Patent Publication Number: US-2021164860-A1

Title: Water leak detection device and integration platform

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application claims the benefit of U.S. Provisional Application Ser. No. 62/695,548 filed Jul. 9, 2018, which is incorporated herein by reference in its entirety. 
    
    
     TECHNICAL FIELD 
     The present disclosure relates generally to a water leak detection device, and, more particularly, to an overall water meter integration platform utilizing the water leak detection device and other connected devices. 
     BACKGROUND 
     Plumbing systems are a critical aspect of all modern buildings and introduce significant risk due to the nature of inserting a pressurized water supply throughout a structure. Plumbing leaks, from small drips to burst pipes, can cause significant damage to any building. Burst pipes and water damage present significant costs for repairs and increased water and sewer bills for homeowners and property managers. Further, billions of dollars are paid by insurers in water damage claims every year. A significant portion of these costs could have been avoided if water leaks were identified quickly and remediation steps implemented soon thereafter. In addition, leaks, even small drips, waste a significant amount of water, thereby raising water costs and reducing valuable resources. 
     Presently, much of the problems associated with water leaks stem from the relative inaccessibility of plumbing systems in both residential and commercial settings. For example, a leak may be behind a wall or in a cabinet which is not readily apparent. Further, even when leaks are found or major flooding events occur, the procedure for addressing the problem and turning off the water supply may be an unfamiliar process, which may delay remedial actions and enhance damage and costs. In most situations, a water meter and main valve is located at a main water entry point. The water meter tracks usage information for the home or commercial space. The main valve, when closed, ceases the flow of water to the downstream plumbing and in many cases is the only immediate option for stopping a leak or burst pipe flow. The main valve is usually located in a remote location (a basement or utility room) which may or may not be anywhere near the leak or anyone capable of finding the valve and turning off the water. 
     Currently, there are systems which are configured to automatically shut off the water at the main valve under certain conditions. For example, some systems are configured to turn off the main valve when the water flow continues through the water meter for an extended period of time. Other systems rely on an array of sensors and valves spread throughout the building in order to detect various circumstances which cause the main valve to be closed. However, these automatic shutoff systems and other current devices are not ideal and suffer from drawbacks. 
     In particular, systems which monitor the water meter and shut off the valve only when significant deviations from normal flow occur do not address most leak situations and likely will shut off the water well after much of the damage has already occurred. Systems which utilize a suite of sensors throughout a building are complicated and expensive and often rely on specific input from a user to understand when conditions are normal and when a leak is occurring. 
     The present disclosure is directed to overcoming these and other problems of the prior art. 
     SUMMARY 
     In some embodiments, a computer-implemented method for detecting a leak in a water flow system is disclosed. The method includes connecting to a water monitoring device and a third-party system, receiving water flow data from the water monitoring device, receiving location data from the third-party system, determining a threshold parameter based on the location data, and comparing the water flow data to the threshold parameter in order to determine that there is a leak in the water flow system. 
     In other embodiments, a water monitoring system is disclosed. The water monitoring system includes a back-end system comprising an authentication module, a machine learning module, an alerts module, and a third-party module. The back-end system is configured to remotely connect to a local system comprising a water monitoring device and the authentication module is configured to authenticate the connection related to a user. The back-end system is configured to remotely connect to a third-party system using the third-party module and the authentication module is configured to authenticate the connection related to the same user. The back-end system receives data from the local system and the third-party system, the machine learning module is configured to analyze the data to determine one or more rules which identify water usage as intended or unintended, and the alerts module is configured to provide an alert instruction to one or more of the local system, the third-party system, or a client device when the water monitoring device measures water flow data which indicate a leak according to the one or more rules. 
     Additional features and advantages of the invention will be made apparent from the following detailed description of illustrative embodiments that proceeds with reference to the accompanying drawings. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The foregoing and other aspects of the present invention are best understood from the following detailed description when read in connection with the accompanying drawings. For the purpose of illustrating the invention, there are shown in the drawings embodiments that are presently preferred, it being understood, however, that the invention is not limited to the specific instrumentalities disclosed. Included in the drawings are the following Figures: 
         FIG. 1  is a schematic diagram of an exemplary water monitoring system, consistent with disclosed embodiments; 
         FIG. 2  is a schematic diagram of an exemplary back-end system of the water monitoring system of  FIG. 1 , consistent with disclosed embodiments; 
         FIG. 3  is a schematic diagram of an exemplary local system of the water monitoring system of  FIG. 1 , consistent with disclosed embodiments; 
         FIG. 4  is a schematic diagram of an exemplary water monitoring device of the local system of  FIG. 3 , consistent with disclosed embodiments; 
         FIG. 5  is an exploded view of an exemplary water monitoring device, consistent with disclosed embodiments; 
         FIG. 6  is a flowchart of an exemplary learning process, consistent with disclosed embodiments; 
         FIG. 7  is a flowchart of an exemplary learning process using third-party system integration, consistent with disclosed embodiments; 
         FIG. 8  is a flowchart of an exemplary alerting process, consistent with disclosed embodiments; 
         FIG. 9  is a flowchart of an exemplary alerting process using third-party system integration, consistent with disclosed embodiments; 
         FIG. 10  is an exemplary user interface which may be presented using a display, consistent with disclosed embodiments; 
         FIG. 11  is another exemplary user interface which may be presented using a display, consistent with disclosed embodiments; 
         FIG. 12  is another exemplary user interface using a display, consistent with disclosed embodiments; and 
         FIG. 13  is another exemplary user interface which may be presented using a display, consistent with disclosed embodiments. 
     
    
    
     DETAILED DESCRIPTION 
     The present disclosure describes a water leak detection device and an integration platform which allows the detection device to be compatible with other devices and incorporates collected data from all devices within the system, including third-party devices, in order to provide a comprehensive system which fits within an overall building automation scheme. The water leak detection device includes monitoring components which are configured to collect data related to a flow of water into or through a building, such as a residence or commercial structure, and computing components which are configured to establish rules for determining when a leak or other unintended water event occurs within the plumbing system. The integration platform provides a centralized data location for receiving collected data from a user&#39;s water leak detection device, other user&#39;s water leak detection devices, user client devices, and third-party devices and systems, thereby enabling the water leak detection device to easily integrate into a user&#39;s daily life by providing multiple communication options for learning from and alerting the user. The integration platform further enhances the data set which is available to the water leak detection device for setting rules which can be implemented to accurately identify leaks and other unwanted water flows and perform corrective actions accordingly. 
     The water leak detection device is connected to a back-end system which includes a specialized configuration which incorporates machine learning to determine the customized rules for the detection device to use in determining when a leak event occurs. The back-end system collects data from multiple different devices and sources and uses artificial intelligence in order to learn behavior patterns of the users and improve the algorithms that make decisions on system modes and corrective actions. 
     The water leak detection device incorporates an intuitive control system which is convenient to the user. The control panel is configured for providing information to the user and receiving input data. The control panel provides a connection point to the user which is conventionally not present in water meter systems and allows for customized input/output. The control panel eases manual control over the system, providing access to control one or more valves or water flow restriction mechanisms within the plumbing system. The control system further includes network compatibility which allows the control panel to be presented on one more client devices, such as personal computers or mobile devices such as smart phones and tablets. The back-end system may include features which provide a mobile application to the user&#39;s mobile device, thereby serving as the connection point and allowing the user to provide control input and receive information such as statistics and alerts. The integration with third-party systems provides yet another channel for communication with the user. 
     The water leak detection device and integration platform provide an efficient and integrated approach to alerting a user of a leak situation. For example, the integration platform at the local and back-end locations and connections to third-party systems provides several options for performing an action which ultimately helps to temper any damage caused by the leak. In one example, the integration with third-party systems allows an alert to be transmitted in a manner which maximizes the possibility that it will be viewed by the user. Examples include a message pushed to a smart phone, an audible alert played through a security system or smart speaker, or a phone call from an operator. Further, the integration of third-party systems allows for additional data, such as location data, to be considered when determining how to contact the user. For instance, if the user is at home, an alarm may sound, and if the user is away, a call or text message may be sent. These features can carry into other corrective actions, including shutting off the water, which is an action that may be automatically carried out in certain situations. 
     While the embodiments described herein are discussed in relationship to a plumbing system (also referred to herein as a water flow system), it should be understood that the disclosed systems, devices, and components are not limited to being used with water flow systems. The disclosed embodiments may be applicable to other fluid systems, such as natural gas systems, cooling systems, lubrication systems, vehicles, etc. 
       FIG. 1  is a schematic diagram of a water monitoring system  100 . In an exemplary embodiment, the water monitoring system  100  includes a back-end system  110 , a local system  120 , a client device  130 , a third-party system  140 , and a network  150 . As shown, the back-end system  110  may be a central computing system configured to connect to the local system  120 , client device  130 , and third-party system  140  either directly or through the network  150 . The network  150  is shown as a single network but can be multiple different networks facilitating connections between components of the water monitoring system  100 . For example, the back-end system  110  and local system  120  may be directly connected. 
     The back-end system  110  is a computing device which is configured to perform one or more monitoring functions through processing and data communication with the other components of the system  100 . The back-end system  110  may be a server that includes components, including processors, memory, databases, etc., which enable analysis, alerts, and action based on data received from the local system  120  (or other component of system  100 ) and through the transmission of data to the local system  120  (or other component of the system  100 ). 
     The local system  120  is associated with a building or structure  160  which includes a water flow system  170 . The water flow system  170  may be a network of pipes which deliver water to fixtures located throughout the interior and/or exterior of the building  160 . These include kitchen fixtures, bathroom fixtures, outdoor faucets, sprinkler systems, etc. The building  160  may be a residential or commercial building. The building  160  may represent a portion of an overall building, such as a townhome, apartment, or office space. The local system  120  is connected to the water flow system  170 , such as at a main water inlet  180 . 
     The local system  120  is a computing device which is configured to perform one or more monitoring functions through processing and data communication with the other components of the system  100 . The local system  120  may include a personal computing device, such as a laptop or desktop computer, or may be built into a separate device. The local system  120  preferably includes one or more processors and memory devices which enable the monitoring processes described herein. 
     The local system  120  is configured to collect water flow data from the water flow system  170 , such as flow rate, temperature, pressure, etc. The local system  120  is configured to provide the water flow data to the back-end system  110 . The local system  120  is also configured to perform actions based on the water flow data and other inputs. For example, the local system  120  may be configured to provide information to a control panel or the client device  130  to inform a user P. While the local system  120  is shown associated with a single building  160  and user P, it should be understood that a plurality of local systems associated with other buildings  190  may also be connected as part of the water monitoring system  100 . These additional local system provide additional data to the back-end system  110  which can be used in generating rules and features which are provided to the local system  120 . In other words, the back-end system  110  is configured to learn from the data and improve the connected systems over time. 
     The client device  130  is a computing device which is connected to the network  150  for communication with other devices in the water monitoring system  100 . The client device  130  may be, for example, a mobile device such as a smart phone or tablet. In other embodiments, the client device  130  may be a smart speaker, such as those provided by Amazon, Apple, or Google. In other embodiments, the client device  130  may be a laptop or desktop computer. In one aspect, the client device  130  is configured as a terminal to present information to the user P. For example, the client device  130  includes a screen which can provide information, such as measurements, usage statistics, and alerts, to the user P. In another aspect, the client device  130  is configured to collect data as input from the user P. This data may include input such as settings or responses to questions regarding water usage. In another aspect, the data may include data which is normally collected through third-party functions of the client device  130 . For example, the client device  130  may collect location data which is associated with the user P. In another example, the client device  130  may collect voice data based on captured speech of the user P. 
     The third-party system  140  is an additional computing device which is interconnected within the water monitoring system  100 . For example, the third-party system  140  may be associated with a third-party entity, such as an entity which provides a service to the user P and which collects data. In one embodiment, the third-party system  140  is associated with a virtual assistant service, such as Alexa® (Amazon), Siri® (Apple), Google Assistant®, or the like. The third-party system  140  may include a server or other computing device which receives data from a client device, such as the client device  130 . 
     One or more of the back-end system  110 , local system  120 , and third-party system  140  may include a mobile application which is provided to the user P through the client device  130 . The client device  130 , through the mobile application(s) provides an interface for the user P to control one or more aspects of the components of the water monitoring system  100 . The back-end system  110  preferably further includes a configuration which enables and eases integration of third-party services into the overall water monitoring system  100 . For example, the back-end system  110  may be configured with an open API which enables integration of third-party components and services within the same network  150 . In this way, the back-end system  110  is configured to augment the water monitoring services provided through the local system  120  with data and services which are associated with the third-party system  140 . 
     The network  150  includes one or more network connections which enable data transfer between one or more components of the water monitoring system  100 . For example, the network  150  may be the Internet, with each system within the water monitoring system  100  including an Internet connection device. The network  150  may also include one or more connections through Wi-Fi or Bluetooth™ which enable data transfer between components. In some embodiments, the network  150  may include one or more cloud-computing services, such as data storage and transfer. 
       FIG. 2  is a schematic diagram of an exemplary embodiment of the back-end system  110 . The back-end system  110  includes a processor  210 , memory  220 , one or more I/O devices  230 , one or more control modules  240 - 270 , and a data storage device  280 . The processor  210  and memory  220  may be specifically configured to implement the disclosed processes. The I/O device  230  may include, for example, communication components which enable the back-end system  110  to send and receive data to and from other components of the water monitoring system  100 . 
     The control modules  240 - 270  may include, for example, an authentication module  240 , a machine learning module  250 , an alerts module  260 , and a third-party module  270 . The control modules  240 - 270  may be implemented in hardware or software. For example, the control modules  240 - 270  may include software instructions which are stored in the memory  220  or in their own hardware component and which are executed by the processor  210  in order to carry out an associated process. The control modules  240 - 270  are exemplary and other control modules may be used in addition to or in place of the control modules described herein. Moreover, while the control modules  240 - 270  are depicted as separate components of the back-end system  110 , it should be understood that the control modules may be interconnected, combined, or located elsewhere within the water monitoring system  100 . 
     The authentication module  240  is configured to monitor and authenticate data communications between the back-end system  110  and other devices within the water monitoring system  100 . For example, the authentication module  240  is configured to provide an access token for each session between back-end system  110  and local system  120 , back-end system  110  and client device  130 , and back-end system  110  and third-party system  140 . The back-end system  110  stores information about the user P, including information which identifies the local system  120 , client device  130 , and any third-party accounts associated with the user P. The authentication module  240  is configured to store and recall a unique token to distinguish each device upon communication with the back-end system  110 , thereby associating multiple devices with a single user P and enabling the back-end system  110  to distinguish between them. 
     The machine learning module  250  is configured to analyze data received from the network  150  and identify patterns and generate rules which help to define behavior of the user P and the logic which will be used within the water monitoring system  100  for alerting the user P. For example, the machine learning module  250  may receive water usage statistics including amount of water usage and timing of water usage to determine patterns which define the normal usage of water by the user P at the building  160 . The machine learning module  250  may also consider data across multiple local systems associated with the plurality of buildings  190 . The machine learning module  250  is configured to use the usage data to determine rules for when water usage is likely to be planned and when it is indicative of a leak (e.g., pipe burst). The machine learning module  250  may also be configured to determine rules for different water usage modes. For example, the machine learning module  250  may determine when the local system  120  should be placed in a “home” or “away” mode. These modes may include different usage expectations, further refining the capability of the back-end system  110  to determine between genuine water usage and leaks. 
     The alerts module  260  is configured to provide a message or alert to one or more devices within the water monitoring system  100 . For example, the alerts module  260  may receive an indication that a major leak is occurring at the building  160  and route a message to the user P through the local system  120 , the client device  130 , and/or the third-party system  140 . The alerts module  260  is also configured to receive information back from local system  120 , the client device  130 , and/or the third-party system  140  which can be used to stop the alert. For example, the user P may provide input to the client device  130  which is delivered to the alerts module  260 . The input may indicate that the user P is not home and a corrective action, such as turning off the water, should be taken. The alerts can be customized and may depend on the situation. For example, the machine learning module  250  may cause the alerts module  260  to send different messages depending on the type of leak identified and the home/away status of the user P. 
     In an exemplary embodiment, the alerts module  260  is configured to determine an alert instruction depending on the leak situation. The alert instruction may include sending an alert to the user P through the client device P. In another example, the alert instruction may include performing a corrective action, such as by automatically shutting off the water through the local system  120 . 
     The third-party module  270  is configured to facilitate communication between the back-end system  110  and the third-party system  140 . In one embodiment, the third-party module  270  is an open API device which allows various third-party systems  140  to integrate with the back-end system  110 . The third-party module  270  is at least partly responsible for integrating the local system  120  into an overall home automation scheme which accomplishes several objectives. First, the integration of third-party systems  140  provides additional access to data which is relevant to the water monitoring system  100 . Second, the third-party module  270  allows the user P to interact with the back-end system  110  through existing automation tools, such as a smart speaker. In this way, the local system  120  and/or an associated device, becomes an additional automation tool or an Internet of Things device which is part of an open system. 
       FIG. 3  is a schematic diagram of an exemplary embodiment of the local system  120 . The local system  120  may include a water monitoring device  310  which has a local integration platform  315 . The local integration platform includes at least a processor  320 , a memory  330 , and I/O devices  340 . The water monitoring device  310  further includes one or more internal sensors  350  and one or more internal valves  360 . The local system  120  may also include one or more external sensors  370  and one or more external valves  380 . It should be understood that the depiction of the local system  120  is exemplary and that other arrangements are possible, including various connections between the components, integration of the components, or movement of the components elsewhere within the water monitoring system  100 . 
     The water monitoring device  310  is installed in the building  160  to monitor the water flow system  170  at the main water inlet  180 . The integration platform  315  includes computing components which are configured to connect the local system  120  to the network  150  and provide collected data to the back-end system  110  and connects the water monitoring device  310  to the other components of the water monitoring system  100 . The integration platform  315  thereby integrates the water monitoring device  310  into an overall automation scheme and thus can be viewed as an Internet of Things device which serves as a water monitoring data source. The processor  320  is configured to execute instructions stored in the memory  330  in order to perform one or more associated processes, such as determine when and what data to send to the back-end system  110 . The I/O devices  340  include an interface such as a touch screen which presents information to the user P. The I/O devices  340  also include data connection elements which facilitate data communication with the other components of the water monitoring system  100 . 
     The internal sensors  350  and internal valves  360  are mechanical components of the water monitoring device  310 . The internal sensors  350  may include, for example, flow rate, temperature, and pressure sensors which are configured to measure associated parameters at the main water inlet  180 . The internal valves  360  include one or more valves which are configured to control flow through the water flow system  170  at the main water inlet  180 . The internal valves  360  may include, for example, an automatic shut-off valve which is controlled by the water monitoring device  310  to cease the flow of water. The internal valves  360  may also include a manual shut-off valve which allows the user P to stop the water flow. 
     The external sensors  370  and external valves  380  are mechanical components of the local system  120  which may be located remote from the water monitoring device. The external sensors  370  could include one or more pressure, temperature, moisture sensors, or proximity sensors which are configured to detect a parameter somewhere in the building  160  other than at the main water inlet  180 . For example, a moisture sensor may be placed at a potential leak spot such that the water monitoring device  310  can be configured to detect a leak based on a reading from one of the external sensors  370 . The external valves  380  may include one or more remote valves located somewhere in the water flow system  170  other than the main water inlet  180 . The external valves  380  may be remotely controllable by the local integration platform  315  to turn off only a portion of the water flow in the water flow system  170 . For example, a leak may be detected by an external sensor  370  in a particular room of the building  160  and the water monitoring device  310  may close an external valve  380  associated with that particular room such that the water flow is stopped to the location of the leak, but the entire water flow system  170  is not shut off. 
     In embodiments in which the local system  120  is associated with a commercial building, such as an apartment or office building, the local system may include a plurality of water monitoring devices  310 , each at a separate location (e.g., to monitor separate apartments or offices). A central integration platform  315  may be connected to each of the water monitoring devices  310  and may collect data from each and transmit the data to the back-end system  110 . The integration platform  315  may include an authentication module which is configured to identify the particular water monitoring device  310  and store a token which associates a user P with the correct water monitoring device  310 . 
       FIG. 4  is a schematic diagram of an exemplary embodiment of the water monitoring device  310 . The water monitoring device  310  includes an exemplary embodiment of the local integration platform  315  and a suite of valve and board sensors  410 . The valve and board sensors  410  are connected in relation to a pipe  420  which makes up a portion of the water flow system  170  at the main water inlet  180 . The valve and board sensors  410  preferably include a small leak sensor  430 , a temperature sensor  440 , and a water flow sensor  450 . The small leak sensor  430  is preferably a pressure sensor configured to measure a pressure in the pipe  420 . The small leak sensor  430  is configured to monitor pressure values and provide the data to the local integration platform  315  for being provided to the back-end system  110  and/or for determining the presence of a leak within the water flow system  170 . In another embodiment, the small leak sensor  440  may be a vibration sensor. The vibration sensor may be configured to monitor the water flow system  170  for vibrations which may indicate the presence of a leak. The temperature sensor  440  and water flow sensor  450  are configured to measure associated data (e.g., temperature, flow rate) and provide the data to the local integration platform  315  and, in some cases, the back-end system  110 . The local integration platform  315  is configured to constantly update certain parameters of water flow based on readings from the sensors  430 - 450 . 
     The water monitoring device  310  also includes a valve power supply  460  and at least one valve  470 . The powered valve  470  is configured to cease flow by closing a portion of the pipe  420  at the location of the valve, based on a signal from the power supply  460 . The valve  470  may be, for example, a ball valve which is controlled based on an electric signal. The valve and board sensors  410  further include a controller  480  and memory  490  which are configured to provide signals to close and/or open the valve  460  based on communication with the local integration platform  315 . 
       FIG. 5  is an exploded view of an exemplary embodiment of the water monitoring device  310 . The water monitoring device  310  includes a housing  510  for enclosing the local integration platform  315  and the valve and board sensors  410 . The local integration platform  315  includes computing components including processors and memory  520 , a power source  530 , and a control panel  540 . In one embodiment, the power source  530  may include a power cord for connecting a building power supply (not shown) and/or a back-up battery that may provide power when the primary power source is not available (e.g., during a power outage). The housing  510  includes a window  545  for receiving the control panel  540  and allowing a user P to view and interact with the control panel  540 . 
     In the illustrated exemplary embodiment, the valve and board sensors  410  include flow sensor  550 , pressure sensor  552 , and temperature sensor  554 . Additional components may include, for example, a real time clock  556  and a siren/alarm  558 . The valve and board sensors  410  also include valves for controlling the flow of water through the water monitoring device  310 . The valves include, for example, an automatic shut-off valve  560  and a manual shut-off valve  570 . 
     The water monitoring device  310  further includes a connection element  580 . The connection element  580  includes a pipe section which is configured to be installed into the water flow system  170 . The connection element  580  includes an inlet section and an outlet section which are inserted into the water flow system  170  downstream of the main water inlet  180  such that water flows into the inlet section from the main water inlet  180  and out of the outlet section to the rest of the building  160 . The valve and board sensors  410  are located in and around the connection element  580  in a manner to collect relevant data, such as temperature, pressure, flow rate, vibration, etc (e.g., via sensors  550 ,  552 , and  554 ). The automatic shut-off valve  560  includes a powered valve which is controlled by the local integration platform  315  to open and close at selected times. The housing  510  preferably includes a handle  590  which is connected to the manual shut-off valve  570  which allows the user P to manually turn off the water at the location of the water monitoring device  310 . 
       FIG. 6  is a flowchart of an exemplary learning process  600  for training the back-end system  110  for alerting the user P and controlling the automatic shut-off valve  560  via the water monitoring device  310 . The back-end system  110  performs one or more steps of the learning process  600 , such as by the processor  210  executing instructions stored in the memory  220 . In some embodiments, the modules  240 - 270  or one or more other modules may include software or hardware which is incorporated into the learning process  600 . 
     In step  610 , the back-end system  110  collects data. The local system  120  receives data and transmits the data to the back-end system  110 . In one example, this data includes water monitoring data from the water monitoring device  310 . The water monitoring data includes, for example, water flow statistics gathered by the sensors  430 ,  440 , and  450 . In an embodiment, the back-end system  110  receives a water usage map which includes detailed water use and the associated time of the use, including time, date, day of the week, etc. The back-end system  110  may also collect location data associated with the user P, such as location data collected by the local system  120 . 
     In step  620 , the back-end system  110  receives input setting data. The local system  120  may receive input setting data as input from the user P and provide the data to the back-end system  110 . The input setting data includes information which further provides indicators of normal water usage within the building  160 . For example, the input setting data may include a normal work schedule of the user P such that the back-end system  110  is apprised of when to expect the user P to be home. In another example, the input setting data includes a schedule for normal water use, such as the timing of an irrigation system (e.g., sprinkler system). In another example, the input setting data may include custom water leak thresholds. For example, the user P may input thresholds which represent unintended use of water within the building  160 . In some embodiments, the thresholds may include a “home” mode threshold and an “away” mode threshold. 
     In step  630 , the back-end system  110  requests and receives additional information. The additional information may include further information about normal water usage. For example, the back-end system  110  may identify a water usage event and request additional information, such as whether the water usage event is a normally scheduled occurrence or whether it was a one-off event. The back-end system  110  may provide the request for additional information to the local system  120  and/or directly to the client device  130  such that the user P can provide the requested information. 
     In step  640 , the back-end system  110  analyzes the information received in steps  610 - 630  and determines and/or updates rules associated with normal water usage. For example, the back-end system  110  may identify certain water flow thresholds which would fall outside of normal water use for the particular building  160 . These thresholds may vary depending on the time of day, day of the week, month, etc. The thresholds may additionally or alternatively depend on the location of the user P. For example, if the user P is not at the building  160 , the threshold for normal water use may be lower than when the user P is present. 
     The learning process  600  may take place after an initial installation of a water monitoring device  310  to thereby allow the back-end system  110  to learn about the habits of the user P (and any other associated users). The machine learning module  250  may include one or more software processes which are carried out to generate information requests (step  630 ) and water value thresholds (step  640 ) which are used as rules for determining when a water usage is intended and when it represents an unwanted use, such as a burst pipe. These rules set parameters for when the back-end system  110  and/or local system  120  should provide an alert to the user P and/or should take a corrective action such as shutting off the water by way of the water monitoring device  310 . The learning process  600  may continuously repeat by gathering information from the various components in the water monitoring system  100  (steps  610 - 630 ) and updating the rules based on the collected data and machine learning algorithms (step  640 ). 
       FIG. 7  is a flowchart of another exemplary process  700  for the back-end system  110  to perform machine learning to further establish rules for the water monitoring device  310 . The back-end system  110  may perform one or more steps of process  700  in order to further refine the rules which define actions which are taken by the back-end system  110  and the local system  120 . In some aspects, the process  700  is part of the integration platform of the water monitoring system  100  in that the process  700  includes steps for allowing the third-party system  130  to provide information which is used by the back-end system  110  to control the water monitoring device  310 . 
     In step  710 , the back-end system  110  connects to the third-party system  140 . This may include the back-end system  110  registering an account with the third-party system  140  and performing an authentication with the authentication module  240  in order to associate the third-party account of the user P with their back-end system account. The third-party system  140  may be associated with an automation service, such as those provided by Amazon, Apple, Google, etc. The third-party system  140  may register with the back-end system through the third-party module  270  (e.g., via an open API) in order to integrate third-party services into the back-end system  110 . 
     In step  720 , the back-end system  110  receives data from the third-party system  140 . For example, the back-end system  110  may receive location data from a third-party system  140  with access to location data. In one example, the third-party system  140  is associated with a security system which monitors a status of the building  160 . The security system may track location data which includes whether the user P is at the building  160  or away (i.e., depending on an armed/unarmed status of the security system). In another example, the third-party system  140  may be associated with a location-based service, a feature which has become common with many mobile applications. The third-party system  140  may receive location data associated with the user P based on the location of the client device  130  which is running the associated application. In yet another example, the third-party system  140  is associated with a smart speaker which includes a microphone and is configured to listen for speech within the home to determine whether the user P (or another individual) is present. The location data can also be mapped according to the time, date, and day of the week, indicating when the user P is home and when the user is away. 
     In step  730 , the back-end system  110  updates the rules associated with a local system  120  based on information received from the third-party system  140 . For example, the back-end system  110  (e.g., through the machine learning module  250 ) may modify an expected schedule of when the user is home or away and may match the location data to water usage. This information further provides the back-end system  110  with data which shows patterns of usage of water within an associated building  160 . For example, the back-end system  110  may determine that water usage never exceeds a certain threshold when the user is away from the building  160  and set the threshold for an “away” mode accordingly. 
     In step  740 , the back-end system  110  is configured to set a status for the water monitoring device  310 . For example, the machine learning module  250  may review location information to determine whether the water monitoring device  310  should enter a “home” mode or an “away” mode. Each mode may include different rules or thresholds which indicate a particular action that should be taken. For example, when the water monitoring device  310  is in a “home” mode, the thresholds for normal water use are much higher than the thresholds associated with the “away” mode. 
     In step  750 , the back-end system  110  provides the update rules and status to the water monitoring device  310 . The water monitoring device  310  is thereby continuously updated with rules and a current home/away status through machine learning which accounts for information which is not directly determined by the water monitoring device  310 . In particular, data collected through third-party systems  140  supplements the data and rules from the learning process  600  in order to provide a more comprehensive artificial intelligence scheme which more accurately assesses and identifies water usage. 
       FIG. 8  is a flowchart of an exemplary alert process  800 , consistent with disclosed embodiments. In some embodiments, the local system  120  may perform one or more steps of the process  800  in order to provide an alert to a user. The process  800  may be performed based on rules or control logic which is stored by the local integration platform  315  and which are received from the back-end system  110 . In other embodiments, the back-end system  110  may perform one or steps of the process  800 , such as in situations in which the rules or control logic is stored by the back-end system  110 . 
     In step  810 , the water monitoring device  310  collects water flow data through the sensors (e.g., sensors  430 ,  440 ,  450 ). The water flow data includes one or more of flow rate, temperature, pressure, and vibration measurements which are taken periodically over time. The water monitoring device  310  collects the data and, in step  820  provides the data to the back-end system  110  through the local integration platform  315 . The back-end system  110  receives the data, updates rules through the machine learning processes (e.g., process  600 ), and provides the updated rules back to the water monitoring device  310 . 
     In step  830 , the water monitoring device  310  further performs a check according to the current rules or control logic in order to detect a leak. For example, the water monitoring device  310  may compare a pressure or flow rate measurement to a threshold to determine whether the water usage is likely unintended and thus a leak is occurring. The threshold may be based on the rules and therefore may depend on factors such as day of the week, date, time, home/away status, in order to accurately represent water usage as expected or unexpected. 
     In another example, the water monitoring device  310  may identify a leak, such as a small leak, based on a stored pattern associated with such a leak. For example, a very small flow rate over a long period of time may be associated with a small leak. In another example, the water monitoring device  310  may be configured to identify vibration patterns within the water flow system  170  to identify a leak. 
     In step  840 , the water monitoring device  310  initiates an alert to notify the user P. For example, the water monitoring device  310  may identify an leak and cause an alert instruction to be sent to the client device  130 , thereby alerting the user P of the situation. The alert instruction may depend on the leak which is identified (e.g., based on the severity of the situation). For a large leak (e.g., burst pipe), the water monitoring device  310  may send a push notification (e.g., through a mobile application) which notifies the user P immediately. Other options for alerting the user P include text message, email, phone call, etc. For a small leak, the water monitoring device  310  may only present a notification locally on an interface, such as the control panel  540 . In another embodiment, the water monitoring device  310  may sound an alarm as an audible alert. 
     While the water monitoring device  310  is described as alerting the user P, it should be understood that any of the components of the water monitoring system  100  may cause the alert to be provided to the user P (e.g., through the client device  130 ). For example, the back-end system  110  may identify the leak based on the data received in step  830 . The back-end system  110  may additionally or alternatively perform a leak detection analysis (e.g., step  830 ) to identify abnormal water usage and provide an alert to the client device  130 . 
     The integration platform  315  or back-end system  110  may generate the alert instruction based on a status mode of the water monitoring device  310 . For example, if the water monitoring device  310  is in an “away” mode, the alert instruction may be delivered to a client device  130  which is likely to be with the user P, such as a smart phone. In addition, the alert instruction may be delivered to a third-party system  140 , such as a third-party which may be capable of contacting the user P and/or dispatching a remediation team to the building  160 . If the water monitoring device  310  is in a “home” mode, the alert instruction may include a message to a client device  130  which is likely to be in range of the user P, such as a smart speaker or security system. 
     In step  850 , the water monitoring device  310  performs a corrective action in certain situations. For example, when a major leak is detected (e.g., burst pipe, leak for a long period of time), the water monitoring device  310  may determine that the water should be shut-off to minimize water damage from the potential leak. The water monitoring device  310  may use the stored rules or control logic to determine which leaks identified in step  840  are associated with a corrective action and perform the corrective action when needed. For example, the water monitoring device  310  may send a signal to close the automatic shut-off valve  560 . 
     While the corrective action may be automatic based on the control logic, in other embodiments or instances the corrective action may be performed based on input from the user P. For example, after receiving a notification that a leak is occurring, the user P may enter an instruction to turn off the water. This may include the client device  130  receiving the input instruction and transmitting the instruction to the water monitoring device  310 , either directly or through one or more of the back-end system  110  and third-party system  140 . In another example, the user P may provide input directly to the water monitoring device  130 , such as through the control panel  540 . 
     The water integration platform  315  and/or the back-end system  110  is also configured to determine whether to perform a corrective action based on the alert instruction from step  840 . For example, some alert instructions may include an associated corrective action, such as an automatic water shut-off. In this way, the corrective action may depend on certain conditions, such as the “home” or “away” status of the water monitoring device  130 . 
       FIG. 9  is a flowchart of an exemplary process  900  for providing an alert to the user through an integrated third-party system  140 . The process  900  may be used in addition to or alternative to the process  800  and may be a specific embodiment of the process  800 . The water monitoring device  310  (e.g., through the local integration platform  315 ) may perform one or more steps of the process  900  to communicate with the user P through a client device  130  associated with a third-party system  140 . 
     In step  910 , the water monitoring device  310  identifies abnormal water usage. For example, the water monitoring device  310  may compare water flow data to stored rules or control logic to determine whether a leak is occurring. The water monitoring device  310  may identify a leak through one or more of the leak detection processes described herein. For example, the small leak sensor  430  may detect a water pressure or vibration pattern which indicates a leak has occurred. 
     In some embodiments, the water monitoring device  310  may be connected to a third-party system  140  capable of detecting a leak or other abnormal water usage and communicating the leak to the local integration platform  315  through the network  150 . In one example, the local system  120  may include external sensors  370  which are associated with a third-party service or entity. These sensors  370  may include moisture sensors, flow sensors, pressure sensors, etc. which are located remote from the water monitoring device  310  and which are configured to send signals to the water monitoring device  310  in order to identify leak conditions. These local integration platform  315  may include one or more components which facilitate a connection with the external sensors  370 , such as Wi-Fi or Bluetooth™ connections. In some embodiments, the third-party devices (e.g., external sensors  370 ) may collect data independently and transmit the data to the third-party system  140 . The third-party system  140  may provide the data to the back-end system  110  which in turn communicates with the local system  120 . 
     In step  920 , the water monitoring device  310  categorizes the problem. For example, water monitoring device  310  may determine whether the detected abnormal water usage represents a small leak or a major leak, as these situations may be handled differently. In step  930 , the water monitoring device  310  determines a protocol for alerting the user P based on the categorized leak. For example, if the leak is a small leak, a text message or similar alert may be delivered to the client device  130 . For larger leaks, such as a burst pipe or unattended running faucet, a third-party system  140  may be involved to provide a more comprehensive alert and/or remedy to the situation. 
     In step  940 , a message is pushed through the third-party system  140 . For example, the water monitoring device  310  may determine that a major leak is occurring and perform a process through the local integration platform  315  that provides a message to a user P through a third-party system  140  or associated service. In one example, the local integration platform  315  may include connections (e.g., through the back-end system  110 ) to a third-party security system. The local integration platform  315  may cause a message to be sent to the third-party system  140  associated with the security system, thereby notifying an entity monitoring the status of the building  160 . In another example, the third-party system  140  may be associated with a home automation system. The local integration platform may cause a message to be sent through the home automation system such that the user P may receive water usage alerts in a centralized location with other home automation alerts. 
     It should be understood that the third-party integration messaging is not limited to any particular leaks (e.g., major leaks) and can be used to integrate the water monitoring device  310  and the associated messages into any third-party system. For example, the local integration platform  315  may push a message through a third-party system to alert a user using a client device  130  such as a smart speaker. The smart speaker may produce an audible message which alerts the user P of the small leak and the location, if known. 
     The present disclosure includes one or more systems which provide a water monitoring device for monitoring water usage within a building. The water monitoring system includes one or more components which are configured to collect data from multiple sources, including different water monitoring devices as well as third-party devices, in order to perform machine learning processes in order to generate rules or control logic which accurately represent water flow as either intended or unintended. The integration of third-party devices through an integration platform enables the overall water monitoring system to collect additional data which is useful in assessing water flow, as well as providing additional routes for alerting and/or otherwise communicating with a user (e.g., through a client device). The overall system fits into an overall home automation scheme and provides an additional resource to the user, monitoring the water usage, providing feedback, and taking corrective actions where necessary to avoid unnecessary damage and expense due to unintended water usage. 
       FIGS. 10-13  are exemplary user interfaces  1000 ,  1100 ,  1200 , and  1300  which may be implemented through the back-end system  110  and displayed to a user P through the client device  130 . In one embodiment, interfaces  1000  and  1100  include an exemplary web browser arrangement. Interfaces  1200  and  1300  are suitable for the control panel  540  or a mobile application which is presented through the client device  130 . 
     As shown in  FIG. 10 , the user interface  1000  includes a dashboard section which provides some information about the status of the water monitoring device  310  and the associated water flow. The information may include, for example, the current water flow, past water flow, status of the automatic shut-off valve, status of whether a leak is detected, “home” or “away” mode status, and water temperature (current and past temperature). This information provides a snapshot of the status of the water flow and provides feedback to the user which is not present in a conventional water meter. As shown in  FIG. 11 , the user interface  1100  includes an input settings interface which enables a user P to provide information. For example, the user interface  1100  includes an option for the user P to input customized thresholds for detecting a leak. The user interface  1100  also includes input fields for connecting the water monitoring device  310  to the network  150 , such as a Wi-Fi network. As shown in  FIGS. 12-13 , the user interfaces  1200  and  1300  include another representation of the dashboard and include flow rate, current usage, leak detection, valve status, and “home” or “away” status. 
     The embodiments of the present disclosure may be implemented with any combination of hardware and software. For example, computing platforms (e.g., servers, desktop computer, etc.) may be specially configured to perform the techniques discussed herein. 
     In addition, the embodiments of the present disclosure may be included in an article of manufacture (e.g., one or more computer program products) having, for example, computer-readable, non-transitory media. The media may have embodied therein computer readable program code for providing and facilitating the mechanisms of the embodiments of the present disclosure. The article of manufacture can be included as part of a computer system or sold separately. 
     While various aspects and embodiments have been disclosed herein, other aspects and embodiments will be apparent to those skilled in the art. The various aspects and embodiments disclosed herein are for purposes of illustration and are not intended to be limiting, with the true scope and spirit being indicated by the following claims. The system and processes of the figures are not exclusive. Other systems, processes and interfaces may be derived in accordance with the principles of the invention to accomplish the same objectives. Although this invention has been described with reference to particular embodiments, it is to be understood that the embodiments and variations shown and described herein are for illustration purposes only. Modifications to the current design may be implemented by those skilled in the art, without departing from the scope of the invention.