JP2021189576A - Information processing system - Google Patents

Abstract

SOLUTION: To provide an information processing system for estimating an attribute of a user who uses a washing machine. An information processing system includes an acquisition section and an estimation section. The acquisition section acquires data related to a state of a washing machine. The estimation section estimates an attribute of a user of the washing machine based on the data acquired by the acquisition section with the use of a learned model generated by machine learning.SELECTED DRAWING: Figure 6

Description

Embodiments of the present invention relate to an information processing system.

In recent years, home appliances equipped with a wireless communication function and capable of connecting to the Internet have become widespread. Such a technical field is generally called IoT (Internet of Things), and is attracting attention not only in the field of home appliances but also in various industries. For example, home appliance manufacturers are developing services that enable them to remotely check the status of home appliances from smartphone apps (hereinafter referred to as "home appliances apps") and to operate home appliances remotely. ing.

International Publication No. 2014/097589 Japanese Unexamined Patent Publication No. 2019-063381 Japanese Unexamined Patent Publication No. 2019-096975 Japanese Unexamined Patent Publication No. 2019-154481

However, since the conventional service is based on the user's operation such as the user remotely checking the status of the home appliance or remotely controlling the home appliance, the frequency of the user operating the home appliance application is high. It was expensive and not always convenient for users. Therefore, it is desired to provide a home appliance application that can control home appliances according to the attributes of the user. In order to provide such a home appliance application, it is necessary to estimate the user's attributes regardless of the user's setting action.

An object to be solved by the present invention is to provide an information processing system capable of estimating the attributes of a user who uses a washing machine.

The information processing system of the embodiment has an acquisition unit and an estimation unit. The acquisition unit acquires data regarding the state of the washing machine. The estimation unit estimates the attributes of the user of the washing machine based on the data acquired by the acquisition unit using the trained model generated by machine learning.

The figure which shows the whole structure of the information processing system of an embodiment. The external view which shows the structural example of the washing machine of embodiment. The cross-sectional view which shows the structural example of the washing machine of embodiment. The block diagram which shows the structural example concerning the control of the washing machine of an embodiment. The figure which shows the specific example of the image in the water tank of the washing machine of embodiment. The block diagram which shows the configuration example of the server of an embodiment. The figure which shows the specific example of the data D used for generating the input information for estimation of the number of people in the information processing system of embodiment. The figure which shows the 1st specific example of the temperature change around the washing machine of embodiment. The figure which shows the 2nd specific example of the temperature change around the washing machine of embodiment. The figure which shows the specific example of the user registration information of an embodiment. The figure which shows the flow of the process in the washing machine of embodiment. The figure which shows the flow of processing in the server of embodiment.

Hereinafter, the information processing system of the embodiment will be described with reference to the drawings. In the following description, configurations having the same or similar functions are designated by the same reference numerals. And, duplicate explanation of those configurations may be omitted. "Based on XX" means "based on at least XX" and may include cases based on other factors in addition to XX. The term "based on XX" is not limited to the case where XX is directly used, and may include the case where it is based on a product that has been calculated or processed on XX. The term "XX or YY" is not limited to the case of either XX or YY, and may include both cases of XX and YY. This is also the case when there are three or more selective elements. "XX" and "YY" are arbitrary elements (for example, arbitrary information).

(First Embodiment)
<1. Overall configuration of information processing system>
FIG. 1 is a diagram showing an overall configuration of the information processing system 1 of the embodiment. The information processing system 1 includes, for example, a washing machine 100, a server 200, and a terminal device 300 arranged in each home. However, in the present specification, the "information processing system" does not include the washing machine 100 and the terminal device 300, and may mean only the server 200. The network NW described later includes, for example, the Internet, cellular network, Wi-Fi network, LPWA (Low Power Wide Area), WAN (Wide Area Network), LAN (Local Area Network), other public lines, and dedicated lines. You can use it according to your needs.

The washing machine 100 is arranged in the residence of the user U. The washing machine 100 can communicate with the server 200 via, for example, a wireless router R and a network NW arranged in the residence of the user U. The number of washing machines 100 shown in FIG. 1 is one, but the number is not limited to one, and may be a plurality of washing machines. The configuration of the washing machine 100 will be described in detail later.

The server 200 is composed of one or more server devices SD (for example, a cloud server). The server 200 may be referred to as a "server system". The server 200 may include an information processing unit that performs edge computing or fog computing, such as an information processing unit included in a router in a network NW. The server 200 will be described in detail later.

The terminal device 300 is a device such as a personal computer and can communicate with the server 200 via the network NW. The terminal device 300 includes a display device 300a such as a liquid crystal display or an organic EL display (Electro Luminescence) display. However, the terminal device 300 and the server device SD may be integrally provided by one device.

<2. Washing machine ＞
<2.1 Overall configuration of washing machine>
FIG. 2 is an external view showing a configuration example of the washing machine 100. Further, FIG. 3 is a cross-sectional view showing a configuration example of the washing machine 100. FIG. 3 shows a cross section of the washing machine 100 along the F2-F2 line in FIG. As shown in FIGS. 2 and 3, the washing machine 100 is a drum-type washer-dryer having a drying function, but this does not limit the washing machine of the present embodiment to the drum-type. The washing machine 100 may be a vertical washer / dryer. The washing machine 100 is configured to be connectable to a network by being provided with a wireless module 70.

The washing machine 100 includes, for example, a washing machine main body 1B, a door 1D, a switch 4, and an operation panel 5. In the washing machine 100, the door 1D side is the front side of the washing machine 100, and the opposite side of the surface where the door 1D is located is the rear side of the washing machine 100. In FIG. 1, the XYZ axis coordinate system is applied, and the + X axis in the horizontal direction along the front surface of the washing machine 100, the + Y axis in the depth direction, and the + Z axis in the vertical direction are defined respectively. The description of the XYZ axis coordinate system is the same in the following figure.

The washing machine main body 1B constitutes the outer shell of the washing machine 100, and is formed in the shape of a rectangular box whose front surface is smoothly inclined. An opening 1A is provided in the central portion of the front surface of the washing machine main body 1B. The opening 1A communicates with a rotary tub 13 (FIG. 2) provided inside the washing machine main body 1B. The laundry is put into the rotary tub 13 through the opening 1A. The door 1D is provided so that the opening 1A can be opened and closed. The door 1D is opened by operating the door lever 1DL provided in the vicinity of the opening 1A. Further, a door switch 601 is provided in the opening / closing portion of the door 1D as a sensor for detecting the opening / closing of the door.

A detergent softener charging section 26 (FIG. 3) that can be slid in and out in the front-rear direction is provided on the upper left portion of the front surface of the washing machine main body 1B. When the detergent softener charging unit 26 is housed inside the washing machine main body 1B, the lid surface 26F of the detergent softener charging unit 26 is formed so as to have an inclination similar to the inclination of the front surface of the washing machine main body 1B. The detergent softener charging unit 26 is provided with a detergent case (not shown) for the user to charge the detergent or the softener for each washing operation. The user pulls out the detergent softener charging unit 26, and in a state where the detergent or the softening agent weighed by the user is poured into this detergent case and stored, the detergent softener charging unit 26 is stored in the washing machine main body 1B. Keep it. After that, when the washing machine 100 enters the washing operation, the detergent or the softener that has been poured and stored in the detergent case is put into the water tank 14 (FIG. 3) at a predetermined timing during the execution of the washing operation control. .. Further, the detergent case is provided with a detergent remaining amount sensor 602 and a softener remaining amount sensor 603 as sensors for detecting the remaining amount of the detergent and the softener in the case.

A water supply port 23, an opening 17A, and a lid 34 are provided on the upper surface of the washing machine main body 1B. The water supply port 23 is connected to a water faucet as a water source by a water supply hose (not shown). Then, the water from the tap is supplied to the water tank 14 provided in the washing machine main body 1B through the water supply hose, the water supply port 23, and the water supply valve unit 10 described later.

A lid 34 that can be opened and closed is arranged in the opening 17A. For example, the lid 34 shown in FIG. 1 shows an released state. The opening 17A is used to replenish the replenisher liquid storage unit 17 provided inside the washing machine main body 1B with the washing treatment agent. Detergents, fabric softeners, bleaches and mixed solutions thereof are examples of laundry treatment agents. In the following description, a case where a detergent is used as the first washing treatment agent and a softener is used as the second washing treatment agent will be illustrated. Without being limited to this, for example, a bleaching agent or a mixed solution may be configured to be used in place of or in addition to the above in the same manner.

For example, the replenisher liquid storage unit 17 includes a pump 30 (FIG. 3) and tanks 31 and 32.
The tanks 31 and 32 are containers provided with a liquid receiving port (not shown) at the top. The tanks 31 and 32 store the detergent or softener poured into the inside from the liquid receiving port separately for each tank. A pump 30 is provided in the liquid flow path from the inside of the tanks 31 and 32 to the water tank 14. When the pump 30 is driven, the washing agent in the tanks 31 and 32 is poured into the water tank 14. For example, the timing for driving the pump 30 is determined by the control unit 80 (described later in FIG. 4) according to the washing operation course selected by the user. Although the case of two tanks has been described by exemplifying the tanks 31 and 32, the number of tanks is not limited and may be one or three or more.

The operation panel 5 accepts various settings related to the operation of the washing machine 100. For example, as shown in FIG. 2, the operation panel 5 is provided on the front side of the upper surface of the washing machine main body 1B. The operation panel 5 includes, for example, a display unit 6 and a switch 4. The display unit 6 is formed as, for example, a so-called touch-type operation panel, and includes a touch sensor configured by a capacitive switch. The display unit 6 includes, for example, a liquid crystal display device. The display unit 6 displays an image including a plurality of types of display screens related to washing operation control on the display surface thereof. For example, the operation panel 5 accepts the user's operation for instructing the switching of the control mode of the washing operation control of the washing machine 100 (starting another control mode), and displays the setting contents and the current operation status by the operation. ..

The switch 4 is a power switch of the washing machine 100. A power cord (not shown) is provided on the power supply side of the switch 4, and power is supplied from the outside via the power cord. Further, a power supply unit (not shown) for supplying electric power supplied from the outside to each functional unit of the washing machine 100 is connected to the load side of the switch 4.

Specifically, the power supply unit converts AC power supplied from the outside into DC power and supplies a stabilized voltage to each functional unit of the washing machine 100. For example, the power supply unit includes a DC voltage detection unit (power supply detection unit 604 described later in FIG. 4) (not shown), and supplies the detection result to the control unit 80. For example, when a DC voltage less than a predetermined threshold voltage is output from the power supply unit, the power supply unit outputs a reset request to the control unit 80. When the power is turned on by the switch 4 and the DC voltage exceeding a predetermined threshold voltage is output from the power supply unit, the power supply unit inverts the detection result and cancels the reset request to the control unit 80. After the reset request is released, the washing machine 100 is initialized by the control unit 80, and then various controls including communication control and washing control are possible. The operation of turning on the power by the switch 4 is an example of the operation for starting the washing operation (washing operation start operation). The washing machine 100 starts the washing operation after detecting the washing operation start operation. The order is defined so that the washing machine 100 detects the washing operation start operation and then actually starts the washing operation. There is no limit on the time difference between the two.

Further, the washing machine 100 is connected to a drum motor 7, a drain valve unit 9, a water supply valve unit 10, a drying unit 11, and a pump 30 (automatic charging mechanism). Although detailed description of each is omitted, the drum motor 7 rotates a rotary tank 13 provided in the washing machine main body 1B. The drain valve unit 9 includes a drain valve, a drain pump, and a drain valve motor (not shown), and drains the water in the water tank 14 to the outside of the machine. The water supply valve unit 10 includes a water supply valve, a bath water pump, and a bath water pump motor (not shown), opens and closes communication between the water supply port 23 or the water intake port 24 of the bath water and the inside of the water tank 14, and supplies water into the water tank 14.

The drying unit 11 includes a compressor, a fan, a fan motor, and an exhaust damper motor (not shown), and constitutes a heat pump unit for drying laundry. The pump 30 inputs the detergent or the softener previously stored in the tanks 31 and 32 at the timing of being driven under the control of the control unit 80. The control unit 80 includes a drive circuit for driving each unit. The drum motor 7, the drain valve unit 9, the drying unit 11, the water supply valve unit 10, and the pump 30 are controlled via the control unit 80.

<2.2 Configuration of washing machine sensor group and control unit>
FIG. 4 is a block diagram showing a configuration example relating to the control of the washing machine 100. As shown in FIG. 4, the washing machine 100 has a wireless module 70 for connecting itself to other communication devices so as to be communicable, a control unit 80 for making the own device function as a washing machine, and control as a washing machine. It is equipped with a sensor group SU for acquiring various information necessary for the washing machine. The control unit 80 is communicably connected to the sensor group SU by a communication line inside the washing machine 100, and is wirelessly connected to the server 200 via the wireless module 70. The wireless module 70 wirelessly communicates with the server 200 via the wireless router R.

The control unit 80 is, for example, a processor such as a CPU (Central Processing Unit), a memory such as an SRAM (Static Random Access Memory) or a DRAM (Dynamic Random Access Memory), an SSD (Solid State Drive), an HDD (Hard Disk Drive), or the like. It is configured to include an auxiliary storage device. The control unit 80 functions as a device including a washing control unit 81, a storage unit 82, an information recording unit 83, and an information output unit 84 by the processor reading a program stored in the auxiliary storage device into the memory and executing the program. do. All or part of the functions of the control unit 80 are controlled by hardware (including circuit unit; circuitry) such as ASIC (Application Specific Integrated Circuit), PLD (Programmable Logic Device), or FPGA (Field Programmable Gate Array). It may be realized, or it may be realized by the cooperation of software and hardware.

In order to make the washing machine function as a washing machine, the washing control unit 81 has a drum motor 7, a drain valve unit 9, a water supply valve unit 10, and drying based on an input operation to the operation panel 5 and a detection result of the sensor group SU. It has a function of controlling the operation of the unit 11 and the pump 30. The washing control unit 81 may be configured to perform control based on the washing method set by the user U in addition to the control based on the washing method set in advance in the washing machine 100. For example, the user U sets the washing method by using an application (hereinafter referred to as “washing application”) installed in the operation panel 5 of the washing 100 or the terminal device 300. Hereinafter, the washing method selected in the use of the washing machine 100 is referred to as a "washing course", and among the washing courses, the one set by the user U from the washing application is referred to as an "app application course".

The storage unit 82 stores the identification information I1 and the state information I2. The identification information I1 indicates a device ID assigned to each washing machine 100 to identify the washing machine 100. The state information I2 is time-series information indicating the state of the washing machine 100.

For example, the information recording unit 83 refers to a timer (not shown), associates the information indicating the state of the washing machine 100 with the date and time information, and adds the information to the state information I2 of the storage unit 82. The "state of the washing machine 100" includes, for example, an ON / OFF state of the power of the washing machine 100, an operating state of the drum motor 7, a water supply valve unit 10, a drain valve unit 9, a drying unit 11, and a pump 30. .. Further, the "state of the washing machine 100" may include the state of the item to be washed by the washing machine 100. The date and time information includes information indicating the day of the week and the time.

Further, the information recording unit 83 associates the information indicating the detection result of the sensor group SU with the date and time information, and adds the information to the state information I2 of the storage unit 82. Here, the washing machine 100 has, as the sensor group SU, the water level sensor 605, the foam sensor 606, and the weight sensor 607 in addition to the door switch 601, the detergent remaining amount sensor 602, the softener remaining amount sensor 603, and the power supply detection unit 604. , A dirt sensor 608, a water temperature sensor 609, an in-tank camera 610, an ambient temperature / humidity sensor 611, an outside air temperature sensor 612, and a lint filter switch 613.

The water level sensor 605 is a sensor that measures the water level of the water injected into the water tank 14. The bubble sensor 606 is a sensor that detects bubbles generated inside the water tank 14. Foam is primarily produced by the detergent or fabric softener being agitated in the aquarium 14 during the washing process. Excess or deficiency of detergent or softener can be detected by the degree of foaming. The weight sensor 607 is a sensor that measures the weight of the water tank 14. The weight of the water tank 14 can be used to detect the amount of water injected and the amount of clothes to be washed. The dirt sensor 608 is a sensor that detects water dirt in the water tank 14. The state of clothing in the water tank 14 can be detected by the vibration state of the water tank 14. The water temperature sensor 609 is a sensor that measures the temperature of the water injected into the water tank 14.

The in-tank camera 610 is a camera that captures the inside of the water tank 14 of the washing machine 100. For example, FIG. 5 is a diagram showing a specific example of an image (hereinafter referred to as “in-water tank image”) in which the inside of the water tank 14 of the washing machine 100 is captured by the in-tank camera 610. The detection result of the sensor group SU is the detection result of the various sensors and switches described above. These information may be recorded as raw data or may be recorded in a state where necessary operations (processing) have been performed.

The ambient temperature / humidity sensor 611 is a sensor that detects the temperature and humidity in the vicinity of the washing machine 100. The outside air temperature sensor 612 is a sensor that measures the outside air temperature at the place where the washing machine 100 is installed. When the washing machine 100 is installed outdoors, the outside air temperature sensor 612 may be omitted because the measured temperature of the outside air temperature sensor 612 is the same as the measured temperature of the ambient temperature / humidity sensor 611. The power supply detection unit 604 has a function of detecting a DC voltage as described above. With this function, the power supply detection unit 604 can detect the frequency of the external power supply to which the washing machine 100 is connected.

The information output unit 84 transmits the state information I2 stored in the storage unit 82 to the server 200 via the wireless module 70. The information output unit 84 transmits the state information I2 to the server 200, for example, at a predetermined cycle. At this time, the information output unit 84 associates the state information I2 with the identification information I1 stored in the storage unit 82 and transmits the identification information I1 to the server 200. The identification information I1 is a device ID assigned to each washing machine 100 to identify the washing machine 100. The state information I2 and the identification information I1 are examples of data transmitted from the washing machine 100 to the server 200. Hereinafter, the state information I2 and the identification information I1 are collectively referred to as “data D”.

Instead of the above configuration, the information recording unit 83 may be omitted, and the information output unit 84 may transmit the operating state of the washing machine 100 and the detection result of the sensor group SU to the server 200 in real time. In this case, the server 200 may associate the operating state of the washing machine 100 and the detection result of the sensor group SU with the date and time information.

<3. Server ＞
FIG. 6 is a block diagram showing a configuration example of the server 200. The server 200 includes, for example, an information acquisition unit 201, an information conversion unit 202, a learning unit 203, an estimation unit 204, an information recording unit 205, and an information output unit 206. These functional units are realized by executing a program (software) by a hardware processor such as a CPU included in the server 200. However, all or part of these functional parts may be realized by hardware such as ASIC, PLD, or FPGA (circuit part; including circuitry), or by cooperation between software and hardware. May be good.

Further, the server 200 has a storage unit 207. The storage unit 207 is realized by, for example, a RAM, a ROM, an HDD, a flash memory, or a plurality of combinations thereof. The storage unit 207 stores the storage information I11, the user registration information I12, the learning model L, the estimation model (learned model) M, and the user attribute information I13.

The information acquisition unit 201 acquires (receives) the data D transmitted from the washing machine 100. When the estimation model M is learned, the information acquisition unit 201 collects data D from a plurality of washing machines 100 used in each of the plurality of homes. The information acquisition unit 201 stores the acquired data D as the storage information I11 in the storage unit 207. The information acquisition unit 201 accumulates data D for each washing machine 100, for example, for a predetermined period (for example, 3 months). The information acquisition unit 201 is an example of the “acquisition unit”.

The information conversion unit 202 generates input information to be input to the estimation model M, which will be described later, based on the accumulated information I11 and the learning model L in which the data D of each washing machine 100 is accumulated. The learning model L is a model showing an algorithm for performing machine learning. The estimation model M is a trained model generated by learning the accumulated information I11 using the learning model L, and is a model for estimating the attributes of the user U. For example, as an example of the attributes of the user U, (a) the number of people using the washing machine 100, (b) gender, (c) age, (d) housing form, (e) installation position, (f) working form, (g). Whether or not they are married, (h) the area of residence, etc. can be mentioned. However, the attribute of the user U to be estimated may be a matter other than the matters listed above as long as it is a matter that can be estimated based on the accumulated information I11 with respect to the user U.

(A. Input information for estimating the number of users)
The information conversion unit 202 has, for example, one or more of the following information as input information (hereinafter referred to as "input information for estimating the number of users") to be input to the estimation model M in order to estimate attributes related to the number of users. Is generated based on the accumulated information I11. This information has a correlation with the number of users, at least in the viewpoint described below. Therefore, one or more of the information listed below can be used as the input information of the estimation model M whose output information is the attribute related to the number of users.

-Information about the model of the washing machine 100-Information about the number of washings of the washing machine 100-Information about the amount of clothes washed by the washing machine 100-Information about the washing time of the washing machine 100-Information about the continuous operation of the washing machine 100-Washing Information on stains on clothes washed by machine 100 ・ Information on cleaning frequency of lint filter in washing machine 100 ・ Information on washing course of washing machine 100 ・ Information on setting timer reservation of washing machine 100 ・ Bath in washing machine 100 Information on the use of water-Information on the change of the reserved time in the washing machine 100-Information on the type of clothes washed by the washing machine 100-Information on the time from the end of washing of the washing machine 100 until the clothes are taken out

For example, in general, washing machine manufacturers line up models that have functionality according to the number of people who use the washing machine, so users can select models from the lineup that match the number of people they expect. It is considered to be selected and purchased. Therefore, it is highly likely that the washing machine 100 will be used by a number of users according to the model.

Further, in general, the load applied to the washing machine 100 tends to increase as the number of people using the washing machine 100 (for example, the number of family members living together with the user) increases. Therefore, various index values indicating the magnitude of the load applied to the washing machine 100 have a correlation with the number of people used in the washing machine 100. Here, as index values indicating the magnitude of the load applied to the washing machine 100, "the number of washings of the washing machine 100", "the amount of clothes washed by the washing machine 100", "the washing course of the washing machine 100", and the like. Examples include "washing time of the washing machine 100", "continuous operation of the washing machine 100", "dirt on clothes washed by the washing machine 100", "cleaning frequency of the lint filter in the washing machine 100" and the like.

FIG. 7 is a diagram showing a specific example of data D used for generating input information for estimating the number of users. The data D is time-series data showing the detection result of the sensor group SU. In FIG. 7, the “number of washings of the washing machine 100” can be detected based on, for example, the output of the door switch 601. Further, the "amount of clothes washed by the washing machine 100" can be detected based on, for example, the output of the weight sensor 607 or the water level sensor 605. Further, the "washing time of the washing machine 100" and the "continuous operation of the washing machine 100" can be detected based on, for example, the output of the door switch 601 or the weight sensor 607. Further, "dirt on clothes washed by the washing machine 100" can be detected based on, for example, the output of the dirt sensor 608. Further, the "cleaning frequency of the lint filter in the washing machine 100" can be detected based on the output of the lint filter switch 613.

Also, in general, the method of using the washing machine 100 tends to change depending on whether the person who uses the washing machine 100 is a single person or a family member. Here, as index values regarding the usage of the washing machine 100, "setting of timer reservation of washing machine 100", "use of bath water in washing machine 100", "change of reservation time in washing machine 100" and the like are mentioned. Be done. For example, it is considered that the user who uses the washing machine 100 alone has a large fluctuation in the life pattern, so that the frequency of changing the reservation time of the timer is relatively high. On the other hand, since it is considered that the user who uses the washing machine 100 as a family has a small change in the life pattern, it is considered that the frequency of changing the reservation time of the timer is relatively low.

Further, in general, it is highly likely that the person who uses the bath water in the use of the washing machine 100 is a user who uses the washing machine 100 as a family member. Information on "washing course of washing machine 100", "setting of timer reservation of washing machine 100", "use of bath water in washing machine 100", and "change of reservation time in washing machine 100" is provided to the operation panel 5. It can be detected based on the history information of the operation input performed.

Further, in general, the family composition of the user appears more directly in the type of clothes washed by the washing machine 100. For example, it is highly likely that the family structure of a user in which small-sized clothes are washed is a household with children, and that the number of people using the washing machine 100 is likely to be a plurality of people. Further, in general, it is highly likely that the short time from the end of washing of the washing machine 100 to the removal of clothes indicates that the amount of washing is large, and the user is a family member of the washing machine 100. It is highly probable that you are using. The "type of clothes washed by the washing machine 100" can be detected, for example, based on an image in the aquarium (see FIG. 5). The "time from the end of washing of the washing machine 100 to the removal of clothes" can be detected based on, for example, the output of the door switch 601 or the weight sensor 607.

The information conversion unit 202 acquires "information on the load applied to the washing machine 100", "information on the time from the end of washing of the washing machine 100 until the clothes are taken out", which is acquired based on such data D. "Information on the type of clothes washed by the washing machine 100", "Information on the washing course of the washing machine 100", "Information on setting the timer reservation of the washing machine 100", "Information on the use of bath water in the washing machine 100" , "Information on change of reservation time in washing machine 100" and the like are generated as input information for estimating the number of users.

Another example of the attribute related to the number of people using the washing machine 100 is the household composition of the user. For example, if the number of users of the washing machine 100 is estimated to be one, the household composition of the users can be considered as a single household. Further, for example, when the number of people using the washing machine 100 is estimated to be two and the age is estimated to be the same generation, it can be considered that the household composition of the user is a household of only a couple. In this case, in order to estimate the age and age of the user, the age estimation input information described later may be included in the number estimation input information.

Further, for example, when the number of people using the washing machine 100 is a plurality of people and it is estimated that children and adults are included in the washing machine 100, the household composition of the user can be considered to be a household of parents and children. In this case, in order to estimate the presence or absence of children, information such as age estimation input information and employment form estimation input information, which will be described later, may be included in the number estimation input information.

Further, in this case, if it is estimated that the adult includes an adult of the child-rearing generation and an elderly adult, the household composition of the user can be considered to be a three-generation household. In this case, in order to estimate the age of an adult, information such as input information for age estimation and input information for employment form estimation, which will be described later, may be included in the input information for number estimation.

(B. Input information for estimating gender)
The information conversion unit 202 is, for example, one of the following information as input information (hereinafter referred to as "input information for gender estimation") to be input to the estimation model M in order to estimate the attributes related to the gender of the user U. The above is generated based on the accumulated information I11. This information has a correlation with the gender of the user U, at least in the viewpoint described below. Therefore, one or more of the information listed below can be used as the input information of the estimation model M whose output information is the attribute related to the gender of the user U.

-Information on the type of clothes washed by the washing machine 100-Information on the washing course of the washing machine 100-Information on the brand of the detergent or softener used in the washing machine 100-Information on the physical characteristics of the user U of the washing machine 100

The gender of the user appears more directly in the "clothes type washed by the washing machine 100". For example, whether the clothes washed by the washing machine 100 are for men or women can be determined by, for example, the purpose, size, pattern, color, shape (example of clothing type) of the clothes. In addition, it is generally considered that females are more likely to consider the load on laundry than males. For example, it is considered that women tend to prefer to use "fashionable clothes washing", which is one of the "washing machines 100 washing courses", rather than men. In addition, since such female preference may also appear in the detergent or fabric softener used, it may be possible to estimate the gender of the user when the use of a specific brand of detergent or fabric softener is detected. There is. "Information about the brand of the detergent or the softener used in the washing machine 100" can be identified by, for example, the user registration information I12.

Further, it is considered that the "physical characteristics of the user U" shows a tendency regarding the gender of the user U of the washing machine 100. For example, men tend to be taller than females. Also, male hands and arms tend to be larger and thicker than female hands and arms. In addition, women often wear jewelery on their hands and arms more often than men, and it is often possible to tell from the appearance whether the jewelery is for women or men.

Specifically, the physical characteristics of the user U of the washing machine 100 can be acquired by using, for example, the image data in the water tank of the washing machine 100. For example, the image in the water tank of the washing machine 100 may capture the hand of the user U who puts in and takes out the laundry. Therefore, the information conversion unit 202 detects the hand of the person captured in the image in the aquarium by the image recognition process, and identifies the attribute of the detected hand (for example, size, thickness, presence / absence of decoration, etc.). do. The image data in the water tank is one aspect of the data D. In this case, the image recognition process may be performed by any method as long as it can identify the hand of the person captured in the image in the water tank.

For example, in the image recognition process, a method of identifying the presence / absence and attributes of a human hand can be used based on various feature quantities acquired by using each pixel value of the image data. Therefore, the information conversion unit 202 generates information indicating the identification result of the human hand by the image recognition process as the input information for gender estimation. Since it is considered that each pixel value of the image data includes information indicating the characteristics of the subject, the information conversion unit 202 may generate the image data itself as input information for gender estimation.

(C. Input information for estimating age)
The information conversion unit 202 is, for example, one of the following information as input information (hereinafter referred to as "age estimation input information") to be input to the estimation model M in order to estimate the age-related attribute of the user U. The above is generated based on the accumulated information I11. This information has a correlation with the age of the user U, at least in the viewpoint described below. Therefore, one or more of the information listed below can be used as input information of the estimation model M whose output information is the attribute related to the age of the user U.

-Information about the model of the washing machine 100-Information about the number of washings of the washing machine 100-Information about the type of clothes washed by the washing machine 100-Information about the washing course of the washing machine 100-Information about the continuous operation of the washing machine 100-Washing machine Information on the washing time of 100-Information on the brand of the detergent or softener used in the washing machine 100-Information on the timer reservation setting of the washing machine 100-Information on the stains on the clothes washed in the washing machine 100-In the washing machine 100 Information on the use of bath water-Information on the frequency of cleaning the lint filter in the washing machine 100-Information on the time from the end of washing of the washing machine 100 until the clothes are taken out

The model of the washing machine 100 may have different user preferences depending on the age and age. For example, younger users are more likely to choose a drum-type washing machine, which is a relatively new type of washing machine, and older users are more likely to choose a vertical-type washing machine.

Further, for example, it is considered that an elderly person has a smaller amount of laundry than a young person and is likely to have a smaller load on the washing machine 100. Further, for the same reason, it is considered that an elderly person is less likely to continuously operate the washing machine 100 than a young person. In addition, since elderly people tend to be active earlier in the morning than young people, it is considered that the time to start opening and closing the washing machine 100 is earlier than that of young people. In addition, it is considered that the change in life pattern between weekdays and holidays is smaller in elderly people than in young people. On the other hand, it is thought that the lifestyle patterns of office workers and students change between weekdays and holidays. Furthermore, in the case of students, unlike office workers, even if the lifestyle patterns differ from day to week on weekdays, the same lifestyle patterns tend to be repeated when viewed over a one-week span. Also, depending on the brand of detergent or fabric softener used for washing, the difference in taste depending on the age may be reflected. Similarly, the difference in taste depending on the age may be reflected in the function of the washing machine 100 used. For example, older people are less likely to use the drying function than younger people. For example, the presence or absence of the use of the drying function can be determined based on the history information of the washed washing course.

Therefore, the information conversion unit 202 includes "information on the load related to the washing machine 100", "information on the type of clothes washed by the washing machine 100", "information on the washing course of the washing machine 100", and "continuous operation of the washing machine 100". "Information about the washing machine 100", "Information about the washing time of the washing machine 100", "Information about the brand of the detergent or the softener used in the washing machine 100" are generated as input information for age estimation.

(D. Input information for estimating attributes related to housing form)
The information conversion unit 202 is, for example, one or more of the following information as input information (hereinafter referred to as "input information for housing form estimation") to be input to the estimation model M for estimating attributes related to the housing form. Is generated based on the accumulated information I11. This information has a correlation with the housing form, at least in the viewpoint described below. Therefore, one or more of the information listed below can be used as the input information of the estimation model M whose output information is the attribute related to the housing form.

-Information on the ambient temperature or humidity of the washing machine 100-Information on the outside air temperature at the installation position of the washing machine 100

FIG. 8 is a diagram showing a first specific example of a temperature change at the installation position of the washing machine 100. As shown in FIG. 8, it is considered that the "temperature or humidity around the washing machine 100" shows a tendency of indoor and outdoor temperature changes according to the installation position. For example, condominiums tend to have smaller indoor temperature changes than detached houses. Specifically, the information regarding the temperature at the installation position of the washing machine 100 can be acquired based on the detection result of the ambient temperature / humidity sensor 611 included in the accumulated information I11. Therefore, the information conversion unit 202 generates information indicating the temperature at the installation position of the washing machine 100 as input information for estimating the housing form.

In addition, since condominiums are less affected by outside air temperature than detached houses, indoor temperature changes tend to be smaller regardless of time of day or season. Since indoor temperature changes are not a little affected by outdoor temperatures, "outside air temperature at the installation position of the washing machine 100" is also used as input information for housing form estimation in order to more accurately determine the cause of indoor temperature changes. May be included. In addition to temperature changes, condominiums tend to have smaller indoor humidity changes than detached houses. Therefore, the "humidity around the washing machine 100" may be included in the input information for estimating the housing form as well as the temperature change.

(E. Input information for estimating the attribute related to the installation position of the washing machine 100)
The information conversion unit 202, for example, includes the information listed below as input information (hereinafter referred to as “installation position estimation input information”) to be input to the estimation model M in order to estimate the attributes related to the installation position of the washing machine 100. One or more of them are generated based on the accumulated information I11. This information has a correlation with the installation position of the washing machine 100, at least from the viewpoint described below. Therefore, one or more of the information listed below can be used as input information of the estimation model M whose output information is the attribute related to the installation position of the washing machine 100.

-Information on the ambient temperature or humidity of the washing machine 100-Information on the outside air temperature at the installation position of the washing machine 100

For example, when the washing machine 100 is installed outdoors such as on a balcony or rooftop, the temperature change around the washing machine 100 is the same as the change in the outside air temperature. Therefore, it is highly possible that it is possible to determine whether the installation position of the washing machine 100 is outdoors or indoors based on the temperature change around the washing machine 100. Further, when the washing machine 100 is installed indoors, the characteristics of the room may be reflected in the temperature change around the washing machine 100.

FIG. 9 is a diagram showing a second specific example of the temperature change in the room where the washing machine 100 is installed. For example, in a studio apartment or the like, a washing machine 100 may be installed near the kitchen, and a stove may be installed near the installation position. When the stove is installed near the washing machine 100, it is considered that the temperature near the washing machine 100 rises at the timing of using the stove. Further, since the stove is likely to be used during the meal time, if the temperature frequently rises in the vicinity of the washing machine 100 during the meal time, the place where the washing machine 100 is installed is the kitchen. Probability is high. As described above, the "temperature around the washing machine 100" is considered to have a correlation with the attribute regarding the installation location of the washing machine 100. Therefore, the information conversion unit 202 generates "information about the temperature or humidity around the washing machine 100" and "information about the outside air temperature at the installation position of the washing machine 100" as input information for estimating the installation position.

Further, when the washing machine 100 is installed indoors, it is considered that the "humidity around the washing machine 100" shows a humidity change according to the use of the space of the installation place. For example, when the washing machine 100 is installed near the kitchen, the humidity tends to be temporarily increased by the steam generated during cooking. Further, even in a space other than the kitchen, when the space is used, the humidity tends to temporarily increase due to the exhalation of the user, drinks, and the like. In addition, the degree of the tendency for the humidity to increase temporarily may differ depending on the use of the space, and the tendency appears as a feature such as the amount of change in humidity and the time zone when the humidity changes. Therefore, the information conversion unit 202 generates information indicating the humidity around the washing machine 100 as input information for estimating the installation position.

(F. Input information for estimating employment form)
Among the information listed below, the information conversion unit 202, for example, as input information to be input to the estimation model M for estimating the attributes related to the employment form of the user U (hereinafter referred to as "input information for employment form estimation"). One or more is generated based on the accumulated information I11. This information has a correlation with the employment form of the user U, at least from the viewpoint described below. Therefore, one or more of the information listed below can be used as the input information of the estimation model M whose output information is the attribute related to the employment form of the user U.

-Information on the type of clothes washed by the washing machine 100-Information on the weekly usage cycle in the washing machine 100 (hereinafter referred to as "washing cycle") -Information on the change of the reserved time in the washing machine 100-Information on the change of the reservation time in the washing machine 100- Information on the presence or absence of the terminal device 300 in the vicinity

For example, attributes such as the working style and working hours of the user U are considered to appear in the life pattern of the user U. Further, a part of the life pattern of the user U appears in the usage pattern of the washing machine 100, and the usage pattern of the washing machine 100 appears in the fluctuation pattern of the load applied to the washing machine 100. For example, when the work mode of the user U is part-time, it appears as a feature that the time interval from the end of the use of the washing machine 100 to the restart is relatively short (for example, less than several hours). When the work mode of the user U is full-time, the time interval from the end of the use of the washing machine 100 to the restart of the washing machine 100 is relatively long (for example, 10 hours or more).

Further, for example, the working time zone of the user U is a time zone from the end of the use of the washing machine 100 to the resumption of use, and the life pattern appears in the usage pattern of the washing machine 100. For example, in the lifestyle pattern of user U whose working hours are from 9:00 am to 20:00 pm, the use of the washing machine 100 is temporarily stopped before 9:00 am on weekdays, and the use of the washing machine 100 is used after 20:00 pm. Appears as a usage pattern of the washing machine 100 to restart. In addition, the lifestyle pattern of user U, whose working hours are from 21:00 pm to 6:00 am the next morning, temporarily ends the use of the washing machine 100 before 21:00 pm on weekdays, and uses the washing machine 100 after 6:00 am the next morning. Appears as a usage pattern of the washing machine 100 to restart.

Further, for example, when the user is a student, the washing machine 100 may be used in a different pattern every weekday, but the washing machine 100 may be used in a repeat of almost the same pattern every week. As a usage pattern, the life pattern of the user U appears. It is also possible to estimate the user's life pattern based on the presence / absence of the user at the user's home. For example, the presence / absence of a user can be estimated based on the presence / absence of a user terminal (for example, a smartphone, a tablet, a notebook computer, etc.) around the washing machine 100, for example. Further, the presence or absence of the user terminal in the vicinity of the washing machine 100 may be estimated based on the position information of the user terminal, or may be estimated based on whether or not the user terminal exists on the same subnet as the washing machine 100. May be good.

(F. Input information for estimating whether or not the user U of the washing machine 100 is married)
The information conversion unit 202, for example, serves as input information (hereinafter referred to as "marriage estimation input information") to be input to the estimation model M in order to estimate the attributes related to whether or not the user U is married (married / unmarried). One or more of the listed information is generated based on the accumulated information I11. This information has a correlation with whether or not the user U is married, at least from the viewpoint described below. Therefore, one or more of the information listed below can be used as input information of the estimation model M whose output information is the attribute related to the presence or absence of marriage of the user U.

-Information on the number of washings of the washing machine 100-Information on the type of clothes washed by the washing machine 100-Information on the washing course of the washing machine 100-Information on the washing time of the washing machine 100-Information on the continuous operation of the washing machine 100-Washing Information on the time from the end of washing of the machine 100 until the clothes are taken out-Information on the timer reservation setting of the washing machine 100-Information on the dirt on the clothes washed by the washing machine 100-Use of bath water in the washing machine 100 Information on-Information on the frequency of cleaning the lint filter in the washing machine 100

It is considered that the "load on the washing machine 100" shows a tendency regarding whether or not the user U is married. For example, if either the men's clothing or the women's clothing is stored in the water tank 14 of the washing machine 100, it is considered that the user U is likely to be unmarried, and the men's clothing is considered to be unmarried. If both the and women's clothing are stored in the aquarium 14, User U is likely to be married. Further, when the clothing for adults and the clothing for children are stored in the water tank 14, it is highly likely that the user U is married.

Therefore, the information conversion unit 202 generates information indicating the result of clothing identification by the image recognition process as input information for marriage estimation. Further, since it is considered that each pixel value of the image data includes information indicating the characteristics of the subject, the information conversion unit 202 may generate the image data itself as input information for marriage estimation.

Further, it is considered that the "physical characteristics of the user U of the washing machine 100" also shows a tendency regarding whether or not the user U is married. For example, as a physical feature showing a tendency regarding the presence or absence of marriage, the presence or absence of a marriage ring can be mentioned. If it is identified by the image recognition process that the wedding ring is attached to the hand of the user U captured in the image in the aquarium, it is considered that the user U is likely to be married.

Therefore, the information conversion unit 202 generates information indicating the identification result of the physical feature by the image recognition process as the input information for marriage estimation. Further, since it is considered that each pixel value of the image data includes information indicating the characteristics of the subject, the information conversion unit 202 may generate the image data itself as input information for marriage estimation.

(H. Input information for estimating the residential area)
Among the information listed below, the information conversion unit 202, for example, as input information (hereinafter referred to as "input information for estimation of residential area") to be input to the estimation model M in order to estimate the attributes related to the residential area of the user U. One or more is generated based on the accumulated information I11. This information has a correlation with the residential area of the user U, at least in the viewpoint described below. Therefore, one or more of the information listed below can be used as input information of the estimation model M whose output information is the attribute related to the residential area of the user U.

-Information on the ambient temperature or humidity of the washing machine 100-Information on the frequency of the external power supply of the washing machine 100

It is considered that the "temperature around the washing machine 100" shows a tendency regarding the residential area of the user U. For example, when the washing machine 100 is installed indoors, the temperature around the washing machine 100 is considered to change relative to the outside air temperature, and the outside air temperature shows a different distribution depending on the area. Therefore, it is considered that the temperature around the washing machine 100 reflects the climate of the residential area of the user U. Further, it is considered that the "humidity around the washing machine 100" also shows a tendency regarding the residential area of the user U. Furthermore, the combination of "temperature around the washing machine 100" and "humidity around the washing machine 100" is considered to characterize the climate of the residential area in more detail.

Therefore, the information conversion unit 202 generates information indicating the temperature around the washing machine 100 as input information for estimating the residential area. The information conversion unit 202 may acquire the temperature information of each place from the server of the Japan Meteorological Agency, and may use the comparison result between the acquired temperature information of each place and the detection result of the ambient temperature / humidity sensor 611 as the input information for estimating the residential area.

The water used by the user U for washing may have properties characteristic of the residential area (for example, soft water or hard water). Therefore, if the water quality of the water used in the washing machine 100 can be measured, there is a possibility that the user's residential area can be estimated based on the measured water quality. Therefore, when the washing machine 100 has a water quality sensor for measuring the water quality of the water used, the information conversion unit 202 generates information on the water quality of the water used in the washing machine 100 as input information for estimating the residential area. You may.

Further, the "information regarding the frequency of the external power supply" is information indicating whether the AC frequency of the external power supply to which the washing machine 100 is connected is 50 Hz or 60 Hz. This information is derived, for example, based on the detection result of the power supply detection unit 604 included in the data D. Here, the frequency of the commercial power supply in eastern Japan is 50 Hz, and the frequency of the commercial power supply in western Japan is 60 Hz.

Next, the learning unit 203 will be described. By applying the various input information described above to the learning model L of machine learning, the learning unit 203 generates an estimation model M for estimating the attributes of the user U as a learned model. When the information about the washing machine 100 is input, the estimation model M is learned to output the estimation result of the attribute of the user U of the washing machine 100.

In the present embodiment, the learning unit 203 estimates the estimation model MB for estimating the attributes related to the number of users, the estimation model MB for estimating the attributes, and the attributes related to age as the estimation model M for estimating the attributes of the user U. Estimated model MC, estimation model MD for estimating attributes related to housing form, estimation model ME for estimating attributes related to installation position, estimation model MF for estimating attributes related to employment form, estimation model MG for estimating attributes related to marital status, residence Generate an estimation model MH that estimates the attributes related to the area.

In addition, in this specification, "learning" may mean either unsupervised learning or supervised learning. In the following, the case where the estimation model M is mainly generated by supervised learning will be described, but the estimation model M may be generated by unsupervised learning. For example, the learning unit 203 applies the various estimation input information generated by the information conversion unit 202 and the user registration information I12 to the learning model L as teacher data, and the relationship between the various estimation input information and the user registration information I12. By learning sex, estimated models MA to MH are generated as trained models. For the learning model L, for example, a neural network, reinforcement learning, deep learning, and the like can be used, but the learning model L is not limited to these. The learning model L may generate a regression curve or a classifier as a learning result of the above relationship. For example, an SVM (Support Vector Machine), a decision tree, a random forest, a k-nearest neighbor method, or the like may be used as the learning model L other than the neural network.

FIG. 10 is a diagram showing a specific example of the user registration information I12. In the user registration information I12, for each user U identification information (user ID), the number of users of the washing machine 100 owned by the user, the installation position, the gender of the user U, the age, the working style, the presence or absence of marriage, and the residential area. , The form of the dwelling in which the user resides (dwelling form), the brand of the softener or detergent used, and the like. However, the attribute of the user U to be estimated may be a matter other than the matters listed above as long as it is a matter that can be estimated based on the accumulated information I11 with respect to the user U. The learning unit 203 performs machine learning using the data in which each attribute of the user U and various estimation input information having a correlation with them are associated with each other as the teacher data, and thereby each attribute of the user U. It is possible to learn the relationship between and various estimation input information.

The association between each attribute of the user U and various estimation input information may be performed manually or mechanically. For example, the learning unit 203 may obtain the strength of the correlation between the various estimation input information and each attribute, and generate the teacher data by associating the various estimation input information with the attribute having the strongest correlation.

The estimation unit 204 uses the estimation model M obtained by the learning unit 203, and is a determination target based on the data D received from the washing machine 100 (hereinafter referred to as “determination target washing machine 100”) to be determined for the user attribute. The user attribute of the user U of the washing machine 100 is estimated. Here, "estimation" is not limited to determining one most probable attribute candidate, and the probability of each of a plurality of attribute candidates (for example, the possibility that the number of users is one: 10%, the number of users). There is a possibility that there are 2 people: 20%, a possibility that the number of users is 3, 50%, and a possibility that the number of users is 4 or more: 20%).

The estimation unit 204 inputs various estimation input information based on the data D obtained from the determination target washing machine 100 into the estimation model M, and uses the user attributes of the user U of the determination target washing machine 100 as output information of the estimation model M. Get the estimation result. In the present embodiment, the estimation unit 204 uses the above-mentioned input information for estimating the number of users, input information for gender estimation, input information for age estimation, and housing form as input information based on the data D obtained from the washing machine 100 to be determined. Input information for estimation, input information for installation position estimation, input information for employment form estimation, input information for marriage estimation, and input information for residential area estimation are input to the corresponding estimation models MA to MH, and the estimation models MA to MH are used. The output information includes (a) the number of people using the washing machine 100, (b) gender, (c) age, (d) housing type, (e) installation position, (f) working type, (g) marital status, and ( h) Output the estimation result of the residential area.

The information recording unit 205 stores the user attributes estimated by the estimation unit 204 in the storage unit 207 as the user attribute information I13.

The information output unit 206 transmits the user attribute information I13 obtained by the above-mentioned processing to the terminal device 300. As a result, the user attribute information I13 can be used for product development and service provision.

<4. Processing flow>
Next, the flow of processing will be described.
FIG. 11 is a diagram showing a flow of processing in the washing machine 100. First, the control unit 80 determines whether or not the power of the washing machine 100 is turned on (S101). When the power of the washing machine 100 is turned off, the control unit 80 waits until the power of the washing machine 100 is turned on.

On the other hand, when the power of the washing machine 100 is turned on (S101: YES), the control unit 80 transmits the detection result detected by the sensor group SU to the server 200 in a predetermined cycle or in real time (S102). ).

Next, the control unit 80 determines whether or not the power of the washing machine 100 has been turned off (S103). When the power of the washing machine 100 is ON (S103: NO), the control unit 80 repeats the process of S102. On the other hand, when the power of the washing machine 100 is turned off (S103: YES), the control unit 80 ends a series of processes. Then, the washing machine 100 repeats the above-mentioned processing (S101 to S103) for a predetermined period, for example.

FIG. 12 is a diagram showing a processing flow in the server 200. As a premise, the data D transmitted from the washing machine 100 to the server 200 is acquired by the information acquisition unit 201 and stored as the storage information I11.

First, the information conversion unit 202 generates the above-mentioned various estimation input information based on the accumulated information I11 (S201). Next, the estimation unit 204 inputs the generated input information for estimation into the estimation models MA to MH, respectively, and obtains an estimation result regarding the attribute of the user U as output information (S202). Next, the information output unit 206 causes the terminal device 300 to output information indicating the estimation result regarding the attribute of the user U estimated by the estimation unit 204.

<5. Action>
As a comparative example, it is conceivable to collect the attribute information of the user U by having the user enter information such as gender, age, number of users, and residential area on a questionnaire or the Web. However, in these cases, the input may be troublesome and the user may not input the information, and the information input by the user may not match the actual situation. It is also difficult to confirm the detailed usage status with the user in the form of a questionnaire.

On the other hand, in the present embodiment, the information processing system 1 is acquired by the information acquisition unit 201 using the information acquisition unit 201 that acquires the data D transmitted from the washing machine 100 and the machine-learned estimation model M. It includes an estimation unit 204 that estimates the attributes of the user U of the washing machine 100 based on various estimation input information obtained from the data D. According to such a configuration, the attribute of the user U can be estimated based on the usage result of the washing machine 100 of the user U without input by the user U. As a result, it is possible to reduce the burden of collecting the attribute information of the user U.

The server 200 may be mounted by using a plurality of information processing devices connected so as to be communicable via a network. In this case, each functional unit included in the server 200 may be distributed and mounted in a plurality of information processing devices. For example, the learning unit 203 and the estimation unit 204 may be mounted on different information processing devices.

In the above embodiment, the case where the server 200 estimates the information regarding the user attribute of the washing machine 100 by machine learning of the stored information has been described, but the washing machine 100 estimates the user attribute at a designated timing. Not only that, the estimation result may be accumulated, and the user's attribute regarding the change may be estimated based on the accumulated estimation result. For example, the server 200 may estimate changes in the family structure of users based on changes in the number of users. For example, if the number of users increases from one to two, the server 200 may presume that the user's marital status has changed from unmarried to married.

According to at least one embodiment described above, the acquisition unit for acquiring data regarding the state of the washing machine and the trained model generated by machine learning are used, and the data is acquired based on the data acquired by the acquisition unit. By having an estimation unit that estimates the attributes of the user of the washing machine, it is possible to estimate the attributes of the user who uses the washing machine regardless of the setting action of the user.

Although some embodiments of the present invention have been described, these embodiments are presented as examples and are not intended to limit the scope of the invention. These embodiments can be implemented in various other embodiments, and various omissions, replacements, and changes can be made without departing from the gist of the invention. These embodiments and variations thereof are included in the scope of the invention described in the claims and the equivalent scope thereof, as are included in the scope and gist of the invention.

1 ... Information processing system, 100 ... Washing machine, 1A ... Opening, 1B ... Washing machine body, 1D ... Door, 1DL ... Door lever, 4 ... Switch, 5 ... Operation panel, 6 ... Display, 7 ... Drum motor, 8 ... Power supply unit, 9 ... Drain valve unit, 10 ... Water supply valve unit, 11 ... Drying unit, 13 ... Rotating tank, 14 ... Water tank, 17 ... Replenisher liquid storage unit, 17A ... Opening, 23 ... Water supply port, 24 ... Intake port , 26 ... Detergent softener input part, 26F ... Lid surface, 30 ... Pump, 31, 32 ... Tank, 34 ... Lid, 601 ... Door switch, 602 ... Detergent remaining amount sensor, 603 ... Softener remaining amount sensor, 604 ... Power supply detector, 605 ... water level sensor, 606 ... bubble sensor, 607 ... weight sensor, 608 ... dirt sensor, 609 ... water temperature sensor, 610 ... tank camera, 611 ... ambient temperature / humidity sensor, 612 ... outside air temperature sensor, 613 ... Lint filter switch, 70 ... wireless module, 80 ... control unit, 81 ... washing control unit, 82 ... storage unit, 83 ... information recording unit, 84 ... information output unit, 200 ... server, 201 ... information acquisition unit, 202 ... Information conversion unit, 203 ... learning unit, 204 ... estimation unit, 205 ... information recording unit, 206 ... information output unit, 207 ... storage unit, 300 ... terminal device, 300a ... display device.

Claims (9)

The acquisition unit that acquires data on the state of the washing machine,
Using the trained model generated by machine learning, an estimation unit that estimates the attributes of the user of the washing machine based on the data acquired by the acquisition unit, and an estimation unit.
Information processing system equipped with. The data includes information on the model of the washing machine, information on the number of washings, information on the amount of clothes washed by the washing machine, information on washing time, information on continuous operation of the washing machine, and washing by the washing machine. Information on stains on clothes, information on how often the lint filter of the washing machine is cleaned, information on the washing course used in the washing machine, information on setting timer reservations, information on the use of bath water, and changes in reservation time. Containing any one or more of information regarding the type of clothes washed by the washing machine, or information regarding the time from the end of washing until the clothes are taken out.
The estimation unit estimates an attribute related to the number of people using the washing machine as an attribute of the user.
The information processing system according to claim 1. The data may be information about the type of clothes washed by the washing machine, information about the washing course used in the washing machine, information about the brand of the detergent or fabric softener used, or the physical characteristics of the user of the washing machine. Contains any one or more of the information about
The estimation unit estimates an attribute related to the gender of the user as an attribute of the user.
The information processing system according to claim 1. The data includes information on the model of the washing machine, information on the number of washings, information on the type of clothes washed by the washing machine, information on the washing course used in the washing machine, information on the continuous operation of the washing machine, and the like. Information on washing time, information on the brand of detergent or softener used in the washing machine, information on timer reservation setting of the washing machine, information on stains on clothes washed in the washing machine, bath water in the washing machine. Contains any one or more of information regarding the use of the laundry, information regarding the frequency of cleaning the lint filter in the washing machine, and information regarding the time from the end of washing until the clothes are taken out.
The estimation unit estimates an attribute related to the age of the user as an attribute of the user.
The information processing system according to claim 1. The data includes one or more of information about the temperature around the washing machine, information about the humidity around the washing machine, or information about the outside air temperature at the location where the washing machine is installed.
The estimation unit estimates an attribute related to the living form of the user as an attribute of the user.
The information processing system according to claim 1. The data includes one or more of information about the temperature around the washing machine, information about the humidity around the washing machine, or information about the outside air temperature at the location where the washing machine is installed.
The estimation unit estimates an attribute related to the installation position of the user as an attribute of the user.
The information processing system according to claim 1. The data relates to information on the type of clothes washed by the washing machine, information on the usage cycle for each predetermined period in the washing machine, information on changing the reservation time, and information on the presence or absence of a user terminal in the vicinity of the washing machine 100. Contains any one or more of the information
The estimation unit estimates an attribute related to the working form of the user as an attribute of the user.
The information processing system according to claim 1. The data includes information on the number of washings of the washing machine, information on the type of clothes washed by the washing machine, information on the washing course selected in the washing machine, information on the washing time of the washing machine, and information on the washing machine. Information on continuous operation, information on the time from the end of washing until clothes are taken out, information on timer reservation settings of the washing machine, information on stains on clothes washed by the washing machine, bath water in the washing machine. Contains any one or more of the information regarding the use of the lint filter and the frequency of cleaning the lint filter in the washing machine.
The estimation unit estimates, as an attribute of the user, an attribute relating to the presence or absence of marriage of the user.
The information processing system according to claim 1. The data includes any one or more of information about the temperature around the washing machine, information about the humidity around the washing machine, or information about the frequency of the external power source of the washing machine.
The estimation unit estimates an attribute related to the residential area of the user as an attribute of the user.
The information processing system according to claim 1.

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