservation systems are not able to provide more nuanced water conservation assistance to people. For instance, there are many sources of water use in a typical home. These typically showers, bath tubs, kitchen faucets, bathroom faucets, washing machines, dish washers, outdoor spickets, swimming pools, and toilets. Each of these vary by manufacturer, by the usage habits of users, and based on local water pressure. Traditional water conservation efforts have not been able to provide effective water conservation assistance based on the particular circumstances of individual water sources within a home, and the habits and patterns of the people that use them. 
     What is needed is a method and system that solves the long-standing technical problem of water conservation systems that do not provide individualized water conservation assistance based on the unique circumstances of each home and the people within it. 
     SUMMARY 
     Embodiments of the present disclosure provide one or more technical solutions to the technical problem of water conservation systems that do not provide individualized water conservation assistance based on the unique circumstances of each home and the people within it. Embodiments of the present disclosure utilize a water flow monitoring device positioned in a plumbing system. The water flow monitoring device tracks the flow of water in a portion of the plumbing system and outputs waterflow monitoring data to a cloud-based waterflow analysis system. The waterflow analysis system analyzes the waterflow monitoring data in accordance with one or more machine learning models. The waterflow analysis system outputs waterflow notification data to a computing device of the user. The computing device of the user displays the waterflow notification data to the user. The user is able to take water conservation actions based on the waterflow notification data. 
     Embodiments of the present disclosure address some of the shortcomings associated with traditional water conservation systems. Machine learning analysis is performed on waterflow metrics to determine water conservation data that can be provided to users based on the characteristics of the plumbing situations of the users. The various embodiments of the disclosure can be implemented to improve the technical fields of water conservation, data management, data processing, and data transmission. Therefore, the various described embodiments of the disclosure and their associated benefits amount to significantly more than an abstract idea. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a block diagram of a system for assisting users to conserve water, in accordance with one embodiment. 
         FIG. 2A  is a block diagram of a waterflow monitoring device, in accordance with one embodiment. 
         FIG. 2B  is an illustration of a waterflow monitoring device, in accordance with one embodiment. 
         FIG. 2C  is an illustration of components of a waterflow monitoring device, in accordance with one embodiment. 
         FIG. 2D  is an illustration of components of a waterflow monitoring device, in accordance with one embodiment. 
         FIG. 3  is a block diagram of a production environment for assisting users to conserve water, according to one embodiment. 
         FIG. 4  is a graph illustrating current draw from a waterflow monitoring device and current produced by a micro hydropower generator, in accordance with one embodiment. 
         FIG. 5  illustrates a functional flow diagram of a process for assisting users to conserve water, in accordance with one embodiment. 
         FIG. 6  illustrates a functional flow diagram of a process corresponding to a hardware logic flowchart for a waterflow monitoring device, in accordance with one embodiment. 
         FIG. 7  illustrates a functional flow diagram of a process corresponding to a firmware logic flowchart for a waterflow monitoring device, in accordance with one embodiment. 
         FIG. 8  illustrates a functional flow diagram of a process corresponding to a service flowchart for a waterflow analysis system, in accordance with one embodiment, in accordance with one embodiment. 
         FIG. 9  illustrates a functional flow diagram of a process for utilizing a waterflow monitoring device, a waterflow analysis system, and a user computing device  106  to assist the user in conserving water, in accordance with one embodiment. 
         FIG. 10  is a flow diagram of a process for assisting users to conserve water, in accordance with one embodiment. 
     
    
    
     Common reference numerals are used throughout the FIGs and the detailed description to indicate like elements. One skilled in the art will readily recognize that the above FIGs are examples and that other architectures, modes of operation, orders of operation, and elements/functions can be provided and implemented without departing from the characteristics and features of the invention, as set forth in the claims. 
     DETAILED DESCRIPTION 
     Embodiments will now be discussed with reference to the accompanying FIGs, which depict one or more exemplary embodiments. Embodiments may be implemented in many different forms and should not be construed as limited to the embodiments set forth herein, shown in the FIGs, and/or described below. Rather, these exemplary embodiments are provided to allow a complete disclosure that conveys the principles of the invention, as set forth in the claims, to those of skill in the art. 
       FIG. 1  illustrates a block diagram of a production environment  100  for assisting users to conserve water, according to one embodiment. The production environment  100  includes a water conservation system  101 . The water conservation system  101  includes a waterflow monitoring device  102  positioned in a plumbing system  108 , a waterflow analysis system  104 . And a user computing device  106 , according to various embodiments. The waterflow monitoring device  102 , the waterflow analysis system  104 , and the user computing device  106  are communicatively connected by one or more networks  110 . 
     In one embodiment, the waterflow monitoring device  102  is disposed within the plumbing system  108 . The waterflow monitoring device  102  can be disposed in the plumbing system  108  in order to monitor the flow of water through one or more plumbing fixtures. Plumbing fixtures can include toilets, kitchen faucets, bathroom faucets, showers, bathtubs, washing machines, dishwashing machines, swimming pool, sprinkling systems, or other types of plumbing fixtures that are known at the time of filing of this application, or that may become known after the time of filing of this application. 
     In one embodiment, the waterflow monitoring device  102  measures the waterflow through the one or more plumbing fixtures. The waterflow monitoring device  102  transmits waterflow monitoring data from the waterflow monitoring device  102  to the waterflow analysis system  104 . The waterflow monitoring data indicates characteristics of the flow of water through the one or more plumbing fixtures monitored by the waterflow monitoring device  102 . The waterflow monitoring data can include a time periods or lengths of time during which water flowed through the one or more plumbing fixtures. The waterflow monitoring data can include flow rates of the water through the one or more plumbing fixtures during the times that water flowed through the one or more plumbing fixtures. The flow rate data can include dates and times of waterflow. The waterflow monitoring data can include ranges of time during which water flowed. The waterflow monitoring data can include total waterflow for a given period of time. The waterflow monitoring data can include other kinds of data indicative of water use in waterflow through the one or more plumbing fixtures, as will be apparent to those of skill in the art in light of the present disclosure. 
     In one embodiment, the waterflow monitoring device  102  can also measure the flow of water by detecting the water pressure. The total flow of water during a period of time can be calculated based on the water pressure and the duration of waterflow. Higher water pressure results in a higher flow rate of water in terms of volume per second. Accordingly, in one embodiment, the total flow of water can be calculated by the waterflow monitoring device  102  based on the water pressure and the length of time of waterflow. 
     In one embodiment, the waterflow monitoring device  102  is connected to a local network. The local network can include a local area network (LAN), wireless area network (WAN), or other types of networks that are used to connect devices to each other and/or to the Internet in a home, a business, an educational institution, a government institution, or other types of institutions or facilities in which plumbing fixtures are commonly used. 
     In one embodiment, the waterflow monitoring device  102  connects to the networks  110  by connecting to a wireless router. The wireless router can host a wireless network. The waterflow monitoring device  102  can connect to a network via a Wi-Fi protocol a Bluetooth protocol, a ZigBee protocol, or other types of wireless communication protocols. 
     In one embodiment, the waterflow monitoring device can be connected to the one or more networks  110  via a wired connection. The wired connection can include an ethernet connection, a USB connection, or other types of wired connections such as are known at the time of filing or become known after the time of filing. 
     In one embodiment, the waterflow analysis system  104  receives the waterflow monitoring data from the waterflow monitoring device  102  via the one or more networks  110 . The waterflow analysis system  104  analyzes the waterflow monitoring data and identifies characteristics of the waterflow through the one or more plumbing fixtures. The waterflow analysis system  104  generates waterflow notification data based on the characteristics of the waterflow. 
     In one embodiment, the waterflow analysis system  104  is a cloud-based analysis system. The waterflow analysis system  104  can include analysis models implemented in one or more servers, one or more computing devices, one or more virtual machines, one or more machine images, or other computing systems capable of performing analysis on the waterflow monitoring data at a location remote from the waterflow monitoring device  102 . 
     In one embodiment, the one or more networks  110  include the Internet. The waterflow monitoring device  102  passes the waterflow monitoring data to the cloud-based water analysis system  104  via the Internet and any intervening networks. 
     In one embodiment, after the waterflow analysis system  104  has generated waterflow notification data, the waterflow analysis system  104  outputs the waterflow notification data to the user computing device  106 . The waterflow analysis system  104  is connected to the user computing device  106  by the one or more networks  110 . The one or more networks  110 , as described previously, can include one or more of the Internet, LANs, WANs, or other communication networks. 
     In one embodiment, the user computing device  106  includes a computing device of the user. The user can include an individual or organization that has installed the waterflow monitoring device  102  or that monitors the plumbing system  108 . The user computing device  106  can include a personal computing device of the user such as a mobile phone, a tablet, a laptop computer, a desktop computer, a voice recognition device, a wearable smart device, such as a smart watch, or other types of personal computing devices that are accessible by the user. 
     In one embodiment, the user computing device  106  receives the waterflow notification data from the waterflow analysis system  102  and displays the waterflow notification data to the user. When the user accesses the user computing device  106 , the user can view the waterflow notification data. The user can then take water conservation measures, if needed, based on the waterflow notification data. 
     In one embodiment, the user first receives waterflow notification data on a user computing device  106  that is not able to display the full text or images associated with the waterflow notification data. For example, the user may receive a waterflow notification in the form of an audio alert from a voice recognition listening device. The user may then check a second user computing device, such as a smart phone, for example, in order to receive the full text or images of the waterflow notification data. Accordingly, the user may receive a first portion of waterflow notification data, such as an alert, on a first user computing device  106  and may then check a second user computing device  106  in order to fully view the waterflow notification data. 
     In one embodiment, the waterflow notification data includes analysis generated by the waterflow analysis system  104  based on the waterflow monitoring data received from the waterflow monitoring device  102 . The waterflow notification data can include data indicating how much water is used by the one or more plumbing fixtures monitored by the waterflow monitoring device  102 , how often water flows through the one or more plumbing fixtures, times of day that water flows through the one or more plumbing fixtures, aggregated data reflecting waterflow over a particular time period, comparison data indicating how much water was used in one aggregation period versus another aggregation period, changes in waterflow patterns, or other types of waterflow analysis. 
     In one embodiment, the waterflow notification data can include an amount of waterflow through the plumbing fixture in particular instances. In an example in which the plumbing fixture is a toilet, the waterflow notification data can indicate how much water was used on each individual flush. The waterflow notification data can also indicate the average amount of water used per flush. The waterflow notification data can also indicate the number of flushes per day. The waterflow notification data can indicate the times of day of individual toilet flushes. The waterflow notification data can also include waterflow quantification and time data for plumbing fixtures other than toilets. All of this data can inform the user and assist the user in the user&#39;s water conservation efforts. 
     In one embodiment, the waterflow notification data includes one or more recommended actions to be taken by the user. For example, the waterflow notification data can indicate that there is likely a leak associated with the one or more plumbing fixtures based on the waterflow monitoring data. The waterflow notification data can encourage the user to check for a leak or replace a plumbing fixture. The waterflow notification data can recommend to the user ways that the user could better conserve water based on the analysis of the waterflow monitoring data. 
     In one embodiment, the waterflow analysis system  104  generates waterfowl notification data based on water usage of a community of users of the waterflow analysis system  104 . The waterflow analysis system can generate notification data that informs a user how the user&#39;s water usage compares to other users of the waterflow analysis system  104 . For example, the waterflow notification data can indicate to the user that a plumbing fixture of the user utilizes more water on average than plumbing fixtures of that type of other users. The waterflow notification data can include recommendations to improve conservation of water in the plumbing fixture. If the waterflow notification data indicates that the plumbing fixture uses less water than typical plumbing fixtures of that type, then the waterflow notification data can include positive feedback for the user to encourage the user in the user&#39;s water conservation efforts. The waterflow notification data may request that the user provide the model of the plumbing fixture so that the waterflow analysis system can provide recommended models of plumbing fixtures to possibly replace an inefficient model. 
     In one embodiment, as will be set forth in more detail below, the waterflow analysis system  104  includes one or more analysis models that implement machine learning processes. The analysis models implement the machine learning processes to understand the plumbing circumstances of the user, to identify the types of plumbing fixtures monitored by the waterflow monitoring device  102 , and to identify water conservation practices that can be implemented by the user based on the individual characteristics of the user and the user&#39;s plumbing circumstances. 
     In one embodiment, the user computing device  106  is a smart phone. The smart phone includes a waterflow analysis application associated with the waterflow analysis system  104 . The waterfall analysis application displays the waterflow notification data to the user. The waterflow analysis application can also push notifications to the user even when the user is not actively using the waterflow analysis application. For example, the waterfall analysis application can cause the smart phone to output a notification sound. The waterflow analysis application can cause a notification icon to appear on a home screen of the smart phone. The waterflow analysis application can cause an LED or other visual indicator to blink or otherwise illuminates in order to indicate to the user that a waterflow notification is available to be viewed by the user. 
       FIG. 2A  is a block diagram of a waterflow monitoring device  102 , according to one embodiment. The waterflow monitoring device  102  includes a water input port  202 , a water output port  204 , a water throughput  206 , a micro hydropower generator  208 , a waterflow sensor  209 , a microcontroller  210 , a memory  212 , a wireless transceiver  216 , a reset button  218 , a battery  220 , and a charging port  222 , according to various embodiments. The components of the waterflow monitoring device  102  enable the waterflow monitoring device  102  to perform waterflow monitoring processes related to one or more plumbing fixtures and to provide waterflow monitoring data to a waterflow analysis system  104 . 
     In one embodiment, the waterflow monitoring device  102  is configured to be installed in a plumbing system  108  upstream from one or more plumbing fixtures to be monitored by the waterflow monitoring device  102 . For example, the waterflow monitoring device  102  can be disposed in the plumbing system as a portion of the plumbing system  108  through which water must flow in order to reach the one or more plumbing fixtures to be monitored by the waterflow monitoring device  102 . Thus, any water that flows through the one or more plumbing fixtures to be monitored by the waterflow monitoring device  102  must first flow through the waterflow monitoring device  102 . This enables the waterflow monitoring device  102  to perform accurate waterflow monitoring operations on the one or more plumbing fixtures. 
     In one embodiment, the waterflow monitoring device  102  includes an ultrasound waterflow sensor. The waterflow monitoring device  102  can be disposed in the plumbing system at a position external to a water pipe through which water flows to a plumbing fixture. The waterflow monitoring device  102  can use ultrasound waves to measure the flow of water through the water pipe. The waterflow monitoring device  102  can then output waterflow monitoring data to the waterflow analysis system for analysis. 
     In one embodiment, the waterflow monitoring device  102  utilizes the water input port  202  and the water output port  204  to connect into a plumbing system  108  upstream from the one or more plumbing fixtures to be monitored by the waterflow monitoring device  102 . The water input port  202  can include a connection, such as a threaded connection, a water pipe connector, a water tube connector, or another kind of connector that enables the waterflow monitoring device  102  to be properly connected to a plumbing outlet from which the one or more plumbing fixtures receive water. The water output port  204  likewise includes a connection, such as a threaded connection, a water pipe connector, a water tube connector, or another kind of connector that enables the waterflow monitoring device to output water to the one or more plumbing fixtures to be monitored by the waterflow monitoring device  102 . 
     In one example, in accordance with one embodiment, the waterflow monitoring device  102  is configured to be connected to a toilet to monitor waterflow through the toilet. Typically, toilets receive water from a plumbing outlet attached to or protruding from the bathroom wall. The water input port  202  is configured to be connected to the plumbing outlet attached to or protruding from the bathroom wall such that the waterflow monitoring device  102  receives water from the plumbing outlet. The water input port  202  can be configured to properly connect to a standard wall plumbing outlet. The water output port  204  is configured to attach to a water inlet of the toilet so that the waterflow monitoring device  102  can provide water to the toilet via the water output port  204 . The water output port  204  can be configured to properly connect to a standard toilet inlet port. The water input port  202  and the water output port  204  can also be configured to be connected to hoses that can connect to the plumbing outlet and the inlet port of the toilet. 
     In one embodiment, the waterflow monitoring device  102  includes the water throughput  206 . The water throughput  206  passes water from the water input port  202  to the water output port  204 . Thus, any water that is supplied to the one or more plumbing fixtures passes through the water throughput  206 . The throughput can include a copper plumbing pipe, a plastic plumbing pipe, a flexible funding tube, or other tubes or pipes that can be utilized in plumbing systems to pass water to a plumbing fixture. The water throughput  206 , the water input port  202 , and the water output port  204  can be a single unitary piece, in one embodiment. In other embodiments, the water input port  202 , the water output port  204 , and the water throughput  206  are separate parts that are securely connected together to pass water without leaking. 
     In one embodiment, the waterflow monitoring device  102  includes a micro hydropower generator  208 . The micro hydropower generator  208  is connected to, or is part of, the water throughput  206 . As water flows through the water throughput  206 , the micro hydropower generator  208  generates electricity. The flowing water through the water throughput  206  causes a movable portion of the micro hydropower generator  208  to move, thereby causing the micro hydropower generator  208  to generate electricity. 
     In one embodiment, the waterflow monitoring device includes a waterflow sensor  209 . The waterflow  209  sensor senses the flow of water through the water throughput  206 . The waterflow sensor  209  can output waterflow signals indicating that water is flowing. The waterflow signals can also indicate the flow rate of the water. In one embodiment, the waterflow sensor includes components positioned within the water throughput  206  to sense waterflow. Alternatively, the waterflow sensor  209  senses waterflow without any components positioned in the water throughput  206 . In one embodiment, if the waterflow sensor  209  is a purely external waterflow sensor, then the waterflow monitoring device  102  may not include the water input port  202 , the water output port  204 , the water throughput  206 , and the micro hydropower generator  208 . 
     In one embodiment, the micro hydropower generator  208  also corresponds to a waterflow sensor. The micro hydropower generator  208  senses waterflow through the water throughput  206 . The micro hydropower generator  208  enables the waterflow monitoring system  102  to sense and monitor the flow of water through the water throughput  206 . Accordingly, in one embodiment, the waterflow sensor  209  is part of the micro hydropower generator  208 . 
     In one embodiment, the waterflow monitoring device includes a microcontroller  210  and a memory  212 . The microcontroller  210  is configured to execute software instructions stored in the memory  212 . In one embodiment, the memory  212  includes firmware data  214 . The firmware data  214  corresponds to the basic operating instructions for the waterflow monitoring device  102 . The microcontroller  210  executes the firmware data  214  and operates in accordance with the firmware data  214 . 
     In one embodiment, the microcontroller  210  receives waterflow signals from the waterflow sensor  209 . The waterflow signals are indicative of the flow of water through the water throughput  206 . The microcontroller  210  generates waterflow monitoring data by processing the waterflow signals. The waterflow monitoring data includes the types of waterflow monitoring data described above, in a format that can be received and processed by the waterflow analysis system  104 . 
     In one embodiment, the memory  212  also stores waterflow monitoring data prior to transmission of the waterflow monitoring data to the waterflow analysis system  104 . The memory  212  can also store network connection data, and data related to the type or types of plumbing fixtures monitored by the waterflow monitoring device. In one embodiment, the memory  212  is part of the microcontroller  210 . Alternatively, the memory  212  can be in a separate chip from the microcontroller  210 . 
     In one embodiment, the waterflow monitoring device  102  includes a wireless transceiver  216 . The wireless transceiver  216  is controlled by the microcontroller  210  in accordance with the firmware data  214 . The wireless transceiver  216  is configured to make a network connection with a wireless network in the vicinity of the waterflow monitoring device  102 . The wireless transceiver  216  can provide waterflow monitoring data to the waterflow analysis system  104  via the wireless network and other intervening networks. The wireless transceiver  216  can be configured to communicate via one or more wireless communication protocols including, but not limited to, a Wi-Fi protocol, a Bluetooth protocol, a ZigBee protocol, or a Z-Wave protocol. 
     In one embodiment, the waterflow monitoring device  102  includes a reset button  218 . The reset button enables a user to either wake-up the microcontroller  210  or to reset the firmware data  214  to factory condition. For example, by pressing the reset button  218  for the first selected duration, the reset button can output an interrupt that wakes of the microcontroller  210 . By pressing the reset button  218  for a second selected duration longer than the first selected duration, the reset button  218  can cause a factory reset of the firmware data  214 . The reset button  218  can be positioned within a housing of the waterflow monitoring device  102  and can be accessible via a hole of relatively small diameter, for example the diameter of a common paperclip. Those of skill in the art will understand, in light of the present disclosure, that a reset button can be positioned or configured other than as described above without departing from the scope of the present disclosure. 
     In one embodiment, the waterflow monitoring device  102  includes a battery  220 . The battery  220  can power the electronic components of the waterflow monitoring device  102 . The battery  220  can include a rechargeable battery. The rechargeable battery can include a lithium-ion battery or other types of rechargeable batteries known in the art at the time of filing or such as may become known after the time of filing. In an alternative embodiment, the battery  220  can include a common replaceable battery that can be conveniently accessed and replaced by the user. 
     In one embodiment, the waterflow monitoring device  102  includes a charging port  222 . The charging port  222  can receive a charging cable to recharge the battery  220 , or to otherwise provide power to the electronic components of the waterflow monitoring device  102 . The charging port  222  can include a micro USB port, a USB C port, a USB 2.0 port, a USB 3.0 port, or other types of charging ports known in the art at the time of filing or such as may a become known after the time of filing. 
     In one embodiment, the waterflow monitoring device  102  includes a casing or housing. The casing can include a durable material such as plastic or metal. The casing can enclose the electronic components such as the micro hydropower generator  208 , the microcontroller  210 , the memory  212 , the wireless transceiver  216 , the reset button  218 , the battery  220 . The charging port  222  can include an opening in the casing. The casing can also enclose entirely, or partially, the water throughput  206 . In one embodiment, the casing is approximately spherical with a radius of about 7 cm. The casing can include a shapes other than spherical with a volume larger or smaller than the volume of the sphere of radius 7 cm. 
     In one embodiment, the waterflow monitoring device  102  has two modes, sleep mode and active mode. In order to more efficiently use the battery  220 , the waterflow monitoring device  102  is most commonly in a sleep mode. Battery usage is minimal when the waterflow monitoring device  102  is in sleep mode. In one embodiment, bringing the waterflow monitoring device  102  into or out of sleep mode or active mode corresponds to bringing the microcontroller  210  into or out of sleep mode or active mode. 
     In one embodiment, there are three different ways to bring the waterflow monitoring device  102  out of sleep mode to active mode. Plugging the power cable into the charging port  222  can bring the waterflow monitoring device  102  out of sleep mode. When a charging cable is plugged into the charging port  222 , the waterflow monitoring device  102  is brought to and stays in active mode indefinitely. After the charging cable is unplugged, the waterflow monitoring device  102  returns to sleep mode following the firmware signal. 
     In one embodiment, the waterflow monitoring device  102  can be brought out of sleep mode via the reset button  218 . For example, inserting a paper clip into the hole in the casing that gives access to the reset button  218  and pressing the reset button can bring the waterflow monitoring device  102  out of sleep mode. The firmware then puts the waterflow monitoring device  102  back to sleep mode after firmware defined tasks have been completed. 
     In one example, in accordance with one embodiment, there are two different pushes of the reset button  218  that the waterflow monitoring device  102  accepts: a short push and a long push. The short push is defined by a push less than 2 seconds in length, in one embodiment. The short push brings the waterflow monitoring device  102  to active mode and allows the firmware to put it back to sleep mode, as will be set forth in more detail below. The long push is, for example, a push that is longer than  10  seconds in duration. The long push brings the waterflow monitoring device  102  back active mode and the firmware can detect the long push and reset the waterflow monitoring device  102  to the factory settings, according to one embodiment. Other lengths of time can be used without departing from the scope of the invention. 
     In one embodiment, the micro hydropower generator  208  can bring the waterflow monitoring device  102  out of sleep mode. In one embodiment, when the micro hydropower generator  208  detects waterflow it brings the waterflow monitoring device  102  out of sleep mode and sends the waterflow data to the firmware. 
     In one embodiment, the micro hydropower generator  208  performs three primary functions. The micro hydropower generator  208  can function as a waterflow sensor. The micro hydropower generator  208  can function as a device power supply. The micro hydropower generator  208  can function as a charging power source for the battery  220 . 
     In one embodiment, the micro hydropower generator senses waterflow. When the micro hydropower generator  208  detects a waterflow, the micro hydropower generator  208  brings the waterflow monitoring device  102  out of sleep mode. The firmware takes over after the waterflow monitoring device  102  becomes active. 
     In one embodiment, the micro hydropower generator  208  acts as a power supply for the electronic components of the waterflow monitoring device  102  in certain circumstances. When the microcontroller  210  of the waterflow monitoring device  102  is started, the initial inrush current may be too much for the micro hydropower generator  208  to handle. However, after the initial inrush phase has passed, the micro hydropower generator  208  can generate enough power to support the waterflow monitoring device  102 . The solution is to use the battery  220  if the micro hydropower generator  208  does not have enough power and to switch to micro hydropower generator  208  when the micro hydropower generator  208  is able to generate a sufficient amount of energy. 
     In one embodiment, the micro hydropower generator  208  acts as a charging source for the battery  220 . After the initial inrush phase, the micro hydropower generator  208  can generate more power than the waterflow monitoring device  102  needs. Therefore, the waterflow monitoring device  102  uses the excess power generation to recharge the battery  220 . With this design the battery power can last much longer. 
     In one embodiment, there are dual power suppliers inside of the waterflow monitoring device  102 , the battery  220  and the micro hydropower generator  208 . In one embodiment, there are three cases in which the waterflow monitoring device  102  is powered by the battery  220 . The battery is used as the power device to cover the initial inrush current when the waterflow monitoring device  102  is powered on. After water flow has stopped, the battery  220  is used to send monitoring data to the waterflow analysis system  104  and to handle any response data from the waterflow analysis system  104 . Additionally, if the waterflow pressure is too low, the micro hydropower generator  208  may not be able to generate enough power to power the waterflow monitoring device  102 . In this case the battery  220  is used to power the entire process. On the other hand, if the water flow pressure is sufficient for the micro hydropower generator  208  to entirely power the waterflow monitoring device  102 , the firmware switches to the micro hydropower generator  208  as the power source. 
     In one embodiment, the waterflow monitoring device  102  includes a valve that close off the water throughput  206  so that no water can flow to the plumbing fixture. In one embodiment, the valve can be electronically controlled by the microcontroller  210 . The microcontroller can electronically close the valve using the battery  220  in order to prevent any water from flowing through the water throughput  206 . In one embodiment, the waterflow analysis system  104  can send a command to the waterflow monitoring device to close the valve, for example when there is a leak detected by the waterflow analysis system  104  and the user has not responded waterflow notification data indicating that there is a leak. 
     In one embodiment, the user can input a command to the user computing device  106  to cause the waterflow monitoring device  104  to shut close the valve in order to stop waterflow in the event of a leak or other circumstances. For example, the user can access via the user computing device  106  a software application associated with the waterflow monitoring system  104  and can input a command to shut the valve and prevent further waterflow. The waterflow monitoring device receives the command either directly from the user computing device  106  or via the waterflow analysis system  104  and executes the command. In one embodiment the user computing device  106  is a smart phone. The software application can be an application implemented in the smart phone. 
       FIG. 2B  is an illustration of a waterflow monitoring device  102 , in accordance with one embodiment. The waterflow monitoring device  102  includes a casing  223 . The charging port  222  is positioned on the casing such that a user can plug a charging cable into the charging port  222  to charge the battery of the waterflow monitoring device  102 . The casing includes a relatively small opening through which the reset button  218  can be accessed by inserting a long thin instrument, such as a straightened paperclip. A threaded water output port  204  protrudes from the casing  223 . A threaded water input port  202  also protrudes from the casing  223 , though the elevated perspective view of  FIG. 2B  obscures the water input port  202 . The threaded water input and output ports can be utilized to install the water monitoring device  102  in a plumbing system, for example, between a wall mounted bathroom water outlet and a toilet. In one embodiment, hoses can be utilized to connect the waterflow monitoring device  102  in the plumbing system. 
     In one embodiment, the water throughput  206  and the electrical components of the waterflow monitoring device  102  are positioned within the casing  223 . The casing  223  can be made from a ceramic material, a plastic material, or another suitable material. 
       FIG. 2C  is an illustration of components of a waterflow monitoring device  102  without the casing  223 , in accordance with one embodiment. The view of  FIG. 2C  illustrates the micro hydropower generator  208 . When water flows through the water input port  202  to the water output port  204  via the water throughput  206 , the micro hydropower generator  208  generates electricity. The micro hydropower generator  208  can also act as a flow sensor that outputs sensor signals indicating that water is flowing, as well as the flow rate of the water. Alternatively, dedicated sensors can be positioned to sense one or more waterflow related parameters. 
     In one embodiment, the waterflow monitoring device  102  includes a printed circuit board  227 . The printed circuit board  227  includes electrical components of the waterflow monitoring device  102 . The printed circuit board  227  can include the microcontroller  210 , the wireless transceiver  216 , the memory  212 , the reset button  218 , and the battery  220 , according to various embodiment. The micro hydropower generator  208  is coupled to the printed circuit board  227  via connector cables  225 . The connector cables  225  can include power cables and signal cables. In one embodiment, the printed circuit board  227  is positioned within the casing  223 . 
       FIG. 2D  is an illustration of components of a waterflow monitoring device  102  with a portion of the micro hydropower generator  208  removed, in accordance with one embodiment. The view of  FIG. 2D  illustrates the water throughput  206  by which water flows from the water input port  202  to the water output port  204 . A turbine wheel  229  of the micro hydropower generator  208  is positioned in the water throughput  206 . When water flows through the water throughput  206 , the flowing water turns the turbine wheel  229 , causing the micro hydropower generator  208  to generate electricity. Additionally, or alternatively, the motion of the turbine wheel  229  can be utilized to sense the flow rate of the water, according to an embodiment. Those of skill in the art will recognize, in light of the present disclosure, that other types of micro hydropower generators  208  can be implemented without departing from the scope of the present disclosure. 
       FIG. 3  is a block diagram of a production environment  300  for assisting users to conserve water, according to one embodiment. The production environment  300  includes a service provider computing environment  310  for assisting users to conserve water, according to various embodiments. The service provider computing environment  310  represents one or more computing systems such as a server or distribution center that is configured to receive, execute, and host one or more data management systems (e.g., applications) for access by one or more users, for predicting characteristics of transactions of users of a data management system, according to one embodiment. The service provider computing environment  310  represents a traditional data center computing environment, a virtual asset computing environment (e.g., a cloud computing environment), or a hybrid between a traditional data center computing environment and a virtual asset computing environment, according to one embodiment. The service provider computing environment  310  includes a waterflow analysis system  104 , which is configured to assist user of the waterflow analysis system  104  to conserve water. 
     The waterflow analysis system  104  includes an interface module  320 , user profile data  322 , plumbing fixture profile data  324 , an analysis model  326 , a waterflow notification database  328 , a machine learning training model  330 , computing resources  332 , and a waterflow monitoring database  334 , according to various embodiments. 
     In one embodiment, the waterflow analysis system  104  utilizes the interface module  320  to communicate with the user computing device  106  and with the waterflow monitoring device  102 . The interface module  320  can receive data from both the waterflow monitoring device  102  and the user computing device  106 . The interface module  320  can output data to both the waterflow monitoring device  102  and the user computing device  106 . 
     In one embodiment, when a user first purchases a waterflow monitoring device  102  and seeks to receive waterflow conservation assistance from the waterflow analysis system  104 , the user must first register with the waterflow analysis system  104 . In this case, the user can utilize the user computing device  106  to set up an account with the waterflow analysis system  104 . 
     In one embodiment, when the user sets up an account with the waterflow analysis system  104 , the user provides user configuration data  350  to the waterflow analysis system  104 . Setting up an account can include providing account access credentials including a username, a password, and an email address. The user can then use these access credentials to log into the waterflow analysis system  104  in the future. 
     In one embodiment, the user configuration data  350  includes additional data about the user. The user configuration data  350  can include a name of the user, an address of the user, a geolocation of the user, and a telephone number of the user. The user configuration data  350  can also include data regarding whether water conservation assistance will be provided for a residential setting, a commercial setting, an educational setting, a government institution setting, a recreation setting, or other types of settings. The user configuration data  350  can also include payment data for providing payments to the waterflow analysis system  104 . 
     In one embodiment, the user can provide device configuration data  352  to the waterflow analysis system  104  via the user computing device  106 . The interface module  320  receives the waterflow monitoring device  102  configuration data  352 . The waterflow monitoring device  102  configuration data  352  corresponds to characteristics regarding the waterflow monitoring device  102  that the user has installed or will install in the plumbing system  108 . The waterflow monitoring device  102  configuration data  352  enables the waterflow analysis system  104  to activate the waterflow monitoring device  102 , to receive data from the waterflow monitoring device  102 , and to output data to the waterflow monitoring device  102 . 
     In one embodiment, the waterflow monitoring device  102  configuration data  352  can include an identification number or serial number associated with the waterflow monitoring device  102  that is to be activated. The waterflow monitoring device  102  configuration data  352  can also include login credentials for a local network via which the waterflow monitoring device  102  will communicate with the waterflow analysis system  104 . The identification number and serial number enable the waterflow analysis system  104  to authenticate and activate the waterflow monitoring device  102 . The activation process can include the interface module  320  sending activation data to the waterflow monitoring device  102  via the one or more networks  110 . 
     In one embodiment, the waterflow monitoring device  102  configuration data  352  can include data regarding a type of plumbing fixture that the waterflow monitoring device  102  will monitor. For example, the waterflow analysis system  104  can prompt the user, via the user computing device  106 , to select from one or more types of plumbing fixtures. The plumbing fixture options can include a toilet, kitchen sink, a bathroom sink, a shower, a bathtub, a dishwasher, a washing machine, or other types of plumbing fixtures. 
     In one embodiment, after the user has selected the type of plumbing feature, the waterflow analysis system  104  can prompt the user to provide a brand of the plumbing fixture. The waterflow analysis system  104  can provide a list of known manufacturers. The waterflow analysis system  104  can then prompt the user to select a model of the plumbing fixture. If the brand or the model are not available as options presented by the waterflow analysis system  104 , the user can manually enter the brand and model of the plumbing fixture. 
     In one embodiment, the waterflow monitoring device  102  configuration data  352  can include a number of people that may commonly use the plumbing fixture. For example, if the plumbing fixture is enabled residential location, then the waterflow monitoring device  102  configuration data  352  can include a number of people that live at the residence. 
     In one embodiment, once the waterflow monitoring device  102  has been installed and activated, the waterflow analysis system  104  receives waterflow monitoring data  354  from the waterflow monitoring device  102 . The waterflow monitoring data  354  indicates characteristics of the flow of water through the plumbing fixture monitored by the waterflow monitoring device  102 . The waterflow monitoring data  354 can include a time periods or lengths of time during which water flowed through the plumbing fixture. The waterflow monitoring data  354  can include flow rates of the water through the plumbing fixture during the times that water flowed through the plumbing fixture. The flow rate data can include dates and times of waterflow. The waterflow monitoring data  354  can include ranges of time during which water flowed. The waterflow monitoring data  354  can include other kinds of data indicative of water use in waterflow through the plumbing fixture, as will be apparent to those of skill in the art in light of the present disclosure. 
     In one embodiment, the interface module  320  outputs waterflow notification data  356  to the user computing device  106  so that the user computing device  106  can display the waterflow notification data  356  to the user. The waterflow notification data  356  can include analysis generated by the waterflow analysis system  104  based on the waterflow monitoring data received from the waterflow monitoring device  102 , as will be described in more detail below. The waterflow notification data  356  can include data indicating how much water is used by the plumbing fixture monitored by the waterflow monitoring device  102 , how often water flows through the plumbing fixture, times of day that water flows through the plumbing fixture, aggregated data reflecting waterflow over a particular time period, comparison data indicating how much water was used in one aggregation period versus another aggregation period, changes in waterflow patterns, or other types of waterflow analysis. 
     In one embodiment, the waterflow notification data  356  includes one or more recommended actions to be taken by the user. For example, the waterflow notification data  356  can indicate that there is likely a leak associated with the one or more water fixtures based on the waterflow monitoring data. The waterflow notification data  356  can encourage the user to check for a leak or replace a plumbing fixture. The waterflow notification data  356  can recommend to the user ways that the user could better conserve water based on the analysis of the waterflow monitoring data  354 . 
     In one embodiment, the user can install multiple waterflow monitoring devices  102 . The user can configure and activate each waterflow monitoring device  102  with the waterflow analysis system  104 . Each activated waterflow monitoring device  102  is linked to the account of the user. Each waterflow monitoring device  102  provides waterflow monitoring data  354  to the waterflow analysis system  104 . The waterflow analysis system  104  provides waterflow notification data  356  to the user regarding each installed waterflow monitoring device  102 . Thus, although the description of the waterflow analysis system  104  is set forth primarily with regards to a single waterflow monitoring device  102 , the waterflow analysis system  104  can perform the described functions for multiple waterflow monitoring devices  102  for each user of the waterflow analysis system  104 . 
     In one embodiment, the waterflow analysis system  104  includes user profile data  322 . The user profile data  322  includes user data  358 . The user data  358  can include the data provided by the user with the user configuration data  350 . The user data  358  can include a name of the user, a location of the user, a number of waterflow monitoring devices  102  associated with the user, the type of account that the user has with the waterflow analysis system  104 , payment data of the user, or other data provided by the user or collected related to the user. The user profile data  322  can also include authentication data  360  corresponding to the login credentials of the user for accessing the waterflow analysis system  104 . 
     In one embodiment, the waterflow analysis system  104  also includes plumbing fixture profile data  324 . The plumbing fixture profile data  324  corresponds to the characteristics of the plumbing fixture associated with the waterflow monitoring device  102 . The plumbing fixture profile data  324  can include data indicating the type of the plumbing fixture, the brand of the plumbing fixture, and the model of the plumbing fixture. The plumbing fixture profile data  324  can also include data generated by the waterflow analysis system  104  related to the plumbing fixture based on analysis of the waterflow monitoring data  354 . In one embodiment, the plumbing fixture profile data  324  can include short leakage threshold data  362  and long leakage threshold data  364 , as will be described in more detail below. 
     In one embodiment, the waterflow analysis system  104  includes a waterflow monitoring database  334 . The waterflow monitoring database  334  includes historical waterflow monitoring data  366 . The historical waterflow monitoring data  366  includes all of the waterflow monitoring data  354  collected by the waterflow analysis system  104  from the waterflow monitoring device  102 . As new waterflow monitoring data  354  is received from the waterflow monitoring device  102 , the historical waterflow monitoring data  366  is updated. 
     In one embodiment, the waterflow analysis system  104  includes an analysis model  326 . The analysis model  326  is configured to analyze the waterflow monitoring data  354 . The analysis model  326  generates waterflow notification data  356  based on the waterflow monitoring data  354 . 
     In one embodiment, the analysis model  326  is trained with a machine learning process to identify characteristics of the plumbing fixture associated with the waterflow monitoring device  102  based on the waterflow monitoring data  354 . The machine learning process trains the analysis model  326  based on the known waterflow characteristics of a large database of plumbing fixtures. The machine learning process trains the analysis model  326  based on the known characteristics of plumbing fixtures and the known waterflow monitoring data associated with other users of the waterflow analysis system  104 . 
     In one embodiment, the analysis model  326  is trained by the machine learning process to learn the characteristics of the plumbing fixture and the usage habits of those that use the plumbing fixture. The analysis model  326  is trained to determine when the plumbing fixture is likely leaking and to generate waterflow notification data  356  indicating to the user that the plumbing fixture is likely leaking. The analysis model  326  is trained to determine whether better conservation habits on the part of the user can reduce the amount of waterflow through the plumbing fixture and to generate waterflow notification data  356  indicating overuse of the plumbing fixture and tips for conserving water. 
     In one embodiment, the analysis model  326  utilizes historical waterflow monitoring data  366  when learning the characteristics of the plumbing fixture and when determining waterflow notification data  356  to be sent to the user. The historical waterflow monitoring data  366  analyzed by the analysis model  326  can include waterflow monitoring data from a selected time period, such as the previous week, the previous month, or the entirety of the historical waterflow monitoring data received from the waterflow monitoring device  102 . 
     In one embodiment, the analysis model  326  also utilizes the most current waterflow monitoring data  368  when learning the characteristics of the plumbing fixture when determining waterflow notification data  356  to be sent to the user. For example, the analysis model  326  can compare the current waterflow monitoring data  368  to the historical waterflow monitoring data  366  in order to generate waterflow analysis data  370 . The analysis model  326  can utilize the waterflow analysis data  370  to update the characteristics of the plumbing fixture and to generate waterflow notification data  356  to be sent to the user. 
     In one nonlimiting example of operation of the waterflow analysis system  104 , the plumbing fixture monitored by the waterflow monitoring device  102  is a toilet. Knowing the average time needed to fill the water tank per normal flush for each individual toilet can be very helpful in providing useful waterflow notification data  356  to the user. With this data, the analysis model  326  can detect water waste and notify the user regarding how to use the toilet more efficiently. 
     Individual toilets can have widely varying characteristics. For example, different models of toilets can have different sizes of water tanks. The size of the water tank can determine both how much water is used for flush and how long it takes to refill the tank. To assist in water conservation, some toilets include dual flush buttons that enables selective flushing for purely liquid waste or combinations of solid and liquid waste. The same model of toilet installed in different locations can have different durations to fill the water tank. For example, at the top of a high-rise building it can take longer to fill the tank because the water pressure can be lower than at lower levels. Because of wear and tear, the duration of filling up the water tank can change after years of use. Additionally, people can add objects into the water tank to take away some volume of the water tank in order to conserve water. Accordingly, the water use characteristics of individual toilets can vary widely even before accounting for the particular habits of users. 
     The water monitoring system  104  utilizes the analysis model  326 , the firmware data  214  of the waterflow monitoring device  102 , and the user computing device  106  to assist users to conserve water. The firmware data  214  acts as an executor that executes rules pushed down from the waterflow analysis system  104  and uploads the waterflow monitoring data  354  to the waterflow analysis system  104  based on the rules. The user computing device  106 , for example a mobile phone, acts as a deliverer or notifier that delivers alerts in a timely manner so that users can react accordingly. The analysis model  326  is the brain that analyzes the waterflow monitoring data  354  and generates the waterflow notification data  356 . 
     In one embodiment, one function of the analysis model  326  is to detect whether there is a water leak in the toilet. Before the analysis model  326  can detect leaking water, it will determine the average time taken to fill the water tank per normal flush. After the analysis model  326  has determined the average refill time for a flush, the analysis model  326  can determine one a leakage has occurred. In one alternate embodiment, the waterflow monitoring device  102  can calculate the average time for a flush and provide that data to the waterflow analysis system  104  with the waterflow monitoring data  354 . 
     In one embodiment, there are two type of the water leakages that can be detected by the analysis model  326 : a long water leakage and a short water leakage. The long water leakage can correspond to waterflow to the toilet that continues for a duration of time longer than 3 times the average water tank refill time. Other thresholds other than a factor of three can be utilized to detect long water leakage. A short water leakage can correspond to more than 50% of the last N flushes using less than half of the average time, where N is a selected number of flushes. A short water leakage can occur when the rubber flipper in the toilet tank fails to seal the drainage. Other thresholds can be used to determine that a short water leakage has occurred. 
     In one embodiment, after a new water monitoring device  102  has been installed to monitor toilet, there can be multiple phases using different algorithms to calculate the average time for a flush. In one example, there are three different phases using different algorithms to calculate the average time for flush: a beginning phase, and learning phase, and an advanced phase. Those of skill in the art will recognize, in light of the present disclosure, that a waterflow monitoring device  102  can utilize fewer or more phases than three, without departing from the scope of the present disclosure. 
     In one embodiment, the beginning phase corresponds to a phase in which the waterflow monitoring device  102  has been newly installed. Because the waterflow monitoring device  102  has been newly installed, there is no previous data that can be used to calculate the average time per flush. In one example, a default average time is selected. The default average time can be a duration that is longer than most toilet flush refills. In one example, the initial default average time can be two minutes. In this case, the long water leakage would take six minutes to be determined and reported to the user, if the long water threshold is three times the average flush duration. 
     In one embodiment, in the learning phase, some simple calculations can be used to determine the average flush refill time. After a selected number of flush refill times have been observed, the average time can be calculated based on the observed flush refill times. The learning phase can include sorting the observed flush refill times from shortest to longest, excluding detected leakages. The learning phase then normalizes the observed flush refill durations by taking off 20% from the longest waterflow and 20% from the shortest waterflow from the sorted data points. After more data points are collected, for example, more than 20 data points, the average time can be calculated. The reason to do this is that users flush manually, and individual pushes on the flush button or lever are not consistent. With the remaining 60% of the middle section of the sorted data points, a simple calculation can be performed to determine the average, for example by summing the durations and dividing by the number of data points. Those of skill in the art will recognize, in light of the present disclosure, that other algorithms or methods can be used to determine an average flush time during the learning phase without departing from the scope of the present disclosure. 
     In one embodiment, in the advanced phase begins after a selected number of months, for example 3, or after a selected number of toilet flushes have been observed, for example 100. With the larger pool of data points, the normal distribution (bell curve) of statistics is used to calculate the probability of the recent water flow data. By using the normal distribution calculation, the advanced phase calculates the standard deviation of toilet flush durations based on the pool of recent data points. The first standard deviation has about 68.52% of the data points. The average time is calculated based on this 68.52% of the data points. The standard deviation can be recalculated periodically, for example, weekly or monthly. 
     In one embodiment, the plumbing fixture profile data  324  includes the short leakage threshold data  362  indicating the short leakage threshold determined based on the calculated average toilet flush and selected factors as set forth above. The plumbing fixture profile data  324  also includes long leakage threshold data  364  indicating the long leakage threshold. The long leakage threshold data  364  includes the long leakage threshold data calculated based on the average flush duration and other factors as set forth above. The analysis model  326  can determine a leak by comparing one or more recent observed flush durations to the short leakage threshold and the long leakage threshold. The analysis model  326  can generate waterflow notification data  356  indicating that there is a leak. The waterflow notification data  356  is sent to the user computing device  106  so that the user can observe the notification and take action. 
     In one embodiment, the waterflow notification data  356  can include messages congratulating the user on water conservation steps taken by the user, such as reducing the volume of the toilet water tank. The waterflow notification data  356  can encourage users to take water conservation measures based on the analysis of the waterflow monitoring data  354 . 
     While a particular example has been described in which a waterflow monitoring device  102  is installed to monitor a toilet, those of skill in the art will recognize, in light of the present disclosure, that the waterflow monitoring device  102  and the analysis model  326  can analyze water usage and generate waterflow notification data  356  for other types of plumbing fixtures. Using the principles set forth above, the analysis model  326  can learn the behaviors and characteristics of other plumbing fixtures, identify leaks in other plumbing fixtures, identify wasteful use of other plumbing fixtures, identify abnormal use of other plumbing fixtures, and provide alerts, advice, and encouragement in the waterflow notification data  356 . 
     In one embodiment, the waterflow analysis system  104  utilizes the machine learning training module  330  to train the analysis model  326  in accordance with one or more machine learning processes. The machine learning training module  330  trains the analysis model  326  with one or more machine learning process to identify characteristics of the plumbing fixture associated with the waterflow monitoring device  102  based on the waterflow monitoring data  354 . The machine learning process trains the analysis model  326  based on the known waterflow characteristics of a large database of plumbing fixtures. The machine learning process trains the analysis model  326  based on the known characteristics of plumbing fixtures and the known waterflow monitoring data associated with other users of the waterflow analysis system  104 . 
     In one embodiment, the machine learning training module  330  can utilize training set data  374  to train the analysis model  326  in accordance with a supervised machine learning process. The training set data  374  can include the known characteristics of various brands and models of plumbing fixtures. For toilets, the training set data could include average tank sizes, average flush times, leakage rates, and types of flushing mechanisms. The training set data  374  can also include the observed usage habits of a large variety of users that have use the various plumbing fixtures. Based on the training set data, the machine learning training module  330  can train the analysis model  326  to identify or predict characteristics of plumbing fixtures and predicted usage based on typical usage patterns of users who have used those plumbing fixtures. 
     In one embodiment, the analysis model  326  includes a logistic regression model. In one embodiment, the analysis model  326  includes a random forest model. In one embodiment, the analysis model  326  includes a linear regression model. In one embodiment, the analysis model  326  includes a linear discriminant model. In one embodiment, the analysis model  326  includes a neural networks model. In one embodiment, the analysis model  326  includes a support vector machines model. In one embodiment, the analysis model  326  includes a decision tree model. In one embodiment, the analysis model  326  utilizes a latent Dirichlet allocation (LDA) model. In one embodiment, the analysis model  326  includes a naïve Bayes model. In one embodiment, the analysis model  326  includes a K nearest neighbors model. Additionally, or alternatively, the analysis model  326  can utilize other types of models or algorithms, as discussed herein, and/or as known in the art at the time of filing, and/or as becomes known after the time of filing. 
     In one embodiment, the analysis model  326  utilizes both supervised and unsupervised machine learning. The unsupervised learning includes, in one embodiment, one or more of an LDA model, a probabilistic topic model, a clustering model, or other kinds of unsupervised learning. The supervised learning includes, in one embodiment, a multiclass classifier or another kind of supervised learning model. 
     In one embodiment, the analysis model  326  includes a recurrent neural network. The recurrent neural network can include a plurality of nodes. Connections between the nodes form a directed graph along a sequence. The recurrent neural network can exhibit dynamic temporal behavior for a time sequence. The recurrent neural network can use its internal memory to process sequences of inputs. In one embodiment, the recurrent neural network is an attention based recurrent neural network. 
     In one embodiment, the waterflow analysis system  104  includes a waterflow notification database  328 . The waterflow notification database  328  includes historical waterflow notification data  372  including waterflow notifications that have been sent to users in the past. 
     In one embodiment, the computing resources  332  include processing resources  376  and memory resources  378 . The processing resources  376  include one or more processors. The memory resources  378  include one or more memories configured as computer readable media capable of storing software instructions and other data. The processing resources  376  are capable of executing software instructions stored on the computer readable media. In one embodiment, the various components, models, modules, databases, and engines of the waterflow analysis system  104  utilize the computing resources  332  to assist in performing their various functions. Alternatively, or additionally, the various components, modules, databases, and engines can utilize other computing resources. 
     In one embodiment, the user computing device is a smart phone. The smart phone includes the waterflow analysis application associated with the waterflow analysis system  104 . The user accesses the waterflow analysis application to connect the waterflow monitoring device  102  to a local communication network, such as a wireless communication network. The user uses the waterflow analysis application to communicate with the waterflow analysis system, for example to set up an account with the waterflow analysis system, to configure waterflow notification devices, and to receive waterflow notification data  356  from the waterflow analysis system  104 . The smart phone can include computing resources including processing resources and memory resources to execute the application. In one embodiment, portions of the waterflow analysis system  104  can be implemented within the waterflow analysis application. 
     In one embodiment, the user can utilize multiple user computing devices to communicate with the waterflow analysis system  104 . For example, the user can use multiple desktop computers, laptop computers, tablets, smart phones, and wearable devices as user computing devices  106  to access the waterflow analysis system  104  to receive waterflow analysis services. 
     In one embodiment, a waterflow monitoring device  102  needs a stable power supply to work properly. In the most of house of US there is no power outlet next a toilet. Using an external standard USB charger may not be a good idea in this case. Another choice would be using batteries. Since the battery power wouldn&#39;t last very long and changing batteries is not very convenient in the location in which the waterflow monitoring device  102  is installed. Therefore, at the initial design phase, one of the preferred specifications is that the power supply has to last at least 6 months without replacing or recharging, if not longer. 
     In one embodiment, the waterflow monitoring device  102  can be equipped with a micro hydropower generator. Compatible micro hydropower generators can be bought off the shelf or purchased online. In one embodiment, a micro hydropower generator can provide about 0.3 watts power with a tap water pressure of 60 psi. Thus, in one embodiment, a micro hydropower generator can handle 60 mA at 5 V with 60 psi pressure. Those of skill in the art will understand, in light of the present disclosure, that a micro hydropower generator can have different operating specifications and characteristics than those described above without departing from the scope of the present disclosure. 
       FIG. 4  is a graph  400  illustrating current draw from a waterflow monitoring device and current produced by a micro hydropower generator, in accordance with one embodiment. 
     In one embodiment, at time T 0  waterflow begins, for example when a toilet is flushed. T 1  corresponds to a time at which current generation and current draw are unstable. T 2  corresponds to a stable period during which current draw and current generation are stable. T 3  corresponds to a time when waterflow is stopped, for example, when a toilet water tank is full. T 4  corresponds to a time at which the waterflow monitoring device  102  is turned off or enters sleep mode. 
     In one embodiment, When waterflow starts and the microcontroller wakes up at T 0 , there is a current inrush. During the current inrush, the waterflow monitoring device  102  needs a relatively high amount of power to operate. After about a little less than one second, the microcontroller operation becomes steady and the power consumption is reduced dramatically. After the waterflow stops, the waterflow monitoring device  102  needs to transmit waterflow monitoring data wirelessly to the waterflow analysis system  104 , thereby requiring another relatively power consumption until the completion of the process. 
     Accordingly, in one embodiment, there is a power ramp up at the initial wake-up and during the transmission of the waterflow monitoring data. 
     In one embodiment, there are four power consumption periods during the entire process. During the circuit inrush period the micro hydropower generator is just ramping up and can&#39;t support the waterflow monitoring device  102 . The waterflow monitoring device  102  can depend on the battery to run. The second period is called the vulnerable period because the micro hydropower generator is still not stable. The battery is still a better option. The third period is called the stable period because current draw and current generation are stable. As can be seen from the graph  400 , if the water pressure is high enough, the micro hydropower generator can not only provide the power that the waterflow monitoring device  102  needs during this stable period, there is also some extra power which can be used to recharge the battery. 
     In one embodiment, when the water flow stops the situation is changed. The waterflow monitoring device  102  can&#39;t turn off the power at the moment because the data needs to be sent to the cloud services. Because there is no power from the micro hydropower generator anymore, everything relies on the battery again. Accordingly, three out of four periods depend on the battery power. 
     In one embodiment, there is only one period during which the micro hydropower generator generates sufficient power consistently to power the device. If this period can generate more power than the other three periods spend, the device in theory can be run without external power for a very long time. If the water pressure is not high enough, then the micro hydropower generator can&#39;t generate enough power to meet the needs of the waterflow monitoring device  102 . In this situation, using an external charger may be the only choice remaining. 
     In one embodiment, during this whole process there are a couple of key moments, T 0 , T 2 , T 3  and T 4 . In one embodiment, the process starts at T 0 . At T 2  the logic circuit and the firmware work together to flip the power source of the waterflow monitoring device  102  from the battery to the micro hydropower generator if the micro hydropower generator generates enough power. In one embodiment, the flip happens only when both following conditions are true: the firmware sends the flipping signal to the logic circuit after waiting from T 0  to T 2  and the logic circuit detects the micro hydropower generator voltage is 4.2 V or higher after applying the load. At T 3 , since the micro hydropower generator stops generating power, the logic circuit detects 0 V. The logic circuit flips back to battery. At T 4 , the firmware has completed all tasks. It sends the turn-off signal to the latching circuit to bring the waterflow monitoring device  102  back to sleep mode. 
       FIG. 5  illustrates a functional flow diagram of a process  500  for assisting users to conserve water, in accordance with one embodiment. The process  500  can complete the various process steps using any of the methods, processes, and procedures discussed above with respect to  FIGS. 1-4 . 
     Referring to  FIG. 5 , and the description of  FIGS. 1-4  above, at block  502  the waterflow monitoring device  102  monitors waterflow in a plumbing fixture with a waterflow monitoring device, according to one embodiment. From block  502  the process proceeds to block  504 . 
     At block  504  the waterflow monitoring device  102  outputs waterflow notification data to the waterflow analysis system, according to one embodiment. From block  504  the process proceeds to block  506 . 
     At block  506  the waterflow analysis system  104  receives waterflow data from the waterflow monitoring device, according to one embodiment. From block  506  the process proceeds to block  508 . 
     At block  508  the waterflow analysis system  104  analyzes waterflow data with a machine learning module, according to one embodiment. From block  508  the process proceeds to block  510 . 
     At block  510  the waterflow analysis system  104  generates waterflow notification data based on analysis by machine learning module, according to one embodiment. From block  510  the process proceeds to block  512   
     At block  512  the waterflow analysis system  104  outputs waterflow notification data to the user computing device, according to one embodiment. From block  512  the process proceeds to block  514 . 
     At block  514  the user computing device  106  receives waterflow notification data from the waterflow analysis system, according to one embodiment. From block  514  the process proceeds to block  516 . 
     At block  516  the user computing device  106  displays the waterflow notification data to the user, according to one embodiment. 
     Those of skill in the art will recognize, in light of the present disclosure, that the process  500  can include different steps and different orders of steps, other than those represented in  FIG. 5  without departing from the scope of the present disclosure. 
       FIG. 6  illustrates a functional flow diagram of a process  600  corresponding to a hardware logic flowchart for a waterflow monitoring device  102 , in accordance with one embodiment. The process  600  can complete the various process steps using any of the methods, processes, and procedures discussed above with respect to  FIGS. 1-5 . 
     Referring to  FIG. 6 , and the description of  FIGS. 1-4  above, at block  602  the waterflow monitoring device  102  is in sleep mode, according to one embodiment. The process proceeds from block  602  to block  604 . 
     At block  604  if the charging cable is plugged in to the waterflow monitoring device  102 , the process proceeds to block  610  and  612 . At block  610  the microcontroller is booted, according to one embodiment. At block  612  the battery is recharged, according to one embodiment. From block  610  the process proceeds to block  614 . At block  614  firmware processing tasks are performed, according to one embodiment. 
     At block  604  if the charging cable is not plugged in, the process proceeds to block  606 . At block  606  if the reset button is depressed, the process proceeds to block  616 , according to one embodiment. At block  616  the microcontroller is booted, according to one embodiment. From block  616  the process proceeds to block  618 . At block  618  the microcontroller is notified that the reset button is still depressed, according to one embodiment. If at block  620  the reset button is still depressed, then the process proceeds to block  614 , according to one embodiment. If at  620  the reset button is no longer depressed, then the process proceeds to block  608 , according to one embodiment. 
     At block  606 , if the reset button is not depressed, then the process proceeds to block  608 , according to one embodiment. If at block  608  waterflow is not detected, then the process proceeds to block  602 , according to one embodiment. If at block  608  waterflow is detected, then the process proceeds to block  622 , according to one embodiment. At block  622 , the microcontroller is booted, according to one embodiment. From block  622 , the process proceeds to block  624  and  628 . At block  624  waterflow data is sent to the microcontroller, according to one embodiment. From block  624  the process proceeds to block  626 . At block  626 , if the waterflow has not stopped the process proceeds to block  624 , according to one embodiment. At block  626  if the waterflow has stopped, then the process proceeds to block  614 . At block  628  if the micro hydropower generator is generating enough power, then the process proceeds to block  630 , according to one embodiment. At block  630  the battery is recharged by the micro hydropower generator, according to one embodiment. At block  628  if the micro hydropower generator is not generating enough power, the process proceeds to block  624 . 
     Those of skill in the art will recognize, in light of the present disclosure, that the process  600  can include different steps and different orders of steps, other than those represented in  FIG. 6  without departing from the scope of the present disclosure. 
       FIG. 7  illustrates a functional flow diagram of a process  700  corresponding to a firmware logic flowchart for a waterflow monitoring device  102 , in accordance with one embodiment. The process  700  can complete the various process steps using any of the methods, processes, and procedures discussed above with respect to  FIGS. 1-6 . 
     Referring to  FIG. 7 , and the description of  FIGS. 1-4  above, at block  702  the process starts, according to one embodiment. From block  702  the process proceeds to block  704 . At block  704  if the waterflow monitoring device  102  is not configured that the process proceeds to block  714 , according to one embodiment. From block  714  the process proceeds to block  716 . At block  716 , if a configuration request is received, then the process proceeds to block  718 , according to one embodiment. At block  718  the waterflow analysis system is called to add the waterflow monitoring device  102 , according to one embodiment. At block  720 , if the waterflow monitoring device  102  has been added successfully, then the waterflow monitoring device  102  switches to a data collection operation at block  722 , according to one embodiment. From block  722 , the process proceeds to block  708 , according to one embodiment. If at block  720  the waterflow monitoring device  102  is not successfully added, then the process returns to block  716 . At block  716  if a configuration request is not received, then the process proceeds to block  724 , according to one embodiment. At block  724 , if the waiting time has exceeded 10 minutes, then the process proceeds to block  726 . At block  726 , the waterflow monitoring device  102  is put into sleep mode, according to one embodiment. From block  726  the process proceeds to block  728 . At block  728  the process ends. At block  724 , if the waiting time does not exceed 10 minutes, then the process returns to block  716 , according to one embodiment. 
     At block  704 , if the waterflow monitoring device  102  is configured, then the process proceeds to block  706 . At block  706 , if the reset button is depressed, then the process proceeds to block  712 . If at block  712  the reset button has been depressed for longer than 10 seconds, then the process proceeds to block  710 . At block  710 , the configuration data is cleared and the firmware is reset to factory condition, according to one embodiment. From block  710  the process proceeds to block  714 . If at block  712  the reset button is not depressed longer than 10 seconds, then the process proceeds to block  708 . 
     At block  706 , if the reset button is not depressed, then the process proceeds to block  708 . At block  708 , if waterflow is not detected, then the process proceeds to block  750 . At block  750 , the waterflow monitoring device  102  is put into sleep mode, according to one embodiment. From block  750 , the process proceeds to block  752 . At block  752  the process ends, according to one embodiment. 
     If at block  708  waterflow is detected, then the process proceeds to block  730 . At block  730 , the process waits, for three seconds, for example, and then collects voltage generated by the micro hydropower generator, according to one embodiment. From block  730  the process proceeds to block  732 . At block  732 , the data flow monitoring device  102  switches from being powered by the battery to being powered by the micro hydropower generator, according to one embodiment. From block  732  the process proceeds to block  734 . At block  734  if the waterflow has stopped, then the process proceeds to block  744 . At block  744 , the waterflow duration is counted, according to one embodiment. From block  744  the process proceeds to block  746 . At block  746 , if the waterflow duration is greater than a threshold, then the process proceeds to block  748 . At block  748 , waterflow monitoring data is sent to the waterflow analysis system  104 , according to one embodiment. From block  748  the process returns to block  734 . If at block  746  the duration of the waterflow is less than a threshold, the process returns to block  734 . 
     At block  734  if the waterflow has stopped, then the process proceeds to block  736 . At block  736 , waterflow monitoring data is sent to the waterflow analysis system  104 , according to one embodiment. From block  736  the process proceeds to block  738 . At block  738 , newly calculated threshold data is saved, according to one embodiment. From block  738  the process proceeds to block  740 . At block  740 , the waterflow monitoring device  104  is put into sleep mode, according to one embodiment. From block  740  the process proceeds to block  742 . At block  742  the process ends, according to one embodiment. 
     Those of skill in the art will recognize, in light of the present disclosure, that the process  700  can include different steps and different orders of steps, other than those represented in  FIG. 7  without departing from the scope of the present disclosure. 
       FIG. 8  illustrates a functional flow diagram of a process  800  corresponding to a service flowchart for a waterflow analysis system  104 , according to one embodiment, in accordance with one embodiment. The process  800  can complete the various process steps using any of the methods, processes, and procedures discussed above with respect to  FIGS. 1-7 . 
     Referring to  FIG. 8 , and the description of  FIGS. 1-4  above, at block  802 , the waterflow analysis system  104  receives waterflow monitoring data from the waterflow monitoring device  102 , according to one embodiment. From block  802  the process proceeds to block  804 . At block  804 , if the waterflow monitoring data includes duplicate flow data, then the process proceeds to block  806 . If the waterflow monitoring data does not include duplicate flow data, then the process proceeds to block  810 . At block  806 , the duplications are ignored, according to one embodiment. From block  806  the process proceeds to block  810 . 
     At block  810 , if the waterflow monitoring data includes overlapping data, then the process proceeds to block  812 . At block  812 , the overlapping data is merged. From block  812 , the process proceeds to block  814 . At block  810 , if the waterflow monitoring data does not include overlapping data, then the process proceeds to block  814 . 
     At block  814 , if the waterflow monitoring data includes future dated flow data, then the process proceeds to block  816 . At block  816 , the future dated flow data is removed. From block  816  the process proceeds to block  818 . At block  814 , if the waterflow monitoring data does not include future dated flow data, then the process proceeds to block  818 . 
     At block  818 , the waterflow monitoring data is entered into the waterflow monitoring database, according to one embodiment. From block  818  the process proceeds to block  820 . At block  820 , new leaking threshold data is calculated based on the new waterflow monitoring data and recent historical waterflow monitoring data, according to one embodiment. From block  820  the process proceeds to block  822 . At block  822 , the new water leaking threshold is output to the waterflow monitoring device  102 . From block  822  the process proceeds to end at block  824 , according to an embodiment. From block  818 , the process also proceeds to block  826 . At block  826 , the analysis model is invoked asynchronously, according to one embodiment. From block  826  the process proceeds to block  828 . 
     In one embodiment, if the long flow threshold is exceeded at block  828 , then the process proceeds to block  830 . At block  830 , a long waterflow alert message is sent to the user computing device  106 , according to one embodiment. From block  830 , the process proceeds to block  832 . At block  828 , if the long flow threshold is not exceeded, then the process proceeds to block  832 . 
     At block  832 , if the short flow threshold is exceeded, then the process proceeds to block  834 . At block  832 , if the short flow threshold is not exceeded then the process proceeds to block  836 . At block  834 , a short flow waterflow alert message is output to the user computing device  106 , according to one embodiment. From block  834 , the process proceeds to block  836 . At block  836 , if a low battery is reported from the waterflow monitoring device  102 , the process proceeds to block  838 . At block  836  of a low battery is not reported, the process proceeds to block  840 . At block  838 , a low battery alert is sent to the user computing device  106 , according to one embodiment. From block  838 , the process proceeds to block  840 . At block  840  the process ends, according to one embodiment. 
     Those of skill in the art will recognize, in light of the present disclosure, that the process  800  can include different steps and different orders of steps, other than those represented in  FIG. 8  without departing from the scope of the present disclosure. 
       FIG. 9  illustrates a functional flow diagram of a process  900  for utilizing a waterflow monitoring device  102 , a waterflow analysis system  104 , and a user computing device  106  to assist a user in conserving water, in accordance with one embodiment. The process  900  can complete the various process steps using any of the methods, processes, and procedures discussed above with respect to  FIGS. 1-7 . 
     Referring to  FIG. 9 , and the description of  FIGS. 1-4  above, the dashed boxes delineate which process functions are performed by the waterflow monitoring system  102 , which process functions are performed by the waterflow analysis system  104 , and which process functions are performed by the user computing device  106 . 
     At block  902 , the process starts in the waterflow monitoring system  102 . From block  902 , the process proceeds to block  904 . At block  904  the waterflow monitoring system  102  requests to add the waterflow monitoring device  102  to the waterflow analysis system  104 , according to one embodiment. From block  904 , the process proceeds to block  906 . Additionally, or alternatively, the process of the waterflow analysis system  104  can start at  903  with a request to add a new device. From block  903  the process proceeds to block  906 . At block  906 , the waterflow analysis system  104  registers the waterflow monitoring device  102  as ready for use, according to one embodiment. From block  906  the process proceeds to block  908 . At block  908 , the average waterflow time is set to a default value, according to one embodiment. From block  908 , the process proceeds to block  910 . At block  910 , the waterflow monitoring device  102  stores the average flow time threshold, according to one embodiment. From block  910  the process proceeds to block  912 . 
     At block  912 , if waterflow has started, then the process proceeds to block  914 . At block  914 , the waterflow duration is recorded by the waterflow monitoring device  102 . From block  914  the process proceeds to block  916 . At block  916 , if the waterflow has not stopped, then the process proceeds to block  918 , according to one embodiment. At block  918 , if the waterflow duration threshold has not been exceeded, then the process returns to block  914 . At block  916 , if the waterflow has stopped, then the process proceeds to block  920 . At block  918 , if the waterflow duration threshold has been exceeded, then the process proceeds to block  920 . 
     At block  920 , the waterflow analysis system  104  adds waterflow data points received in the waterflow notification data from the waterflow monitoring device  102 , according to one embodiment. From block  920 , the process proceeds to block  922 . At block  922  if the long waterflow threshold has been exceeded, then the process proceeds to block  924 . At block  924 , the waterflow analysis system  104  outputs waterflow notification data including a long leakage alert to the user computing device  106 , according to one embodiment. From block  924  the process proceeds to block  926 . At block  926 , the user computing device  106  displays the long leakage alert to the user, according to one embodiment. At block  922 , if the long waterflow threshold has not been exceeded, and the process proceeds to block  928 . 
     At block  928 , if the short waterflow threshold has been exceeded, then the process proceeds to block  930 . At block  930 , the waterflow analysis system  104  sends waterflow notification data to the user computing device  106  including a short leakage alert, according to one embodiment. The user computing device  106  displays the short leakage alert to the user at block  932 , according to one embodiment. At block  928 , if the short waterflow threshold has not been exceeded, then the process proceeds to block  934 . 
     At block  934 , if the waterflow monitoring device  102  is in the beginner phase, then the process proceeds to block  938 . At block  938 , the waterflow analysis system  104  continues to use the default average time, according to one embodiment. From block  938 , the process proceeds to block  910 . If the waterflow monitoring device  102  is not in the beginner phase at block  934 , the process proceeds to block  936 . At block  936 , if the waterflow monitoring device  102  in the learning phase, then the process proceeds to block  940 . At block  940 , the waterflow analysis system  104  recalculates the threshold with a simple average. From block  940 , the process proceeds to block  910 . At block  936 , if the process is not in the learning phase, then the process proceeds to block  942 . At block  942 , the waterflow analysis system  104  recalculates the threshold with a normal distribution, according to one embodiment. From block  942 , the process returns to block  910 . 
     Those of skill in the art will recognize, in light of the present disclosure, that the process  900  can include different steps and different orders of steps, other than those represented in  FIG. 9  without departing from the scope of the present disclosure. 
     Embodiments of the present disclosure address some of the shortcomings associated with traditional water conservation systems. Machine learning analysis is performed on waterflow metrics to determine water conservation data that can be provided to users based on the characteristics of the plumbing situations of the users. The various embodiments of the disclosure can be implemented to improve the technical fields of water conservation, data management, data processing, and data transmission. Therefore, the various described embodiments of the disclosure and their associated benefits amount to significantly more than an abstract idea. 
       FIG. 10  illustrates a flow diagram of a process  1000  for assisting users to conserve water, according to various embodiments. 
     Referring to  FIG. 10  and the description of  FIGS. 1-9  above, in one embodiment, process  1000  begins at BEGIN  1002  and process flow proceeds to MONITOR WATERFLOW THROUGH A PLUMBING FIXTURE WITH A WATERFLOW MONITORING DEVICE DISPOSED IN A PLUMBING SYSTEM  1004 . 
     In one embodiment, at MONITOR WATERFLOW THROUGH A PLUMBING FIXTURE WITH A WATERFLOW MONITORING DEVICE DISPOSED IN A PLUMBING SYSTEM  1004 , waterflow is monitored through a plumbing fixture with a waterflow monitoring device disposed in a plumbing system, using any of the methods, processes, and procedures discussed above with respect to  FIGS. 1-9 . 
     In one embodiment, once waterflow is monitored through a plumbing fixture with a waterflow monitoring device disposed in a plumbing system 
     at MONITOR WATERFLOW THROUGH A PLUMBING FIXTURE WITH A WATERFLOW MONITORING DEVICE DISPOSED IN A PLUMBING SYSTEM  1004  process flow proceeds to GENERATE WATERFLOW MONITORING DATA INDICATIVE OF A FLOW OF WATER THROUGH THE PLUMBING FIXTURE  1006 . 
     In one embodiment, at GENERATE WATERFLOW MONITORING DATA INDICATIVE OF A FLOW OF WATER THROUGH THE PLUMBING FIXTURE  1006 , waterflow monitoring data is generated indicative of a flow of water through the plumbing fixture, using any of the methods, processes, and procedures discussed above with respect to  FIGS. 1-9 . 
     In one embodiment, once waterflow monitoring data is generated indicative of a flow of water through the plumbing fixture at GENERATE WATERFLOW MONITORING DATA INDICATIVE OF A FLOW OF WATER THROUGH THE PLUMBING FIXTURE  1006 , process flow proceeds to OUTPUT THE WATERFLOW MONITORING DATA TO A CLOUD-BASED WATERFLOW ANALYSIS SYSTEM  1008 . 
     In one embodiment, at OUTPUT THE WATERFLOW MONITORING DATA TOA CLOUD-BASED WATERFLOW ANALYSIS SYSTEM  1008 , the waterflow monitoring data is output to a cloud-based waterflow analysis system, using any of the methods, processes, and procedures discussed above with respect to  FIGS. 1-9 . 
     In one embodiment, once the waterflow monitoring data is output to a cloud-based waterflow analysis system at OUTPUT THE WATERFLOW MONITORING DATA TO A CLOUD-BASED WATERFLOW ANALYSIS SYSTEM  1008 , process flow proceeds to ANALYZE THE WATERFLOW MONITORING DATA WITH A MACHINE LEARNING TRAINED ANALYSIS MODEL OF THE WATERFLOW MONITORING SYSTEM  1010 . 
     In one embodiment, at ANALYZE THE WATERFLOW MONITORING DATA WITH A MACHINE LEARNING TRAINED ANALYSIS MODEL OF THE WATERFLOW MONITORING SYSTEM  1010 , the waterflow monitoring data is analyzed with a machine learning trained analysis model of the waterflow monitoring system, using any of the methods, processes, and procedures discussed above with respect to  FIGS. 1-9 . 
     In one embodiment, once the waterflow monitoring data is analyzed with a machine learning trained analysis model of the waterflow monitoring system at ANALYZE THE WATERFLOW MONITORING DATA WITH A MACHINE LEARNING TRAINED ANALYSIS MODEL OF THE WATERFLOW MONITORING SYSTEM  1010 , process flow proceeds to GENERATE WATERFLOW NOTIFICATION DATA BASED ON THE ANALYSIS OF THE WATERFLOW MONITORING SYSTEM AND INCLUDING A NOTIFICATION REGARDING THE FLOW OF WATER THROUGH THE PLUMBING FIXTURE  1012 . 
     In one embodiment, at GENERATE WATERFLOW NOTIFICATION DATA BASED ON THE ANALYSIS OF THE WATERFLOW MONITORING SYSTEM AND INCLUDING A NOTIFICATION REGARDING THE FLOW OF WATER THROUGH THE PLUMBING FIXTURE  1012 , waterflow notification data is generated based on the analysis of the waterflow monitoring system and including a notification regarding the flow of water through the plumbing fixture, using any of the methods, processes, and procedures discussed above with respect to  FIGS. 1-9 . 
     In one embodiment, once waterflow notification data is generated based on the analysis of the waterflow monitoring system and including a notification regarding the flow of water through the plumbing fixture at GENERATE WATERFLOW NOTIFICATION DATA BASED ON THE ANALYSIS OF THE WATERFLOW MONITORING SYSTEM AND INCLUDING A NOTIFICATION REGARDING THE FLOW OF WATER THROUGH THE PLUMBING FIXTURE  1012 , process flow proceeds OUTPUT THE WATERFLOW MONITORING DATA TO A USER OF THE WATERFLOW ANALYSIS SYSTEM  1014 . 
     In one embodiment, at OUTPUT THE WATERFLOW MONITORING DATA TO A USER OF THE WATERFLOW ANALYSIS SYSTEM  1014 , the waterflow monitoring data is output to a user of the waterflow analysis system, using any of the methods, processes, and procedures discussed above with respect to  FIGS. 1-9 . 
     In one embodiment, once the waterflow monitoring data is output to a user of the waterflow analysis system 
     at OUTPUT THE WATERFLOW MONITORING DATA TO A USER OF THE WATERFLOW ANALYSIS SYSTEM  1014 , process flow proceeds to END  1016 . 
     In one embodiment, at END  1016  the process for assisting users to conserve water is exited to await new data and/or instructions. 
     As noted above, the specific illustrative examples discussed above are but illustrative examples of implementations of embodiments of the method or process for assisting users to conserve water. Those of skill in the art will readily recognize that other implementations and embodiments are possible. Therefore, the discussion above should not be construed as a limitation on the claims provided below. 
     In one embodiment, a computing system implemented method assists users to conserve water. The method includes monitoring waterflow through a plumbing fixture with a waterflow monitoring device disposed in a plumbing system, generating waterflow monitoring data indicative of a flow of water through the plumbing fixture, and outputting the waterflow monitoring data to a cloud-based waterflow analysis system. The method includes analyzing the waterflow monitoring data with a machine learning trained analysis model of the waterflow monitoring system, generating waterflow notification data based on the analysis of the waterflow monitoring system and including a notification regarding the flow of water through the plumbing fixture, and outputting the waterflow monitoring data to a user of the waterflow analysis system. 
     In one embodiment, a system for assisting users to conserve water, the system includes at least one processor at least one memory coupled to the at least one processor. The at least one memory has stored therein instructions which, when executed by any set of the one or more processors, perform a process. The process includes monitoring waterflow through a plumbing fixture with a waterflow monitoring device disposed in a plumbing system, generating waterflow monitoring data indicative of a flow of water through the plumbing fixture, and outputting the waterflow monitoring data to a cloud-based waterflow analysis system. The process includes analyzing the waterflow monitoring data with a machine learning trained analysis model of the waterflow monitoring system, generating waterflow notification data based on the analysis of the waterflow monitoring system and including a notification regarding the flow of water through the plumbing fixture, and outputting the waterflow monitoring data to a user of the waterflow analysis system. 
     In one embodiment, a computing system implemented method assists users to conserve water. The method includes receiving, with a waterflow analysis system, waterflow monitoring data from a waterflow monitoring device indicating characteristics of waterflow in a plumbing fixture monitored by the waterflow monitoring device and analyzing the waterflow monitoring data with an analysis model of the waterflow monitoring system. The method includes generating waterflow notification data based on the analysis of the waterflow monitoring system and including a notification regarding the flow of water through the plumbing fixture and outputting the waterflow monitoring data to a user of the waterflow analysis system. 
     In one embodiment, a system assists users to conserve water. The system includes at least one processor at least one memory coupled to the at least one processor. The at least one memory has stored therein instructions which, when executed by any set of the one or more processors, perform a process. The process includes receiving, with a waterflow analysis system, waterflow monitoring data from a waterflow monitoring device indicating characteristics of waterflow in a plumbing fixture monitored by the waterflow monitoring device and analyzing the waterflow monitoring data with an analysis model of the waterflow monitoring system. The process includes generating waterflow notification data based on the analysis of the waterflow monitoring system and including a notification regarding the flow of water through the plumbing fixture and outputting the waterflow monitoring data to a user of the waterflow analysis system. 
     In one embodiment, A waterflow monitoring device includes a water input port configured to connect to a plumbing outlet of a plumbing system, a water output port configured to connect to a water input port of a plumbing fixture, and a water throughput configured to pass water from the plumbing outlet to the water input port of the plumbing fixture. The waterflow monitoring device includes a micro hydropower generator configured to detect waterflow through the water throughput, to generate electricity from the waterflow through the water throughput, and to generate waterflow signals indicative of waterflow through the water throughput. The waterflow monitoring device includes a wireless transceiver configured to pass data to a cloud-based waterflow analysis system via one or more networks and to receive data from the cloud-based waterflow analysis system via the one or more networks. The waterflow monitoring device includes a microcontroller coupled to the micro hydropower generator and the wireless transceiver and configured to generate waterflow monitoring data from the waterflow signals and to cause the wireless transceiver to output the waterflow monitoring data to the cloud-based waterflow analysis system. 
     The disclosed embodiments provide one or more technical solutions to the technical problem of ineffective water management systems. These and other embodiments of a water conservation system are discussed in further detail below. 
     Utilizing cloud-based machine learning trained analysis models to analyze water monitoring data is a technical solution to a long-standing technical problem and is not an abstract idea for at least a few reasons. First, utilizing cloud-based machine learning trained analysis models to analyze water monitoring data is a technical solution to a long-standing technical problem and is not an abstract idea for at least a few reasons is not an abstract idea because it is not merely an idea itself (e.g., can be performed mentally or using pen and paper). Second, utilizing cloud-based machine learning trained analysis models to analyze water monitoring data is a technical solution to a long-standing technical problem and is not an abstract idea because it is not a fundamental economic practice (e.g., is not merely creating a contractual relationship, hedging, mitigating a settlement risk, etc.). Third, utilizing cloud-based machine learning trained analysis models to analyze water monitoring data is a technical solution to a long-standing technical problem and is not an abstract idea for at least a few reasons is not an abstract idea because it is not a method of organizing human activity (e.g., managing a game of bingo). Fourth, although mathematics may be used to generate an analytics model, the disclosed and claimed methods and systems for assisting users to conserve water are not an abstract idea because the methods and systems are not simply a mathematical relationship/formula. 
     Utilizing cloud-based machine learning trained analysis models to analyze water monitoring data is a technical solution to a long-standing technical problem and is not an abstract idea because utilizing machine learning processes to better understand water monitoring data yields significant improvement to the technical fields of water conservation, water management, electronic data management, data processing, and data transmission, according to one embodiment. 
     As a result, embodiments of the present disclosure allow for reduced use of processor cycles, memory, and power consumption, by improving the effectiveness of water conservation systems. Consequently, computing and communication systems implementing or providing the embodiments of the present disclosure are transformed into more operationally efficient devices and systems. In addition to improving overall computing performance, utilizing cloud-based machine learning trained analysis models to analyze water monitoring data significantly improves the field of water conservation and water management systems by more efficiently providing personalized content to users, according to one embodiment. Therefore, both human and non-human resources are utilized more efficiently. Furthermore, by utilizing a time-location enrichment model to better understand transaction description strings, loyalty in the water conservation system is increased. This results in repeat customers, efficient water conservation services, and reduced abandonment of use of the water management system, according to one embodiment. 
     Herein, the term “production environment” includes the various components, or assets, used to deploy, implement, access, and use, a given application as that application is intended to be used. In various embodiments, production environments include multiple assets that are combined, communicatively coupled, virtually and/or physically connected, and/or associated with one another, to provide the production environment implementing the application. 
     As specific illustrative examples, the assets making up a given production environment can include, but are not limited to, one or more computing environments used to implement the application in the production environment such as a data center, a cloud computing environment, a dedicated hosting environment, and/or one or more other computing environments in which one or more assets used by the application in the production environment are implemented; one or more computing systems or computing entities used to implement the application in the production environment; one or more virtual assets used to implement the application in the production environment; one or more supervisory or control systems, such as hypervisors, or other monitoring and management systems, used to monitor and control assets and/or components of the production environment; one or more communications channels for sending and receiving data used to implement the application in the production environment; one or more access control systems for limiting access to various components of the production environment, such as firewalls and gateways; one or more traffic and/or routing systems used to direct, control, and/or buffer, data traffic to components of the production environment, such as routers and switches; one or more communications endpoint proxy systems used to buffer, process, and/or direct data traffic, such as load balancers or buffers; one or more secure communication protocols and/or endpoints used to encrypt/decrypt data, such as Secure Sockets Layer (SSL) protocols, used to implement the application in the production environment; one or more databases used to store data in the production environment; one or more internal or external services used to implement the application in the production environment; one or more backend systems, such as backend servers or other hardware used to process data and implement the application in the production environment; one or more software systems used to implement the application in the production environment; and/or any other assets/components making up an actual production environment in which an application is deployed, implemented, accessed, and run, e.g., operated, as discussed herein, and/or as known in the art at the time of filing, and/or as developed after the time of filing. 
     As used herein, the terms “computing system”, “computing device”, and “computing entity”, include, but are not limited to, a virtual asset; a server computing system; a workstation; a desktop computing system; a mobile computing system, including, but not limited to, smart phones, portable devices, and/or devices worn or carried by a user; a database system or storage cluster; a switching system; a router; any hardware system; any communications system; any form of proxy system; a gateway system; a firewall system; a load balancing system; or any device, subsystem, or mechanism that includes components that can execute all, or part, of any one of the processes and/or operations as described herein. 
     In addition, as used herein, the terms computing system and computing entity, can denote, but are not limited to, systems made up of multiple: virtual assets; server computing systems; workstations; desktop computing systems; mobile computing systems; database systems or storage clusters; switching systems; routers; hardware systems; communications systems; proxy systems; gateway systems; firewall systems; load balancing systems; or any devices that can be used to perform the processes and/or operations as described herein. 
     As used herein, the term “computing environment” includes, but is not limited to, a logical or physical grouping of connected or networked computing systems and/or virtual assets using the same infrastructure and systems such as, but not limited to, hardware systems, software systems, and networking/communications systems. Typically, computing environments are either known environments, e.g., “trusted” environments, or unknown, e.g., “untrusted” environments. Typically, trusted computing environments are those where the assets, infrastructure, communication and networking systems, and security systems associated with the computing systems and/or virtual assets making up the trusted computing environment, are either under the control of, or known to, a party. 
     In various embodiments, each computing environment includes allocated assets and virtual assets associated with, and controlled or used to create, and/or deploy, and/or operate an application. 
     In various embodiments, one or more cloud computing environments are used to create, and/or deploy, and/or operate an application that can be any form of cloud computing environment, such as, but not limited to, a public cloud; a private cloud; a virtual private network (VPN); a subnet; a Virtual Private Cloud (VPC); a sub-net or any security/communications grouping; or any other cloud-based infrastructure, sub-structure, or architecture, as discussed herein, and/or as known in the art at the time of filing, and/or as developed after the time of filing. 
     In many cases, a given application or service may utilize, and interface with, multiple cloud computing environments, such as multiple VPCs, in the course of being created, and/or deployed, and/or operated. 
     As used herein, the term “virtual asset” includes any virtualized entity or resource, and/or virtualized part of an actual, or “bare metal” entity. In various embodiments, the virtual assets can be, but are not limited to, virtual machines, virtual servers, and instances implemented in a cloud computing environment; databases associated with a cloud computing environment, and/or implemented in a cloud computing environment; services associated with, and/or delivered through, a cloud computing environment; communications systems used with, part of, or provided through, a cloud computing environment; and/or any other virtualized assets and/or sub-systems of “bare metal” physical devices such as mobile devices, remote sensors, laptops, desktops, point-of-sale devices, etc., located within a data center, within a cloud computing environment, and/or any other physical or logical location, as discussed herein, and/or as known/available in the art at the time of filing, and/or as developed/made available after the time of filing. 
     In various embodiments, any, or all, of the assets making up a given production environment discussed herein, and/or as known in the art at the time of filing, and/or as developed after the time of filing, can be implemented as one or more virtual assets. 
     In one embodiment, two or more assets, such as computing systems and/or virtual assets, and/or two or more computing environments, are connected by one or more communications channels including but not limited to, Secure Sockets Layer communications channels and various other secure communications channels, and/or distributed computing system networks, such as, but not limited to: a public cloud; a private cloud; a virtual private network (VPN); a subnet; any general network, communications network, or general network/communications network system; a combination of different network types; a public network; a private network; a satellite network; a cable network; or any other network capable of allowing communication between two or more assets, computing systems, and/or virtual assets, as discussed herein, and/or available or known at the time of filing, and/or as developed after the time of filing. 
     As used herein, the term “network” includes, but is not limited to, any network or network system such as, but not limited to, a peer-to-peer network, a hybrid peer-to-peer network, a Local Area Network (LAN), a Wide Area Network (WAN), a public network, such as the Internet, a private network, a cellular network, any general network, communications network, or general network/communications network system; a wireless network; a wired network; a wireless and wired combination network; a satellite network; a cable network; any combination of different network types; or any other system capable of allowing communication between two or more assets, virtual assets, and/or computing systems, whether available or known at the time of filing or as later developed. 
     As used herein, the term “user” includes, but is not limited to, any party, parties, entity, and/or entities using, or otherwise interacting with any of the methods or systems discussed herein. For instance, in various embodiments, a user can be, but is not limited to, a person, a commercial entity, an application, a service, and/or a computing system. 
     As used herein, the term “relationship(s)” includes, but is not limited to, a logical, mathematical, statistical, or other association between one set or group of information, data, and/or users and another set or group of information, data, and/or users, according to one embodiment. The logical, mathematical, statistical, or other association (i.e., relationship) between the sets or groups can have various ratios or correlation, such as, but not limited to, one-to-one, multiple-to-one, one-to-multiple, multiple-to-multiple, and the like, according to one embodiment. As a non-limiting example, if the disclosed system and method for providing access control and enhanced encryption determines a relationship between a first group of data and a second group of data, then a characteristic or subset of a first group of data can be related to, associated with, and/or correspond to one or more characteristics or subsets of the second group of data, or vice-versa, according to one embodiment. Therefore, relationships may represent one or more subsets of the second group of data that are associated with one or more subsets of the first group of data, according to one embodiment. In one embodiment, the relationship between two sets or groups of data includes, but is not limited to similarities, differences, and correlations between the sets or groups of data. 
     As used herein, the term storage container includes, but is not limited to, any physical or virtual data source or storage device. For instance, in various embodiments, a storage container can be, but is not limited to, one or more of a hard disk drive, a solid-state drive, an EEPROM, an optical disk, a server, a memory array, a database, a virtual database, a virtual memory, a virtual data directory, or other physical or virtual data sources. 
     As used herein, the term application container includes, but is not limited to, one or more profiles or other data sets that allow users and processes to access only particular data within a file system related to a storage container. For instance, in various embodiments, an application container can include, but is not limited to, a set of rules, a list of files, a list of processes, and/or encryption keys that provide access control to a file system such that a user associated with the application container can only access data, files, objects or other portions of a file system in accordance with the set of rules, the list of files, the list of processes, and/or encryptions keys. 
     As used herein, the term file includes, but is not limited to, a data entity that is a sequence of bytes that can be accessed individually or collectively. 
     As used herein the term data object includes, but is not limited to, a data entity that is stored and retrieved as a whole, or in large chunks, rather than as a sequence of bytes. 
     In the discussion above, certain aspects of one embodiment include process steps and/or operations and/or instructions described herein for illustrative purposes in a particular order and/or grouping. However, the particular order and/or grouping shown and discussed herein are illustrative only and not limiting. Those of skill in the art will recognize that other orders and/or grouping of the process steps and/or operations and/or instructions are possible and, in some embodiments, one or more of the process steps and/or operations and/or instructions discussed above can be combined and/or deleted. In addition, portions of one or more of the process steps and/or operations and/or instructions can be re-grouped as portions of one or more other of the process steps and/or operations and/or instructions discussed herein. Consequently, the particular order and/or grouping of the process steps and/or operations and/or instructions discussed herein do not limit the scope of the invention as claimed below. 
     As discussed in more detail above, using the above embodiments, with little or no modification and/or input, there is considerable flexibility, adaptability, and opportunity for customization to meet the specific needs of various parties under numerous circumstances. 
     In the discussion above, certain aspects of one embodiment include process steps and/or operations and/or instructions described herein for illustrative purposes in a particular order and/or grouping. However, the particular order and/or grouping shown and discussed herein are illustrative only and not limiting. Those of skill in the art will recognize that other orders and/or grouping of the process steps and/or operations and/or instructions are possible and, in some embodiments, one or more of the process steps and/or operations and/or instructions discussed above can be combined and/or deleted. In addition, portions of one or more of the process steps and/or operations and/or instructions can be re-grouped as portions of one or more other of the process steps and/or operations and/or instructions discussed herein. Consequently, the particular order and/or grouping of the process steps and/or operations and/or instructions discussed herein do not limit the scope of the invention as claimed below. 
     The present invention has been described in particular detail with respect to specific possible embodiments. Those of skill in the art will appreciate that the invention may be practiced in other embodiments. For example, the nomenclature used for components, capitalization of component designations and terms, the attributes, data structures, or any other programming or structural aspect is not significant, mandatory, or limiting, and the mechanisms that implement the invention or its features can have various different names, formats, or protocols. Further, the system or functionality of the invention may be implemented via various combinations of software and hardware, as described, or entirely in hardware elements. Also, particular divisions of functionality between the various components described herein are merely exemplary, and not mandatory or significant. Consequently, functions performed by a single component may, in other embodiments, be performed by multiple components, and functions performed by multiple components may, in other embodiments, be performed by a single component. 
     Some portions of the above description present the features of the present invention in terms of algorithms and symbolic representations of operations, or algorithm-like representations, of operations on information/data. These algorithmic or algorithm-like descriptions and representations are the means used by those of skill in the art to most effectively and efficiently convey the substance of their work to others of skill in the art. These operations, while described functionally or logically, are understood to be implemented by computer programs or computing systems. Furthermore, it has also proven convenient at times to refer to these arrangements of operations as steps or modules or by functional names, without loss of generality. 
     Unless specifically stated otherwise, as would be apparent from the above discussion, it is appreciated that throughout the above description, discussions utilizing terms such as, but not limited to, “activating”, “accessing”, “adding”, “aggregating”, “alerting”, “applying”, “analyzing”, “associating”, “calculating”, “capturing”, “categorizing”, “classifying”, “comparing”, “creating”, “defining”, “detecting”, “determining”, “distributing”, “eliminating”, “encrypting”, “extracting”, “filtering”, “forwarding”, “generating”, “identifying”, “implementing”, “informing”, “monitoring”, “obtaining”, “posting”, “processing”, “providing”, “receiving”, “requesting”, “saving”, “sending”, “storing”, “substituting”, “transferring”, “transforming”, “transmitting”, “using”, etc., refer to the action and process of a computing system or similar electronic device that manipulates and operates on data represented as physical (electronic) quantities within the computing system memories, resisters, caches or other information storage, transmission or display devices. 
     The present invention also relates to an apparatus or system for performing the operations described herein. This apparatus or system may be specifically constructed for the required purposes, or the apparatus or system can comprise a general-purpose system selectively activated or configured/reconfigured by a computer program stored on a computer program product as discussed herein that can be accessed by a computing system or other device. 
     Those of skill in the art will readily recognize that the algorithms and operations presented herein are not inherently related to any particular computing system, computer architecture, computer or industry standard, or any other specific apparatus. Various general-purpose systems may also be used with programs in accordance with the teaching herein, or it may prove more convenient/efficient to construct more specialized apparatuses to perform the required operations described herein. The required structure for a variety of these systems will be apparent to those of skill in the art, along with equivalent variations. In addition, the present invention is not described with reference to any particular programming language and it is appreciated that a variety of programming languages may be used to implement the teachings of the present invention as described herein, and any references to a specific language or languages are provided for illustrative purposes only and for enablement of the contemplated best mode of the invention at the time of filing. 
     The present invention is well suited to a wide variety of computer network systems operating over numerous topologies. Within this field, the configuration and management of large networks comprise storage devices and computers that are communicatively coupled to similar or dissimilar computers and storage devices over a private network, a LAN, a WAN, a private network, or a public network, such as the Internet. 
     It should also be noted that the language used in the specification has been principally selected for readability, clarity and instructional purposes, and may not have been selected to delineate or circumscribe the inventive subject matter. Accordingly, the disclosure of the present invention is intended to be illustrative, but not limiting, of the scope of the invention, which is set forth in the claims below. 
     In addition, the operations shown in the FIGs, or as discussed herein, are identified using a particular nomenclature for ease of description and understanding, but other nomenclature is often used in the art to identify equivalent operations. 
     Therefore, numerous variations, whether explicitly provided for by the specification or implied by the specification or not, may be implemented by one of skill in the art in view of this disclosure.