Control of a household appliance

Abstract

The present disclosure relates to household appliances. For example, a method of enabling the control of a household appliance is proposed. The method may, in some implementations, comprise determining (320) a user behaviour which is common to multiple users, where the multiple users are associated with respective household appliances located within a common geographic area, and transmitting (330) a message to instruct a certain household appliance to be controlled based on the determined user behaviour common to the multiple users.

Description

[0001] CONTROL OF A HOUSEHOLD APPLIANCE

[0002] Technical Field

[0003] The present disclosure relates to household appliances. More specifically, the present disclosure for example relates to methods, corresponding apparatuses (e.g., servers) and computer programs for controlling and operating household appliances.

[0004] Background

[0005] With the advance of Internet-of-Things, it is becoming increasingly popular to include network communication capability in household appliances and to establish smart homes with several communicatively connected household appliances. In smart homes, there is a general need to provide automatic control of the operation of the various household appliances. In the existing prior art, attempts have therefore been made, e.g., by household appliance manufacturers to improve the automatic control of household appliances.

[0006] Summary

[0007] It is in view of the above considerations and others that the various embodiments of the present invention have been made. The present disclosure recognizes that there is a need for continued improvements of the existing art described above.

[0008] A general object of the present disclosure is therefore to provide improved control and operation of a household appliance.

[0009] According to a first aspect, a computer-implemented method of enabling the control (and / or operation) of a household appliance is provided. The computer- implemented method comprises: determining a user behaviour common to multiple users, the multiple users being associated with respective household appliances located within a common geographic area; and transmitting a message to instruct a household appliance, located in the common geographical area, to be controlled and operated based on the determined user behaviour of said multiple users. In some embodiments, the user behaviour common to the multiple users may relate to how the respective household appliances are being used at a particular time or in a particular time period. The particular time or time period may be a current time or current time period. The user behaviour common to the multiple users may relate to how the respective household appliances are being used at the particular time or in the particular time period in the sense that it relates to whether or not the appliances are being used and / or whether or not the appliances are being operated in a particular manner at the given time or within the time period. The user behaviour common to multiple users may relate to turning on or off a household appliance, increasing or decreasing an operating parameter of the household appliance and / or performing a specific activity on the household appliance at the time or in the time period.

[0010] The user behaviour common to the multiple appliances may relate to the multiple users using their respective household appliances at the time or in the time period and transmitting a message to instruct a household appliance may comprise transmitting a message to instruct a household appliance that is not being used at the time or in the time period.

[0011] The user behaviour common to the multiple appliances may relate to the multiple users operating their respective household appliances to perform an activity at the time or in the time period and transmitting a message to instruct a household appliance may comprise transmitting a message to instruct a household appliance that is not being operated to perform that activity at the time or in the time period. The user behaviour common to the multiple appliances may relate to appliance cleaning activity within a time period following a holiday. Alternatively, the user behaviour common to the multiple appliances may relate to increasing or decreasing an operating parameter to improve air quality or decrease environmental air temperature.

[0012] The user behaviour common to the multiple appliances may relate to the multiple users not using their respective household appliances at the time or in the time period. The household appliances may be washing machines and the message may indicate the possibility of bad weather.

[0013] In some embodiments, the computer-implemented method may comprise, responsive to the user behaviour common to multiple users, transmitting said message to a user terminal associated with a user of a household appliance to prompt the user to control (and / or operate) said household appliance through a user interface of the user terminal or the household appliance.

[0014] In some embodiments, the computer-implemented method may comprise, responsive to said determining the user behaviour common to multiple users, transmitting said message to the user terminal to prompt the user to instruct the user terminal to transmit a control message to said household appliance for controlling said household appliance based on the determined user behaviour of said multiple users.

[0015] In some embodiments, the computer-implemented method may comprise: obtaining data related to the common geographic area; and collecting, or otherwise obtaining, data related to the user behaviour of multiple users associated the common geographic area of said obtained data. In certain embodiments, the computer- implemented method may comprise: determining Internet Protocol (IP) addresses associated with respective household appliances within a defined geographic area; and collecting, or otherwise obtaining, data related to the user behaviour of multiple users associated with the household appliances associated with said obtained IP addresses.

[0016] As used throughout this disclosure, the IP address referred to can be any IP associated with the home appliance exposed to the server. It may for example be the IP address of a router such as a Wi-Fi router to which the appliance is connected. The public IP address of a home router provides a good estimation of the geographic area in which the household appliance(s) in question is / are located, or placed.

[0017] For example, collecting, or otherwise obtaining, data related to the user behaviour of multiple users may comprise: collecting, or otherwise obtaining, historical data related to operational settings used by the household appliances during past operations.

[0018] Additionally, or alternatively, collecting, or otherwise obtaining, data related to the user behaviour of multiple users may comprise: collecting, or otherwise obtaining, current data related to operational settings used by the household appliances during current operations. In some embodiments, the method further comprises determining the location of the appliance based on an IP address associated with the appliance.

[0019] In some embodiments, the method may further comprise receiving a message from said household appliance, identifying the IP address associated with said household appliance by analysing said message, determining a geographic area corresponding to said IP address associated with said household appliance, wherein the common geographic area corresponds to said determined geographic area and wherein transmitting a message to instruct a household appliance to be controlled based on the determined user behaviour common to said multiple users is performed following determination of the geographic area corresponding to said IP address associated with the household appliance.

[0020] According to a second aspect, a server is provided. The server comprises at least one processor; and at least one memory, wherein the at least one memory comprises instructions executable by the at least one processor whereby the server is operative to perform the method according to the first aspect.

[0021] According to a third aspect, a computer program is provided. The computer program comprises instructions which, when executed on at least one processor, cause the at least one processor to carry out the method according to the first aspect. A carrier comprising the computer program of the third aspect is also provided. The carrier may be any one of an electronic signal, an optical signal, a radio signal, or a computer- readable medium.

[0022] According to a fourth aspect, a method of operating a household appliance, the method comprising: causing a message to be sent, over one or more networks, to a server, the message indicating a geographical area associated with the household appliance; receiving instructions to control the household appliance based on a determined user behaviour common to multiple users associated with respective household located within a common geographical area corresponding to said geographical area; and in response thereto controlling the household appliance accordingly. The user behaviour common to the multiple users may relate to whether or not the appliances are being used and / or whether or not the appliances are being operated in a particular manner at a given time or within a given time period.

[0023] According to a fifth aspect, a household appliance is provided. The household appliance comprises: at least one processor; and at least one memory, wherein the at least one memory comprises instructions executable by the at least one processor whereby household appliance is operative to perform the method according to the fourth aspect.

[0024] According to a sixth aspect, a computer program is provided. The computer program comprises instructions which, when executed on at least one processor, cause the at least one processor to carry out the method according to the fourth aspect. A carrier comprising the computer program of the sixth aspect is also provided. The carrier may be any one of an electronic signal, an optical signal, a radio signal, or a computer-readable medium.

[0025] According to a seventh aspect, a computer-implemented method of enabling the control (and / or operation) of a household appliance is provided. The computer- implemented method comprises: determining a user behaviour common to multiple users, the multiple users being associated with respective household appliances located within a common geographic area; and transmitting a message to instruct a household appliance, located in the common geographical area, to be controlled and operated based on the determined user behaviour of said multiple users, wherein the household appliance is determined to be located in the common geographical area based on an IP address associated with the appliance.

[0026] In some embodiments, the computer-implemented method may comprise, responsive to determining the user behaviour common to multiple users, transmitting said message to a user terminal associated with a user of a household appliance to prompt the user to control (and / or operate) said household appliance through a user interface of the user terminal or the household appliance or prompt the user to instruct the user terminal to transmit a control message to said household appliance for controlling said household appliance based on the determined user behaviour of said multiple users. In some embodiments, the computer-implemented method may comprise: determining Internet Protocol (IP) addresses associated with respective household appliances within a defined geographic area; and collecting, or otherwise obtaining, data related to the user behaviour of multiple users associated with the household appliances associated with said obtained IP addresses.

[0027] In some embodiments, the user behaviour common to the multiple users comprises user behaviour relate to setting of hard water settings.

[0028] In some embodiments, the computer-implemented method may further comprise: identifying n number of geographic areas, e.g., within an initial wide area; collecting, or otherwise obtaining, data related to water hardness of each one of the identified n number of geographic areas; and categorizing the identified n number of geographic areas into different water hardness classes depending on the water hardness. For example, said common geographic area may be a geographic area, which share the same water hardness class.

[0029] For example, the different water hardness classes may comprise: (a) Hard Water (HW) area; (b) Medium Hard Water (MHW) area; (c) Medium Soft Water (MSW) area; and (d) Soft Water (SW) area.

[0030] In some embodiments, categorizing the identified n number of geographic areas into different water hardness classes may include applying an unsupervised machine learning (ML) algorithm. For example, the unsupervised ML algorithm may include / <- means clustering.

[0031] In some embodiments, transmitting the message comprises transmitting a message with a recommendation or instruction to set a water hardness setting based on the determined user behaviour common to the multiple users.

[0032] In some embodiments, the method further comprises determining the location of the appliance based on an IP address associated with the appliance.

[0033] In some embodiments, the method may further comprise receiving a message from said household appliance, identifying an IP address associated with said household appliance by analysing said message, determining a geographic area corresponding to said IP address associated with said household appliance, wherein the common geographic area corresponds to said determined geographic area and wherein transmitting a message to instruct a household appliance to be controlled based on the determined user behaviour common to said multiple users is performed following determination of the geographic area corresponding to said IP address associated with the household appliance.

[0034] According to an eight aspect, a server is provided. The server comprises at least one processor; and at least one memory, wherein the at least one memory comprises instructions executable by the at least one processor whereby the server is operative to perform the method according to the seventh aspect.

[0035] According to a ninth aspect, a computer program is provided. The computer program comprises instructions which, when executed on at least one processor, cause the at least one processor to carry out the method according to the seventh aspect.

[0036] The various aspects and embodiments described throughout this disclosure allow for improved control (and / or operation) of a household appliance. For example, by controlling a household appliance based on user behaviour in the proposed way it is made possible to achieve improved quality of operations, energy savings and / or increased life span of the household appliances. As a bonus, some implementations allow for the identification of patterns and trends in user behaviour, leading to improved user experience and personalized recommendations. In turn, this may benefit the user’s experience when using his or her household appliances.

[0037] Brief Description of the Drawings

[0038] These and other aspects, features and advantages will be apparent and elucidated from the following description of various embodiments, reference being made to the accompanying drawings, in which:

[0039] Fig. 1 is a block diagram illustrating an environment with one or more household appliances communicatively connected to a server via a network;

[0040] Fig. 2 is flowchart illustrating a method of controlling and operating a household appliance, in accordance with some embodiments;

[0041] Fig. 3 is a signalling diagram illustrating an example of signalling between various components of the system in Fig. 1; Fig. 4 is a flowchart illustrating another method of controlling and operating a household appliance, in accordance with some embodiments.

[0042] Fig. 5 is a block diagram illustrating a server, in accordance with some implementations; and

[0043] Fig. 6 is a block diagram illustrating a household appliance, in accordance with some implementations.

[0044] Detailed Description of Embodiments

[0045] The present invention will now be described more fully hereinafter. The invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided by way of example so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those persons skilled in the art. Like reference numbers refer to like elements throughout the description.

[0046] As described earlier, some existing solutions for controlling and operating a household appliance may be perceived as inadequate by some users. It is therefore a general object of embodiments described herein to allow for improved control (and / or operation) of a household appliance.

[0047] To address this, and in accordance with an aspect, described herein is a method for enabling the control (and / or operation) of a household appliance. A past and / or current user behaviour common to multiple (i.e., two or more) users is determined. The multiple users are associated with respective household appliances located, or otherwise placed, within a common geographic area. Furthermore, the household appliance to be controlled and operated can be controlled and operated based on the determined past and / or current user behaviour of said multiple users. Specifically, appropriate settings and processes for the household appliance can be predicted, or determined, based on the determined past and / or current user behaviour of several users associated with respective household appliances located at a common geographic area and, then, the household appliance in question can be controlled and operated accordingly.

[0048] By controlling a household appliance based on past and / or current user behaviour in the proposed way it is made possible to achieve at least one out of improved quality of operations, energy savings and increased life span of the household appliances. As a bonus effect, some implementations allow for the identification of patterns and trends in user behaviour, leading to improved user experience and personalized recommendations. In turn, this may benefit the user’s experience when using his or her household appliances.

[0049] Reference is now made to Fig. 1, which illustrates an example system 100 in accordance with some aspects of the present invention. A user environment 110, which may be embodied as a smart home, comprises one or several household appliances 112. Furthermore, one or several user terminals 114 are operable to control the operation of the one or several household appliances 112. The user terminal(s) 114 may be smartphone(s) (e.g., mobile telephone(s)) or a tablet computer(s). However, they could also be desktop computer(s) or other electronic device(s).

[0050] Also, a server 130 is operable to communicate with the one or several household appliances, e.g. via network 120. The network 120 may include any of a variety of networks, including a local area network (LAN), wide area networks (WAN), personal area networks (PAN), wireless networks, wired networks, the Internet, or a combination of such networks. A Wi-Fi router 140, including a modem, may be provided in the network to route the communication between the appliance and the server. The router may implement a wireless access point for communicating with devices in the household, including the household appliance, using for example Wi-Fi technology, and a modem for communicating with a public network, such as the Internet. As a specific example, the one or more networks may include a Wi-Fi LAN network, in the user environment, and the Internet and the network may also include a Wi-Fi router. In other implementations, the network may include, additionally or alternatively, a home hub and / or one or more Bluetooth networks, such as a Bluetooth Lower Energy (BLE) network.

[0051] Reference is now made to Fig. 2, which shows a flow chart illustrating an example of a method 200 of enabling the control (and / or operation) of a household appliance 112. The method 200 is computer-implemented. In some implementation, the method 200 is performed by, or otherwise executed in, server 130. In the example described in conjunction with Fig. 2, the household appliance is exemplified as a dishwasher or a washing machine.

[0052] Action 210: A past user behaviour common to multiple (i.e., several) users is determined. Said multiple users are associated with respective household appliances located within a common geographic area.

[0053] Action 212: In some embodiments, to determine the user behaviour common to multiple users, data related to the common geographic area is obtained. For instance, in some implementations Internet Protocol (IP) addresses associated with respective household appliances are obtained, since IP addresses may be indicative of the geographic area. Based on the IP addresses, the household appliances located within a common geographical area can then be determined. For example, in some implementations the geographic area indicated by the IP address can be retrieved from a look-up table or similar. The IP address associated with the household appliance can be any IP associated with the home appliance exposed to the server. In some implementations it is the IP address of a home router, for example a Wi-Fi router as shown in Figure 1. Specifically, the IP address associated with the appliance may be the public IP address of a router connected to the appliance. The router may for example using Network Address Translation (NAT), as in known to the skilled person, to map private IP addresses of the devices in the household to the public IP address of the router and to replace the source or destination IP address, based on the mapping, in packets exchanged between the appliance and the server. Depending on the communication technology used it could alternatively be the IP address of the household appliance, for example an Intranet IP address or a public IP address if the household appliance is directly connected to a telecommunication network. The IP address of the router, or appliance, provides a good estimation of the geographic area in which the household appliance(s) in question is / are located, or placed.

[0054] Action 214: Further, in some embodiments, to determine the user behaviour common to multiple users, data related to the past user behaviour of multiple users associated with the common geographic area is collected, or otherwise obtained. For instance, in some implementations, data related to the past user behaviour of multiple users associated with the household appliances determined to be located within a common geographical area based on their associated IP addresses is collected. Behaviours common to that geographical area can then be determined from the collected data.

[0055] As discussed above, IP addresses (e.g. of routers associated with the household appliance(s) in question) may be used in some implementations to establish or otherwise determine the past user behaviour of users common to a same or similar geographic are. In other implementations, it is for example possible or conceivable to use postal codes or regional telephone codes indicative of a geographic area. As will be appreciated, as used herein the expression “geographic area” should be understood in broad sense. That is, it is sufficient if the obtained IP addresses (or the postal codes, or the regional telephone codes) coarsely indicate a geographic area.

[0056] As discussed above, the method 200 involves determining a past user behaviour common to several users, the several users being associated with respective household appliances of a common geographic area. The user behaviour may be past user behaviour in the sense that the settings have already been set on the other appliances or the appliances have already been instructed to do something. However, the settings or processes may also be current settings or processes. For example, the settings may have been set in the very recent past. Some of the processes may not even have finished or may be about to start if a start time has been set in the future. As such, the method may involve collecting, or otherwise obtaining, current data related to operational settings used by the household appliances during current operations. Additionally or alternatively, action 214 involves collecting, or otherwise obtaining, historical data related to operational settings used by the household appliances during past operations. This historical data may not relate to current data.

[0057] In some implementations, the behaviour of the multiple users may not be identical. Instead, the behaviour may be common in the sense that it is representative of the multiple users. For example, it could be an average setting or value for the users or a typical or similar behaviour. In other implementations, determining the common behaviour would comprise determining a behaviour that is the common in the sense that it is the same, or at least similar, for the multiple users. As a specific example of obtaining data related to geographical area, the server could gather one or more out of IP addresses, telephone number and postcodes associated with a plurality of all connected household appliances (action 212) and group the connected appliances into a set of geographical areas. As an example of obtaining data related to common behaviour in a geographical area, the servers could then gather the hard water setting value that have been set by the users in each of the areas in the set (action 214). For each geographical area, the servers could calculate an average value, for example a trimmed or truncated mean that excludes outliners. In other words, in one example, the determined past behaviour common to multiple users could be the setting of a hard water setting value corresponding, substantially, to the average hard water setting value. The truncated mean is representative for the multiple users in a specific geographical area.

[0058] Although it has been described with respect to operation 212 and 214 that data related to geographical area is obtained and data related to past behaviour in the geographical areas is then obtained, the order may be varied. For example, the method may involve obtaining and analysing behaviour of multiple, or all, geographical areas to look for patterns and only once those patterns have been established the method may determine which specific geographical areas the patterns are associated with. In other examples, location data and behaviour data may be analysed together and the common geographical areas may be created and updated, based on user behaviour pattern, as part of the analysis. For example, the location data and behaviour data may be analysed using an unsupervised or supervised machine learning algorithm, as will be described in more detail below.

[0059] In the following, certain optional actions, or method steps, related to analysis of a common behaviour related to water hardness will be described. These steps are described purely as an example and other types of common behaviour and analysis of that common behaviour are contemplated.

[0060] Action 220: One or a plurality (i.e., two or more) of geographic areas, i.e. n number of geographic areas, is / are identified. That is n is an integer equal to or more than 2. Action 230: Data related to water hardness of each one of the identified n number of geographic areas is collected, or otherwise obtained or retrieved.

[0061] Action 240: The identified n number of geographic areas are categorized or otherwise grouped into different water hardness classes depending on the water hardness. The different classes of water hardness may e.g. include the following classes, or groups:

[0062] (a) Hard Water (HW) area;

[0063] (b) Medium Hard Water (MHW) area;

[0064] (c) Medium Soft Water (MSW) area; and

[0065] (d) Soft Water (SW) area.

[0066] Other classes, or groupings, of water hardness may of course be applied based on specific system requirements and / or user demands and this should preferably be tested and evaluated.

[0067] Other examples of water hardness classes are shown in the tables below.

[0068] Table 1. Example of water hardness classes using an international unit (mmol) for water hardness.

[0069]

[0070] Table 2. Example of water hardness classes using German degree of measurement for the water hardness.

[0071] Action 242: In some implementations, categorizing the identified n number of geographic areas into different water hardness classes may include applying an unsupervised machine learning, ML, algorithm.

[0072] For example, the unsupervised ML algorithm may include / <- means clustering. L means clustering is a commonly used unsupervised machine learning algorithm, which can be used to group data points into clusters based on their similarity. The goal of / <- means clustering is to partition a given dataset into k clusters, with each data point belonging to the cluster with the closest mean. The value of k, which is the number of clusters, is typically chosen by the user or operator and is based on domain knowledge or by experimenting with different values. The value of k could for example be decided based on the number of appliance settings or application process variations available for water hardness. As is well-known, the algorithm works by first randomly selecting k points from the dataset as the initial centroids for the k clusters. Then, each data point is assigned to the closest centroid based on its distance from the centroid. After all data points have been assigned to clusters, the centroids of the clusters are recalculated as the mean of all the data points belonging to that cluster. This process may be repeated until the centroids no longer move significantly or a predetermined number of iterations is reached.

[0073] There are several benefits to using unsupervised machine learning algorithms such as / <- means clustering over non-ML solutions for clustering and segmentation tasks. For example, the scalability may be improved as unsupervised ML algorithms like / <- means clustering can be applied to large datasets with many dimensions, making them more scalable than traditional clustering methods. This means they can be used to find patterns and groupings in large datasets much faster than manual or rule-based methods. Furthermore, the unsupervised ML algorithms like / <- means clustering may be more objective than human-driven clustering methods, as they are not influenced by human biases or pre-conceptions. This can lead to more accurate and unbiased clustering results. Still further, unsupervised ML algorithms can be automated, meaning that once they are trained, they can be applied to new data without the need for manual intervention. This can save time and effort, especially when dealing with large datasets. Moreover, unsupervised ML algorithms can help discover hidden patterns and relationships in data that may not be directly apparent to humans. This can lead to new insights and discoveries that may not have been possible with traditional clustering methods. Still further, unsupervised ML algorithms like / <- means clustering can adapt to changes in the data over time, meaning that they can be used for ongoing analysis and segmentation tasks.

[0074] The use of an unsupervised ML algorithm, such as / <- means clustering, in categorizing geographic areas into different water hardness classes has the advantage of enabling accurate and efficient classification, facilitating targeted water hardness strategies for the household appliance to be controlled and operated.

[0075] In Fig. 2, actions 220 through 240 are schematically illustrated as separate actions, or method steps, subsequent to action 210 and prior to action, or method step, 250. In other implementations, these actions, or method steps, could instead be viewed as a way of implementing actions 210, 212 and 214 and thus represent sub-actions, or sub-steps, of action 210.

[0076] Action 250: A message is transmitted to instruct a household appliance to be controlled and operated based on the determined past user behaviour of said multiple users.

[0077] Action 252: As an example, and responsive to the determination of the past user behaviour common to multiple users, said message is transmitted to a user terminal associated with a user of a household appliance to prompt the user to control (and / or operate) said household appliance through a user interface of the user terminal. In some implementations, said message is transmitted to the user terminal to prompt the user to transmit a control message to said household appliance for controlling said household appliance based on the determined past user behaviour of said multiple users.

[0078] Continuing with the above example of average hard water setting, the message could include advice to be displayed on a display of the user terminal and the displayed advice could for example be “Hello, your hard, water settings may not be appropriate for your area. Your current setting is 8 indicating very hard water. The average setting of users in your area is 2, but you may have a private water supply. If you wish for your settings to be changed automatically, click “yes” ”. The message may be sent to all users or only to those users whose behaviour or appliance settings / processes deviate more than a threshold percentage from the determined common behaviour, in this case the truncated mean.

[0079] However, although a specific implementation has been described with respect to action 252, other implementations are of course conceivable. In other implementations, said message is transmitted directly to the household appliance to, for example, directly control the setting of the appliance or directly control the appliance to carry out a process.

[0080] Reference is now made to Fig. 3, which shows a signalling diagram intended to elucidate an example scenario when an example embodiment of the present disclosure is reduced into practice. In this example scenario, the household appliance 112 is exemplified as a dishwasher or, alternatively, a washing machine.

[0081] This use case scenario recognizes the fact that operational settings of the household appliance 112 can be based on user behaviour of multiple users in the same IP geo-location where the water hardness is the same or similar for the household appliances associated with this IP geo-location.

[0082] In this example, a server 130 is operable to obtain 310 various data from or via network 120.

[0083] It should be appreciated that the server 130 may obtain IP addresses associated with respective household appliances within a defined geographic area. The server may obtain the IP addresses by analysing the header of packets of data, making up messages, received from the appliance. For example, the messages may be messages received when the appliance first enrols with the server and / or it may be messages received by the server for appliance monitoring and / or control purposes. As mentioned above, the IP addresses may be the public IP address added to the packets by a home router. The obtained IP addresses are indicative of the geographic area. Typically, household appliances 112 within the same or similar geographic area will experience similar water hardness levels and hence users of these household appliances 112 oftentimes benefit from applying similar operational settings when controlling and operating these household appliances 112. Additionally, the server 130 may collect, or otherwise obtain, data related to the user behaviour of multiple users with household appliances associated with said obtained IP addresses. The server 130 is thus operable to determine 320 a past and / or current user behaviour common to multiple users, whereby the multiple users are associated with respective household appliances located within the common geographic area as being indicated by the obtained IP addresses. Again, and as discussed above, there are other conceivable ways of determining the common geographic area and IP addresses are just one useful example.

[0084] As described above, the server may make use of ML to group or otherwise categorise geographic areas with corresponding water hardness levels. As is also described above, an unsupervised ML algorithm such as / <- means clustering may be used for this purpose.

[0085] Based on the determined past and / or current user behaviour among user within a certain geographic area sharing the same or similar water hardness level, the server 130 is operable to recommend appropriate operational settings for the control of the household appliance. In the example in Fig. 3, the recommendation is sent or otherwise transmitted 330 in a message from the server 130 to a user terminal 114 (e.g., a smartphone) associated with a user of the household appliance 112. The message is received 330 by the user terminal and prompts the user to control and / or operate the household appliance 112 in accordance with the recommendation, i.e. the recommended operational settings that are based on past and / or current user behaviour of many users within the same area and who share similar water hardness conditions.

[0086] The user of the user terminal 114 may hence interact with and operate a user interface of the user terminal to enable the control 350 of the operational settings of the household appliance 112 in accordance with the recommendation. For example, the user may interact with and operate the user interface of the user terminal to transmit 340 a message to the household appliance 114 for controlling the household appliance 114 in accordance with the recommendation, i.e. based on the determined past and / or current user behaviour of the multiple users being associated with household appliances within the same area. The message may for example comprise an instruction to change a setting of the appliance, such as the water hardness setting in memory to one out of a number of different settings. For example, three settings representing soft, medium or hard water may be used. Of course, more levels may be used as exemplified in the tables above. The appliance may then use this setting to control the appliance in an appropriate manner or to provide suggestions to the user. For example, the appliance may recommend to the user to run cleaning cycles more often or to use more detergent if the water hardness level is higher than a threshold than if the water harness level is equal to or lower than the threshold. Alternatively, or additionally, the control message may for example comprise an instruction to change a setting controlling the temperature of a cycle, or parts of a cycle and / or, in auto-dosing appliances, change a setting controlling the amount of detergent that is used and / or, in appliances with water softener, chance a setting to control the amount of water softener used and / or, in tumble dryers or washer dryers, change the length of the cycle. For example, if the water hardness level is higher than a threshold, the message may comprise an instruction to control the washing machine or dishwasher to use more water softener or more detergent than if the water hardness level was equal to or lower than the threshold. Although it has been described above that the user terminal may send data to the appliance and the appliance may make recommendations to the user based on that data, in other implementations, the user terminal may, in addition or as an alternative to the described functionality, may provide its own recommendations to the user via the user interface of the terminal. Following user inputs on the user interface of the user terminal, the user terminal may then generate and send a control message to the appliance.

[0087] Although it has been described that the user operates a user interface of the user terminal to enable the control 350 of the operational settings of the household appliance 112 in accordance with the recommendation, the user may alternatively or additionally be prompted to operate a user interface of the appliance to enable the control. Accordingly, the user terminal may not send data and / or a control message to the appliance. Further, as mentioned above, the server may, additionally or alternatively, send a control message directly to the appliance.

[0088] The generation and transmission of the control message from the server may be initiated following a number of different triggers depending on the implementation. For example, in some implementations, the collection and analysis of common behaviours associated with geographical areas may be carried out and updated at appropriate times, e.g. every few weeks, days, hours or minutes, and the results may be stored in memory. The server may then decide to push out control messages to appliances and / or user terminals of all users associated with the appliances for which behaviour was analysed if the common behaviour is applicable to the appliance but has not been implemented. These processes may be triggered by the server itself. Alternatively or additionally, and as schematically shown in the flowchart of FIG. 4, the server may receive 410 a request with instructions to check whether there may be any common behaviour recommendations relevant for a particular appliance or a group of appliances. This message could be generated automatically when a user first registers an appliance, at regular intervals and / or when the user selects an option in the user interface of the appliance or user terminal. The message could be generated in, and sent from, the user terminal, the appliance or the server. The appliance for which the request is generated may not be one of the appliances for which associated user behaviour was analysed to obtain the common behaviours. This would for example be true if the appliance has just been connected and registered. However, it may be located within a relevant geographical area. The server would then check 420 the IP address associated with the appliance (for example by analysing an enrolment request for the appliance and / or messages exchanged with the appliance) and determine 430 whether there are any common behaviours associated with a geographical area linked to that IP address which may be applicable to the appliance. For example, as mentioned above, all messages received from the appliance, for example via a home router, may have a header including an IP address, for example the IP address of the home router, and the associated geographical area can be looked up using a database. If there are common behaviours associated with the geographical area which may be applicable to the appliance, the server would then generate an appropriate control message and send 450 it to the appliance and / or user terminal. The server may first look up the user terminal(s) linked to the relevant appliance to determine where to send the control message, for example if the request to check common user behaviour was not received from a user device. The server may only send 450 the control message if the settings or present user behaviour associated with the appliance deviates from the common behaviour, for example if it deviates from the common behaviour by more than a predetermined threshold 440. For example, the control message may be sent if a setting of the appliance deviates from a computed “common” setting more than a threshold or if a process usually and / or currently carried out by the user on the appliance, or by the appliance automatically, deviates more than a threshold from a common process, wherein the deviation may be determined according to a predefined scale and / or according to an algorithm. The control message may for example inform the user that settings associated with water hardness level of other appliances in the same geographical area indicate that the water hardness level is high and that settings / processes for high water hardness level would be appropriate if the household is connected to a public water supply and / or does not have a household water softener installed. The server may take into account any information the user entered when the user registered the appliance, such as information about water softeners installed in the household and the source of the water supply of the household, before / when it generates the message. Of course water hardness level is just one example and the control message may include other information and / or commands for other settings or processes, as will be described in more detail below. If instead a common behaviour applicable to the household appliance is not found or if the user behaviour associated with the appliance does not deviate from the common user behaviour, the server may not send 460 a control message. In some implementations or circumstances, the common behaviour analysis may be performed or updated every time a request to check if there are any common behaviours applicable to the appliance is received. This may be the case when the common behaviour may change quickly, for example if it is related to the weather. If the common behaviour computations are carried out periodically, the frequency of re-computation may be higher for common behaviours that are likely to change more often (e.g. related to the weather) than the frequency of re-computation for behaviours that are likely to change less often (e.g. water hardness level). It will be appreciated that although specific steps, and an order, has been described with respect to Figure 4, some steps may not be carried out, additional steps may be carried out or a different order may be used. Specifically, in some implementations of the method, a check to determine if the user behaviour of an appliance deviates from a common user behaviour (operation 440) may not be carried out before the control message is sent.

[0089] By controlling a household appliance based on past (and / or current) user behaviour in the proposed way it is made possible to achieve at least one out of improved quality of operations, energy savings and increased life span of the household appliances. Again, it is a bonus that some implementations allow for the identification of patterns and trends in user behaviour, leading to improved user experience and personalized recommendations. In turn, this may benefit the user’s experience when using his or her household appliances.

[0090] It should therefore be appreciated that, in some implementations, it may be possible to predict appropriate operational settings of the household appliance based on the past (and / or current) user behaviour of many users within the same are and who are believed to share certain operational circumstances of their household appliances such as the above example with water hardness for washing machines or dishwashers.

[0091] Reference is now made to Fig. 5, which illustrates the server 130, or server system, described in conjunction with Figs. 1-3 in accordance with some implementations .

[0092] The server 130 typically includes one or more processing units (CPU’s) 502, one or more network interfaces 504, memory 506, and one or more communication buses 508 for interconnecting these components. Although throughout the description will refer to a server, it will be understood that the server could comprise at least parts of one or more distinct servers, including an Internet-of-things (loT server), communicatively connected in a computing cloud. The functions of the server, described herein, could in some implementations be distributed across one or more of the servers of the computing cloud. Memory 506 may include high-speed random access memory, such as DRAM, SRAM, DDR RAM, or other random access solid state memory devices; and may include non-volatile memory, such as one or more magnetic disk storage devices, optical disk storage devices, flash memory devices, or other non-volatile solid state storage devices. Memory 506 may optionally include one or more storage devices remotely located from the CPU(s) 502. Data and computer program code (instructions) can be accessed from the remotely located storage device, for example downloaded via a wireless interface, to be loaded into the one or more processing units 502. Memory 506, or alternately the non-volatile memory device(s) within memory 506, includes a non-transitory computer readable storage medium. Memory 506 or the computer readable storage medium of memory 506 stores the computer program product 510 which is programmed with computer program code (instructions) that when loaded into a processor device, will perform a method, for instance the method 200 explained with reference to Fig. 2, In some implementations, memory 506 or the computer readable storage medium of memory 506 stores the following programs, modules and data structures, or a subset thereof:

[0093] • an operating system 510 that includes procedures for handling various basic system services and for performing hardware dependent tasks;

[0094] • a network communication module 512 that is used for connecting the server 130 to other computers via the one or more communication network interfaces 504 (wired or wireless) and one or more communication networks 120 (Figs. 1 and 3), such as the Internet, other wide area networks, local area networks, personal area network, metropolitan area networks, VPNs, and so on;

[0095] • one or more server application module(s) 514 for enabling the server 130 to perform the functions offered by the server 130, including but not limited to: o an IP address access module 516 for accessing IP addresses associated with household appliances 112 (Figs. 1 and 3), wherein the IP addresses are indicative of IP-geolocations of the household appliances 112; o a water hardness (WH) access module 518 for accessing water hardness data related to different IP-geolocations; o a context tracking module 520 for tracking and storing the context of users of household appliances, including storing, among other data, the operational settings currently used by users as well as operational settings used in the past by the users, whereby the context tracking module 520 can be seen as a module for keeping track of past and current user behavior through the operational settings applied, or otherwise used, by the household appliances 112; and o a ML-based clustering module 522 for categorizing identified geographic areas into different water hardness classes depending on water hardness, wherein, as mentioned above purely as an example, the ML-based clustering module 522 may apply an unsupervised machine learning, ML, algorithm such as / <- means clustering.

[0096] • one or more server data module(s) 530 for storing data related to the server 130, including but not limited to: o a household settings database 532 including a library of operational settings of the household appliances 112 (Figs. 1 and 3); o a context database 534 including information associated with one or more users, wherein context information includes operational settings currently used by one or more household appliances associated with the users as well as operational settings used in the past by the one or more household appliances associated with the users; o a user profile database 536 including account information for a plurality of users, each account including e.g. one or more out of user histories, user preferences, environmental factors associated with the user’s household such as water supply information and determined user interests; o an IP address lookup database 538 including an array of data for mapping IP addresses with corresponding IP geolocations; o a water hardness (WH) database 540 storing water hardness data for different IP geolocations and, for example, including an array of data for mapping geographic areas with corresponding water hardness classes; o a database 542 here called Big Data Database which stores any and possibly all results / outcomes from past / current analyses of geographical areas and common behavior, e.g., the outcome of the optional k- clustering.

[0097] It will be appreciated that although the components of the server has been described to include data and computer programs for determining water hardness levels and settings, this is only one example and the server may include data and computer program instructions for determining and storing any behaviour common to users associated with respective appliances located within a common geographical area.

[0098] Reference is now made to Fig. 6, which is a block diagram illustrating an example implementation of a household appliance 112. The household appliance 112 includes one or more processing units (CPU’s) 602, one or more network interfaces 61010, memory 612, and one or more communication buses 514 for interconnecting these components. The household appliance 112 typically also includes a user interface 604. The user interface 604 includes user interface elements that enable output 606 to be presented to a user, including e.g. a visual display and / or a speaker. The user interface 604 includes user interface components that facilitate user input 608 such as a keyboard, a mouse, a touch sensitive display, or other input buttons 608.

[0099] Memory 612 may include high-speed random access memory, such as DRAM, SRAM, DDR RAM, or other random access solid state memory devices; and may include non-volatile memory, such as one or more magnetic disk storage devices, optical disk storage devices, flash memory devices, or other non-volatile solid state storage devices. Memory 612 may optionally include one or more storage devices remotely located from the CPU(s) 602. Memory 612, or alternately the non-volatile memory device(s) within memory 612, includes a non-transitory computer readable storage medium. In some implementations, memory 612 or the computer readable storage medium of memory 612 stores the following programs, modules and data structures, or a subset thereof:

[0100] • an operating system 616 that includes procedures for handling various basic system services and for performing hardware dependent tasks;

[0101] • a network communication module 618 that is used for communicatively connecting the household appliance 112 to other component such as a user terminal 114 and / or a server 130 (Figs. 1 and 3) via the one or more communication network interfaces 610 (wired or wireless) and one or more communication networks, such as the Internet, other wide area networks, local area networks, metropolitan area networks, personal area network, VPNs, and so on;

[0102] • a presentation module 620 for enabling presentation of content at the household appliance 112 through the output mechanisms 606 associated with the user interface 604;

[0103] • one or more household appliance application module(s) 622 for enabling the household appliance to perform one or more functions described herein, e.g. to be controlled and operated in accordance with past and / or current user behavior of multiple users; and

[0104] • a household appliance data module 624 for storing data related to the household appliance such as operational settings data, user data, etc.

[0105] Other conceivable user case examples of the inventive concept

[0106] A reader of this disclosure will appreciate that the present disclosure recognizes the fact that a household appliance is operable to be controlled and operated based on, or in dependence on, past and / or current user behaviour, which is common to multiple users of a common geographic area or place.

[0107] In the example embodiments described hitherto, the household appliances have been exemplified as dishwashers or washing machines. In such example embodiments, the disclosure therefore recognizes the fact that users, or operators, experiencing similar water conditions (water hardness) are likely to operate their dishwashers or washing machines in similar manners.

[0108] In the following, additional conceivable examples, forming part of this disclosure, will be briefly discussed to give further context to the reader to appreciate the inventive concept presented throughout this disclosure. These examples are provided by way of example and with the intention to fully convey the scope of the present invention to those persons skilled in the art.

[0109] For example, a computer-implemented method (e.g., performed by a server or other computer system) for predicting and initiating household appliance processes based on common user behavior is provided.

[0110] The computer-implemented method may for example include:

[0111] (a) analyzing user activity patterns and determining suitable times for running household appliance processes;

[0112] (b) predicting appropriate processes based on the user's past behavior;

[0113] (c) providing suggestions for appliance processes to the user; and

[0114] (d) automatically starting household appliance processes.

[0115] Although some of the examples above have been given with respect to water hardness settings which would be expected to remain the same over time for a geographical area, the user behaviour common to multiple appliances may alternatively relate to behaviour that may change over time. For example, the user behaviour common to the multiple users may relate to how the respective appliances are being used at a specific time or in a time period. The specific time and time period may be a current time and current time period respectively. Specifically, in some embodiments, it may relate to whether the appliances are being used or not used at a given time or within a given time period. Additionally or alternatively, it may relate to whether the appliances are being operated in a specific manner at the given time or in the time period. It may further relate to patterns of use of the appliances over time. If a large proportion of the household appliances in a geographical area are used at a given time or within a time period, a message may be sent to a household appliance not being used at that time or within that time period and vice versa. Furthermore, if a large proportion of the household appliances are used in the same or similar way, for example, a certain activity is started for a large proportion of the appliances at the given time or at a time that falls within a certain time period, a message may be sent to a household appliance not used this way (at the given time or within the time period), if appropriate. The use may involve turning on or off the appliance, increasing or decreasing an operating parameter of the appliance and / or running an activity such as a cycle, program and / or process. The user may cause the appliance use by performing an action on the appliance or by remote control or the appliance may cause the use automatically, for example based on settings which may have been previously accepted or selected by the user. As examples, running a cycle, program or process may include running any cycle, program or process, such as any cooking process or any laundry process, or it may include a specific cycle, program or process, such as a specific laundry cycle or cooking program or cleaning cycle or program for the appliance. As an example, the operating parameter may be an operating level such as an operating level of an air conditioner, humidifier, de-humidifier or purifier. As examples, the operating parameter may involve a fan speed, or the temperature or humidity of the air output by the air conditioner, humidifier or de-humidifier. The time period may be a short time period such as a few minutes or a few hours or it may be a longer time period such as a larger number of hours, a few days or a few weeks. The specific time may be a time that the server checks which appliance are in use or not in use and, for example, what processes they are running. The determined behaviour may be a past behaviour and / or it may be a current behaviour.

[0116] More specifically, some settings, for example operating levels and / or fan speeds of air conditioners and air purifiers change frequently depending on environmental factors such as temperature and air quality. Also, use of appliances in terms of when users decide to run a wash, cook or clean their appliances also changes depending on the time of day, time of year and the weather at that time. A determined user behaviour common to the multiple users based on which a message to control other appliances in the same geographical area may be transmitted may then be the turning on or off the respective appliances, increasing or decreasing an operating parameter of the respective appliances and / or performing an activity such as a cycle, program and or process at a specific time or at a time that falls within a specific time period. For example, the method may detect common trends over time and provide instructions and recommendations based on those trends. The common trends may be based on historical data. In some implementations, the common user behaviour may be determined as a common user behaviour that deviates from a normal behaviour. The “normal” behaviour may be the average or mean setting, use frequency for a particular process per appliance, or total number of uses of a process across the multiple appliances, or a larger set of appliances including the multiple appliances, over a certain time period depending on what is appropriate for that setting or process. The time period may be short, such as a short time period at a particular time of day, or it may be longer, such as a number of days, weeks or months. The determined common user behaviour may then be determined based on trends in the geographical area. Control of other appliances in the area based on the determined common user behaviour may be considered if the overall appliance use in the geographical area deviates from normal behaviour by a certain threshold. Additionally or alternatively, control of another appliance in the area based on the determined common user behaviour may be considered if the use of the other appliance deviates from the common behaviour by a certain threshold. In both cases, the deviation may be determined according to a predefined scale and / or according to an algorithm. It will be appreciated that the common and / or “normal” behaviour does not have to be determined with respect to the average or mean but can be judged against some other appropriate measure. It will be appreciated that a common user behaviour for a geographical area does not require that all considered appliances in the area are used or used in the same or similar way. A common user behaviour can be determined when a majority, more than a predetermined percentage and / or a sufficiently large proportion of household appliances are used or used in the same or similar way. As an example, the appliances which are used in the same or similar way may be counted and compared to the number of appliances not used in the same or similar way in the area and a determination about whether a common behaviour exists for that area can then be made based on that comparison. Appliances in the area that are not used in the same or similar way may then be controlled based on the determined common user behaviour of the multiple users associated with the household appliances that are used in the same or similar way. The analysis may take into account the “normal” or average use or any other measure relevant for the application. Some specific examples of user behaviour common to multiple users relating to how the respective household appliances are being used at a specific time or in a time period will be described below. It will be appreciated that although some examples already mentioned herein and some of the Figures herein have been described with respect to past behaviour and / or water hardness, the methodologies, systems, apparatus and some of the implementations described also apply to current behaviour and behaviour not relating to water hardness. Specifically, it will be appreciated that the environments, household appliance, server and overall methodology described in the drawings can also be used in and / or apply to embodiments and examples in which the common user behaviours relate to how the respective appliances are being used at a specific time or in a time period, including the specific examples described below.

[0117] In some implementations, the household appliance is an oven and the method further comprises sending a reminder message to remind the user to clean the oven based on activity of other appliance users in the same area. For example, the server may determine that a large number of users in a country or region of a country run an oven cleaning process after significant holidays when users in some regions of the world tend to have used their ovens a lot, such as Christmas, New Year Celebrations or Thanksgiving, and the server may transmit a message suggesting, or including control instructions related to, an oven cleaning process to other users in the same region. The message may alternatively or additionally include cleaning guidance or tips. The server may for example determine that a large number of users are all operating their ovens in a particular manner in that they are all running a cleaning process on their ovens over a time period of a number of days following a significant holiday. In some embodiments, the server may further determine that the number of users running the cleaning process is significantly higher compared to a yearly average. Additionally, the server may determine that the user behaviour, in the same time period, with respect to a certain oven, deviates from the common behaviour determined. In other words, the server may determine that the user is not operating the oven to perform a cleaning activity in the time period, for example because the user hasn’t started an oven cleaning process after a portion of the time period has elapsed. In some implementations, the message may be sent based on the analysis of the user's activity pattern and on determining that the user is in the kitchen. For example, the message may be sent after dinner on the weekend if an analysis of the user’ s activity pattern suggest that the user is not in the habit of using the oven after dinner on that day. The message may be displayed on the user interface of the appliance and / or the user terminal and, optionally, the user may be prompted to confirm that the oven should automatically start a cleaning program.

[0118] In some implementations, the household appliance is a washing machine and the method further comprises analyzing the total volumes of washing machine use compared to normal volumes of usage in a geographical area and optionally correlating it with weather data. For example, the server may determine that the instantaneous washing machine use or the washing machine use over a time period of a number of minutes or hours has gone down significantly compared to a yearly, seasonal or monthly average instantaneous use or a yearly, seasonal or monthly average use over the time period. This may indicate that the users think it is going to rain later and don’t want to hang their washing out to dry. For example, the method may further comprise sending a message indicating the possibility of bad weather approaching based on a user's intention to put on a wash when few other people are doing so. Specifically, the message may be sent in response to a user’s input to start a laundry cycle and a determination that washing machine usage is lower than a threshold in the area.

[0119] In some implementations, the household appliance is an air conditioner (AC) and the method further comprises adjusting, or suggesting an adjustment of, the temperature settings of the AC based on patterns of use of other users in the geographical area. As a specific example, the method may comprise automatically reducing the temperature settings based on a determination that multiple users in the same geographical area are reducing the temperature setting and that this may indicate that it is a warm day. Alternatively the method may comprise sending a message to a user device recommending or prompting the user to control their AC in accordance with the common user behaviour. For example, the server may determine that a large proportion of appliances in a geographical area are being operated in a particular manner in that an operating parameter of the large proportion of appliances in the geographical area is increased, for example to above a certain threshold, or decreased, for example to below a certain threshold, within a time period. The behaviour may be measured over a time period of a number of minutes or hours. Alternatively, the server may determine a high instantaneous use of air conditioners in the geographical area, or a high instantaneous use of air conditioners involving an operating parameter above or below a threshold in the geographical area, at a certain time. Additionally, it may determine that the user behaviour, in the same time period or at the certain time, with respect to a certain appliance deviates from the common behaviour. The operating parameter may be a temperature setting as described above and / or an operating level or fan speed and it may be changed to decrease environmental air temperature. These findings can be further correlated with weather data to improve accuracy and suggestions and / or control of the appliances.

[0120] In some implementations, the household appliance is an air purifier and the method further comprises adjusting, or suggesting an adjustment of, the operation of the air purifier based on patterns of use of other users in the geographical area. For example, the air purifier may detect high levels of pollution in a specific area due to traffic or forest fires. For example, users may increase the fan speed settings of the air purifier when the air quality gets worse, to improve air quality, and the method may include collecting and analysing the new settings. As a specific example, the method may comprise determining that multiple users in the same geographical area are operating their appliances in a particular manner in that they are changing the operating level of their air purifiers, to for example increase the speed of a fan, which may indicate a change in air pollution levels, and based on that determination automatically changing the mode of a specific air purifier in the geographical area. The behaviour may be measured over a time period of a number of minutes or hours. Alternatively, the server may determine a high instantaneous use of air purifiers in the geographical area, or a high instantaneous use of air purifiers involving an operating parameter above or below a threshold in the geographical area, at a certain time. The method may comprise determining that the user behaviour, in the same time period or at the same time, with respect to the specific air purifier deviates from the common behaviour. The findings can be further correlated with air quality sensor data in the geographical area to improve accuracy and suggestions and / or control of the appliances. Instead of automatically changing the mode of the specific air purifier in the geographical area, a message to an associated user device with recommendations or prompts to control the air purifier may be sent, as has been indicated in other examples for other types of appliances above.

[0121] The various aspects and embodiments described throughout this disclosure allow for improved control (and / or operation) of a household appliance. For example, by controlling a household appliance based on common user behaviour in the proposed way it is made possible to achieve improved quality of operations, energy savings and / or increased life span of the household appliances. As a bonus effect, some implementations allow for the identification of patterns and trends in user behaviour of multiple users within a common area, leading to improved user experience and personalized recommendations.

[0122] As will be appreciated by those skilled in the art, modifications and other variants of the described aspects and embodiments will come to mind to one skilled in the art having benefit of the teachings presented in the foregoing description and associated drawings. Therefore, it is to be understood that the embodiments are not limited to the specific example embodiments described in this disclosure and that modifications and other variants are intended to be included within the scope of this disclosure. Furthermore, although specific terms may be employed herein, they are used in a generic and descriptive sense only and not for purposes of limitation. Therefore, a person skilled in the art would recognize numerous variations to the described embodiments that would still fall within the scope of the appended claims. As used herein, the terms “comprise / comprises” or “include / includes” do not exclude the presence of other elements or steps. Furthermore, although individual features may be included in different claims, these may possibly advantageously be combined, and the inclusion of different claims does not imply that a combination of features is not feasible and / or advantageous. In addition, singular references do not exclude a plurality.

Claims

CLAIMS1. A computer- implemented method of enabling the control of a household appliance, the method comprising: determining a user behaviour common to multiple users, the multiple users being associated with respective household appliances located within a common geographic area; and transmitting a message to instruct a household appliance, determined to be located in the common geographical area, to be controlled based on the determined user behaviour common to said multiple users, wherein the user behaviour common to the multiple users relates to how the respective household appliances are being used at given time and / or in a time period.

2. A computer-implemented method according to claim 1, wherein the user behaviour common to multiple users relate to turning on or off a household appliance, increasing or decreasing an operating parameter of the appliance and / or performing a specific activity on the household appliance at the given time or in the time period.

3. A computer-implemented method of any one of the preceding claims, wherein the user behaviour common to the multiple appliances relate to the multiple users using their respective household appliances at the given time or in the time period and transmitting a message to instruct a household appliance comprising transmitting a message to instruct a household appliance that is not being used at the time or in the time period.

4. A computer-implemented method of any one of the preceding claims, wherein the user behaviour common to the multiple appliances relate to the multiple users operating their respective household appliances to perform an activity at the given time or in the time period and transmittinga message to instruct a household appliance comprising transmitting a message to instruct a household appliance that is not being operated to perform that activity at the given time or in the time period.

5. A computer-implemented method of any one of the preceding claims, wherein the user behaviour common to the multiple appliances relate to appliance cleaning activity within a time period following a holiday.

6. A computer-implemented method of any one of claims 1 to 4, wherein the user behaviour common to the multiple appliances relate to increasing or decreasing an operating parameter to improve air quality or decrease environmental air temperature.

7. A computer-implemented method of any one of claims 1 to 3, wherein the user behaviour common to the multiple appliances relate to the multiple users not using their respective household appliances at the given time or in the time period.

8. A computer-implemented method of claim 7, wherein the household appliances are washing machines and the message indicates the possibility of bad weather.

9. The computer-implemented method according to any one of the preceding claims, further comprising: responsive to determining the user behaviour common to multiple users, transmitting said message to a user terminal associated with a user of a household appliance to prompt the user to control and operate said household appliance through a user interface of the user terminal.

10. The computer-implemented method according to claim 9, further comprising:responsive to said determining the user behaviour common to multiple users, transmitting said message to the user terminal to prompt the user to instruct the user terminal to transmit a control message to said household appliance for controlling said household appliance based on the determined user behaviour of said multiple users.

11. The computer-implemented method according to any one of the preceding claims, comprising: obtaining data related to the common geographic area; and collecting, or otherwise obtaining, data related to the user behaviour of multiple users associated the common geographic area of said obtained data.

12. The computer-implemented method according to claim 11, comprising: obtaining IP addresses associated with respective household appliances within a defined geographic area; and collecting, or otherwise obtaining, data related to the user behaviour of multiple users with household appliances associated with said obtained IP addresses.

13. The computer-implemented method according to claim 11 or 12, wherein collecting, or otherwise obtaining, data related to the user behaviour of multiple users comprises: collecting, or otherwise obtaining, historical data related to operational settings used by the household appliances during past operations.

14. The computer-implemented method according to claim 11 or 12, wherein collecting, or otherwise obtaining, data related to the user behaviour of multiple users comprises: collecting, or otherwise obtaining, current data related to operational settings used by the household appliances during current operations.

15. The computer-implemented method of any one of claim 1 to 14, further comprising determining the location of the appliance based on an IP address associated with the appliance.

16. The computer-implemented method of claim 15, further comprising receiving a message from said household appliance, identifying the IP address associated with said household appliance by analysing said message, determining a geographic area corresponding to said IP address associated with said household appliance, wherein the common geographic area corresponds to said determined geographic area and wherein transmitting a message to instruct a household appliance to be controlled based on the determined user behaviour common to said multiple users is performed following determination of the geographic area corresponding to said IP address associated with the household appliance.

17. A server (130), comprising: at least one processor (502); and at least one memory (506), wherein the at least one memory (506) comprises instructions executable by the at least one processor (502) whereby the server (130) is operative to perform the method according to any one of the claims 1-16.

18. A computer program comprising instructions which, when executed on at least one processor, cause the at least one processor to carry out the method according to any one of the claims 1-16.

19. A method of operating a household appliance, the method comprising:causing a message to be sent, over one or more networks, to a server, the message indicating a geographical area associated with the household appliance; receiving instructions to control the household appliance based on a determined user behaviour common to multiple users associated with respective household located within a common geographical area corresponding to said geographical area; and in response thereto controlling the household appliance accordingly, wherein the common user behaviour relates to whether or not the appliances are being used and / or whether or not the appliances are being operated in a particular manner at a given time or within a given time period.

20. A household appliance (112), comprising: at least one processor (602); and at least one memory (612), wherein the at least one memory (612) comprises instructions executable by the at least one processor (602) whereby household appliance (112) is operative to perform the method according to claim 19.

21. A computer program comprising instructions which, when executed on at least one processor, cause the at least one processor to carry out the method according to claim 19.

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