Information processing apparatus and program

Abstract

To appropriately acquire information relating to a user's excretion and provide information based on the excretion.SOLUTION: An information processing apparatus comprises a control unit and a storage unit. The storage unit stores a first learned model that has been learned in advance and a second learned model that has been learned in advance. The control unit inputs user history information and excretion information into the first learned model, outputs prediction information relating to the next excretion of the user, inputs the user history information and the prediction information into the second learned model and outputs a recommendation so that a state of the excretion becomes an ideal state.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to an information processing device and the like. [Background technology]

[0002] For example, the care support device described in Patent Document 1 includes a first sensor that detects information related to the excrement of the care recipient, a second sensor that detects information related to the sleep state of the care recipient, and a controller. The controller executes a detection process that detects excretion by the care recipient using the detection value of the first sensor, a determination process that determines the sleep state of the care recipient using the detection value of the second sensor, and an output process that detects excretion by the care recipient and, if it is determined that the care recipient is not in a deep sleep state, outputs information indicating that excretion has occurred. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent Publication No. 2021-124961 Summary of the Invention [Problem to be solved by the invention]

[0004] The present disclosure aims to obtain appropriate information regarding a user's excretion and to provide information based on excretion. [Means for solving the problem]

[0005] The information processing device of the present disclosure is an information processing device including a control unit and a memory unit, wherein the memory unit stores a first trained model that has been trained in advance and a second trained model that has been trained in advance, and the control unit inputs a user's history information and excretion information into the first trained model and outputs prediction information regarding the user's next excretion, and inputs the user's history information and the prediction information into the second trained model and outputs recommendations so that the excretion state will be ideal.

[0006] In addition, the program of the present disclosure causes a computer having a memory unit that stores a first trained model that has been trained in advance and a second trained model to realize the following steps: inputting a user's history information and excretion information into the first trained model and outputting prediction information regarding the user's next excretion; and inputting the user's history information and the prediction information into the second trained model and outputting recommendations so that the excretion state will be ideal. [Effects of the Invention]

[0007] According to the present disclosure, it is possible to obtain appropriate information regarding the user's excretion and provide information based on the excretion. [Brief explanation of the drawings]

[0008] [Figure 1] FIG. 1 is a diagram illustrating an entire system according to a first embodiment. [Figure 2] FIG. 1 is a diagram illustrating a feces detection device according to a first embodiment. [Figure 3] FIG. 1 is a diagram illustrating a feces detection device according to a first embodiment. [Figure 4] FIG. 2 is a diagram illustrating a sensor device according to the first embodiment. [Figure 5] FIG. 2 is a diagram illustrating a functional configuration of hardware of a first server device in the first embodiment. [Figure 6] FIG. 2 is a diagram illustrating a functional configuration of hardware of a second server device in the first embodiment. [Figure 7] FIG. 2 is a diagram illustrating the functional configuration of hardware of a terminal device according to the first embodiment. [Figure 8] FIG. 2 is a diagram illustrating a software configuration according to the first embodiment. [Figure 9] FIG. 2 is a diagram illustrating an example of a data structure in the first embodiment. [Figure 10] FIG. 2 is a diagram illustrating screen transitions in the first embodiment. [Figure 11] FIG. 3 is a diagram illustrating a display screen of an overall dashboard in the first embodiment. [Figure 12] FIG. 2 is a diagram illustrating a display screen of a personal dashboard in the first embodiment. [Figure 13] FIG. 2 is an enlarged view of a portion of the personal dashboard according to the first embodiment. [Figure 14] FIG. 2 is a diagram illustrating a display screen of a personal analysis page in the first embodiment. [Figure 15] FIG. 4 is a diagram illustrating an operation of selecting an ideal state in the first embodiment. [Figure 16] FIG. 10 is a diagram illustrating a display screen of a monthly history screen in the first embodiment. [Figure 17] FIG. 4 is a diagram illustrating a main process in the first embodiment. [Figure 18] FIG. 10 is a diagram illustrating an excretion notification determination process in the first embodiment. [Figure 19] 5A to 5C are diagrams illustrating an excretion detection process in the first embodiment. [Figure 20] FIG. 4 is a diagram illustrating an excretion prediction process in the first embodiment. [Figure 21] FIG. 10 is a diagram illustrating an overall dashboard display process in the first embodiment. [Figure 22] FIG. 10 is a diagram illustrating a personal dashboard display process in the first embodiment. [Figure 23] FIG. 10 is a diagram illustrating a personal analysis screen display process in the first embodiment. [Figure 24]FIG. 10 is a diagram illustrating a first trained model generation process in the first embodiment. [Figure 25] FIG. 10 is a diagram illustrating a second trained model generation process in the first embodiment. [Figure 26] FIG. 1 is a diagram illustrating the state of fecal moisture content. [Figure 27] FIG. 10 is a diagram illustrating a sensor device according to a second embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0009] Hereinafter, one embodiment of the present invention will be described with reference to the drawings. Specifically, the case where the system of the present invention is applied will be described, but the scope of application of the present invention is not limited to this embodiment.

[0010] [1. First embodiment]

[0011] [1.1 Overall System] FIG. 1 is a diagram illustrating an overall overview of system 1. As shown in FIG. 1, system 1 includes a feces detection device 3 capable of detecting a user's feces. The feces detection device 3 includes a feces detection device 10 that is attached to, for example, a diaper, underwear, or the like to detect the user's feces, and a feces detection device 15 that is attached to another location, for example, a toilet bowl, to detect the user's feces. One or more feces detection devices 10 may be attached to each user. Furthermore, the feces detection device 15 may be an inspection device that is attached to a location other than a toilet, for example, by medical staff.

[0012] The feces detection device 3 may be capable of communicating with the network NW via an access point AP by wireless communication means. The network NW may be, for example, a local area network (LAN) within a facility, or a wide area network (WAN) such as the Internet. It may also be a network in the form of a combination of multiple networks such as LANs and wide area networks.

[0013] The system 1 also includes a server device 5. The server device 5 may include one or more server devices, and includes a first server device 20 and a second server device 30 in FIG.

[0014] The first server device 20 is a server device that can manage information to support users and predict the user's condition, etc. The second server device 30 is a server device that manages various information related to users. For example, it may be a server device that manages electronic medical records that include information related to the user's illnesses, etc.

[0015] Furthermore, a terminal device 7 may be further connected to the network NW. The terminal device 7 is a device that allows a general computer, medical personnel such as a doctor, nurse, pharmacist, or laboratory technician, or facility staff (hereinafter referred to as staff, etc.) to input or display information. For example, the terminal device 40 may be installed in a nurse's station, etc.

[0016] The system 1 may also include a measuring device 52 or a measuring device 54 for measuring the user's status. The measuring device 52 is, for example, provided between the user and a bed device (mattress) and detects the user's vibrations. The vibrations detected by the measuring device 52 are the user's body movements, and the measuring device 52 may acquire the user's biological information (heart rate, respiratory rate, activity level) or the user's sleep state (whether the user is awake or asleep) based on the detected vibrations. The measuring device 54 acquires the user's biological information at any timing. For example, the measuring device 54 can measure and acquire the user's body temperature, blood pressure, blood glucose level, SpO2 value, etc. at any timing. The measuring device 54 may also acquire these biological information values ​​at predetermined time intervals.

[0017] The device may further include an environment measuring device 56 that measures the environment. The environment measuring device 56 acquires environment information such as temperature and humidity. The environment measuring device 56 can acquire values ​​based on the environment information (environment information values) at predetermined time intervals.

[0018] That is, the measuring device 52 can acquire continuous biometric information of the user, while the measuring device 54 can acquire discrete biometric information of the user (biometric information acquired sporadically).

[0019] [1.2 Overview of the feces detection device] An overview of the feces detection device 3 will now be described. As explained in Figure 1, the feces detection device 3 includes a feces detection device 10 that is attached to the underwear or diaper of the user, and a feces detection device 15 that is attached to the toilet.

[0020] [1.2.1 How to wear] An example of a method for attaching the feces detection device 10 will be described with reference to the drawings. Figure 2 is a diagram showing an example of a method for attaching the feces detection device 10 to a diaper A10.

[0021] The diaper A10 is for receiving excrement such as urine and feces from a user, and is, for example, a disposable paper diaper. The diaper A10 may also be a cloth diaper. As shown in Fig. 2, the feces detection device 10 is attached to a main body A12 of the diaper A10, which is worn on the lower abdomen of the user.

[0022] The main body A12 has a ventral portion A13 located on the ventral side of the user, a dorsal portion A14 located on the dorsal side of the user, and a crotch portion A15 that is recessed between the ventral portion A13 and the dorsal portion A14 and through which the user's crotch passes. The main body A12 has a laminated structure made of nonwoven fabric, resin film, absorbent sheet, etc.

[0023] The back portion A14 is provided with a pair of hook-and-loop fasteners A14a. The user can put on the diaper A10 by adhering the hook-and-loop fasteners A14a to the abdominal portion A13. The sheet portion A17 is made of, for example, a white nonwoven fabric and is positioned between the user's buttocks or private parts and the main portion A12 to receive the user's excrement.

[0024] The feces detection device 10 is provided in the diaper A10. The feces detection device 10 includes a detection unit 10a having an illumination unit and a light receiving unit, a cable 10b extending from the detection unit 10a, and a main body 10c connected to the cable 10b and having a control unit. In addition to the control unit, the main body 10c also has a memory unit and an alarm unit.

[0025] As shown in the figure, the detection unit 10a and the cable 10b are disposed inside the diaper A10 in a state where they are inserted inside the bag-shaped sheet portion 10d.

[0026] The bag-shaped sheet portion 10d is made of a waterproof bag with an opening 10e on one side in the longitudinal direction. The detection unit 10a and cable 10b are inserted into the bag-shaped sheet portion 10d through the opening 10e and are disposed in the diaper A10 in this state. The bag-shaped sheet portion 10d is formed to a length such that the opening 10e is located outside the diaper A10 when disposed in the diaper A10. The bag-shaped sheet portion 10d is made of, for example, a waterproof transparent film with a nonwoven fabric bonded to the outer peripheral surface.

[0027] The detector 10a detects the color of feces adhering to the bag-shaped sheet portion 10d and transmits a detection signal (information on the received light color) to the control unit of the main body portion 10c via the cable 10b. The detector and cable 10b are protected from contamination by excrement by the waterproof bag-shaped sheet portion 10d. This allows the feces detection device 10 to be easily reused by simply replacing the diaper A10 and the bag-shaped sheet portion 10d.

[0028] The main body 10c is located outside the diaper A10 when the detection unit 10a is disposed inside the diaper A10. That is, the length of the cable 10b is set so that the main body 10c is located outside the diaper A10 when the detection unit 10a is disposed inside the diaper A10. The main body 10c is attached to the diaper A10 by, for example, a hook-and-loop fastener or a clip (neither of which are shown).

[0029] The feces detection device 10 may be attached to the diaper A10 in different ways. For example, as shown in Fig. 3, the feces detection device 10 may be provided between the sheet portion A17 and the main body portion A12. The sheet portion A17 is made of, for example, a white nonwoven fabric, and is positioned between the user's buttocks or private parts and the main body portion A12 to receive the user's excrement.

[0030] Here, the feces detection device 10 has a light emitting section 10L that emits light and a light receiving section 10R that receives light.

[0031] For example, the illuminating unit 10L may emit light continuously when powered on, or may emit light intermittently, for example, every several tens of seconds to several minutes. The light receiving unit 10R receives reflected light of the light emitted from the illuminating unit 10L.

[0032] Specifically, in the case of FIG. 3, the light-receiving unit 10R receives reflected light from the sheet portion A17. When the user has not defecated, the light-receiving unit 10R receives white light reflected from the sheet portion A17. On the other hand, when the user has defecated, the sheet portion A17 changes from white to the color of feces. As a result, when the user has defecated, the light-receiving unit 10R receives feces-colored light reflected from the sheet portion A17. In addition, in the case of FIG. 2, the light-receiving unit 10R may receive reflected light from the bag-shaped sheet portion 10d. Furthermore, the light-receiving unit 10R may have a color sensor and a transmission sensor as a sensor device. Note that, as will be described in detail later, it is preferable that the present embodiment has multiple light-receiving units 10R. There are multiple ways to arrange the light-receiving units 10R, and specific arrangement methods will be described later.

[0033] 2 and 3 are merely for explaining how to arrange the feces detection device 10, and specific methods for detecting excrement, types of sensors, etc. will be described later.

[0034] [1.3 Hardware Configuration] Next, the hardware configuration of each device will be described.

[0035] [1.3.1 Feces detection device] The configuration of the feces detection device 10 will be described with reference to Fig. 4(a). The feces detection device 10 is configured to include one or more of a control unit 100, a memory unit 110 (including storage, ROM, and RAM), a first sensor unit 120, a second sensor unit 130, and a communication unit 190 as necessary.

[0036] The control unit 100 controls the entire feces detection device 10. The control unit 100 realizes various functions by reading and executing various programs stored in the storage unit 110, which is a storage device. The control unit 100 may be realized by one or more control devices / arithmetic units (CPUs (Central Processing Units), SoCs (System on a Chip)). The control unit 100 may also be configured by a control circuit.

[0037] The memory unit 110 stores various types of information as data. The memory unit 110 is generally a device including one or more of a storage, a ROM, and a RAM, and stores data in any of them as needed. The memory unit 110 of this embodiment is generally preferably configured with a semiconductor memory or the like.

[0038] The first sensor unit 120 is a sensor that acquires the status of the user's excrement. For example, an optical sensor is preferably used as the sensor. The first sensor unit 120 has one or more sensors. The first sensor unit 120 outputs, as sensor information, the intensity and color of light emitted from a light source (illumination unit) and received by a light receiving unit from each sensor.

[0039] The communication unit 190 communicates with other devices. For example, the communication unit 190 transmits sensor information output by the first sensor unit 120 to other devices using a short-range wireless communication method (for example, Bluetooth (registered trademark) or the like). If the communication unit 190 is a communication module compatible with wireless LAN, it connects to a network via an access point or the like and transmits the sensor information to other devices (for example, the first server device 20 or the like). The communication unit 190 may provide communication using a method that enables mobile communication such as 4G / LTE / 5G / 6G. The communication unit 190 may also be an interface (for example, USB) for communicating with other devices.

[0040] (sensor device) The sensor device constituting the first sensor section 120 will be described with reference to Fig. 4(b). Fig. 4(b) is a diagram illustrating a detection section 10T of the feces detection device 10.

[0041] The detection unit 10T has a light-emitting unit and a light-receiving unit as necessary. In this embodiment, it has at least one light-emitting unit and multiple light-receiving units. The feces detection device 10 receives light emitted from the light-emitting unit with the light-receiving unit, and can obtain a color signal and light intensity using the light. For example, when the first sensor unit 120 is a color sensor, the feces detection device 10 can obtain the color of the feces. Furthermore, the first sensor unit 120 can obtain the light transmittance from the light intensity. Since the light color and light transmittance can be obtained from the sensor device, the system can obtain the properties of the feces (such as the hardness of the feces).

[0042] Furthermore, by arranging a plurality of light receiving units 122, it is possible to obtain the amount of feces excreted by the user.

[0043] 4(b) schematically shows the arrangement of the light receiving unit 122 in the detection unit 10T, with two light receiving units 124 and 126 arranged in a housing 10K. The housing 10K is then provided in a pad 10P. The pad 10P is provided in the diaper A10, and is preferably arranged so as to be positioned along the buttocks of the user from the dorsal side to the ventral side.

[0044] In this embodiment, by providing the detection unit 10T with a plurality of light receiving units 122, it is possible to detect the feces excreted by the user as a line or a surface rather than as a point. For example, even if the feces of the user are excreted within the range of the dashed line us1, they can be detected using the light receiving unit 126.

[0045] The light receiving unit 122 may also be arranged above and below the housing 10K. In FIG. 4(c), the light receiving unit 122 is arranged above and below the housing 10K. For example, instead of the light receiving unit 124 shown in FIG. 4(b), light receiving units 124a and 124b are arranged above and below the housing 10K, sandwiching the housing 10K. Similarly, instead of the light receiving unit 126, light receiving units 126a and 126b are arranged. By arranging the light receiving units 122 above and below, the feces detection device 10 can detect feces using the same algorithm regardless of the up-down orientation. For example, even if the user's feces are excreted within the range of the dashed line us2, they can be detected using the light receiving units 126a and 126b.

[0046] The number of light receiving sections 122 is just an example, and may be at least two. For example, as shown in Fig. 4(c), four light receiving sections 122 may be arranged.

[0047] [1.3.2 First server device] The configuration of the first server device 20 will be described with reference to FIG.

[0048] The control unit 200 controls the entire first server device 20. The control unit 200 realizes various functions by reading and executing various programs stored in a storage unit 210 (for example, storage 210A or ROM 210B) which is a storage device. The control unit 200 may be realized by one or more control devices / arithmetic units (CPUs (Central Processing Units), SoCs (System on a Chip)). The control unit 200 may also be configured by a control circuit.

[0049] Various types of information are stored as data in the storage unit 210. The storage unit 210 is generally a device including one or more of a storage 210A, a ROM 210B, and a RAM 210C, and stores data in any of these as needed.

[0050] Storage 210A is a non-volatile storage device capable of storing programs and data. For example, it may be configured as a storage device such as an HDD (Hard Disk Drive) or an SSD (Solid State Drive). Storage 210A may also be configured as an externally connectable USB memory or memory card. Storage 210A may also be, for example, a storage area on the cloud.

[0051] The ROM 210B is a non-volatile memory that can retain programs and data even when the power is turned off.

[0052] The RAM 210C is a main memory that is mainly used when the control unit 200 executes processing. The RAM 210C is a rewritable memory that temporarily stores programs read from the storage 210A or the ROM 210B, and data including execution results.

[0053] The operation unit 250 accepts operation inputs from an operator such as a user or an administrator to the first server device 20. For example, the operation unit 250 may be an input device such as a keyboard or a mouse that is directly connected to the first server device 20. Alternatively, the operation unit 250 may be another remotely connected terminal device that can be operated by the first server device 20.

[0054] The output unit 260 outputs the content processed by the first server device 20. For example, the output unit 260 may be a display device such as a display, or a display control device that outputs information to a display. The output unit 260 may also be a printing device such as a printer, or an alarm device that outputs an alarm sound, etc.

[0055] The output unit 260 may also have a function of providing a user interface (WEB interface) that outputs information to other devices via the communication unit 290. The output unit 260 may also have a function of sending notifications or emails to other terminal devices via the communication unit 290.

[0056] The communication unit 290 communicates with other devices. The communication unit 190 is a network interface card connectable to a network, and communicates with other devices by connecting to the network. The communication unit 290 may also acquire sensor information from the feces detection device 10. The communication unit 290 may also provide communication using a method that allows mobile communication such as 4G / LTE / 5G / 6G. The communication unit 290 may also be an interface (for example, USB) for communicating with other devices.

[0057] [1.3.3 Second server device, terminal device] The second server device 30 and the terminal device 40 have the same hardware configuration as that described for the first server device 20.

[0058] For example, as shown in FIG. 6, the second server device 30 includes a control unit 300, a memory unit 310 (storage 312, ROM 314, and RAM 316), an operation unit 350, an output unit 360, and a communication unit 390.

[0059] As shown in FIG. 7, the terminal device 40 includes a control unit 400, a memory unit 410 (storage 412, ROM 414, and RAM 416), an operation unit 450, an output unit 460, and a communication unit 490.

[0060] Detailed explanations of the second server device 30 and the terminal device 40 have been given for the first server device 20, so detailed explanations will be omitted. The first server device 20, the second server device 30, and the terminal device 40 may have one or more of the configurations shown in Figs. 5 and 6 as necessary.

[0061] 1.3.4 Other equipment The other devices also have components such as a control unit, a storage unit, and a communication unit, and are capable of transmitting necessary information to the first server device 20.

[0062] For example, the feces detection device 15 can be provided in a toilet (e.g., a fixed toilet or a portable toilet). The feces detection device 15 may be a device that is permanently installed in the toilet, or a device that is installed later. The feces detection device 15 is equipped with a photographing means such as a camera device, and can photograph feces, which are excrement of the user. The feces detection device 15 then transmits image data of the photographed feces to the first server device 20. The feces detection device 15 may also analyze the photographed image of the feces and transmit information such as the color and condition of the feces (e.g., the amount of feces, the properties of the feces, etc.) to the first server device 20. The feces detection device 15 may also be realized by installing an application on a terminal device such as a user's smartphone.

[0063] The measuring device 52 detects the user's body movements and acquires biometric information such as the user's heart rate and breathing based on the body movements. For example, the measuring device 52 acquires the heart rate, breathing rate, and activity level as values ​​related to the user's biometric information, and transmits them to the first server device 20. The measuring device 52 may also acquire the user's sleep state (whether the user is asleep or awake, etc.) as the user's state, or the user's getting out of bed, state of being in bed, and posture while in bed (for example, sitting on the edge of the bed, lying on one side, lying on one's back, lying on one's stomach, etc.), and transmit these to the first server device 20. The measuring device 52 is a device that can mainly continuously acquire the user's biometric information and state of the user.

[0064] The measuring device 54 can measure the user's biological information at any timing. For example, the measuring device 54 measures and acquires values ​​of the user's biological information such as body temperature, heart rate, blood pressure (systolic blood pressure, diastolic blood pressure), blood glucose level, SpO2, etc. Then, the measuring device 54 transmits the acquired biological information to the first server device 20.

[0065] Each device transmits various information to the first server device 20, but may also transmit it to the second server device 30, which is an electronic medical record server that manages information related to the user's illness and medical treatment, as necessary.

[0066] [1.4 Software Configuration] Next, the software configuration will be described with reference to FIG.

[0067] [1.4.1 Storage section] First, a description will be given of the information stored in the storage unit 210. Note that, although the information is described as being stored in the storage unit 210 in Fig. 8(a), one or more pieces of information may be stored in another device, for example, the second server device 30. Furthermore, one or more pieces of information may be stored in a storage area on a cloud on a network.

[0068] The storage unit 210 stores user information about each user. Here, the user information includes, for example, one or more of basic information, excretion information, diaper information, medication information, disease information, diet information, biological information, and condition information. Each piece of information will be explained below.

[0069] The basic information storage area 211 stores basic information. Here, the basic information is basic information about a user. Fig. 9(a) is an example of basic information, and for example, an ID (e.g., "Usr001"), a user's name (e.g., "Mr. A"), sex (e.g., "male"), and date of birth (e.g., "1950 / 05 / 01") may be stored as identification information for uniquely identifying a user.

[0070] The basic information may also include disease information. The disease information may be input by, for example, staff such as doctors or nurses. The disease information may include not only information about the user's disease (e.g., high blood pressure, diabetes, constipation), but also information such as the presence or absence of allergies, foods that should be avoided, the amount of activity required, and the amount of fluid required. The disease information may also be acquired from the second server device 30, which is an electronic medical record server.

[0071] The excretion information storage area 212 stores excretion information. Here, the excretion information is information related to the user's excretion. In this embodiment, the excretion mainly refers to feces, but information related to the user's excretion is stored.

[0072] For example, Figure 9(b) is an example of excretion information, which stores, for example, the user's ID (e.g., "Usr001"), the date and time when the user's excretion was detected (e.g., "2024 / 02 / 01 11:15:10"), feces information, and one or more pieces of historical information when the user's excretion was detected. For example, the history information may include the location where the user excreted (e.g., "diaper"), the user's medication information may include the date and time of the last medication administered to the user after the user excreted and the details of the medication administered to the user (e.g., "2024 / 02 / 01 09:10:05 1 drop of X medicine"), and the user's dietary information may include the date and time of the last meal consumed by the user after the user excreted and the details of the meal (e.g., "2024 / 02 / 01 08:59:10 bread"), the amount of fluid the user has consumed that day (e.g., "450 ml"), and related information.

[0073] Here, stool information is information about the condition of the stool. The stool information includes the color of the user's stool, the properties of the stool, and the amount of stool excreted. Here, stool color changes continuously, but can be broadly classified into, for example, black, brown, yellow, and other colors. Furthermore, stool properties change continuously, such as hardness, but can be broadly classified into watery, soft, and solid. Furthermore, stool properties include the shape and softness of the stool.

[0074] The location of excretion is the location where the user excreted feces, and can include, for example, "diaper," "toilet (fixed)," and "toilet (portable)."

[0075] The excretion information may include, for example, information about the user's urine, as information about the user's excretion. For example, the excretion information may include the time when the user urinates and the amount of urine.

[0076] The related information can store related information from the history information as needed at the timing when the user's excretion is detected. For example, one or more pieces of information such as biometric information values ​​(e.g., heart rate, respiratory rate) when the user excretes, diaper information, information related to illness, and information related to sleep may be stored. In other words, the related information can be information that the first server device 20 can obtain at the timing when the user's excretion is detected.

[0077] The history information storage area 213 stores information about the user's history. Here, the user's history is information about the user up to the time of acquisition. For example, it is information that can be acquired by the user information acquisition unit 203 at the time of acquisition. As an example, as shown in FIG. 8(b), the history information storage area 213 secures areas for a diaper information storage area 213A that stores diaper information as the user's history information, an eating and drinking information storage area 213B that stores eating and drinking information, a biological information storage area 213C that stores biological information, and a status information storage area 213D that stores status information. The history information may also include environmental information acquired by the environment measurement device 56.

[0078] The diaper information storage area 213A stores information about the user's diapers. For example, FIG. 9(c) shows an example of diaper information, which stores information about diaper changes in association with the user's ID. For example, the information may include the date and time of diaper change (e.g., "2024 / 01 / 31 22:25:30"), the diaper size (e.g., "medium"), and the type of diaper (e.g., "paper").

[0079] The diet information storage area 213B stores diet information. The diet information is information about the food and drink consumed by the user, and may include, for example, information about the meal contents, such as the type of meal, the type of ingredients, the intake amount of each meal, and the intake amount of ingredients. It may also include the amount of water consumed by the user. The diet information may also include, for example, the date and time when the user ate a meal.

[0080] The biological information storage area 213C stores the biological information of the user. The biological information relates to the biological information of the user and includes values ​​acquired from the measuring device 52 or the measuring device 54 and values ​​input by staff or the like. The biological information may include, for example, information such as heart rate, respiratory rate, SpO2, activity level, blood glucose level, etc. The biological information may also include information such as height and weight. The biological information may further include the date and time when the measuring device 52 or the measuring device 54 acquired the biological information for each user.

[0081] The status information storage area 213D stores status information related to the user's status. Here, the status information related to the user's status includes one or more pieces of information other than the above-mentioned information. For example, the status information may include at least one piece of information related to the user's sleep, information related to the user's daytime activities, activity content, activity duration, activity frequency, and activity amount.

[0082] The status information may also include information regarding the recorded content of care provided to the user. For example, the recorded content of care input by a staff member or the like from the terminal device 40 may be transmitted to the first server device 20 or the second server device 30. For example, the care provided by a staff member or the like to a user (e.g., assistance with excretion, changing the user's position) may generally be transmitted to the second server device 30 and recorded therein. The terminal device 40 displays a screen for recording the care provided by the staff member or the like. When the staff member or the like inputs the recorded content of the care provided on the screen, the terminal device 40 receives and acquires the recorded content of care. The terminal device 40 transmits the recorded content of care to the first server device 20 or the second server device 30. The first server device 20 may acquire the recorded content of care from the second server device 30 and store it as status information.

[0083] The drug information storage area 213E stores information about medications taken by a user or medications prescribed by a doctor (hereinafter referred to as medication, etc.). The drug information may include the date and time when the user was given medication, the type of medication, and the dosage (or the amount of medication). The drug information may also be obtained from the second server device 30, which is an electronic medical record server.

[0084] The schedule information storage area 215 stores information based on the situation expected for the user after the acquisition time as schedule information. In this embodiment, the schedule information storage area 214 secures areas for a menu information storage area 214A for storing menu information and a support information storage area 214B for storing support information, as shown in Fig. 8(c).

[0085] The menu information storage area 214A stores menu information, which is information about the menu that the user plans to eat and drink. The menu information may include information about the types and amounts of ingredients, and information about the dishes. The menu information may also include information about salt content, sugar content, calories, etc. The menu information may also include information about the amount of water the user should consume.

[0086] The support information storage area 214B stores support information, which is information related to support for the user. The support information includes information other than menus as future information for the user. The support information may include the user's scheduled toileting time and scheduled diaper changing time. The support information may also include medication schedule information as information on the user's planned medication, etc. The medication schedule information may include the timing at which the user will take medication (e.g., laxative, etc.), the dosage (dose), the scheduled time of medication, etc.

[0087] The trained model storage area 220 is an area for storing trained models. In this embodiment, a first trained model 222 and a second trained model 224 are stored in the trained model storage area 220.

[0088] The first trained model 222 is a trained model that has been trained based on multiple pieces of information, such as basic information, excretion information, history information, and schedule information. For example, when the first trained model 222 receives the user's basic information, history information, and schedule information, it outputs information about the user's excretion. For example, the first trained model 222 is a trained model that can output the predicted timing of the user's next excretion and the condition of the excreted stool (such as the color and properties of the stool).

[0089] The second trained model 224 is a trained model that has been trained based on, for example, multiple pieces of information such as basic information, excretion information, history information, and schedule information, as well as information on ideal stool provided by staff, etc. Based on one or more of these pieces of information, the second trained model 224 may be a classification model that can output information on whether the color of the user's stool is the desired color or whether the properties of the user's stool are the desired properties.

[0090] The second trained model 224 can further use schedule information to output whether the color and properties of future stool are the desired stool properties. That is, the second trained model 224 can output the state for ideal excretion of the user. For example, the ideal excretion state (ideal timing for excreting stool, ideal color of stool, ideal properties of stool) may be defined as a reward function, and one or more values ​​input in the history information and the schedule information that maximize the reward may be output.

[0091] The second trained model 224 may output information on a first flag indicating whether the predicted color of the user's stool is the target color or not, and information on a second flag indicating whether the properties of the user's stool are the target properties or not. The information on the first flag and the second flag does not need to be binary, and may be provided in three or four stages.

[0092] The second trained model 224 may output recommendation information necessary for the user's next stool to have a target color or properties. The second trained model 224 may be, for example, a decision tree model.

[0093] In this case, when the predicted color of the stool when the user next defecates is not the target color or the predicted characteristics of the stool are not the target characteristics, the second trained model 224 identifies the group whose conditions for each branch are closest to the input information about the user and the information indicating what should be avoided, and in which the stool will be the target color or characteristics when the user next defecates, and outputs the conditions that differ from the input biological information, excretion information, and information indicating what should be avoided as suggested content.

[0094] Note that both the first trained model 222 and the second trained model 224 may be models trained by machine learning, deep learning, or reinforcement learning. For convenience of explanation, the first trained model 222 and the second trained model 224 will be described using decision tree models, but are not limited to this, and for example, support vector machines, logistic regression, etc. may also be used.

[0095] 1.4.2 Control Unit The control unit 200 executes the programs stored in the storage unit 210 to realize the following functions.

[0096] The excretion information acquisition unit 201 acquires excretion information based on information acquired from the feces detection device 10 or the feces detection device 15. Specifically, the excretion information is acquired by combining the timing acquired by the feces detection device 10 or the feces detection device 15 and the feces state acquired by the feces state acquisition unit 202, and is stored in the excretion information storage area 212.

[0097] The feces state acquisition unit 202 acquires and determines the state of feces based on signals acquired from the feces detection device 10 and the feces detection device 15.

[0098] For example, when the feces state acquisition unit 202 acquires sensor information from the feces detection device 10, it acquires the feces state based on the sensor information. Also, when the feces state acquisition unit 202 acquires image data from the feces detection device 15, it acquires the feces state based on the image data.

[0099] The stool condition is obtained by acquiring stool color information (first stool information), which is information about the color of the stool, stool condition information (second stool information), which is information about the condition of the stool, and stool volume information (third stool information), which is the amount of stool the user has excreted.

[0100] There are various possible colors of stool. In addition to the common colors such as "brown" and "yellow," colors such as "black," "red," "white," and "green" are included. In this embodiment, the colors are classified as "yellow," "brown," "black," and "other."

[0101] The stool properties may be determined, for example, according to the Bristol Scale (BSS). Stool properties may be classified as "hard stool," "slightly hard stool," "normal stool," "slightly soft stool," "mud-like stool," or "watery stool." In this embodiment, the stool property information is classified as "watery," "soft," or "solid." In this way, the stool properties can be identified based on the shape and softness of the stool.

[0102] The amount of stool may be divided into multiple stages, for example, based on weight, etc. For example, a typical amount of stool, about 150 g to 200 g, may be classified as "medium," amounts smaller than that as "small," and amounts larger than that as "large."

[0103] The stool state acquisition unit 202 can acquire the state of the stool based on the excretion signal acquired from the stool detection device 10. As a method for the stool state acquisition unit 202 to acquire the state of the stool, for example, the method disclosed in JP 2023-021018 A (filing date: July 26, 2022, priority date: July 28, 2021, invention name: stool detection system and stool detection device) can be used.

[0104] Furthermore, the stool state acquisition unit 202 can acquire the state of the stool based on the excretion signal acquired from the stool detection device 15. For example, the control unit 200 acquires image data of an image of stool excreted by the user from the stool detection device 15 as the excretion signal. The control unit 200 can analyze the acquired image data and acquire stool color information, stool property information, and stool volume information. Furthermore, the control unit 200 can acquire stool color information, stool property information, and stool volume information by inputting the acquired image data into a trained model.

[0105] The user information acquisition unit 203 acquires user information and stores the acquired user information in the storage unit 210 as necessary.

[0106] The excretion notification unit 204 issues a notification in accordance with the excrement excreted by the user. The excretion notification unit 204 issues a notification that the user has excreted, for example, based on a signal received from the feces detection device 10 or the feces detection device 15. Details of the excretion detection and notification operations of the excretion notification unit 204 will be described later.

[0107] The recommendation output unit 205 outputs a recommendation based on the user information to bring the user into an appropriate state regarding excretion. Here, the recommendation output unit 205 may output a recommendation by, for example, displaying a message or a pop-up display on the output unit 700 (display device) of the terminal device 40. In addition to displaying a message, the recommendation output unit 205 may output the recommendation by means of sound, light, vibration, or the like, or by sending a notification to another device (such as an alarm, email, or pop-up display notification).

[0108] In addition, when the recommendation output unit 205 outputs a recommendation, it acquires and outputs information that can be recommended using the first trained model 222 and the second trained model 224.

[0109] For example, the recommendation output unit 205 acquires the timing of the user's next excretion and the state of the excreted stool (such as the color and properties of the stool) as prediction information by inputting one or more pieces of information from among basic information, excretion information, history information, and schedule information into the first trained model 222. Then, the recommendation output unit 205 may recommend that it is time for the user to excrete, or may output a recommendation for the number of excretions.

[0110] Furthermore, the recommendation output unit 205 can acquire parameters that enable the user to achieve ideal excretion (for example, in the case of stool, the state in which ideal stool is excreted) by inputting one or more pieces of information from among basic information, excretion information, history information, and schedule information into the second trained model 224. Then, the recommendation output unit 205 can output recommendations based on the acquired parameters so that the user's stool will be ideal. Furthermore, the recommendation output unit 205 can output recommendations so that the user will achieve ideal stool, based on the state of the next stool excreted predicted based on the first trained model 222 and the state of the ideal stool.

[0111] The model generation unit 206 performs learning from the input information and generates a trained model. In this embodiment, it generates a first trained model 222 and a second trained model 224. The process of generating trained models will be described later.

[0112] The UI providing unit 207 provides a display screen as one of user interfaces (UI) from the first server device 20 to the terminal device 40 or the like. The UI providing unit 207 transmits, for example, XML data or the like to the terminal device 40. The terminal device 40 displays a display screen using an installed application based on the received XML. The terminal device 40 may also access the first server device 20 using a browser application. The UI providing unit 207 provides, for example, display information such as HTML to the terminal device 40. The terminal device 40 can display a display screen based on the received display information.

[0113] The UI providing unit 207 may be executed in the terminal device 40. In this case, the first server device 20 transmits necessary information (e.g., excretion information, user information, etc.) to the terminal device 40. The terminal device 40 displays a display screen by the UI providing unit 207 that can be realized by an installed application.

[0114] [1.5 System Overview] Next, the overall system of this embodiment will be described. Fig. 10 is a diagram illustrating screen transitions in the overall system of this embodiment. First, in this system, an overall dashboard screen W10 is displayed, which displays the status of all users.

[0115] [1.5.1 Overall Dashboard Screen] The overall dashboard screen W10 is a first display screen that allows the status of all target users to be confirmed. An example of the overall dashboard screen, display screen W100, is shown in FIG.

[0116] The display screen W100 displays information for each user in parallel. For example, the area R100 displays information for one user.

[0117] The area R100 is divided into a first information area R102 that displays information for the user, a second information area R104 that displays an icon (graphics, text) for identifying the user and their name, and a third information area R106 that displays predetermined information. The area R100 also has a timeline area J100 that displays the user's history and predictions for the 12 hours before and after the present. The first information area R102 may, for example, display recommendations for the user. For example, in FIG. 11, a recommended bowel movement time and medication recommendations are displayed.

[0118] 11, the third information area R106 displays the amount of moisture and information about the excretion location using distinguishable displays (e.g., icons). The third information area R106 displays, for example, the location where the user's excretion was detected (e.g., a diaper or a toilet).

[0119] Furthermore, the timeline region J100 displays, in chronological order, distinguishable displays based on user information of past users and distinguishable displays based on predicted information of future users. For example, J106 is a line indicating the present. Here, region J102 of region R100 displays, in chronological order, information regarding past medication, etc., and information regarding bowel movements, etc., based on history information, using icons, text, etc. Furthermore, region J104 of region R100 displays, in chronological order, the user's medication schedule, etc., and predicted excretion timing, based on schedule information, using icons, text, etc.

[0120] [1.5.2 Personal Dashboard Screen] As shown in Figure 10, when an individual is selected on the overall dashboard screen W10, the screen transitions to the individual dashboard screen W12. The individual dashboard screen is a display screen that allows the user to check the stool characteristics and color of each individual user.

[0121] FIG. 12 shows an example of the display screen W110, which is a personal dashboard screen. For example, the display screen W110 plots defecation information on an XY graph for each piece of history information as a display item. For example, in FIG. 10, as history information, a graph G110 categorized based on food based on dietary information, a graph G112 categorized based on water content, and a graph G114 categorized based on medication details are displayed. Each graph is an XY graph with the Y axis (vertical axis) representing stool color and the X axis (horizontal axis) representing stool properties, and each graph is plotted based on excretion information. For example, when one plotted point is selected, the control unit 200 may display the user's history information (e.g., information about the meal at that time, information about water content, information about medication, information about biological information values, information about sleep, etc.).

[0122] The graphs G110, G112, and G114 may display points differently depending on the parameter values ​​corresponding to each item. For example, in dietary information, when the main ingredients of a food are extracted as "fish," "meat," "buckwheat noodles," and "bread," the points may be plotted in different colors corresponding to each of the ingredients. The shape may also be changed depending on each value. For example, the shape may be switched between "circle," "triangle," "square," etc. depending on the medication content.

[0123] For example, Figure 13 is an enlarged view of the dosage and graph shape shown in graph G114 for explanatory purposes. The vertical axis (Y axis) indicates the color of the stool. The horizontal axis (X axis) indicates the nature of the stool. As shown in Figure 13, graph G114 indicates three drops of laxative as a "circle," two drops as a "square," and one drop as a "triangle."

[0124] For example, by checking the graph in Figure 13, we can see that the stool of a user who received three drops of laxative is biased towards watery stool. Furthermore, the number of plotted points for a user who received one drop of laxative is small, indicating that the user is not passing stool very often. This makes it easy for staff to confirm that two drops of laxative is the appropriate dosage for this user.

[0125] Furthermore, for example, plot P110 indicates that the user's excretion status was that the stool color was "brown" and the stool properties were "watery." Furthermore, the shape of the plot is "circular," which indicates that three drops of laxative were prescribed. Furthermore, when plot P110 is selected, the control unit 200 may provide further detailed information. For example, the user's history information corresponding to plot P110 (e.g., information about meals, information about biological information values, information about sleep, information about activity level, etc.) may be displayed in a pop-up display or by switching the screen.

[0126] The display screen W110 may also display information entered by staff, information based on the user's chief complaint, or other information determined by the system. Area R110 in Fig. 12 displays information entered by staff regarding the user's physical condition this week.

[0127] Additionally, area R112 displays items for selecting extraction conditions when displaying the graph plot. For example, when a type of meal is selected in area R112, the points on the graph are redrawn based on the excretion information corresponding to the selected meal. For example, when a staff member selects "500ml to 700ml" as the amount of fluid, the points corresponding to the amount of fluid "500ml to 700ml" from the excretion information are displayed on the graph.

[0128] The staff member or the like can select one or more items in area R112. In addition, the staff member or the like can select one or more parameter values ​​for each item. This makes it possible to visually confirm the user's condition (e.g., dietary content, water amount, effects of medication, relationship with excretion location, relationship with activity level) and excretion status (e.g., stool properties, stool color, stool volume, etc.).

[0129] In addition, in area R112, a message based on a recommendation output from recommendation output unit 205 is displayed.

[0130] [1.5.3 Analysis screen] 10, when a predetermined operation (for example, an analysis operation) is performed on the personal dashboard screen W12, the screen transitions to the analysis screen W14. The analysis screen is a screen for learning the appropriate fecal properties, color, etc., from the excretion information for each user into the trained model.

[0131] FIG. 14 shows an example of a display screen W120, which is an analysis screen. On the display screen W120, a graph G120 is displayed in which the stool properties and stool color are plotted on an XY graph based on the excretion information. In this XY graph, the vertical axis (Y axis) represents the stool color, and the horizontal axis (X axis) represents the stool properties. On the display screen W120, an area R120 is displayed in which parameters for an item are selected. A staff member or the like may select a plot in the graph by surrounding it with, for example, a finger, a touch pen, or a mouse (e.g., a virtual line J120), and apply it as training data. Furthermore, if a trained model already exists, re-training may be performed based on the currently selected excretion information.

[0132] The excretion information displayed by J120 indicates the ideal excretion information. That is, the ideal state is selected as the user's stool state by a doctor, nursing staff, etc. For example, the content based on the currently selected history information or the selected range of the currently displayed excretion state becomes information indicating the ideal stool.

[0133] Furthermore, area R122 displays non-recommended foods. Non-recommended foods may be input by staff or may be output by the recommendation output unit 205 as recommendation information and displayed based on that information.

[0134] Fig. 15 is a diagram for explaining a typical screen transition when appropriate information from the excretion information is selected by a staff member, etc. Fig. 15 explains a case where moisture content and medication, etc. are used as extraction conditions.

[0135] As shown in Figure 15(a), the staff member selects the total amount of fluid as the extraction condition and "3 drops of X drug" as the medication. At this time, points corresponding to the stool condition and color are plotted on the graph based on the excretion information that falls within the upper limit.

[0136] Since the stool becomes watery with "3 drops of drug X," the staff member reselects the extraction condition as "2 drops of drug X." Figure 15(b) shows the graph redrawn based on the reselected extraction condition.

[0137] Furthermore, the staff reselected the conditions that fall within the range of "700ml to 1300ml" as the amount of fluid that the user thought was appropriate. Figure 15(c) shows the graph redrawn based on the reselected extraction conditions.

[0138] At this point, the conditions on the graph have been narrowed down to a certain extent, and the points drawn based on the excretion information have also been narrowed down. Then, staff members can circle the area of ​​the stool condition that they consider appropriate, and the system will learn that the stool condition (fecal properties, stool color) in the circled area is appropriate for the extracted conditions.

[0139] As a result, the model generation unit 206 generates a trained model using, for example, in Figure 15(c), the excretion information contained in the enclosed area (for example, the content contained in the excretion information described in Figure 9(b)), the properties of the stool, and the color of the stool.

[0140] [1.5.4 Monthly History Screen] 10, when a predetermined operation (for example, a monthly display operation) is performed on the personal dashboard screen W12, the screen transitions to a monthly history screen W16. The monthly history screen W16 is a screen that displays the monthly history for each user based on excretion information, history information, etc.

[0141] An example of the display screen W130, which is a monthly history screen, is shown in FIG. 16. The display screen W130 showing the monthly history screen is a screen that displays a list of information related to the user's excretion. For example, the display screen W130 displays daily information in an area R130. The daily information may include, for example, information related to medication, information related to the toilet (information related to the location of excretion), and the state of excretion. The state of excretion may display information related to urine or feces.

[0142] The display screen W130 can also execute a report output function. For example, when a staff member or the like selects a button B130, an excretion report is output. The excretion report may be printed or output to a file.

[0143] Furthermore, when a staff member or the like selects any date on the monthly history screen W16, the personal dashboard screen W12 corresponding to the selected date may be displayed. This allows the staff member or the like to easily check the status of the user by checking the personal dashboard screen W12 when they are concerned about the user's excretion on the monthly history screen W16.

[0144] [1.6 Processing flow] The flow of processing in this embodiment will be described. Note that the following processing will be described as being executed by the control unit 200 of the first server device 20, but it may be executed in the system 1. For example, the first server device 20 may be executed by one or more server devices, and therefore, the processing may be realized by a cooperation of multiple server devices. Also, some functions may be executed by an application executed on the terminal device 40.

[0145] [1.6.1 Overall flow] 17 is a diagram illustrating the overall processing flow of this embodiment. First, when the excretion prediction timing arrives, the control unit 200 executes the excretion prediction process (S102; Yes → S104). The details of the excretion prediction process will be described later, but the control unit 200 executes the excretion prediction process and outputs prediction information. The excretion prediction timing is, for example, the following timing.

[0146] At a predetermined time For example, the control unit 200 may execute the excretion prediction process at predetermined times such as 6:00 AM and midnight every day.

[0147] Timing triggered by the user's action For example, the control unit 200 may execute the excretion prediction process when the user has a meal or when the user has excreted.

[0148] Timing of operation by staff etc. For example, when a staff etc. operates to execute the excretion prediction process at any timing, the control unit 200 may execute the excretion prediction process.

[0149] Next, the control unit 200 executes an overall dashboard display process (S106). The overall dashboard display process provides an interface for displaying the overall dashboard in the terminal device 40, for example.

[0150] When the control unit 200 detects that the user has excreted (S108; Yes), it executes an excretion detection process (S110). Here, the control unit 200 may detect that the user has excreted in the following cases.

[0151] When excretion is detected by the feces detection device 10 attached to the diaper. For example, the control unit 200 detects that the user has excreted when the excretion notification unit 204 notifies the user that excretion has occurred.

[0152] When excretion is detected by the feces detection device 15. For example, the control unit 200 detects that the user has excreted when the feces detection device 15 notifies the control unit 200 that the user has excreted.

[0153] When information that a user has excreted is input by a user, staff member, etc. The control unit 200 detects that the user has excreted, for example, when the user notifies the user through the operation user interface that they have excreted.

[0154] Then, when a user is selected on the overall dashboard screen, the control unit 200 reads out the information of the selected user and displays the personal dashboard (S112; Yes→S114).

[0155] 17, in the excretion detection process (S110), the control unit 200 may always perform the detection of whether or not excretion has occurred (S108). The control unit 200 may also detect the user's excretion in real time and perform the process as needed.

[0156] [1.6.2 Notification Judgment Process] Next, the process of determining whether or not to notify staff or the like depending on the state of excretion of the user when the feces detection device 10 is attached to the diaper will be described with reference to FIG.

[0157] First, the feces detection device 10 will be described as having a transmission sensor and a color sensor as sensor devices. The sensor devices are attached over a plurality of areas.

[0158] First, the control unit 200 determines whether the detected color is within a first range (S132). For example, when the feces detection device 10 is attached to a white diaper, the control unit 200 determines whether the output color of the color sensor is white. Then, when the detected color is within the first range, that is, when it is almost the same color as the diaper, the control unit 200 determines that there is no feces and proceeds to step S144 (S132; Yes).

[0159] Here, the control unit 200 determines whether a predetermined time has elapsed since the last detection of defecation (S144). Here, the predetermined time may be a time set by a staff member or a time determined based on the user's disease information. In addition, the predetermined time may be specifically 24 hours, 48 ​​hours, or the like.

[0160] If the control unit 200 determines in S144 that a predetermined time or more has elapsed, it sets the notification content to "constipation." Then, the control unit 200 executes processing with the notification content set to "constipation" and "notification given" (S148). Furthermore, if the time during which no stool is detected is less than the predetermined time in S144, the control unit 200 continues processing without notification (S144; No→S150).

[0161] Returning to S132, when the color detected by the color sensor is outside the first range, i.e., when stool is detected (S132; No), the control unit 200 determines whether the color is abnormal (S134). For example, when the output value from the color sensor indicates red, the control unit 200 determines that bloody stool has been detected (S134; Yes) and sets the notification content to "color or higher" (S142). Then, the control unit 200 executes processing by determining that there is an abnormality in the color of the stool and indicating "notification given" (S148). Note that, as for color abnormalities, the control unit 200 detects bloody stool if the color is red, but may also detect other color abnormalities. For example, when the control unit 200 detects black stool, the control unit 200 may notify of color abnormality as it may be due to bleeding in the upper digestive tract.

[0162] Returning to S134, if the color of the stool is not abnormal (S134; No), the control unit 200 determines whether the transmittance of the transmission sensor is below the threshold (S136; Yes) and whether the amount of stool is above the threshold (S138; Yes).

[0163] Here, when the transmittance of the transmittance sensor is equal to or less than a threshold value, the control unit 200 determines that the stool has a predetermined property. For example, when the user has a small amount of watery stool, the transmittance becomes high, so the control unit 200 does not process the stool as notified even if the user has excreted. Also, when the transmittance is high, it may be urine only, so the control unit 200 does not process the stool as notified.

[0164] Furthermore, when the amount of stool is equal to or greater than the threshold, the control unit 200 sets the notification content to "excretion has occurred" (S140) and processes the notification as having occurred (S148).

[0165] Here, when "notification enabled" is selected, the control unit 200 notifies staff, etc. By receiving the notification, the staff, etc. can, for example, know if there is something abnormal in the user's stool. Furthermore, by receiving the notification, the staff, etc. can know when it is time to change the user's diaper. The notification may be sent to the terminal device 40, for example. For example, the terminal device 40 may display a message to that effect, or may notify by vibration, sound, or light. Furthermore, a notification device may be installed near the feces detection device 10, or the notification may be sent from the feces detection device 10. Furthermore, the control unit 200 may notify staff, etc., by email or via social media, for example.

[0166] [1.6.3 Excretion detection processing] The excretion detection process will be described with reference to Fig. 19. When the control unit 200 receives an excretion signal from the feces detection device 10 or the feces detection device 15, it determines the excrement excreted by the user, such as the color and properties of the feces (S162). The control unit 200 may also determine the amount of feces from the excretion signal (S164).

[0167] Specifically, the stool condition acquisition unit 202 may acquire the color, properties, and amount of stool and output them as stool information.

[0168] Next, the control unit 200 acquires the time when the user excretes and stores it as excretion information (S166). Here, the control unit 200 stores necessary information from the stool information and history information as excretion information.

[0169] At this time, the control unit 200 determines whether learning (relearning) of the first trained model 222 is necessary based on the excretion information (S168). When the control unit 200 determines that learning (relearning) of the first trained model 222 is necessary (S168; Yes), it acquires the necessary history information (S170) and generates (relearns) the first trained model 222 based on the timing (excretion time) of the user's excretion (S172). The control unit 200 can retrain the first trained model 222 by inputting the history information, the timing of the user's excretion, the color of the stool, and the properties of the stool into the first trained model 222 as new data.

[0170] Note that, at an early stage, the user's excretion information and history information may be used to generate the first trained model 222. For example, the control unit 200 may use the user's history information as an explanatory variable and the excretion information as a target variable, and generate the first trained model 222 using these as feature quantities.

[0171] [1.6.4 Excretion prediction processing] The excretion prediction process will be described with reference to Fig. 20. The control unit 200 acquires excretion information (S202). The control unit 200 also acquires history information (S204). The control unit 200 also acquires schedule information (S206).

[0172] Here, the control unit 200 uses the information acquired from S202 to S206 as the objective variable, predicts excretion information using the first trained model 222, and outputs the predicted information (S208). Specifically, the control unit 200 uses the information acquired from S202 to S206 as the objective variable, predicts the user's excretion timing and the state of the stool at that excretion timing (stool color, stool properties) using the first trained model 222, and outputs the predicted information (S208). In addition, the control unit 200 uses the information acquired from S202 to S206 as the objective variable, and acquires the user's ideal state of stool at that time (ideal stool state) using the second trained model 224 (S210).

[0173] At this time, when the stool condition predicted in S208 is not included in the ideal stool condition acquired in S210 (S212; No), the control unit 200 outputs recommendation information to make the stool condition ideal (S214). For example, the control unit 200 acquires the conditions necessary to achieve the target stool color or stool properties using the second trained model 224, and determines the acquired conditions as the recommendation content.

[0174] Specifically, the control unit 200 identifies a condition branch that is not satisfied after the condition that the user's stool color is the desired color and the user's stool properties are the desired stool properties, and outputs a proposal for the condition branch as recommendation information. For example, if the user's stool color and properties are desired when the amount of laxative administered is increased to three drops, but the user is currently administering only two drops, the control unit 200 determines the proposal content, "Increase the amount of laxative administered to three drops," and outputs this as recommendation information.

[0175] The control unit 200 may also determine whether the user has excreted at the predicted excretion timing (S216). Here, when the user has not excreted at the predicted excretion timing, the control unit 200 may execute excretion recommendation processing (S216; Yes→S218).

[0176] [1.6.5 Overall Dashboard Display Processing] The process of displaying the overall dashboard will be described with reference to Fig. 21. That is, the control unit 200 executes the overall dashboard display process, whereby the overall dashboard screen is provided to the terminal device 40 via the UI providing unit 207. The terminal device 40 displays the overall dashboard screen by accessing the first server device 20 using, for example, a web browser or an application.

[0177] The control unit 200 acquires history information and excretion information (S232). The control unit 200 also acquires schedule information (S234). The control unit 200 also acquires prediction information (S236). Upon acquiring this information, the control unit 200 displays an overall dashboard for each user based on the history information, excretion information, schedule information, and prediction information (S238). As an example, the overall dashboard screen is displayed on the display unit of the terminal device 40 that is accessing the web.

[0178] The overall dashboard screen has been described in Fig. 11. That is, as shown in Fig. 11, icons based on past medications and the like based on history information are displayed in the timeline area J102 for each user. Also, icons based on past excretion timing based on excretion information are displayed in area J102.

[0179] Furthermore, the timing of medication, etc. is displayed in region R104 based on the schedule information. Specifically, the control unit 200 acquires the scheduled date and time when the user will administer medication, etc. from the support information included in the schedule information. Then, the control unit 200 displays the timing of medication, etc. as an icon based on the scheduled date and time.

[0180] Furthermore, the control unit 200 displays an icon in the area J104 based on the timing of excretion predicted based on the prediction information.

[0181] [1.6.6 Personal Dashboard Display Processing] The process of displaying the personal dashboard will be described with reference to Fig. 22. That is, the control unit 200 executes the personal dashboard display process, whereby a personal dashboard screen is provided to the terminal device 40 via the UI providing unit 207. The terminal device 40 displays the personal dashboard screen by accessing the first server device 20 using, for example, a web browser or an application.

[0182] The control unit 200 acquires history information and excretion information (S252). The control unit 200 plots the acquired excretion information on a graph. For example, in the case of the personal dashboard display screen described in FIG. 12, graphs are displayed that are plotted based on the contents of food, water amount, medication, etc.

[0183] Each graph has the stool characteristics on the horizontal axis and the stool color on the vertical axis. The graph plots stool information for each parameter based on the stool information based on the excretion information. For example, as shown in the enlarged view of FIG. 13, when the amount of medication is used as a standard, the color or shape of the plot is changed for each attribute value (3 drops of laxative, 2 drops of laxative, 1 drop of laxative), and the graph is plotted based on the excretion information. In FIG. 12, each piece of defecation information is plotted based on "food" (eating and drinking information), "hydration amount" (eating and drinking information), and "medication content" (medication and drinking information). Therefore, staff and the like can easily check the state of defecation simply by checking the screen.

[0184] Furthermore, the graph based on the defecation information may be displayed with, for example, a graph showing the time elapsed from administration of medication to defecation and the amount of defecation on the horizontal axis. Furthermore, each graph may be displayed with the vertical and horizontal axes reversed.

[0185] Furthermore, when there is recommendation information, the control unit 200 displays the recommendation information (S256; Yes → S260). For example, in the case of the personal dashboard display screen of FIG. 12, the recommendation information may be displayed in area R112. For example, the recommendation output unit 205 may output a recommendation so that the next excretion will be in the ideal state by using the first trained model 222 and the second trained model 224. Furthermore, the recommendation output unit 205 may refer to the excretion information and output a recommendation so that the next excretion will be in the ideal state based on the fact that the color and properties of the previous excretion were not in the ideal state.

[0186] Furthermore, if the display conditions are changed by a staff member or the like (S262; Yes), a display is executed in which the plot is replotted based on the changed display information (S264). For example, when a display condition is selected by the user in area R112 in Fig. 12, the display conditions are changed again and the plot is replotted.

[0187] The control unit 200 executes the overall dashboard display process to display various information on the overall dashboard. For example, in area R102 in FIG. 11, a notification can be sent to the user. For example, when the above-described excretion notification determination process determines that a notification is required, the control unit 200 displays the notification content. This allows the user to be notified of information such as "abnormal stool" (e.g., abnormal stool color, constipation, diaper change, etc.). The control unit 200 may also display information such as the next medication as the notification content. The control unit 200 may also notify the user of other information, such as abnormal stool volume, abnormal urine color, abnormal urine volume, or abnormal stool characteristics, based on the excretion information.

[0188] Furthermore, the control unit 200 may issue a notification when the amount of change exceeds a certain threshold value when compared with the excretion information from several days ago. For example, the control unit 200 calculates the average amount of excretion (amount of stool and urine) of the user over several days from the excretion information. Then, the control unit 200 may issue a notification when the amount of excretion detected this time exceeds a threshold value.

[0189] The control unit 200 may also display information based on the excretion information and medication information for that day, such as the location of excretion (diaper, toilet, portable toilet, other, etc.), the color of stool / urine, the amount of stool / urine, and the nature of stool, in the area J102. The control unit 200 may also display in the area J104 both the content predicted based on the history information and the content determined from the schedule information, or may display them in different colors. For example, the control unit 200 may display the timing and state of excretion predicted based on past medication, diet, water amount, and excretion information.

[0190] [1.6.7 Personal analysis screen display processing] The process of displaying the personal analysis screen will be described with reference to Fig. 23. That is, the control unit 200 executes the personal analysis screen display process, whereby the personal analysis screen is provided to the terminal device 40 via the UI providing unit 207. The terminal device 40 displays the personal analysis screen by accessing the first server device 20 using, for example, a web browser or an application.

[0191] The control unit 200 reads the excretion information for a predetermined period (S282), and plots the excretion information on a graph based on the read excretion information (S284).

[0192] Here, when the display conditions are changed by a staff member or the like, the excretion information is re-extracted according to the display conditions, and re-plotting is performed based on the re-extracted excretion information (S288). Here, when an ideal region is selected by a staff member or the like (S290; Yes), the stool corresponding to the excretion information included in the ideal region is considered to be the ideal stool (ideal stool), and a second trained model 224 is generated (S292). Here, when a second trained model 224 already exists, re-learning is performed based on the excretion information included in the selected region.

[0193] [1.6.8 Monthly history screen display processing] The process of displaying the monthly history screen will be described. That is, the control unit 200 executes the monthly history screen display process, and the monthly history screen is provided to the terminal device 40 via the UI providing unit 207. The terminal device 40 displays the personal analysis screen by accessing the first server device 20 using, for example, a web browser or an application.

[0194] The control unit 200 can display information based on the history information, for example, on a calendar-like screen. For example, the control unit 200 can display, on each date on the calendar, one or more of the following: excretion location (diaper, toilet, portable toilet, other, etc.), number of excretion times for each excretion location, stool information (stool properties such as solid stool, muddy stool, watery stool, etc.), stool color, stool volume, urine color, urine volume, water volume, food intake, type of medication, amount of medication, and time. The control unit 200 can also arbitrarily switch this information by operation of a staff member, etc.

[0195] Furthermore, average values ​​may be displayed on the monthly history screen. The control unit 200 may display average values ​​for each week and / or month. For example, the control unit 200 can display the average stool properties for each week and / or month, the amount of stool, the average number of stools, the average amount of urine, the average number of urinations, the average number of excretion times by excretion location, the average amount of water, the average amount of food, the average amount of medication, etc. One or more of these pieces of information can be displayed. Furthermore, staff, etc. can select one or more pieces of information to display.

[0196] Furthermore, when output of an excretion report is selected on the monthly history screen display, the control unit 200 can output an excretion report.

[0197] For example, the excretion report is output for each user and can be used at conferences, etc. The excretion report can include the average stool properties for each user by week and / or month, the amount of stool, the average number of stools, the average amount of urine, the average number of urinations, the average number of excretion times by excretion location, the average amount of water, the average amount of food eaten, the average amount of medication, etc. The control unit 200 can also output daily information for each user as an excretion report. The control unit 200 may output the excretion report on paper or as a document file.

[0198] [1.6.9 First trained model generation process] Next, the process by which the model generation unit 206 generates the first trained model 222 will be described with reference to FIG.

[0199] The control unit 200 acquires the user's history information (diaper information, dietary information, biological information, condition information, etc.) and the user's excretion information (S302). Specifically, it is preferable that the control unit 200 acquires data on meals (e.g., type of meal, type of ingredient, intake amount for each meal, intake amount of ingredients), data on medication (e.g., type of medication, dosage), and amount of fluid intake as the user's history information. The control unit 200 also acquires the timing of the previous excretion. The control unit 200 also acquires the timing of new excretion from the excretion information. The control unit 200 then generates data for learning.

[0200] The control unit 200 generates the first trained model 222 by performing machine learning using the user's history information and excretion information related to the previous excretion as explanatory variables and excretion information based on the user's latest defecation as a target variable (S304). Here, the first trained model 222 may be generated based on the excretion information, for example, the timing of excretion.

[0201] [1.6.10 Second trained model generation process] Next, the process by which the model generation unit 206 generates the second trained model 224 will be described with reference to FIG.

[0202] The second trained model 224 is generated after obtaining information from the user in the second mode regarding points surrounded by shapes and lines and other points, as well as information indicating content to be avoided.

[0203] The control unit 200 receives input from the staff or the like and acquires information indicating whether the color of the user's stool is the desired color or not, whether the properties of the user's stool are the desired properties or not (for example, information regarding the points surrounded by the line J120 in FIG. 14 and other points), and information indicating what the user should avoid (avoidance information) input by the staff or the like (S322). The control unit 200 generates learning data from the acquired information (S324).

[0204] The control unit 200 acquires the target user's excretion information (timing of the previous stool excretion, stool condition), history information (information on meal contents (e.g., type of meal, type of ingredients, intake amount of each meal, intake amount of ingredients), information on medication etc. (e.g., type of medicine, dosage), amount of water intake) (S326).

[0205] If learning data has already been generated, the control unit 200 may set the information (learning data) acquired in S322 together with the corresponding user's excretion information and history information as new learning data and add it to the existing learning data.

[0206] The control unit 200 generates the second trained model 224 by performing machine learning on the learning data of each user using the information on ideal stool, avoidance information, history information, excretion information, etc. as explanatory variables and the information on ideal stool of each user (information indicating whether each user's stool was the target stool color or information indicating whether each user's stool was the target stool properties) as objective variables. Here, the second trained model 224 may be a decision tree model.

[0207] When the second trained model 224 is a decision tree model, the control unit 200 may store, as a decision table, information indicating the conditions for each branch of the decision tree model and whether each group after the final branch is a group that has the target stool color or target stool properties (S328).

[0208] Note that the model generation unit 206 may make the following modifications to the generation process of the first trained model 222 and the second trained model 224.

[0209] If the number of data sets for learning data is less than the desired number, the control unit 200 may output a message to the display device indicating that the number of data sets is small.

[0210] Furthermore, if no objective variable has been set, the control unit 200 may display a message urging the user to change the mode to a different mode and set the objective variable. In this case, the control unit 200 outputs a message to the terminal device 40 indicating that no objective variable has been set. When displaying the message, the terminal device 40 may display a message urging the user to change the mode to a different mode and set the objective variable if it has acquired information indicating that no objective variable has been set.

[0211] Furthermore, the second trained model 224 does not have to be a decision tree model, but can be any model that can output conditions under which the user's stool will have the target stool color or properties, and for example, machine learning or deep learning using a neural network may be used.

[0212] If the number of data sets of learning data does not reach a desired number or more, the control unit 200 may output to the terminal device 40 a message indicating that the objective variable cannot be set.

[0213] In an embodiment, for example, the control unit 200 can derive the influence of at least one of the user's past dietary details, medication details, water intake, and activity level on the excretion results. Based on the derived results, the control unit 200 may provide, for example, recommended nursing care information for each of multiple users as recommendation information. Furthermore, the excretion results may include, for example, at least one of urine color, urine volume, stool color, stool consistency, stool volume, and excretion time.

[0214] In addition, the information about nursing care recommended as recommendation information may include, for example, information about at least one of the amount of fluid needed, the amount of activity needed, diaper changing time, diaper pad size, foods that should be avoided, and medications.

[0215] The control unit 200 may, for example, present the current health condition of each of a plurality of users. The health condition may include, for example, at least one of normal, diarrhea, constipation, lack of exercise, and lack of hydration.

[0216] Furthermore, the system 1 may detect changes in the physical condition of each of the multiple users. When an abnormal change in physical condition is detected, the system 1 enables early diagnosis or treatment. Furthermore, by detecting changes in physical condition, the system 1 enables appropriate decision-making even in cases where the caregiver is inexperienced or lacks knowledge.

[0217] For example, the control unit 200 may collectively present information on at least one of the amount of fluid, dietary content, and medication content, and excretion information (excretion results) for each of multiple users. The excretion information may include, for example, at least one of the color of stool, the properties of stool, the amount of stool, the frequency of stool, the color of urine, the amount of urine, and the frequency of urination.

[0218] Furthermore, for example, the control unit 200 may present the relationship between water content and stool properties by using the second trained model 224. For example, the control unit 200 may present the relationship between urine volume and stool condition. For example, the control unit 200 may present the relationship between daytime urination volume and nighttime urination volume by using the second trained model 224. For example, the control unit 200 may present the relationship between stool color and food ingredients and identify food ingredients that are difficult to digest by using the second trained model 224. For example, the control unit 200 may present the relationship between medication, etc. and stool properties and present predicted information on at least one of the number of bowel movements and bowel frequency by using the second trained model 224.

[0219] The recommendation information may include, for example, information regarding the recommended moisture content (such as the appropriate daily moisture content or its lower and upper limits). The recommendation information may include, for example, information regarding at least one of the recommended daytime diaper volume and the recommended evening diaper volume. The recommendation information may include, for example, information regarding non-recommended food ingredients.

[0220] The recommendation information may include, for example, information regarding the amount of medication (e.g., laxative, etc.). The recommendation information may include, for example, information regarding the predicted time for providing toileting care after medication (e.g., laxative, etc.). The recommendation information may include, for example, information regarding the predicted time for changing a diaper after medication (e.g., laxative, etc.).

[0221] The recommendation information may include, for example, information about walking time as information about the activity. The recommendation information may include, for example, information about recommended training to improve frequent urination. The recommendation information may change the recommended activity content depending on, for example, the user's condition (for example, whether the user can maintain a sitting position).

[0222] In an embodiment, the recommendation information may be presented to at least one of, for example, a caregiver, a physical therapist, an occupational therapist, a nurse, and a doctor. For example, by comparing with past excretion information, people who need treatment are appropriately extracted, and appropriate measures are promoted. For example, early diagnosis is possible. For example, early treatment is possible.

[0223] For example, if your stool is black, it could be a sign of a problem with your stomach, esophagus, or duodenum. For example, if your stool is white, it could be a sign of a problem with your liver, gallbladder, or bile duct. For example, if your stool is red, it could be a sign of a problem with your large intestine or anus. For example, if your stool is green, it could be a sign of a problem with your large intestine or small intestine. For example, if your stool is white and you have diarrhea, it could be a sign of rotavirus infection.

[0224] For example, if your urine is dark yellow, it could be due to dehydration. For example, if your urine is clear (light in color), it could be due to overhydration or decreased kidney function. For example, if your urine is red, it could be due to hematuria or hemoglobinuria. For example, if your urine is orange or brown, it could be due to bilirubinuria or myoglobinuria. For example, if your urine is purple, it could be due to purple urine bag syndrome. For example, if your urine is black, it could be related to taking medication for Parkinson's disease. For example, if your urine is white, it could be due to cystitis.

[0225] For example, it is possible to identify users who are constipated based on information on at least one of whether or not they have had a bowel movement and the amount of bowel movement. This can prompt appropriate measures to be taken. For example, it can enable early diagnosis. For example, it can enable early treatment. For example, it can contribute to the prevention of disorders such as intestinal obstruction.

[0226] For example, bowel movements in diapers can be well controlled. For example, toilet guidance or toilet training can be effectively implemented based on excretion prediction, thereby improving the user's ADL.

[0227] The system 1 according to the embodiment may be combined with a sleep sensor. For example, the recommendation information output from the control unit 200 may be changed based on the results obtained by the sleep sensor. For example, if the sleep sensor determines that the patient has had sufficient sleep and that the defecation state is stable, toilet guidance is recommended. On the other hand, if the sleep sensor determines that the patient has not had sufficient sleep, toilet guidance is not provided, and, for example, defecation in a diaper is recommended.

[0228] For example, information obtained by the system 1 according to the embodiment may be utilized to reduce the effort required for recording. For example, the following related information, including at least one of past fluid intake, dietary content, medication intake, and excretion information, may be utilized in conferences and daily records. For example, the system 1 may be used as a communication tool between multiple professions in conferences. For example, discussions may be held regarding at least one of the type of medication, the amount of medication, the shape of food, the amount of food, the amount of fluid, the timing of toilet guidance, and toilet training. For example, improvement of ADL may be promoted.

[0229] The related information includes, for example, dietary details, medication details, fluid intake, and activity level, as well as the degree of influence on excretion results (excretion information). The excretion results include at least one of urine color, urine volume, stool color, stool consistency, stool volume, and excretion time.

[0230] The related information may include, for example, at least one of the following for a specified period: average fluid intake, food intake, medication content, post-medication bowel movement results, number of bowel movements, and amount of bowel movements. The post-medication bowel movement results include at least one of the time between the time of taking the medication and the time of bowel movement, color of the stool, properties of the stool, and amount of the stool.

[0231] The related information may include information regarding at least one of a tally of stool characteristics and a tally of stool color for a specified period.

[0232] For example, at least a part of the recommendation information and at least a part of the related information may be used by a nutritionist, who can consider appropriate dietary content (amount / shape) and appropriate amount of water based on information collected regarding at least one of stool properties, stool volume, frequency of stool, urine volume, frequency of urination, water volume, and dietary content (amount / shape).

[0233] For example, at least a portion of the recommendation information and at least a portion of the related information may be used by at least one of a doctor and a nurse. For example, values ​​related to at least one of the following for a specified period of time may be presented: water volume, dietary content (amount / shape), stool properties, stool volume, stool frequency, stool leakage frequency, urine volume, urinary frequency, urinary leakage frequency, and number of excretion times at excretion sites. These values ​​may be averages of values ​​for a specified period of time. These values ​​are compared with past values. This makes it easier for at least one of a doctor and a nurse to make various decisions. For example, based on the health condition and excretion prediction, toilet guidance or toilet training plans can be efficiently developed to improve the user's ADL.

[0234] Various judgments include the following. For example, if the user is not taking laxatives, has two bowel movements per week, and there are no problems with stool color, stool texture, stool volume, or stool leakage, it is determined that medication is not necessary. For example, if the user is taking laxatives and has watery stools and a large amount of stool, it is determined that a change in the amount or type of medication should be considered. For example, if the user is taking laxatives, it is determined that a change in the amount or type of medication should be considered and the training content should be reviewed depending on an increase or decrease in the number of bowel movements or urination.

[0235] For example, at least a part of the recommendation information and at least a part of the related information may be applied to home nursing. For example, when home nursing is performed infrequently, this information is effectively used. For example, this information can provide information about the user's physical condition. Abnormalities can be recognized early. For example, early diagnosis is possible. Early treatment is possible.

[0236] For example, at least a portion of the recommendation information and at least a portion of the related information may be related to users who do not require care. This information may be used, for example, for health management of healthy individuals.

[0237] [1.7 Application Examples] Below, some application examples when the above-described embodiment is used will be described.

[0238] [1.7.1 Determination of stool characteristics] The nature of the stool may be determined, for example, from the amount of light (reflectance) acquired by the light-receiving unit 122 of the sensor. For example, FIG. 26 is a graph schematically showing the reflectance of the sensor and the moisture content of the stool. Here, it can be seen from the light received by the light-receiving unit 122 that the higher the reflectance, the less moisture the stool contains. Therefore, when the reflectance acquired from the sensor is high, the nature of the stool is solid stool. Also, it can be seen that the lower the reflectance acquired from the sensor, the more moisture the stool contains. Therefore, when the reflectance acquired from the sensor is low, the nature of the stool is watery stool.

[0239] [1.7.2 Toilet Guidance Processing] The control unit 200 can predict the timing at which the user will excrete by using the first trained model 222. For example, the control unit 200 predicts the time at which the user will excrete from the first trained model 222 based on the basic information, excretion information, and history information. For example, in S208 of FIG. 20, the control unit 200 predicts the timing at which the user will excrete.

[0240] The control unit 200 may then notify the user when it is time for the user to defecate. For example, the control unit 200 may notify the user himself / herself to encourage defecation. For example, the control unit 200 may notify a terminal device held by the user or a wearable device worn by the user. The notification may be, for example, a message displayed, or may be a sound, vibration, or light notification.

[0241] The control unit 200 may also notify staff etc. For example, the control unit 200 notifies a terminal device etc. carried by the staff etc. of the timing when the user needs to defecate. Based on the notification, the staff etc. can take actions to encourage the user to defecate or provide assistance.

[0242] At this time, the user and / or staff, etc. may input whether or not excretion has actually occurred to the system 1. At this time, the feces detection device 10 and the feces detection device 15 may notify the system 1 when they actually detect excretion. When excretion information is sent to the first server device 20 from one or more of the user, staff, etc., the feces detection device 10, and the feces detection device 15, the first server device 20 determines whether learning is necessary (S168 in FIG. 19). Then, if necessary, the first server device 20 (control unit 200) re-learns the first trained model 222 (S170, S172).

[0243] [1.7.3 Recommendation Processing] The control unit 200 can predict the timing and state of excretion of the user by using the first trained model 222 and the second trained model 224. Therefore, the control unit 200 may output recommendation information based on the state of excretion.

[0244] (1) Determining whether to use the feces detection device 10 For example, the control unit 200 predicts the time when the user will defecate from the first trained model 222 based on the basic information, medication information in the history information (such as the dosage administered to the user and the time of administration), and dietary information. In this case, when the predicted time when the user will defecate is between midnight and 6:00, the control unit 200 may output recommendation information for detecting defecation using the feces detection device 10.

[0245] (2) Recommendations for diaper use and type For example, the control unit 200 predicts the stool properties and stool volume using the second learning model based on medication information (such as the dosage of medication administered to the user, the time of administration, etc.) and dietary information (such as menu information including meal contents, and fluid intake, etc.) in the history information. If the control unit 200 predicts that the user's stool properties will be watery or that the stool volume will be large, the control unit 200 may output recommendation information recommending the use of diapers. The control unit 200 may also output recommendation information such as a larger diaper size than normal. The control unit 200 may also input information such as biological information and condition information as history information into the first trained model 222 as explanatory variables to predict the stool properties and stool volume.

[0246] (3) Recommendations based on the difference between predicted and actual excretion timing For example, the control unit 200 may make a recommendation when the actual time of excretion deviates from the time predicted for the user to excrete output from the first trained model 222. In particular, in this case, it is preferable for the control unit 200 to recommend the timing of urine rather than feces as the type of excretion.

[0247] For example, the control unit 200 may output recommendation information such as, "About two hours have passed since the normal timing of excretion. Your awareness of the need to urinate may be declining. Please use a urination prediction device to monitor the situation."

[0248] Below, we will explain some specific recommendations. Note that the following recommendations will focus on particularly effective recommendations, but are not limited to these.

[0249] (A) The factor is moisture content For example, when the properties of the user's stool are watery or hard, the control unit 200 identifies the cause by using the first trained model 222 and the second trained model 224. In this case, when the control unit 200 identifies that the cause is moisture content, it outputs the following recommendation.

[0250] (A-1) Watery stool For example, when the control unit 200 determines that the stool to be excreted is watery, it may display a recommendation such as, "Because you have taken XX drops of laxative, please be careful about the amount of fluid intake. If you consume more than XX ml of fluid, there is a high probability that stool leakage will occur."

[0251] In addition, when the nature of the stool actually detected by the stool detection device 10 and the stool detection device 15 is watery, the control unit 200 may output a message saying, "A large amount of watery stool has been excreted. To prevent dehydration, please have the remaining XX ml of water drink in XX divided doses."

[0252] Such a message is output by the control unit 200 because the user may be drinking too much water and may be dehydrated, and therefore the amount of water needs to be adjusted. The control unit 200 may further adjust the amount of water recommended based on the temperature, etc.

[0253] (A-2) When the stool is hard For example, when the control unit 200 determines that the stool to be excreted will be hard, it may output a message saying, "The patient is not drinking enough water, and the stool may become hard. Please give the patient the remaining XX ml of water in XX divided doses."

[0254] In such cases, the user's fluid intake may be insufficient, so it is important for the user to check their fluid intake on a daily basis and encourage them to replenish fluids as necessary, and therefore the control unit 200 outputs the message described above.

[0255] (B) The cause is diet When the control unit 200 identifies that the cause is food, it outputs the following recommendation.

[0256] (B-1) Watery stool For example, when the control unit 200 determines that the stool to be excreted will be watery, it may display a recommendation such as, "On the days when you ate XX and YY from today's menu, you had watery stool. By changing X to Z, the nutritional balance will not change significantly and you will likely be able to avoid watery stool."

[0257] In addition, when the stool properties actually detected by the stool detection device 10 and the stool detection device 15 are watery, the control unit 200 may display a recommendation such as, "You ate XX in the past and had watery stools XX times. We recommend that you check for allergies or intolerances to that food."

[0258] In such cases, since the condition may be due to indigestion or malabsorption of food, the control unit 200 outputs the above-mentioned message to advise the patient to eat a balanced diet including dietary fiber to aid digestion.

[0259] (B-2) When stool is hard For example, when the control unit 200 determines that the stool to be excreted is hard, it may display a recommendation such as "Adding ZZ to today's menu may soften the stool."

[0260] In addition, when the stool properties actually detected by the stool detection device 10 and the stool detection device 15 are hard stool, the control unit 200 may display a recommendation such as, "You ate ○○ in the past, and have been constipated ○○ times, resulting in hard stool. We recommend that you check for allergies or intolerances to that food."

[0261] In such a case, it is necessary to check whether the meal contains a sufficient amount of dietary fiber, and therefore the control unit 200 outputs the above-mentioned message.

[0262] (C) The factor is exercise (amount of activity) For example, when the control unit 200 determines that the stool to be excreted is hard, it may display a recommendation such as, "People with the same characteristics as Mr. A seem to tend to find that doing XX exercises relieves constipation. Why not try doing XX exercises for 10 minutes during the day?"

[0263] In addition, when the characteristics of the stool actually detected by the stool detection device 10 and the stool detection device 15 are hard stool, the control unit 200 may display a recommendation such as, "Your activity level has decreased by about XX compared to last month. You have also had constipation XX times, and your stool has become hard. We encourage you to exercise, such as taking a walk for XX steps."

[0264] (D) The cause is a drug. (D-1) Watery stool For example, when the control unit 200 determines that the properties of the stool to be excreted are watery, it may display a recommendation such as, "Based on predictions taking into account diet, fluid intake, activity level, etc., it is predicted that there is a high probability of stool leakage due to watery stool when A drops of the planned X agent are administered. It has been estimated that the number of drops with the lowest possibility of stool leakage is B drops. We hope this information is helpful."

[0265] In addition, when the stool properties actually detected by the stool detection device 10 and the stool detection device 15 are watery stool, the control unit 200 may display a recommendation such as, "You have taken ○○ medication in the past, and your constipation has changed to excessive watery stool ○○ times. We recommend that you consult with your doctor and review your medication."

[0266] In such cases, since there is a possibility that the medication being taken is causing a side effect or that the patient is taking an overdose, the control unit 200 outputs the message described above, and the patient needs to consult a doctor or nurse and consider reviewing the medication.

[0267] (D-2) When stool is hard For example, when the control unit 200 determines that the stool to be excreted is hard, it may display a recommendation such as, "Based on predictions made taking into account diet, fluid intake, activity level, etc., it is predicted that constipation will continue with the planned C drops of agent X. We estimate that the number of drops that will most likely relieve constipation is D drops. We hope this information is helpful."

[0268] In addition, when the stool properties actually detected by the stool detection device 10 and the stool detection device 15 are hard stool, the control unit 200 may display a recommendation such as, "You have taken ○○ medication in the past and have suffered from constipation ○○ times. We recommend that you consult with your doctor and review your medication."

[0269] In such a case, the control unit 200 outputs the above-mentioned message because there is a possibility that a side effect of a particular medicine is causing constipation and the patient needs to periodically review the medication.

[0270] For example, the control unit 200 may predict that the stool will be hard based on the medication (drug administration) history of analgesics (opioids), antidepressants, antipsychotics, antiepileptics, etc. Furthermore, when the control unit 200 detects that the stool is hard, it may refer to the medication (drug administration) history and recommend an appropriate dosage (drug administration) of analgesics (opioids), antidepressants, antipsychotics, antiepileptics, etc.

[0271] Furthermore, the control unit 200 may predict that the stool will be watery based on the medication (medication) history of antibiotics, antivirals, nonsteroidal anti-inflammatory drugs (NSAIDs), some antidepressants, etc. Furthermore, when the control unit 200 detects that the stool is watery, it may refer to the medication (medication) history and recommend an appropriate dosage (medication) of antibiotics, antivirals, nonsteroidal anti-inflammatory drugs (NSAIDs), some antidepressants, etc.

[0272] Furthermore, the control unit 200 may determine whether a specific nutritional supplement, such as an iron supplement or a vitamin supplement, is being used when the stool properties change from normal.

[0273] (E) The cause is illness. When the cause is an illness, it is preferable that the control unit 200 outputs a recommendation according to the illness. For example, when watery stool is predicted or detected, it may be a sign of an infection or a digestive system disease, and it is necessary to consult a doctor early and undergo an appropriate examination, so the control unit 200 outputs, for example, a message urging a medical examination.

[0274] Furthermore, when hard stool is predicted or detected, the control unit 200 outputs a message, for example, urging the patient to seek medical attention, since chronic intestinal inflammation or a digestive system disease may be causing the hard stool and the patient may need to be diagnosed and receive appropriate treatment by a doctor as necessary.

[0275] (F) Recommendations other than factors For example, the control unit 200 may output recommendations based on the color of excreted stool or urine. For example, based on the color of the stool or urine that will be excreted in the future, the control unit 200 can output a message in advance such as, "Because you are taking X, it is expected that white stool will be excreted. Please be careful as this may reduce the detection accuracy of the sensor." In addition, the control unit 200 can output a message such as, "The color of your stool is XX, and the color of your urine is XX. There may be a problem with your large intestine or anus, so you should consult a doctor early and undergo appropriate examinations."

[0276] [2. Second Embodiment] Next, a second embodiment will be described. The second embodiment is an embodiment in which the arrangement of the light receiving section 122 is a variation.

[0277] In the first embodiment, the light receiving units 122 are arranged linearly in the detection unit 10T, but they may also be arranged in a planar manner. For example, FIG. 27(a) is a diagram showing the sensor 10Ta. The sensor units 1220 are arranged in a lattice pattern on the substrate 10Ka. FIG. 27(b) is a side view of the sensor 10Ta. The sensor unit 1220 may be configured such that the light receiving units 1222 and the light emitting units 1224 are integrated together. Alternatively, the light receiving units 1222 and the light emitting units 1224 may be covered by a lens 1226.

[0278] The light receiving unit 1222 and the light emitting unit 1224 may be provided in different locations. The substrate 10Ka may be simply a wiring instead of a substrate. The substrate 10Ka may be provided with not only the sensor unit 1220 but also, for example, a communication unit.

[0279] In this way, since the sensor units 1220 are arranged in a grid pattern, it is possible to more accurately determine the amount of excrement (for example, the amount of feces or urine) depending on the contamination range.

[0280] 25(c), the substrate 10Kb may have slits. By providing slits in the substrate 10Kb, feces and urine excreted by the user can be absorbed more efficiently by the pad through the slits.

[0281] Furthermore, the shape of the substrate does not necessarily have to be rectangular. For example, as shown in Figure 25(d), the shape of the substrate 10Kc may be cross-shaped.

[0282] [3. Modifications] The present disclosure is not limited to the above-described embodiments, and various modifications are possible. In other words, embodiments obtained by combining technical means that are appropriately modified within the scope of the present disclosure are also included in the technical scope.

[0283] Although the above-mentioned embodiments are described separately for convenience of explanation, they can be combined to the extent possible. Furthermore, the present invention intends to obtain rights to any of the technologies described in the specification through amendments or divisional applications, etc.

[0284] In addition, the programs that run on each device in each embodiment are programs that control the CPU, etc. (programs that make a computer function) so as to realize the functions of the above-described embodiments. Information handled by these devices is temporarily stored in a temporary storage device (e.g., RAM) during processing, and then stored in various ROMs and HDDs, and is read, modified, and written by the CPU as needed.

[0285] Here, the recording medium for storing the program may be any of semiconductor media (e.g., ROM, non-volatile memory card, etc.), optical recording media / magneto-optical recording media (e.g., DVD (Digital Versatile Disc), CD (Compact Disc), BD (Blu-ray (registered trademark) Disc), etc.), magnetic recording media (e.g., magnetic tape, flexible disk, etc.), etc.

[0286] Furthermore, when distributing the program in the market, the program can be stored in a portable recording medium and distributed, or transferred to a server computer connected via a network such as the Internet. In this case, the storage device of the server device is also included in the present disclosure.

[0287] Furthermore, the above-mentioned data may not be stored within the device, but may be stored in an external device and called up as needed. For example, the data may be stored in a network attached storage (NAS) or on the cloud.

[0288] The scope of the present disclosure is not limited to the configurations explicitly described in the specification, but also includes combinations of the technologies disclosed in the specification. The configurations of the present disclosure for which a patent is sought are set forth in the appended claims, but it is not intended to exclude them from the technical scope on the grounds that they are not set forth in the claims.

[0289] Furthermore, in the above-mentioned specification, the statements "in the case of" and "when" are given as examples and are not intended to limit the configuration to the described contents. The disclosure also includes configurations that are not in these cases or situations, even if they would be obvious to a person skilled in the art, and the applicant intends to obtain rights to them.

[0290] Furthermore, the processes and data flows described in the specification are not limited to the order in which they are described. For example, the patent also discloses configurations in which some processes are deleted or the order is changed, and the patent holder intends to obtain the rights to such configurations.

[0291] Furthermore, although the functions described in the embodiments are executed by each device, they may be realized by one device or may further utilize an external server.

[0292] Furthermore, each functional block or feature of the device used in the above-described embodiments may be implemented or performed by an electrical circuit, for example, an integrated circuit or multiple integrated circuits. The electrical circuit designed to perform the functions described herein may include a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or a combination thereof. The general-purpose processor may be a microprocessor, or a conventional processor, controller, microcontroller, or state machine. The electrical circuit may be composed of digital circuits or analog circuits. Furthermore, as advances in semiconductor technology emerge that replace current integrated circuits, one or more aspects of the present disclosure may also utilize new integrated circuits based on that technology. [Explanation of symbols]

[0293] Flights 10 and 15 detection device 100 control section 110 Storage section 190 Communications Department 20. First server device 200 control section 210 Storage section 210A Storage, 210B ROM, 210C RAM 250 Operation section 260 Output Section 290 Communications Department 30 Second server device 300 control section 310 Storage section 312 Storage, 314 ROM, 316 RAM 350 Control unit 360 Output Unit 390 Communications Department 40 Terminal Equipment 400 control section 410 Storage section 412 storage, 414 ROM, 416 RAM 450 Control unit 460 Output Section 490 Communications Department

Claims

1. An information processing device including a control unit and a storage unit, The storage unit stores a first trained model that has been trained in advance and a second trained model that has been trained in advance; The control unit inputting the user's history information and excretion information into the first trained model, and outputting prediction information regarding the user's next excretion; inputting the user's history information and the prediction information into the second trained model, and outputting a recommendation so that the excretion state becomes an ideal state; Information processing device.

2. The first trained model is a trained model that has been machine-learned using history information of the user as a target variable and excretion information of the user as an explanatory variable, The second trained model is a trained model that uses the user's history information and the user's excretion information as objective variables and trains them as explanatory variables that indicate an ideal state of the user's excretion. The information processing device according to claim 1 .

3. The excretion information includes, as information about the excretion, a color of the stool and a property of the stool, The control unit The excretion information of the user is displayed by plotting the color and properties of the stool on an XY graph; The selected excretion information is learned from the plotted excretion information as an explanatory variable indicating an ideal state as the excretion state of the user. The information processing device according to claim 2 .

4. The control unit When the history information is selected, the excretion information corresponding to the selected history information is extracted and plotted on the XY graph. The information processing device according to claim 3 .

5. The information processing device according to claim 4 , wherein the history information includes information about the user's diet, information about water intake, information about biological information values, and information about sleep.

6. The excretion information includes, as information about the excretion, a color of the stool and a property of the stool, The control unit As the excretion information of the user, the color and properties of the stool are plotted on an XY graph for each piece of history information. The information processing device according to claim 2 .

7. The control unit When the type of history information is selected, excretion information corresponding to the selected history information is extracted and plotted on the XY graph. The information processing device according to claim 6 .

8. The control unit As the history information, XY graphs corresponding to information on the user's diet, information on water intake, and information on medication are displayed. The information processing device according to claim 7 .

9. The control unit Displaying the user's excretion information and predicted information regarding excretion predicted by the first trained model The information processing device according to claim 1 .

10. The control unit In a first area, history information and excretion information of the user are displayed in a chronological order, and In a second area, information scheduled for the user and excretion information predicted by the first trained model are displayed in a time series. The information processing device according to claim 1 .

11. The information processing device according to claim 10 , wherein the first area indicates a time in the past from the present, and the second area indicates a time in the future from the present.

12. The information processing device according to claim 10 , wherein the control unit displays, in a distinguishable manner, a location where the user's excretion is detected in a third area as the information relating to the user's excretion.

13. The information processing device according to claim 10 , wherein the control unit displays information about recommendations for the user in a fourth area.

14. A computer having a storage unit that stores a first trained model that has been trained in advance and a second trained model that has been trained in advance, inputting history information and excretion information of a user into the first trained model and outputting prediction information regarding the next excretion of the user; inputting the user's history information and the prediction information into the second trained model, and outputting a recommendation so that the excretion state becomes an ideal state; A program to make this happen.

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