Recommendation device

Abstract

To provide a recommendation device which can make an appropriate recommendation about a user's state.SOLUTION: A recommendation device comprises: a biological information acquisition unit for acquiring biological information of a user; and a control unit. The control unit is configured to: acquire sleep information including a sleep time in night of the user, on the basis of the biological information of the user; acquire a drowsiness degree in daytime of the user on the basis of the biological information of the user; calculate an ideal sleep time on the basis of the drowsiness degree in daytime; and recommend a sleep time zone to the user so as to make the sleep time of the user the ideal sleep time.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present disclosure relates to a recommendation device and the like. [Background technology]

[0002] Systems for managing the condition of a user have been disclosed. For example, Patent Document 1 discloses an invention for managing the sleeping hours of a user. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2018-166641 A Summary of the Invention [Problem to be solved by the invention]

[0004] The present disclosure provides a recommendation device and the like that is capable of making appropriate recommendations regarding a user's condition. [Means for solving the problem]

[0005] The recommendation device disclosed herein includes a biometric information acquisition unit that acquires a user's biometric information, and a control unit, and the control unit acquires sleep information including the user's nighttime sleep time based on the user's biometric information, acquires the user's daytime sleepiness level based on the user's biometric information, calculates an ideal sleep time based on the daytime sleepiness level, and recommends sleep time periods to the user so as to achieve the ideal sleep time. Effect of the Invention

[0006] According to the present disclosure, it is possible to provide appropriate recommendations regarding the user's condition. [Brief description of the drawings]

[0007] [Figure 1]FIG. 1 is a diagram illustrating an entire prediction system according to a first embodiment. [Diagram 2] FIG. 2 is a diagram illustrating a hardware configuration of a terminal device in the first embodiment. [Diagram 3] FIG. 2 is a diagram illustrating a hardware configuration of a first detection device in the first embodiment. [Figure 4] FIG. 2 is a diagram illustrating a hardware configuration of a second detection device in the first embodiment. [Diagram 5] FIG. 2 is a diagram illustrating a software configuration according to the first embodiment. [Figure 6] FIG. 2 is a diagram illustrating a software configuration according to the first embodiment. [Figure 7] FIG. 4 is a diagram illustrating an example of user information in the first embodiment. [Figure 8] FIG. 2 is a diagram illustrating an example of biological information in the first embodiment. [Figure 9] FIG. 4 is a diagram illustrating an example of first status information in the first embodiment. [Figure 10] FIG. 4 is a diagram illustrating an example of second status information in the first embodiment. [Figure 11] FIG. 4 is a diagram illustrating an example of prediction information in the first embodiment. [Figure 12] FIG. 2 is a diagram illustrating an example of a neural model in the first embodiment. [Figure 13] FIG. 4 is a diagram illustrating an example of action schedule data in the first embodiment. [Figure 14] FIG. 4 is a diagram showing an example of a setting screen for a scheduled activity in the first embodiment. [Figure 15] FIG. 4 is a diagram showing an example of a setting screen for a scheduled activity in the first embodiment. [Figure 16] FIG. 11 is a diagram illustrating an example of a user information acquisition process in the first embodiment. [Figure 17] FIG. 11 is a diagram illustrating an example of a first status information acquisition process in the first embodiment. [Figure 18] FIG. 11 is a diagram illustrating an example of a second state information acquisition process in the first embodiment. [Figure 19] FIG. 2 is a diagram illustrating an example of a learning model generation process in the first embodiment. [Figure 20] FIG. 4 is a diagram illustrating an example of a prediction process in the first embodiment. [Figure 21] FIG. 4 is a diagram illustrating an operation example in the first embodiment. [Figure 22] FIG. 4 is a diagram illustrating an operation example in the first embodiment. [Figure 23] FIG. 4 is a diagram illustrating an operation example in the first embodiment. [Figure 24] FIG. 4 is a diagram illustrating an operation example in the first embodiment. [Diagram 25] FIG. 11 is a diagram illustrating a software configuration according to a second embodiment. [Figure 26] FIG. 11 is a diagram illustrating a recommendation process in the second embodiment. [Figure 27] FIG. 13 is a diagram illustrating an entire prediction system according to a second embodiment. [Figure 28] FIG. 11 is a diagram illustrating a hardware configuration of a server device in a second embodiment. [Figure 29] FIG. 11 is a diagram illustrating a software configuration according to a second embodiment. [Diagram 30] FIG. 11 is a sequence diagram illustrating a process flow in the second embodiment. [Diagram 31] FIG. 13 is a diagram illustrating an entire prediction system according to a third embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0008] Hereinafter, one embodiment of the present invention will be described with reference to the drawings. Systems have been disclosed that manage and predict the user's physical condition and sleep state as a condition of the user, for example, a system that suggests bedtime and wake-up times based on the user's work schedule.

[0009] Further, for example, a technique is known in which a user's sleep status is grasped from the user's biometric information or a questionnaire, and a schedule is proposed for pre-sleep activities such as eating and bathing that affect sleep.

[0010] However, such technology only suggests ways to improve sleep quality based on general behavioral patterns, etc. For example, there has been no proposal from the perspective of what kind of suggestions should be made to a user based on the user's future activity plans.

[0011] In particular, there are times when a user plans a period of time during the day when they should concentrate. In order for the user to concentrate during the period of time, it was not possible to make suggestions on how the user should behave the previous day (e.g., sleep, etc.). In addition, there was no suggestion on what actions the user should take depending on the number of days until the user's schedule.

[0012] In view of such problems, the following embodiment describes a prediction device capable of predicting a user's daily condition based on the user's daily activities including sleep, biological information, etc. Note that the following embodiment is an example for realizing the invention described in the claims, and it goes without saying that the technical scope of the invention described in the claims is not limited to the embodiment.

[0013] [1. First embodiment] [1.1 Overall System] Fig. 1 is a diagram for explaining an overall outline of a prediction system 1. As shown in Fig. 1, the prediction system 1 has a terminal device 10 which is a prediction device. The prediction system 1 also includes a first detection device 20 and a second detection device 30 which can detect and acquire the state of a user.

[0014] The first detection device 20 is, for example, a device placed between the bed device 3 and the mattress 5. The first detection device 20 may be placed under the user P, for example, on the mattress 5, or directly on the floor or tatami mat on which the user P is located.

[0015] The first detection device 20 mainly detects the state (first state) of the user P while he / she is asleep. In particular, the first detection device 20 detects the state (first state) of the user P while he / she is asleep. The second detection device 30 mainly detects the state (second state) of the user P while he / she is active. In this embodiment, the time when the user is asleep refers to the time from when the user enters the bed to when he / she leaves the bed (the time from when the user is in bed to when he / she leaves the bed). The user may be asleep (asleep) or awake while he / she is asleep. The time when the user is asleep refers to the time from when the user falls asleep at night to when he / she wakes up the next morning. The time when the user is active refers to the time from when the user wakes up the morning of the day to when the user falls asleep at night. Depending on the user's life rhythm, the user's activities may be reversed between day and night. In that case, the time when the user sleeps the most continuously in a day may be called the time when the user is asleep, and the other times may be called the time when the user is active.

[0016] The first detection device 20 is a device that detects the state of the user P on the bed device 3. Therefore, the first detection device 20 mainly detects the sleeping state of the user P among the states during sleep (for example, the state of the user P during night time). To be precise, the time from when the user sits in bed until when he actually falls asleep and the time from when he wakes up until when he gets out of bed are included in the time during sleep, but any state other than the active state may be called the sleeping state.

[0017] The second detection device 30 is, for example, a wearable terminal device that can be worn directly by the user P. The second detection device 30 is, for example, a wristband-type device, a watch-type device, or a glasses-type device. The second detection device 30 is a device that the user P wears after waking up, and mainly detects the active state of the user P (for example, the state from waking up to going to bed, the user's state during the day).

[0018] For the sake of convenience, these devices are described separately, but each description can also realize the function of the other device. For example, if the terminal device 10 has an acceleration sensor or the like, placing the terminal device 10 on the mattress 5 can realize the same function as the first detection device 20.

[0019] Furthermore, the second detection device 30 may function as the first detection device 20 by wearing the wearable device even while sleeping.

[0020] In this way, each device may function as another device depending on how it is used. Also, each device may be composed of multiple devices having further functions. For example, the second detection device 30 may include a pulse oximeter and a respiratory test device in addition to the wearable terminal device.

[0021] For example, when a user P is in bed on the mattress 5, the first detection device 20 detects body vibrations (vibrations emitted from the human body) as a biosignal of the user P. Then, based on the detected vibrations, bioinformation of the user P (e.g., information related to heart rate and breathing) is calculated. In this embodiment, values ​​calculated from the bioinformation (e.g., respiratory rate, heart rate, amount of activity) can be output and displayed as bioinformation values ​​of the user P.

[0022] Furthermore, since the terminal device 10 may be a general-purpose device, it is not limited to an information processing device such as a computer, and may be configured as a device such as a tablet or a smartphone. In this embodiment, a smartphone on which an application capable of realizing the functions of this embodiment is installed will be described as an example. Furthermore, the terminal device 10 is a device that can be connected to the first detection device 20 by wire or wirelessly. For example, the terminal device 10 may be an operation device (e.g., a hand switch, an operation remote control, etc.) that is connected to the first detection device 20 by wire.

[0023] The user may be a person undergoing medical treatment or a person in need of nursing care, or may be a healthy person not in need of nursing care, an elderly person, a child, a person with a disability, or even an animal other than a human.

[0024] Here, the first detection device 20 is formed in a sheet shape so as to be thin. As a result, even if it is placed between the bed device 3 and the mattress 5, it can be used without making the user P feel uncomfortable, and therefore it is possible to measure the biological information in bed for a long period of time. That is, when the user is lying down and resting, the biological information value or the like is acquired as the user's state (first state).

[0025] In addition, the first detection device 20 can determine the position and posture of the user P on the bed apparatus 3. For example, it can determine whether the posture of the user P on the mattress 5 is a recumbent position or a sitting position, and it can also determine in which position on the mattress 5 the user P is sleeping and in which direction the user P is sleeping.

[0026] The first detection device 20 only needs to acquire biosignals (body movement, respiratory movement, ballistocardiogram, etc.) of the user P. In this embodiment, the heart rate and respiratory rate can also be calculated based on body vibrations, but for example, an infrared sensor may be used for detection, the biosignals of the user P may be acquired from acquired images, or an actuator with a strain gauge may be used. In addition, by utilizing a built-in acceleration sensor, for example, the device may be realized by a smartphone, tablet, or the like (terminal device 10) placed on the bed device 3.

[0027] [1.2 Hardware configuration] [1.2.1 Common Configuration] Of the individual devices, the control unit, storage, ROM, RAM, display unit, operation unit, and communication unit will be described below.

[0028] The control unit (e.g., control unit 100, control unit 200, control unit 300) is a functional unit for controlling each device as a whole. The control unit realizes various functions by reading and executing various programs stored in storage or ROM. The control unit may be realized by one or more control devices / arithmetic units (CPU (Central Processing Unit), SoC (System on a Chip)).

[0029] Storage (for example, storage 110, storage 210, storage 310) is a non-volatile storage device capable of storing programs and data. For example, it may be configured as a storage device using an HDD (Hard Disk Drive) or a semiconductor memory. Furthermore, the storage may be an externally connectable storage device or storage medium, and may be configured as a USB memory, a CD-ROM drive, or a BD-drive. Furthermore, the storage may be, for example, a storage area on the cloud.

[0030] The ROM (for example, the ROM 120, the ROM 220, and the ROM 320) is a non-volatile memory that can retain programs and data even when the power is turned off. The ROM may store, for example, firmware for each device, initial applications, and the like.

[0031] RAM (e.g., RAM 130, RAM 230, RAM 330) is a main memory that is mainly used by the control unit of each device when executing processing. RAM is a rewritable memory that temporarily holds data including the storage of each device, programs read from the ROM of each device, and execution results.

[0032] The display unit (for example, display unit 140, display unit 240, display unit 240) is a device capable of displaying various information and an execution screen. The display unit may be a device capable of displaying information such as a liquid crystal display or an organic EL display, or may use a light-emitting element such as an LED. For example, the display unit may display the power state of the device or an error state depending on the lighting state of the LED. The display unit may also be configured by an external device. For example, the terminal device 10 may use a display device connected to a terminal capable of outputting video such as HDMI (registered trademark) or DVI.

[0033] Also, for example, the first detection device 20 may display the state of the device on the display unit 140 of the terminal device 10 instead of the display unit 240. In this case, the first detection device 20 does not need to be provided with the display unit 240.

[0034] The operation unit (e.g., operation unit 150, operation unit 250, operation unit 350) is a device through which a user operates each device. The operation unit may be realized by a hardware switch or a software switch. For example, a touch panel integrated with the display unit may be used to realize software keys as the operation unit. The operation unit may also be configured by an external device. For example, the terminal device 10 may use an operation device (e.g., a keyboard, etc.) connected to an interface such as a USB.

[0035] Also, for example, the first detection device 20 may use the operation unit 150 of the terminal device 10 to operate the device instead of the operation unit 250. In this case, the first detection device 20 does not need to be provided with the operation unit 250.

[0036] The communication unit (e.g., communication unit 170, communication unit 270, communication unit 370) is a communication interface that communicates with other devices. For example, it may be a network interface that can provide a wired connection or a wireless connection. In this embodiment, it is possible to communicate with other devices via a network NW. Furthermore, the communication unit may provide a function that allows connection to a mobile network (LTE (Long Term Evolution) / 4G / 5G / 6G).

[0037] 1.2.2 Terminal Equipment As shown in FIG. 2, the terminal device 10 includes a control unit 100, a storage 110 as a memory unit, a ROM 120, and a RAM 130, a display unit 140, an operation unit 150, and a communication unit 170.

[0038] The terminal device 10 may also have a camera unit and an acceleration sensor unit. The camera unit can capture, for example, the terminal device 10, the state of the user, and the environment around the user. By using the camera unit, the terminal device 10 can recognize the facial expression and the movement of the user.

[0039] Furthermore, the acceleration sensor unit can detect the acceleration occurring in the terminal device 10. For example, the terminal device 10 can obtain the activity state of the user by using the acceleration sensor unit.

[0040] [1.2.3 First detection device] As shown in FIG. 3, the first detection device 20 includes a control unit 200, a storage 210 as a memory unit, a ROM 220 and a RAM 230, a display unit 240, an operation unit 250, a sensor unit 260, and a communication unit 270.

[0041] Here, the display unit 240 and the operation unit 250 may be provided as necessary. The display unit 240 may be an element such as an LED that indicates the power-on state or the communication state. Instead of the display unit 240, an output device capable of outputting sound or voice (e.g., a piezoelectric buzzer, a speaker, etc.) may be provided.

[0042] The sensor unit 260 is a first sensor device capable of acquiring the body movement of the user. For example, the sensor unit 260 may be configured with a pressure sensor or a load sensor. The sensor unit 260 detects the body movement of the user. The sensor unit 260 may output, for example, the detected load value or a change in the load value.

[0043] The sensor unit 260 may be an external sensor device connected to the first detection device 20. For example, the sensor unit 260 may be a sensor device that detects the heartbeat of the user by an electrical signal. Also, for example, the change in load may be acquired from a load sensor provided in the bed apparatus 3.

[0044] The communication unit 270 may communicate with, for example, the terminal device 10. For example, the communication unit 270 may connect to the terminal device 10 using Bluetooth (registered trademark). In addition, the communication unit 270 may connect to a network by wireless LAN or the like and communicate with the terminal device 10 via the network, or may communicate with the terminal device 10 using a mobile communication network.

[0045] [1.2.4 Second detection device] As shown in FIG. 4, the second detection device 30 has a control unit 300, a storage 310 as a memory unit, a ROM 320 and a RAM 330, a display unit 340, an operation unit 350, a sensor unit 360, and a communication unit 370.

[0046] Here, the display unit 340 and the operation unit 350 may be provided as necessary. For example, in the case of a wristwatch-type wearable terminal device, the display unit 340 and the operation unit 350 integrated as a touch panel may be provided. Also, if a sensor is simply used, the display unit 340 and the operation unit 350 may not be provided.

[0047] The sensor unit 360 is a second sensor device used to acquire biometric information and an activity state of the user. For example, the sensor unit 360 may be configured with an optical sensor, a moisture detection sensor, a temperature sensor, an acceleration sensor, etc. That is, the sensor unit 360 may be a sensor such as a respiration sensor, an SpO2 sensor, a blood glucose sensor, a heart rate sensor, a blood pressure sensor, a sweat sensor, or a body temperature sensor.

[0048] The sensor unit 360 may be an external sensor device connected to the second detection device 30. For example, the sensor unit 360 may communicate with an external temperature sensor or heart rate sensor via the communication unit 370 and acquire the sensor values ​​output by each sensor.

[0049] [1.3 Software configuration] Next, the software configuration of the prediction system 1 will be described with reference to Fig. 5. Fig. 5 is a diagram that illustrates the software configuration of the terminal device 10, the first detection device 20, and the second detection device 30, and the data stored in the storage unit (storage).

[0050] [1.3.1 First detection device] The first detection device 20 functions as a biological information acquisition unit 202 and a first state determination unit 204 by the control unit 200 executing a program stored in the storage unit.

[0051] The biological information acquiring unit 202 acquires biological information based on a sensor value acquired by the sensor unit 260 .

[0052] For example, the sensor unit 260 may acquire the body vibration of the patient using a pressure sensor, or may acquire the biosignal from the change in the center of gravity position (body movement) of the patient using a load sensor. Alternatively, a microphone may be provided instead of a pressure sensor to acquire the biosignal based on the sound picked up by the microphone. Alternatively, the biosignal may be acquired based on the displacement of the living body or bedding using microwaves or laser speckles. Alternatively, the biosignal may be acquired from the output value of an acceleration sensor or a gravity sensor. It is sufficient that the sensor unit 260 can acquire the biosignal using any method.

[0053] The bioinformation acquiring unit 202 acquires values ​​related to the bioinformation of the user (such as respiration rate, heart rate, and activity level). For example, the respiration rate and heart rate may be obtained based on the respiration interval and heart rate interval by extracting the respiration component and heart rate component from the body movement (body vibration) acquired by the sensor unit 260. In addition, the periodicity of the body movement acquired by the sensor unit 260 may be analyzed (Fourier transform, etc.) and the respiration rate and heart rate may be calculated and acquired from the peak frequency.

[0054] The biometric information acquiring unit 202 may also acquire information (values) related to the biometric information from a device capable of acquiring other biometric information. The biometric information acquiring unit 202 stores the acquired biometric information in the biometric information storage area 212 in chronological order for each user.

[0055] FIG. 8 is a diagram showing an example of biometric information stored in the biometric information storage area 212. FIG. 8 shows an example of biometric information stored in the biometric information storage area 212. The biometric information storage area 212 stores the biometric information in chronological order in association with an ID (e.g., "PB010025") that is the user's identification information. The biometric information stores a heart rate (e.g., "58"), an SpO2 value (e.g., "99"), and a respiratory rate (e.g., "14") as examples of biometric information values ​​in association with a date and time (e.g., "2023 / 06 / 05 04:20:00").

[0056] Here, the bioinformation value may be stored at a predetermined time interval. The timing at which the bioinformation value is stored may be uniform or may vary depending on the bioinformation value. As an example, the bioinformation storage area 212 in FIG. 8 stores the heart rate every minute, and the SpO2 value and the respiratory rate every five minutes.

[0057] The first state determination unit 204 determines the state of the user mainly when the user is asleep among the states of the user. That is, the first state is the state of the user while the user is asleep (in bed apparatus 3), mainly at night, and is the state during the average time when the user sleeps. Moreover, the night determined by the first state may include night, late night, and early morning depending on the user.

[0058] The user's status determined by the first status determination unit 204 is stored in the first status information storage area 214. Fig. 9 is a diagram showing an example of the first status information stored in the first status information storage area 214. The first status information storage area 214 stores information on the user's first status in chronological order for each ID that is identification information of the user.

[0059] Here, the first state includes the following states: The user is out of bed. The user is in bed. The state of the user when the user is in bed. For example, the state of "awake" or "asleep". The state of "asleep" may also be represented by a level indicating the depth of sleep. For example, the state of light sleep may be represented as LV1, and the state of deepest sleep may be represented as LV4, and the state may be stored in stages. - User's posture. For example, the user's posture is "supine", "side-lying", or "prone"

[0060] The biometric information acquiring unit 302 calculates biometric information from the biometric signal of the user detected by the sensor unit 360, and stores the information in the biometric information storage area 312. When the sensor unit 360 is an optical sensor, the biometric information acquiring unit 302 can acquire biometric information such as heart rate, SpO2 value, and blood glucose level. In addition, the biometric information acquiring unit 302 may acquire blood pressure, sweating, body temperature, and the like. The biometric information acquiring unit 302 may also acquire other conditions of the user. For example, by using an acceleration sensor as the sensor unit 360, it may acquire, for example, that the user has coughed, sneezed, or fallen.

[0061] [1.3.2 Second detection device] The second detection device 30 functions as a biological information acquisition unit 302 and a second state determination unit 304 by the control unit 300 executing a program stored in the storage unit.

[0062] The biometric information acquiring unit 302 acquires biometric information based on the sensor value acquired by the sensor unit 360. For example, the biometric information acquiring unit 302 can acquire information such as a heart rate, a respiratory rate, an activity level, and an SpO2 value from the sensor value. The biometric information acquiring unit 302 may also store the acquired biometric information in a chronological order for each user in the biometric information storage area 312. Here, the data stored in the biometric information storage area 312 may be in the same format as that of the biometric information storage area 212 shown in FIG. 8, for example.

[0063] The second state determination unit 304 determines the state of the user mainly when the user is active. That is, the second state is the state of the user from waking up to going to bed (getting out of bed from the bed device 3), mainly during the daytime, and is the state during the average time when the user is active. In addition, the daytime determined by the second state may include morning (after waking up), evening, and night (before going to bed), depending on the user.

[0064] The user's status determined by the second status determination unit 304 is stored in the second status information storage area 314. Fig. 10 is a diagram showing an example of the second status information stored in the second status information storage area 314. The second status information storage area 314 stores information on the second status of the user in chronological order for each ID that is identification information of the user.

[0065] Here, the second state includes the following states: - A state where the user can "concentrate" and feel "drowsy" A state of "hypervigilance" in which the user is not sleepy but cannot concentrate In this embodiment, a state in which the user is not "drowsy" is described as a state in which the user can "concentrate", but there may be multiple other states.

[0066] For example, the following conditions may be determined as the user's condition: (1) A "normal" state as the state of the user. Here, the normal state of the user refers to a state in which the user is not in a state in which the user's concentration is particularly high, but is not in a state in which the user feels drowsy. For example, the normal state of the user may include a state in which the user is moving, or performing daily activities such as eating or taking a bath. (2) A state in which a user can concentrate compared to a "normal" state may be defined as a "concentrated" state. A "concentrated" state may be, for example, a state in which the user can achieve a better score than in a "normal" state when taking a test to check the user's state using an application or the like, or a state in which the user is aware that he or she can concentrate. Also, for example, a state in which the user's heart rate is slightly higher than normal may be defined as a "concentrated" state. (3) As described above, the user's state may be further determined to be a state of "drowsiness" or a state of "hyperarousal."

[0067] The second state may be indicated by a level. For example, a drowsy state may be indicated as LV1, a state where one can concentrate as LV2, and a state of hyperarousal as LV3.

[0068] The second state may indicate other states. For example, a state such as "moving" where the user is moving, or "in bed" where the user is in the bed apparatus 3 may be determined.

[0069] The second state determination unit 304 may determine another state as the second state. For example, the second state determination unit 304 may determine the second state as "coughing" when the user is coughing, as "taking a bath" when the user is taking a bath, or as "exercising" when the user is exercising.

[0070] Moreover, the second state determination unit 304 determines the second state of the user from, for example, a fluctuation in the heart rate of the user. For example, the following documents disclose the relationship between the fluctuation in the heart rate and drowsiness.

[0071] Sang-Ho Jo et al., "Heart Rate Change While Drowsy Driving" J Korean Med Sci (https: / / doi.org / 10.3346 / jkms.2019.34.e56)

[0072] For example, the above-mentioned literature states that "Driving HR showed a trend for increment as compared to day time mean HR, from 85 ± 5.6 to 89.8 ± 5.6 beats / min (by 7%) (P = 0.093). Mean HR while sleepy driving significantly decreased to 81.5 ± 9.2 beats / min by 9.3% ± 7.4% (P = 0.046)," indicating that when one feels "drowsy," there is a decrease in heart rate.

[0073] Therefore, if the acquired heart rate is within ±5% of the average heart rate, the second state determination unit 304 determines that the user is in a state where he or she can concentrate. Also, if the acquired heart rate is 5% or more lower than the average heart rate, the second state determination unit 304 determines that the user is in a sleepy state. Also, if the acquired heart rate is 5% or more higher than the average heart rate, the second state determination unit 304 determines that the user is not sleepy but cannot concentrate, that is, is in a state of hyperarousal.

[0074] The average heart rate described here is an example, and may be determined for each user. For example, the second state determination unit 304 may make the following determination based on the relationship between the heart rates. (1) If your current heart rate is 8% or more lower than your average heart rate, you are sleepy. (2) You can concentrate if your current heart rate is 5% to 10% higher than your average heart rate. (3) If your current heart rate is 10% or more higher than your average heart rate, you are in a state of hyperarousal. (4) Any other condition than the above is normal. For example, the heart rate is within ±5% of the average heart rate. It may be determined that:

[0075] The second state determination unit 304 may use other methods to determine the drowsiness of the user. For example, the second state determination unit 304 may determine drowsiness from the movement of the user's body, or may determine drowsiness from the state of blood flow (for example, parameters such as oxygenated hemoglobin, deoxygenated hemoglobin, and oxygen saturation). For determining drowsiness, for example, conventional technologies such as those described in JP 2008-35964 A, JP 2008-220424 A, JP 2018-27184 A, and JP 2021-18779 A can be used.

[0076] 1.3.3 Terminal Equipment The terminal device 10 functions as an information receiving unit 102, a user information acquiring unit 104, a sleep score calculating unit 106, a learning unit 107, a prediction unit 108, and an action schedule setting unit 109 by the control unit 100 executing a program stored in the storage unit. In addition, a user information storage area 111 for storing user information, a biological information storage area 112 for storing biological information, a first state information storage area 113 for storing first state information, a second state information storage area 114 for storing second state information, a prediction information storage area 115 for storing prediction information, a learning model storage area 116 for storing a learning model, and an action schedule data storage area 117 are secured in the storage 110. In addition, these pieces of information are not limited to the storage 110, and may be stored in an area secured on a cloud service, for example.

[0077] The information receiving unit 102 acquires each piece of information from the first detection device 20 and the second detection device 30 via the communication unit 170. For example, the information receiving unit 102 receives biometric information from the first detection device 20 and the second detection device 30 and stores it in the biometric information storage area 112. Furthermore, the information receiving unit 102 receives first status information from the first detection device 20 via the communication unit 170 and stores it in the first status information storage area 113. Furthermore, the information receiving unit 102 receives second status information from the second detection device 30 via the communication unit 170 and stores it in the second status information storage area 114.

[0078] The user information acquiring unit 104 acquires information about a user. The user information acquiring unit 104 may acquire the user information from the user via, for example, the operation unit 150. The user information acquiring unit 104 may also acquire the user information from, for example, a server device connected via a network. The user information acquiring unit 104 may store the acquired user information in the user information storage area 111.

[0079] An example of the user information will now be described with reference to Fig. 7. Fig. 7 is a diagram showing an example of the user information stored in the user information storage area 111.

[0080] 7(a) stores basic information input by the user. The user information A stores, for example, an ID (e.g., "PB010025") for uniquely identifying the user, a name (e.g., "Suzuki Taro"), a date of birth (e.g., "2000 / 02 / 24"), a sex (e.g., "male"), a height (e.g., "170 cm"), a weight (e.g., "62.0 kg"), and information regarding illness (e.g., "none"). The contents included in the user information A are the contents input by the user via the operation unit 150.

[0081] The user information may also store information by day, such as the user information B in FIG. 7(b). As shown in FIG. 7(b), the user information B stores an ID (e.g., "PB010025"), a date (e.g., "2023 / 06 / 01"), and weight (e.g., "62.5 kg"), blood pressure (e.g., "122 / 70"), and body temperature (e.g., "36.2") measured on the date. For example, the information stored in the user information B may be information input by the user via the operation unit 150, or may be information acquired on an ad hoc basis from another device. For example, the terminal device 10 acquires weight from a weight scale, blood pressure from a blood pressure monitor, and body temperature from a thermometer, and stores them as user information. The user information B may also store information related to the life of the day. For example, in FIG. 7(b), information indicating "drinking alcohol" is stored as life information on the day.

[0082] The time when each piece of user information was acquired may be stored. When multiple pieces of user information were acquired on a given date, multiple pieces of user information may be stored.

[0083] The user information may also store information related to sleep, such as user information C in Fig. 7(c). As shown in Fig. 7(c), the user information C may store an ID (e.g., "PB010025"), a date (e.g., "2023 / 06 / 01"), a bedtime (e.g., "2023 / 05 / 31 22:10"), a wake-up time (e.g., "2023 / 06 / 01 06:45"), and a sleep score (e.g., "89").

[0084] The sleep score is a score for evaluating the quality of sleep based on the user's sleep state throughout the night. Here, the sleep score is information for evaluating sleep, and is calculated by the sleep score calculation unit 106.

[0085] The sleep score calculation unit 106 is a value that evaluates the sleep based on the state of sleep of the user. For example, the sleep score calculation unit 106 calculates a sleep score that evaluates the sleep from parameters such as the depth of sleep, the number of awakenings during sleep, and the sleep duration. Here, the method of calculating the sleep score may be, for example, the method disclosed in Japanese Patent Application Laid-Open No. 2013-45336 (Title of the Invention: Sleep State Evaluation Apparatus, Sleep State Evaluation System and Program, Filing Date: August 25, 2011).

[0086] The learning unit 107 outputs the generated learning model to the learning model storage area 116. The learning model (trained model) in this embodiment is a model that outputs a second state indicating the user's state of concentration, sleepiness, etc. in a day.

[0087] Here, the learning unit 107 may use various methods such as a machine learning model and a deep learning model as the learning model generated. Here, in the machine learning model, methods such as a support vector machine, decision algorithm, random forest, and K-nearest neighbor method may be used. In addition, in the deep learning model, methods such as a convolutional neural network (CNN) and a recurrent neural network (RNN) may be used.

[0088] For example, the learning unit 107 inputs necessary information (parameters) from the user information, the biological information, and the first state information to a neural network composed of multiple layers and neurons included in each layer as shown in FIG. 12. Each neuron receives signals from multiple other neurons and outputs a signal obtained by performing an operation to multiple other neurons. When the neural network has a multi-layer structure, the layers are called an input layer, an intermediate layer (hidden layer), and an output layer in the order in which the signals flow. Various operations (convolution operation, pooling operation, normalization operation, matrix operation, etc.) are performed on the neurons in each layer of the neural network, and the signals flow while changing their shape, and multiple signals are output from the output layer. The multiple output values ​​from the neural network are each output in association with the second state of the user. Then, the learning unit 107 performs a process of predicting (estimating) the state of the user at a certain point in time (for example, the second state such as concentration, drowsiness, or normal) in association with the largest output value among the output values ​​output from the neural network.

[0089] The parameters, which are coefficients used in various calculations of the neural network, are determined by inputting a large number of parameters and the second state of the user that is actually determined based on the parameters into the neural network in advance, propagating the error between the output value and the correct value backward through the neural network using the error backpropagation method, and repeatedly updating the parameters of the neurons in each layer. This process of updating and determining parameters is called learning.

[0090] The structure of the neural network and each of the individual calculations are publicly known techniques explained in books and papers, and any of these techniques may be used.

[0091] The prediction unit 108 applies the input parameters to the learning model generated by the learning unit 107, and predicts the user's state. The prediction unit 108 of this embodiment applies, for example, biometric information, user information, first state information, and second state information to the learning model as parameters, and outputs prediction information predicting the second state of the user (for example, daytime state) to the prediction information storage area 115. In addition, the prediction unit 108 determines the degree of conformity with the behavior schedule set in the behavior schedule setting unit 109, and outputs a recommendation for the user's behavior.

[0092] The activity schedule setting unit 109 sets an activity schedule of the user and stores it as activity schedule data in the activity schedule data storage area 117. An activity schedule refers to the content of a user's desired state for a predetermined period of time, such as one day, two days or more.

[0093] For example, the user sets a time when the user should concentrate as the planned behavior data. The prediction unit 108 compares the second state of the user predicted by the prediction unit 108 with the predicted state of the user included in the planned behavior data, thereby making a recommendation for appropriate behavior.

[0094] 13A is a diagram showing an example of the activity schedule data. The activity schedule data may be set, for example, by day or by week (for example, by day of the week). The activity schedule data may also be set for a special day (for example, a presentation day) or the like.

[0095] In Fig. 13(a), a pattern of planned activities is stored in association with a user ID. In Fig. 13, one or more items indicating whether or not the user has plans can be stored for each date.

[0096] For example, FIG. 13(a) shows a user's scheduled activities from "9 / 1" to "9 / 7". Here, it is preferable for the user to spend as much time in the "concentration" state as possible and as little time in the "sleep" or "hyperarousal" state as possible on the days when the scheduled activities are set.

[0097] The action plan may also store, for example, a time period during which the user should concentrate. For example, when "Exam [Morning] [Afternoon]" is stored, it indicates that it is preferable to concentrate during the morning and afternoon hours. Here, "morning" may be a general time period (e.g., 8:00 to 10:00 in the morning), or the user may be able to set the time period. The user may be able to select one or more time periods, for example, from "early morning", "morning", "afternoon", "evening", "night", and "late night". Furthermore, when setting an action plan, the user may simply set "business trip" without including the time period (e.g., all day). Furthermore, the user may also set any time period.

[0098] Also, one or more types of scheduled activities may be stored in the scheduled activity data. For example, schedules such as "match," "exam," "presentation," "training camp," and "business trip" may be stored. For example, FIG. 13(b) is an example of a table that stores candidates for scheduled activities. Candidate items for scheduled activities and time periods during which concentration should be performed are stored. For example, when a user selects a scheduled activity, the time period during which concentration should be performed is selected. Note that when the time period during which concentration should be performed is not set, the time period during which concentration should be performed may be selected as the whole day.

[0099] The activity schedule setting unit 109 may set the period in which concentration is required on, for example, a setting screen. Fig. 14 is a diagram showing an example of a display screen W100 for setting an activity schedule displayed on the terminal device 10.

[0100] The display screen W100 allows a user to set a daily schedule in the area R100. For example, a user can select a date, and select and register a schedule on which to concentrate. A user may register a schedule that spans multiple days. A user may also register a schedule for each time period.

[0101] The activity schedule setting unit 109 may set the time when the user should concentrate on a date selected from a date display, for example, or may set the time in association with another schedule.

[0102] 15(a) is an example of a schedule setting screen W110 displayed on, for example, a tablet-type terminal device 10. The setting screen W110 may be a general schedule screen used in other services, or may be a dedicated schedule screen.

[0103] The user registers a schedule in the area R110 of the setting screen W110. Here, when a schedule to which, for example, a concentration attribute is added is registered, the terminal device 10 determines that the schedule is a planned activity that requires concentration. For example, the user may set an English conversation plan Y112 as something that requires concentration, and may not particularly set a meeting plan Y114 as something that requires concentration. The user may also register, for example, a plan to rest. The terminal device 10 may determine that the planned rest is a time period during which it is acceptable to feel sleepy.

[0104] Here, the schedule and the state of concentration may be associated with the type of schedule as an action plan, or the schedule may have an attribute. Also, the user may set an arbitrary schedule as a plan to concentrate or a plan to rest.

[0105] Fig. 15(b) is a diagram showing an example of a setting screen. For example, when the user selects "English conversation" in the schedule Y122, a submenu is displayed above the schedule. If the user selects "concentration" from the submenu, the schedule Y112 is set as an action plan for the time period during which the user should concentrate.

[0106] [1.3.4 Alternative Configuration] Note that the specific state of the user may be determined by any device in the prediction system 1. For example, in Fig. 6, the first detection device 20 and the second detection device 30 only acquire biometric information.

[0107] Then, the first detection device 20 transmits the biometric information to the terminal device 10. The second detection device 30 transmits the biometric information to the terminal device 10. The first detection device 20 may further transmit a sensor value output by the sensor unit 260 to the terminal device 10, and the second detection device 30 may further transmit a sensor value output by the sensor unit 360 to the terminal device 10.

[0108] The terminal device 10 has a first state determination unit 103 and a second state determination unit 105. The first state determination unit 103 is the first state determination unit 204 described in FIG. 5. The second state determination unit 105 is the second state determination unit 304 described in FIG.

[0109] Furthermore, the biological information acquisition unit may also be realized by the terminal device 10. In this case, the first detection device 20 and the second detection device 30 may simply be sensor devices capable of outputting sensor values.

[0110] [1.4 Processing flow] The process flow in each device will be described below with reference to the drawings.

[0111] [1.4.1 User information acquisition process] FIG. 16 is an example of an operational flow showing the flow of a user information acquisition process executed by the control unit 100 (user information acquisition unit 104) of the terminal device 10. In FIG.

[0112] The control unit 100 acquires the attributes of the user (step S102). For example, the control unit 100 displays, on the display screen displayed on the display unit 140, an input screen on which the attributes of the user can be input.

[0113] Then, the user uses the input screen to input the user's attributes. Here, the user's attributes include, for example, information such as the user's name and age, as shown in Fig. 7(a). The user's attributes may also include the user's height, weight, etc.

[0114] Next, the user uses the input screen to acquire daily information as information that can be input by the user on a daily basis (Step S104). The daily information is parameters related to the user that change daily, and may be, for example, parameters such as those shown in FIG. 7(b). The daily information may also include measurements acquired from other devices (e.g., a weight scale, a blood pressure monitor, a thermometer, etc.). The user information may also include, for example, information related to the user's lifestyle (e.g., diet, alcohol consumption, medication, etc.).

[0115] The control unit 100 stores the attributes of the user and the daily information of the user as user information in the user information storage area 111 (step S106).

[0116] [1.4.2 First status acquisition process] FIG. 17 is an example of an operational flow showing the flow of a first state acquisition process executed by the control unit 200 (first state determination unit 204) of the first detection device 20.

[0117] When the control unit 200 detects that the user is in bed (step S112), the control unit 200 acquires the biometric information by the biometric information acquisition unit 202 (step S114). The biometric information acquisition unit 202 continues to acquire the biometric information while the user is in bed. That is, the biometric information acquisition unit 202 continues to acquire the biometric information until step S124 in FIG. 17.

[0118] Then, control unit 200 executes a first state determination process (step S116) and determines first state information based on the first state of the user. In the first state determination process, control unit 200 executes first state determination unit 204 to determine the first state of the user. Note that first state determination unit 204 determines the first state of the user and outputs the first state information until the user wakes up. That is, first state determination unit 204 determines the first state and outputs the first state information up to step S124 in FIG. 17.

[0119] When the control unit 200 determines that the user has fallen asleep (step S118; Yes), it stores first state information based on the first state of the user in the first state information storage area 214. Here, the control unit 200 may determine that the user has fallen asleep when, for example, the first state of the user has been determined to be "sleeping" for a predetermined period of time or more, or when it detects that the user is not moving much (no significant body movement is detected).

[0120] When the user falls asleep, the control unit 200 stores the first status information of the user in the first status information storage area 214 (step S120). In addition, the control unit 200 stores the biometric information of the user in the biometric information storage area 212 (step S122).

[0121] Then, the control unit 200 executes steps S120 and S122 until the user wakes up (step S124; No). When the control unit 200 detects that the user has woken up (step S124; Yes), it executes a sleep evaluation process (step S126).

[0122] The sleep evaluation process may be executed in the terminal device 10. At this time, the terminal device 10 may execute the sleep evaluation process, for example, by executing the sleep score calculation unit 106. Furthermore, information related to sleep may be stored in the user information storage area 111 as user information.

[0123] 7(c), the control unit 100 may obtain the "bedtime" and "wake-up time" from the user's sleep onset / asleep state, and store them in the user information storage area 111. The control unit 100 may also store in the user information storage area 111 the sleep score calculated by executing the sleep score calculation unit 106.

[0124] [1.4.3 Second status acquisition process] FIG. 18 is an example of an operational flow showing the flow of the second state acquisition process executed by the control unit 300 (second state determination unit 304) of the second detection device 30.

[0125] The control unit 300 detects a trigger for starting acquisition of the second state (step S132). Here, the trigger for starting acquisition of the second state may be, for example, the following.

[0126] When the second detection device 30 detects that the device is worn by a user When the user performs an operation to start acquiring the second status When a different device (e.g., a sensor device) detects that the user has left the bed and determines to start acquiring the second state. When a preset or predicted start time arrives and it is determined that acquisition of the second state should begin

[0127] When any one of these triggers is detected (step S132; Yes), the biometric information acquisition unit 302 acquires the biometric information (step S134). Furthermore, the second state determination unit 304 executes a second state process (step S136). The second state determination process determines the second state as a state in which the user is active. Here, the second state determination process may determine, for example, "drowsiness," "concentration," "hyperalertness," or "normal" as the second state.

[0128] Then, the control unit 300 stores the biometric information in the biometric information storage area 312, and stores the second status information based on the second status in the second status information storage area 314 (step S138).

[0129] The control unit 300 detects a trigger for ending acquisition of the second state (step S140). Here, the trigger for ending acquisition of the second state may be, for example, the following.

[0130] When the second detection device 30 detects that the device has been removed from the user When a user action is detected to end acquisition of the second state When a different device (e.g., a sensor device) detects that the user is in bed and determines to end acquisition of the second state. When a preset or predicted end time is reached and it is determined that acquisition of the second state is to end When the control unit 300 detects any one of these triggers (step S140; Yes), the control unit 300 ends this process.

[0131] [1.4.4 Learning model generation process] FIG. 19 is an example of an operational flow showing the flow of a learning model generation process executed by the control unit 100 (learning unit 107) of the terminal device 10.

[0132] The learning model is a model obtained by performing machine learning in which the sleep information includes user information, a first state indicating the state of the user while sleeping, and biological information of the user while sleeping, and the second state during the user's activity on the next day (daytime while awake). Here, the activity state of the second state is labeled, for example, as a "concentration" state, a "drowsiness" state, a "hyperarousal" state, or a "normal" state. The learning unit 107 may also perform semi-supervised machine learning to generate the learning model. For example, the learning unit 107 performs unsupervised machine learning for a predetermined period from when the user starts using the system (for example, until the learning unit 107 acquires a characteristic state and generates a learning model). Then, the learning unit 107 performs supervised machine learning after the predetermined period has elapsed (for example, after the learning unit 107 generates a learning model). The trained model may be, for example, a model that has learned a correlation between information (sleep information) regarding the user's sleep on a predetermined day and a second state during the user's activity on the next day of the predetermined day. As a result, by using the trained model, it is possible to predict the condition of the person on the day after a given day based on the sleep information of that day.

[0133] Moreover, the parameters inputted into the learning model are the user information, the first state information, and the biological information during sleep (the biological information acquired by the first detection device 20). Specific processing will be described below.

[0134] First, the control unit 100 (i.e., the learning unit 107) acquires user information from the user information storage area 111 (step S152). The control unit 100 also acquires biometric information from the biometric information storage area 112 (step S154). The control unit 100 also acquires first status information from the first status information storage area 113 (step S156). The control unit 100 also acquires second status information from the second status information storage area 114 (step S158).

[0135] Next, the control unit 100 performs data pre-processing on the acquired information (step S160). The data pre-processing is a process of weighting the information (parameters) acquired in steps S152 to S158 and identifying necessary parameters. Here, the control unit 100 does not necessarily need to acquire all of the information that can be acquired in steps S152 to S158. The control unit 100 acquires one or more pieces of information as parameters as necessary.

[0136] Here, the control unit 100 judges whether prediction information is stored in the prediction information storage area 115 (step S162). That is, the presence of prediction information in the prediction information storage area 115 means that the control unit 100 is outputting prediction information using a learning model, and a valid learning model is already stored in the learning model storage area 116.

[0137] Therefore, the control unit 100 acquires prediction information from the prediction information storage area 115 (step S164), and regenerates (re-learns) the learning model using the prediction information (step S166). At this time, the control unit 100 may re-learn the learning model using the user's condition as teacher data. The control unit 100 may acquire the user's condition determined from bio-information (e.g., heart rate, etc.) as teacher data as the user's condition. The control unit 100 may also acquire the user's condition inputted as teacher data as the user's condition.

[0138] In addition, when there is no prediction information in the prediction information memory area 115, the control unit 100 generates a learning model using the user information, biometric information, first status information, and second status information as parameters, since a valid learning model has not yet been generated (step S168).

[0139] For example, the control unit 100 uses one or more pieces of information from among the user information, the biometric information, and the first status information to group (cluster) and display the second status information. The user provides information such as the user's status (e.g., "sleepy," "concentrated," "hyperawake," "normal") for the grouped second statuses, and the control unit 100 generates a learning model. The control unit 100 also acquires the user's status by having the user take a test using an application during the time of the grouped second status. Then, the control unit 100 stores the generated learning model in the learning model storage area 116.

[0140] [1.4.5 Recommendation Processing] FIG. 20 is an example of an operational flow showing the flow of a first recommendation process executed by the control unit 100 (prediction unit 108) of the terminal device 10.

[0141] The control unit 100 acquires parameters to be applied to the learning model (step S182). The control unit 100 selects one or more of the following parameters as the parameters to be acquired. (1) User Information The control unit 100 acquires one or more pieces of user information from the user information storage area 111. (2) Biometric information The control unit 100 acquires the biometric information from the biometric information storage area 112 . (3) First state The control unit 100 acquires the first status information from the first status information storage area 113.

[0142] Next, the control unit 100 applies the parameters acquired in step S182 to the learning model to output prediction information (step S184). The control unit 100 outputs prediction information, for example, on a daily basis (e.g., the next day, etc.), a weekly basis, or a monthly basis. The control unit 100 may also output prediction information for a specified date or a specified period.

[0143] Next, the control unit 100 compares the prediction information with the activity schedule of the activity schedule data (step S186). Here, the control unit 100 determines, for example, the degree of conformity between the prediction information and the activity schedule.

[0144] Here, the degree of compatibility refers to, for example, judging the degree of compatibility between the user's state set in the action plan (the second state desired by the user) and the user's state output as predicted information (the second state output as predicted information).

[0145] For example, the control unit 100 compares the "concentration" time set in the action schedule with the "concentration" time output as the prediction information. The control unit 100 may calculate the degree of conformity as follows. Suitability = Among the time periods set as "concentration" in the action schedule, the forecast information shows Percentage of time judged

[0146] For example, a user sets "9:00-11:00" and "13:00-14:00" as time periods when he / she should "concentrate" in the planned activities. The control unit 100 predicts that the time period for "concentration" will be "10:00-13:30" as prediction information. In this case, the control unit 100 determines the suitability as 90 minutes / 180 minutes=50%.

[0147] The control unit 100 may also determine the degree of conformance as the degree of agreement for a predetermined period (for example, a day, a time period during the day, or a time period set in a timetable). In this case, the control unit 100 may determine the degree of agreement not only based on the agreement of the "concentration" state, but also on the agreement of states such as "hyperalertness" and "drowsiness."

[0148] In addition, the control unit 100 may evaluate the suitability of "sleepiness" as a negative value. For example, when the predicted information indicates "sleepiness" during a time period when the planned activity indicates "concentration," the suitability may be deducted.

[0149] For example, the user sets "9:00-11:00" and "13:00-15:00" as time periods when the user should "concentrate" in the planned activities. The control unit 100 predicts, as prediction information, that the time period during which the user will "concentrate" will be "10:00-11:00". The control unit 100 also transfers the prediction and predicts that the time period during which the user will "feel sleepy" will be "13:00-13:30". In this case, the control unit 100 may determine the degree of suitability as follows. (Concentration) 60 min / 240 min - (Sleepiness) 30 min / 240 min = 12.5%

[0150] In this case, when "sleepiness" is not taken into consideration, the control unit 100 determines the suitability to be 25%. However, since "sleepiness" occurs during a time period when the user would normally want to "concentrate," the control unit 100 may determine that the suitability is in a lower state.

[0151] The control unit 100 determines whether the degree of suitability is equal to or greater than a threshold (step S188). For example, when "80%" is set as the threshold, if the degree of suitability is equal to or greater than 80%, the control unit 100 determines that the current parameter conditions are appropriate, and displays a recommendation based on the parameters (step S190).

[0152] When the degree of conformance is less than the threshold, the control unit 100 determines whether the degree of conformance does not satisfy the condition a predetermined number of times (step S192). That is, the control unit 100 changes the parameter value in order to increase the degree of conformance (step S194) and outputs the prediction information again (step S184).

[0153] Here, the control unit 100 can change the parameters in various ways, and the following method is considered. (1) Changing the sleep onset time. For example, the control unit 100 advances or delays the sleep onset time. In this case, it is preferable that the control unit 100 changes the parameters so as to advance the sleep onset time. (2) Changing the wake-up time. For example, the control unit 100 advances or delays the wake-up time. In this case, it is preferable that the control unit 100 changes the parameters so as to advance the wake-up time. (3) Changing the sleeping time. For example, the control unit 100 lengthens or shortens the sleeping time. In this case, it is preferable that the control unit 100 changes the parameters so as to lengthen the sleeping time. (4) Changing lifestyle information of the user For example, the control unit 100 changes information related to the user's lifestyle, such as meal times, meal contents, and whether or not the user drinks alcohol.

[0154] In addition, as a method of changing the parameters, the control unit 100 may change the parameters related to the behavior of the day from the second state acquired in the past. For example, the control unit 100 may output prediction information when the user takes a nap.

[0155] Then, the control unit 100 outputs prediction information using an iterative learning model based on the changed parameters, and repeats the process until the degree of conformance becomes equal to or greater than a threshold value.

[0156] However, even if the parameter is changed and the process is repeated, the degree of suitability may not reach or exceed the threshold. Therefore, when the degree of suitability does not satisfy the condition even after the parameter change is repeated a predetermined number of times, the control unit 100 displays a general recommendation (step S196).

[0157] [1.5 Example of operation] Next, an operation example in this embodiment will be described. Fig. 21 is a diagram showing a comparison between the prediction information and the second status information. Fig. 21 also shows information on a daily basis.

[0158] The top graph is a graph based on the prediction information output by the terminal device 10 (prediction unit 108). Here, diagonal lines indicate "normal," black indicates "concentration," white indicates "drowsiness," and shading indicates "hyperarousal."

[0159] In the prediction information output by the terminal device 10, the state is predicted as "concentration" at time t1 and "drowsiness" at time t2, and the state is predicted as "normal" at the other times.

[0160] The graph below the prediction information shows the second state information based on the second state actually determined on the day predicted as the prediction information. In the second state determined by the biological information measured during the user's activity, the predicted time 1 of concentration is consistent, but the drowsiness at time t2 is shorter than the time t3 in the time difference vision.

[0161] Moreover, at time t4, which the terminal device 10 predicted as "normal," the state was determined as "concentration" in the actual second state. During time t4, the state of "hyperarousal" was determined at time t5. At time t6, the terminal device 10 predicted as "normal" with no drowsiness, but the state was determined as "drowsiness" in the actual second state.

[0162] By using such predicted values ​​and actual measured values, the terminal device 10 can perform re-learning. That is, the terminal device 10 performs re-learning using the second state information based on the actual measured values ​​as teacher data.

[0163] The terminal device 10 may output these prediction information as specific parameters such as some biological information value. For example, the line graph at the bottom of Fig. 21 shows a state in which the heart rate is used to determine wakefulness and drowsiness. The line graph at the bottom of Fig. 21 is a graph that represents the rate of variation from the average heart rate in the positive and negative directions, with the average heart rate at the center.

[0164] That is, the terminal device 10 outputs the predicted heart rate as prediction information. The terminal device 10 predicts the time when the predicted heart rate is between 5% and 10% lower than the average heart rate as "concentration". The terminal device 10 predicts the time when the predicted heart rate is likely to be 8% or more lower than the average heart rate as "drowsiness". If the predicted heart rate is within ±5% of the average heart rate, the terminal device 10 predicts the time as "normal". The terminal device 10 may also predict the time when the predicted heart rate is likely to be 10% or more higher than the average heart rate as "hyperarousal".

[0165] Moreover, the terminal device 10 can determine the second state of the user by comparing the heart rate acquired by the second detection device 30 from the biological information acquisition unit 302.

[0166] The terminal device 10 (learning unit 107) can improve the accuracy of prediction by re-learning the learning model using the second status information, which is the actual measured value, as teaching data for the biological information and first status information during sleep and the prediction information output as a predicted value.

[0167] FIG. 22 is an example of a display screen W120 that displays the learning state and is displayed on the display unit 140 of the terminal device 10. As shown in FIG.

[0168] For example, the result of classifying the user's state during a day is displayed in region R120. Region R120 displays the classified time frames as regions R122 and R124.

[0169] In each of the classified regions R122 and R124, pull-down menus that allow the user to select and input the user's state are displayed as items K122 and K124. For example, as shown in item K124, the user can select the user's state for that time period from the pull-down menu. The displayed items may also display the state predicted by the control unit 100 in advance. Even if the state predicted in advance is displayed in the item, the user may reselect the correct state. Furthermore, the user does not need to make a selection (for example, "-") unless he or she is particularly able to concentrate or is sleepy. In this case, the time period may be determined to be a normal state.

[0170] 23 is an example of a display screen W130 that displays an operation screen of a reaction test executed on the terminal device 10. For example, the control unit 100 may present it to the user in order to judge the user's state. The display screen W130 may also be displayed when a function is called by the user. In other words, the reaction test is a test used to judge the user's state, and may be read and displayed by the user at any timing, or may be read and displayed by the control unit 100 for each predetermined condition.

[0171] FIG. 23 shows an example of a reaction test in which numbers are displayed in an area R130. The user is made to select numbers in ascending order, and the state of the user is estimated from the reaction time involved in the selection. For example, if the reaction time, which indicates the time it takes for the user to finish selecting all the numbers, is less than 5 seconds, the control unit 100 may determine that the user is in a "concentration" state. If the reaction time of the user is 10 seconds or more, the control unit 100 may determine that the user is in a "drowsy" state. If the reaction time of the user is within a predetermined time (e.g., less than 5 seconds) but the user makes a predetermined number of mistakes or more (e.g., 3 or more), the control unit 100 may determine that the user is in a "hypervigilance" state.

[0172] 24(a) is an example of a display screen W140 that displays recommendations based on prediction information displayed on the display unit 140 of the terminal device 10. In the display screen W140, the contents set as the user's planned activities are displayed in an area R140. Also, recommendations are displayed in an area R142 to correspond to the planned activities. The recommendation display may display, for example, the user's sleep time, wake-up time, sleep duration, etc. to fulfill the planned activities.

[0173] 24(b) to (d) are examples of recommendation displays displayed in area R142. For example, FIG. 24(b) also shows recommendations regarding the user's lifestyle. For example, if medication will have an effect on the next day, it is preferable for the control unit 100 to recommend the timing of taking the medication. The control unit 100 can also recommend lifestyle habits (for example, whether or not to drink alcohol). The control unit 100 may also output recommendations for changing lifestyle on a weekly basis, for example.

[0174] 24(c), the control unit 100 makes multiple recommendations. For example, the control unit 100 may make stepwise recommendations when the suitability is 90% for the user's sleep time of "8 hours" and 70% for the user's sleep time of "6 hours."

[0175] 24(d) shows a recommendation display when the control unit 100 cannot find parameters for making the degree of compatibility equal to or greater than a predetermined threshold value. In this case, the control unit 100 displays a general recommendation.

[0176] [2. Second embodiment] A second embodiment will be described. The second embodiment is an embodiment in which a recommendation is made to a user to ensure an ideal sleep time by utilizing a daytime sleepiness level. Note that the present embodiment will be described focusing on the differences from the first embodiment, and the description of the configuration and processing common to the first embodiment will be omitted.

[0177] In this embodiment, sleep-related recommendations are made in accordance with the user's schedule by mainly using information related to sleep (for example, user information C in FIG. 7C). For example, the terminal device 10 makes sleep-related recommendations in accordance with important periods for the user.

[0178] 25 is a diagram showing a software configuration in this embodiment. In this embodiment, the control unit 100 recommends a sleeping time zone for the user according to the sleepiness level calculated by the sleepiness level calculation unit 109A.

[0179] Here, the sleeping time period refers to the time period from when the user falls asleep to when he or she wakes up. Here, the sleeping time period includes the following time periods:

[0180] (1) The time period from when the user goes to bed to when the user wakes up (2) The time period from the user's fall asleep time (the time when the user starts sleeping) to the user's wake up time (the time when the user wakes up)

[0181] The control unit 100 recommends a sleeping time period to the user that includes any of the above-mentioned times. For example, the control unit 100 may recommend a time related to the start of sleep, such as "Let's go to bed by X o'clock" or "Let's sleep by X o'clock" to the user. Also, the control unit 100 may recommend a time related to the end of sleep (wake-up), such as "Let's wake up at X o'clock". Also, the control unit 100 may recommend a combination of these two times. Also, the control unit 100 may recommend a time that is the above-mentioned time plus a predetermined time. For example, when the bedtime is 10 p.m., the control unit 100 may recommend "Let's go to bed by 9:30 p.m."

[0182] In addition, the ideal sleep time in this embodiment refers to a sleep time during which the user's sleepiness level during activity (daytime) is preferably 0. Here, the sleepiness level determined as the ideal sleep time is preferably 0, but may be, for example, an extremely small value. For example, when the sleepiness level is the cumulative total of the time during which sleepiness was determined during activity, the sleepiness level may be within a range close to 0 minutes, such as 5 minutes, 10 minutes, or 30 minutes or less. When the sleepiness level is the percentage of the time during activity during which sleepiness was determined, the sleepiness level may be within a range close to 0%, such as 1%, 3%, or 5%.

[0183] In addition, the recommended sleep time in this embodiment refers to a sleep time in which the sleepiness level of the user during the activity does not exceed a predetermined threshold, that is, a sleep time that is a predetermined threshold. For example, if the sleepiness level during the activity is hours, the recommended sleep time is a sleep time in which the sleepiness level during the activity does not exceed 1 hour, assuming that up to 1 hour is an acceptable time. If the sleepiness level is a percentage, the recommended sleep time is a sleep time in which the sleepiness level during the activity does not exceed 10% of the total activity, assuming that up to 10% is an acceptable time. Note that the threshold here is an example, and other numerical values ​​may be used.

[0184] The drowsiness level is expressed as the following values: (1) The total duration of drowsiness detected during the user's awake time or activity time. In other words, the total duration of drowsiness during the daytime of the user. Here, the awake time refers to the time from when the user wakes up in the morning to when they go to bed at night. Moreover, the activity time refers to the time during which the user should be in a normal or awake (hyperawake) state. The terminal device 10 may automatically acquire the awake time from the state of the user detected by the second detection device 30. Moreover, the terminal device 10 may acquire the activity time from the user's settings or schedule. In other words, in this case, the drowsiness level refers to the absolute value of the duration of drowsiness. (2) The proportion of time during which drowsiness was detected during the user's wakefulness or activity time. In other words, in this case, the degree of drowsiness refers to the proportion of time during which the user is relatively drowsy.

[0185] For the drowsiness, for example, "drowsiness" stored in the second status information can be used. Drowsiness can be extracted based on "drowsiness" based on the actual measurement value described in FIG. 21 of the first embodiment. Then, the control unit 100 (drowsiness level calculation unit 109A) calculates the drowsiness level from the extracted drowsiness. [2.1 Recommendation processing]

[0186] Next, the recommendation process of this embodiment will be described. First, in the learning model generation process of FIG. 19, the learning unit 107 may generate a learning model based on the sleeping hours and the drowsiness level during activity (daytime). For example, the learning unit 107 uses the previous day's sleeping hours and sleeping time zone (e.g., bedtime and wake-up time) as input parameters and learns the drowsiness level as teacher data. In this way, the control unit 100 can obtain the sleeping hours and sleeping time zone for achieving a certain drowsiness level by using the learning model. The learning unit 107 may also use a table that associates the sleeping hours with the drowsiness level.

[0187] In this embodiment, first, the control unit 100 acquires an action plan (step S202). For example, the control unit 100 acquires important events (exams, games, etc.) from the action plan data set by the user. For example, the control unit 100 acquires a scheduled date set by the user as the action plan. In addition, attributes may be set together with the scheduled date. The attribute may be, for example, a schedule related to sports such as "athletic meet," "national athletic meet," or "track meet." In addition, the attribute may be, for example, a schedule related to tests such as "regular exams at school," "national exams," or "proficiency exams." In addition, the attribute may be a schedule related to presentations such as "presentations" or "graduation thesis presentations." In addition, the attribute may be set arbitrarily by the user. Then, the control unit 100 recommends information related to sleep (for example, sleep time, sleep time zone, etc.) so that the user will be in good shape for an important event on the scheduled date.

[0188] Next, the control unit 100 may acquire a period until the scheduled action (step S204). For example, the control unit 100 may make a recommendation to reduce the drowsiness level according to the period until the scheduled action.

[0189] The control unit 100 acquires ideal sleep information based on the learning model (step S206). Here, the ideal sleep information may be, for example, an ideal sleep time. The ideal sleep information may also be a sleep time period that results in the ideal sleep time.

[0190] The control unit 100 displays a recommendation based on the acquired ideal sleep information (step S206). Here, the recommendation may be displayed, for example, in the following manner.

[0191] For example, in the learning stage, the control unit 100 learns the ideal sleep time from the daytime sleepiness level. For example, the control unit 100 learns the sleep time at which the daytime sleepiness level of the day becomes 0 from the sleep time from the previous day to the day as the ideal sleep time.

[0192] Then, the control unit 100 displays the ideal sleep time as a recommendation. Here, several processes can be considered as a method for the control unit 100 to display the recommendation. (1) The control unit 100 displays the ideal sleep time. For example, the control unit 100 recommends an ideal sleep time such as "Sleep for 8 hours." When the user sets a wake-up time, the control unit 100 may recommend a time that is an ideal sleep time before the wake-up time as the bedtime. The control unit 100 may also learn a sleep time period that results in a sleepiness level of 0. In this case, the control unit 100 may recommend content such as "Go to bed at 11pm and wake up at 8am."

[0193] (2) The control unit 100 may also make recommendations based on the recommended sleeping time. For example, the control unit 100 acquires a scheduled date set by the user. The control unit 100 then divides a period between the present and the scheduled date into a first period and a second period. The control unit 100 makes recommendations so that the recommended sleeping time is achieved for the first half of the first period starting from the present. The control unit 100 also makes recommendations so that the ideal sleeping time is achieved for the second period approaching the scheduled date. The control unit 100 may also record sleeping time periods that will result in the recommended sleeping time for the first period. The control unit 100 may also record sleeping time periods that will result in the ideal sleeping time for the second period.

[0194] (3) The control unit 100 may further divide the period from the present to the scheduled date into a plurality of periods. In this case, the control unit 100 may recommend a sleep time period for the period from the present to the scheduled date so as to transition from the recommended sleep time to the ideal sleep time.

[0195] As an example, a case where the period from the present to the due date is divided into three equal periods will be described. For example, a first period from the present to the first day, a second period from the first day to the second day, and a third period from the second day to the due date are divided. In this case, the first period, the second period, and the third period may be equal. Also, the first period, the second period, and the third period may be different periods.

[0196] Then, the control unit 100 recommends a sleeping time period during the first period that can ensure a sleeping time in which the user's sleepiness level is a first threshold. Here, the control unit 100 recommends a sleeping time period or a sleeping time period in which the user's sleepiness level is a first threshold (for example, 10% in the case of a percentage).

[0197] In addition, during the second period, the sleep duration or sleep time zone in which the user's sleepiness level is a second threshold value (for example, 5% in the case of a percentage) that is lower than the first threshold value is recommended.

[0198] In addition, during the third period, the system recommends sleep times and sleeping time periods that will ensure that the user's sleepiness level is 0.

[0199] The control unit 100 may perform other controls instead of displaying the recommendations. For example, the control unit 100 may perform a process of setting an alarm at a wake-up time that is the ideal sleep time. Also, the control unit 100 may dim the lights in the room at bedtime and brighten the lights in the room at wake-up time.

[0200] The ideal sleep information may also include, for example, lifestyle information from the previous day. For example, when the learning unit 107 learns, it can learn the lifestyle information from the previous day (for example, the time of dinner, the amount of exercise (number of steps, amount of activity, etc.) from the previous day, the amount of water acquired, whether or not alcohol was consumed) as parameters. The ideal sleep information may include lifestyle information for achieving a predetermined sleepiness level.

[0201] The control unit 100 may also change the content of the recommendation depending on the attributes of the scheduled date. For example, when the scheduled activity is an athletic "match," the control unit 100 may set a higher sleepiness threshold for determining the ideal sleep time and the recommended sleep time because the user will be awakened by the exercise. When the scheduled activity is an "exam," the control unit 100 may set a lower sleepiness threshold for determining the ideal sleep time and the recommended sleep time so that the user will not feel sleepy.

[0202] Furthermore, the control unit 100 may utilize a learning model to predict a time period during the day when the user feels sleepy. In this case, the control unit 100 may encourage the user to exercise or take a nap during the time period when the user feels sleepy.

[0203] In addition, the control unit 100 may shorten the ideal sleep time or the recommended sleep time when the user has set to take a nap. For example, when the learning model is utilized and the ideal sleep time is calculated to be 8 hours, the control unit 100 may calculate the ideal sleep time to be 7 hours if the user exercises. In addition, when the user has set in advance to take a nap, the control unit 100 may shorten the ideal sleep time or the recommended sleep time in the same way as when exercising.

[0204] [2.2 Example of operation] For example, the terminal device 10 may make a recommendation based on the correspondence between the user's planned activity and the number of days until the planned activity. For example, the terminal device 10 may determine, as a result of learning, that the ideal sleep time for the user is 6 to 8 hours (the time when the user's sleepiness level is equal to or lower than a predetermined threshold (for example, the time when the user feels sleepy during the day is less than 1 hour)). Furthermore, when the terminal device 10 learns that the user's sleepiness level is lowest when it is 8 hours, the terminal device 10 may make the following recommendation.

[0205] In this case, for example, the control unit 100 may acquire the ideal sleep information as follows. (1) 3 to 2 months before the planned event The terminal device 10 recommends six hours as the ideal sleep time. Since there is still some time until the planned activity, it is considered a time for the user to work hard to prepare for the planned activity. Therefore, the terminal device 10 recommends increasing the user's activity time (wake time).

[0206] (2) 2 to 1 month before the planned event The terminal device 10 recommends 7 hours as the ideal sleep time. There is some time until the planned activity, and it is considered a time when the user should put some effort into preparing for the planned activity. Therefore, the terminal device 10 recommends adjusting the user's activity time (wake time).

[0207] (3) From one month before the event to the day of the event The terminal device 10 recommends that the ideal sleep time is 8 hours. Since there is not much time left until the planned activity, it is considered a time for the user to adjust his / her physical condition. Therefore, the terminal device 10 recommends that the user reduce the activity time (wake time) of the user.

[0208] [3. Third embodiment] A third embodiment will be described. The third embodiment is an embodiment in which main processing is realized by a server device 40. Note that this embodiment will be described focusing on differences from the first embodiment, and descriptions of configurations and processing common to the first embodiment will be omitted.

[0209] [3.1 Overall structure] 27 is a diagram showing an entire prediction system 1B. The terminal device 10, the first detection device 20, and the second detection device 30 are capable of communicating with a server device 40 via a network NW.

[0210] [3.2 Functional configuration] 28 shows a hardware configuration of the server device 40. The server device 40 has a control unit 400, a storage 410 as a memory unit, a ROM 420 and a RAM 430, and a communication unit 470.

[0211] The explanation of each component is as explained in [1.2.1 Common Components] and is described in the relevant section.

[0212] FIG. 29 is a diagram for explaining the software configuration of the terminal device 10, the first detection device 20, the second detection device 30, and the server device 40. As shown in FIG.

[0213] The server device 40 functions as an information receiving unit 402, a sleep score calculating unit 406, a learning unit 407, a prediction unit 408, and an action schedule setting unit 409 by the control unit 400 executing a program stored in the storage unit.

[0214] The information receiving unit 402 receives user information from the terminal device 10, first status information from the first detecting device 20, and second status information from the second detecting device 30. In addition, the information receiving unit 402 receives biological information during sleep from the first detecting device 20 and biological information during activity from the second detecting device 30.

[0215] Then, the information receiving unit 402 stores the user information in a user information storage area 411, the biometric information in a biometric information storage area 412, the first status information in a first status information storage area 413, and the second status information in a second status information storage area 414.

[0216] In the server device 40, the sleep score calculation unit 406 calculates the sleep score. The learning unit 407 generates learning data and stores it in the learning data storage area 416. The prediction unit 408 uses the learning data based on the parameters to output prediction information and stores it in the prediction information storage area 415. The action schedule setting unit 409 stores action schedule data based on the action schedule set by the user in the action schedule data storage area 417.

[0217] The sleep score calculation unit 406 has the same configuration as the sleep score calculation unit 106 of the first embodiment, the learning unit 107 has the same configuration as the learning unit 107 of the first embodiment, the prediction unit 408 has the same configuration as the prediction unit 108 of the first embodiment, and the behavior schedule setting unit 409 has the same configuration as the behavior schedule setting unit 409 of the first embodiment.

[0218] [3.3 Processing flow] The flow of processing in this embodiment will be described. Note that processing similar to the processing in Fig. 16 to Fig. 20 described in the first embodiment is executed by each device as necessary. Each processing has been described in the first embodiment, so detailed processing description will be omitted.

[0219] 30 is a sequence diagram showing an example of the flow of information between the devices. The terminal device 10 acquires user information (step S1002) and transmits it to the server device 40 (S1004). The server device 40 acquires the user information from the terminal device 10 (S1006). The server device 40 also receives the activity schedule data set by the terminal device 10 in S1006.

[0220] The first detection device 20 acquires biometric information (S1008) and acquires first status information (S1010). Then, the first detection device 20 transmits the biometric information and the first status information to the server device 40 (S1012). The server device 40 receives the biometric information and the first status information from the first detection device 20 (S1014).

[0221] The second detecting device 30 acquires the biometric information (S1020) and acquires the second status information (S1022). Then, the second detecting device 30 transmits the biometric information and the second status information to the server device 40 (S1024). The server device 40 receives the biometric information and the second status information from the second detecting device 30 (S1026).

[0222] The server device 40 generates a learning model based on the user information, the biometric information, the first state information, and the second state information. When the learning model has already been stored, the server device 40 further uses the prediction information and the second state information to regenerate the learning model (S1030).

[0223] The server device 40 then uses the received information and the learning model to output prediction information (S1032). The server device 40 then generates recommendations from the prediction information and the action schedule data, and transmits the recommendations to the terminal device 10 as recommendation information (S1034). The terminal device 10 receives the recommendation information from the server device 40, and outputs the recommendations (S1036).

[0224] [4. Fourth embodiment] A fourth embodiment will be described. The fourth embodiment is an embodiment in which the terminal device 10 or the like communicates with a plurality of server devices 40, which is different from the third embodiment. Note that the present embodiment will be described mainly with respect to the points different from the first and third embodiments, and descriptions of configurations and processes common to the first and third embodiments will be omitted.

[0225] 30 is a diagram illustrating an entire prediction system 1C in the fourth embodiment. In the prediction system 1C, a plurality of terminal devices 10 and the like are connected to a network NW and are capable of communicating with a server device 40.

[0226] That is, in the first and second embodiments, the training data is generated based on information about the same user, but in this embodiment, training data is generated from multiple users.

[0227] As a result, for example, predictive information can be generated from learning data not only of the user using the terminal device 10, but also of user information that is the same or similar to that of the user (e.g., age, gender, medical history, lifestyle pattern, etc.).

[0228] Therefore, even if not much information about a user has been accumulated and prediction cannot be made from the learning data of that user, appropriate measurement information can be output. For example, in the case of a user who goes to bed after 1:00 a.m. every day, it is difficult to predict what the second state will be if the user goes to bed at 10:00 p.m.

[0229] However, by using learning data of users of the same age and sex, it is possible to output prediction information on what the second status information will be.

[0230] [5. 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.

[0231] In addition, although the above-mentioned embodiments are described separately for convenience of explanation, they can be combined to the extent possible. In addition, the present invention intends to obtain rights to any of the technologies described in the specification through amendments or divisional applications, etc.

[0232] In addition, the programs that run on each device in each embodiment are programs that control the CPU and the like (programs that make a computer function) so as to realize the functions of the above-mentioned embodiments. Information handled by these devices is temporarily stored in a temporary storage device (e.g., RAM) during processing, and is then stored in various ROMs and HDDs, and is read, modified, and written by the CPU as necessary.

[0233] Here, the recording medium for storing the program may be any of semiconductor media (e.g., ROM, non-volatile memory cards, 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.

[0234] In addition, when distributing the program on 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 of course included in the present invention.

[0235] Furthermore, the above-mentioned data may not be stored in the device, but may be stored in an external device and called up as appropriate. For example, the data may be stored in a NAS (Network Attached Storage) or on the cloud.

[0236] The scope of the present disclosure is not limited to the configurations explicitly described in the specification, but includes combinations of the technologies disclosed in the present specification. The configurations of the present disclosure that are to be patented are described in the attached claims, but it is not intended to exclude them from the technical scope because they are not described in the claims.

[0237] In addition, in the above-mentioned specification, the statements "in the case of" and "when" are merely examples, and are not intended to limit the configuration to the described contents. Configurations other than these cases and times are also disclosed to the extent that would be obvious to a person skilled in the art, and the company intends to obtain rights to them.

[0238] In addition, the processes and data flows described in the specification are not limited to the order in which they are described. For example, the present application discloses configurations in which some parts of the processes are deleted or the order is changed, and the applicant intends to obtain the rights to such configurations.

[0239] Furthermore, although the functions described in the embodiments are executed by each device, they may be realized by one device, or an external server may be used.

[0240] In addition, each functional block or feature of the device used in the above-mentioned embodiment may be implemented or performed by an electric circuit, for example, an integrated circuit or a plurality of integrated circuits. The electric 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 electric circuit described above may be composed of a digital circuit or an analog circuit. In addition, when a technology for integrated circuitization that replaces current integrated circuits appears due to the progress of semiconductor technology, one or more aspects of the present disclosure may use a new integrated circuit according to that technology. [Explanation of symbols]

[0241] 1. Prediction System 3 Bed equipment 5. Mattress 10 Terminal Equipment 100 control section; 110 storage; 120 ROM; 130 RAM 140 display unit; 150 operation unit; 170 communication unit 20 First detection device 200 control section; 210 storage; 220 ROM; 230 RAM 240 display unit; 250 operation unit; 260 sensor unit; 270 communication unit 30 Second detection device 300 control unit; 310 storage; 320 ROM; 330 RAM 340 display unit; 350 operation unit; 360 sensor unit; 370 communication unit 40 Server device 400 control unit; 410 storage; 420 ROM; 430 RAM 470 Communications Department

Claims

1. The device includes a biometric information acquisition unit that acquires biometric information of a user, and a control unit, The control unit is acquiring sleep information including the user's nighttime sleep time based on the biometric information of the user; acquiring a daytime sleepiness level of the user based on biological information of the user; Calculating an ideal sleep time based on the daytime sleepiness level; Recommending a sleeping time period to the user so as to achieve the ideal sleeping time. Recommendation device.

2. The control unit is A planned date is set by the user, The sleep time when the sleepiness level is 0 is defined as an ideal sleep time. The sleep time when the sleepiness level becomes a first threshold value is set as a recommended sleep time, recommending the sleeping time slot so that the user's sleeping time becomes an ideal sleeping time during a first period from the scheduled date; When the scheduled date is away from the first period or more, the sleep time slot is recommended so that the sleep time of the user is a recommended sleep time. The recommendation device according to claim 1 .

3. The control unit is Divide the period from the present to the scheduled date into multiple periods, The sleep time slot is recommended so as to transition from the recommended sleep time to the ideal sleep time during the period from the present to the scheduled date. The recommendation device according to claim 2 .

4. The control unit is A first period from the present time to a first date, a second period from the first date to a second date, and a third period from the second date to the scheduled date; the first period, the second period, and the third period are equal, recommending a sleep time slot during the first period that can ensure a sleep duration during which the sleepiness level of the user becomes the first threshold value; recommending a sleep time slot during the second period that can ensure a sleep duration during which the sleepiness level of the user is a second threshold value that is smaller than the first threshold value; During the third period, a sleep time period is recommended that ensures a sleep time during which the user's sleepiness level becomes 0. The recommendation device according to claim 2 .

5. The control unit recommends a time when the user falls asleep and / or a time when the user wakes up as the sleep time period. The recommendation device according to claim 1 .

6. The control unit is If the user's sleep time on the previous day is not sufficient to ensure the ideal sleep time, a recommendation is made to exercise or take a nap. The recommendation device according to claim 1 .

7. The control unit is predicting a time period during a day when the user will feel sleepy; Recommending the exercise or nap during the time period when sleepiness occurs. The recommendation device according to claim 6 .

8. The control unit is The user determines the recommended sleep time based on exercise information. The recommendation device according to claim 2 .

9. The control unit is When there is information that the user is taking a nap, the ideal sleep time is shortened. The recommendation device according to claim 1 .

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