Information processing apparatus, learning apparatus, proposal system, and air conditioning system

Abstract

To provide an information processing apparatus, etc. capable of supporting construction of thermal environments in both a bedroom and a bed suitable for an individual user.SOLUTION: An information processing apparatus 100 includes a storage unit 130 and an arithmetic processing unit 120. The storage unit 130 stores attribute information including one or both of user's biological information and user's preference information about coldness and hotness. The arithmetic processing unit 120 specifies to which of a plurality of preset thermal sensation groups the thermal sensation of the user belongs, using user's attribute information stored in the storage unit 130, and determines a combination of a room temperature and warming performance of bedding and sleep wear while the user is sleeping according to the thermal sensation group that the user's thermal sensation belongs. The information processing apparatus 100 further includes an output unit 140 for outputting the combination of the room temperature and the warming performance of bedding and sleep wear while the user is sleeping.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present disclosure relates to an information processing device, a learning device, a proposal system, and an air conditioning system. [Background technology]

[0002] Conventionally, a system is known that calculates the thermal insulation performance of bedding suitable for maintaining a specified in-bed environment based on an estimated indoor temperature in a bedroom, and presents bedding with the calculated thermal insulation performance (see, for example, Patent Document 1). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2020-077277 Summary of the Invention [Problem to be solved by the invention]

[0004] The quality of sleep is affected by the thermal environments in both the bedroom and the bed. However, in a system such as that shown in Patent Document 1, bedding is suggested based on the room temperature of the bedroom, so the user must change the bedding every time the room temperature changes, which places a heavy burden on the user. Furthermore, while the thermal environment suitable for each user when sleeping may vary from person to person depending on the user's metabolic rate, preference for cold and heat, etc., the system shown in Patent Document 1 does not take such individual differences into consideration at all.

[0005] The present disclosure has been made to solve these problems, and its purpose is to provide an information processing device, a learning device, a proposal system, and an air conditioning system that can support the creation of thermal environments in both the bedroom and the bed that are suited to the individual user. [Means for solving the problem]

[0006] The information processing device according to the present disclosure is an information processing device equipped with a computer having a memory unit and an arithmetic processing unit, wherein the memory unit stores attribute information including one or both of a user's biometric information and preference information regarding cold and heat, and the arithmetic processing unit uses the user's attribute information stored in the memory unit to identify to which of a plurality of pre-set thermal sensation groups the user's thermal sensation belongs, and further includes an output unit that determines a combination of the room temperature at the time the user goes to bed and the thermal insulation performance of the bedding and sleeping clothes according to the thermal sensation group to which the user's thermal sensation belongs, and outputs the combination of the room temperature at the time the user goes to bed and the thermal insulation performance of the bedding and sleeping clothes.

[0007] Alternatively, the information processing device according to the present disclosure is an information processing device equipped with a computer having a memory unit and an arithmetic processing unit, wherein the memory unit stores attribute information including one or both of a user's biometric information and preference information regarding cold and heat, and the arithmetic processing unit further includes an output unit that uses a trained model for inferring from the user's attribute information a combination of the room temperature at the time of sleeping and the thermal insulation performance of the bedding and sleeping clothes to infer a combination of the room temperature at the time of sleeping and the thermal insulation performance of the bedding and sleeping clothes from the user's attribute information stored in the memory unit, and outputs the combination of the room temperature at the time of sleeping and the thermal insulation performance of the bedding and sleeping clothes.

[0008] The proposed system according to the present disclosure includes the above-described information processing device and a display unit that displays the combination of the room temperature at the time of the user's sleep and the thermal insulation performance of the bedding and sleepwear output from the output unit.

[0009] The air conditioning system of the present disclosure includes the above-mentioned information processing device and an air conditioning device that adjusts the room temperature of the bedroom where the user sleeps, and the air conditioning device adjusts the room temperature of the bedroom in accordance with the room temperature at the time of the user sleeping output from the output unit.

[0010] The learning device according to the present disclosure includes a data acquisition unit that acquires learning data including attribute information including one or both of a user's biometric information and preference information regarding cold and heat, and a combination of the room temperature when the user goes to bed and the thermal insulation performance of the user's bedding and sleepwear, and a model generation unit that uses the learning data acquired by the data acquisition unit to generate a trained model for inferring the combination of the room temperature when the user goes to bed and the thermal insulation performance of the user's bedding and sleepwear from the user's attribute information. [Effects of the Invention]

[0011] The information processing device, learning device, proposal system, and air conditioning system according to the present disclosure have the effect of being able to assist in creating a thermal environment in both the bedroom and bed that is suitable for the individual user. [Brief explanation of the drawings]

[0012] [Figure 1] 1 is a diagram showing the overall configuration of a system according to a first embodiment. [Figure 2] FIG. 4 is a diagram showing an example of a table for determining a thermal sensation group of a user according to the first embodiment. [Figure 3] FIG. 4 is a diagram showing an example of a table for determining a thermal sensation group of a user according to the first embodiment. [Figure 4] FIG. 4 is a diagram showing an example of a table for determining a thermal sensation group of a user according to the first embodiment. [Figure 5] FIG. 4 is a diagram showing an example of a table for determining a thermal sensation group of a user according to the first embodiment. [Figure 6] FIG. 2 is a diagram showing an example of a table for determining a combination of room temperature when a user goes to bed and the heat retention performance of bedding and sleepwear according to the first embodiment. [Figure 7] FIG. 3 is a diagram showing an example of a condition input screen according to the first embodiment. [Figure 8] 4 is a flowchart showing an example of the operation of the system according to the first embodiment. [Figure 9] FIG. 10 is a diagram showing the overall configuration of a system according to a second embodiment. [Figure 10] FIG. 10 is a block diagram showing the configuration of a learning device according to a second embodiment. [Figure 11] FIG. 10 is a diagram illustrating an example of a neural network in the learning device according to the second embodiment. [Figure 12] FIG. 10 is a flowchart showing an example of the operation of the learning device according to the second embodiment. [Figure 13] FIG. 10 is a flowchart showing an example of the operation of the system according to the second embodiment. [Figure 14] FIG. 1 is a diagram illustrating an example of a configuration for realizing the functions of an information processing device and a learning device according to the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0013] Embodiments of an information processing device, a learning device, a proposal system, and an air conditioning system according to the present disclosure will be described with reference to the accompanying drawings. In each drawing, identical or corresponding parts are designated by the same reference numerals, and redundant descriptions are appropriately simplified or omitted. For convenience, the following description may express the positional relationship of each structure based on the illustrated state. Note that the present disclosure is not limited to the following embodiments, and any combination of the embodiments, any modification of any component of each embodiment, or any omission of any component of each embodiment are possible within the scope of the present disclosure.

[0014] Embodiment 1 A first embodiment of the present disclosure will be described with reference to Figs. 1 to 8. Fig. 1 is a diagram showing the overall configuration of the system. Figs. 2 to 5 are each a diagram showing an example of a table for determining a user's thermal sensation group. Fig. 6 is a diagram showing an example of a table for determining a combination of room temperature at the time of sleeping of a user and the thermal insulation performance of bedding and sleepwear. Fig. 7 is a diagram showing an example of a condition input screen. Fig. 8 is a flow chart showing an example of the operation of the system.

[0015] The proposed air conditioning system according to this embodiment makes suggestions to the user and air-conditions the bedroom to help the user sleep comfortably and with high quality. As shown in FIG. 1, the proposed air conditioning system according to this embodiment includes an information processing device 100. The information processing device 100 includes a data acquisition unit 110, an arithmetic processing unit 120, and a storage unit 130. The arithmetic processing unit 120 is, for example, a processor 101, which will be described later. The storage unit 130 is, for example, a memory 102, which will be described later. In this way, the information processing device 100 includes a computer having the arithmetic processing unit 120 and the storage unit 130.

[0016] The data acquisition unit 110 acquires various types of information used for information processing in the proposed air conditioning system from information sources. In the configuration example described here, the information sources include a temperature sensor 11, a biological sensor 12, and an input unit 20. The information sources, the temperature sensor 11, the biological sensor 12, and the input unit 20, are communicatively connected to the information processing device 100. Communication between these information sources and the information processing device 100 may be wireless or wired.

[0017] The temperature sensor 11 is a sensor that detects the indoor temperature (room temperature) of the target bedroom. The temperature sensor 11 may be built into the air conditioner 220 described below. The biosensor 12 is a sensor that detects biometric information of the target user. A specific example of the biosensor 12 is a sensor that detects the body surface temperature of the user. The sensor that detects the body surface temperature of the user may be, for example, an infrared sensor that detects the body surface temperature without contacting the user, or may be a wearable sensor worn by the user.

[0018] The input unit 20 is operated by a user to input various types of information. The input unit 20 is capable of communicating with the information processing device 100. Specific examples of the input unit 20 that can be used include terminal devices such as smartphones, tablet terminals, personal computers, and smart watches, as well as information appliances such as smart speakers and smart televisions. Furthermore, a remote control for an air conditioner 220, which will be described later, may also be used as the input unit 20.

[0019] The input unit 20 includes an attribute information input unit 21. The attribute information input unit 21 is used by a user to input his or her own attribute information. The user's attribute information input to the attribute information input unit 21 includes one or both of the user's biometric information and the user's preference information regarding heat and cold. The user's biometric information is, for example, information such as the user's height, weight, age, and sex. The user's preference information regarding heat and cold is, for example, information indicating whether the user is sensitive to heat or cold, and information indicating whether the user prefers a cool environment or a warm environment.

[0020] The data acquisition unit 110 acquires data detected by the temperature sensor 11 and the biometric sensor 12, as well as data input to the attribute information input unit 21 of the input unit 20. The data acquired by the data acquisition unit 110 is stored in the storage unit 130. That is, the storage unit 130 stores attribute information including one or both of the user's biometric information and preference information regarding cold and heat. The user's biometric information stored in the storage unit 130 may include the user's body surface temperature data detected by the biometric sensor 12, and data such as the user's height, weight, age, and gender input to the attribute information input unit 21.

[0021] The calculation processing unit 120 uses the attribute information of the user stored in the storage unit 130 to identify to which of a plurality of preset thermal sensation groups the user's thermal sensation belongs. Several examples of identifying the thermal sensation group to which the user's thermal sensation belongs will be described. In the examples to be described, there are five preset thermal sensation groups. That is, the thermal sensation groups are classified into five levels from "1" to "5." Thermal sensation group "1" is the thermal sensation group to which people who are most sensitive to heat belong. Furthermore, thermal sensation group "5" is the thermal sensation group to which people who are most sensitive to cold belong. However, the number of preset thermal sensation groups is not limited to five. That is, four or less, or six or more thermal sensation groups may be preset.

[0022] First, a first example will be described with reference to FIG. 2. In this first example, a thermal sensation group to which a user's thermal sensation belongs is identified based on preference information from among the user's attribute information. In this first example, the user operates the attribute information input unit 21 to select and input preference information that suits their preference from five options ranging from "sensitive to heat" to "sensitive to cold." For example, the storage unit 130 pre-stores table data for determining thermal sensation groups as shown in FIG. 2. This determination table data associates the user's preferences for heat and cold with thermal sensation groups. The arithmetic processing unit 120 then refers to the table data for determining thermal sensation groups stored in the storage unit 130 and identifies the thermal sensation group to which the user's thermal sensation belongs in accordance with the user's preference information.

[0023] Next, a second example will be described with reference to FIG. 3. Like the first example, this second example also identifies the thermal sensation group to which the user's thermal sensation belongs based on preference information from the user's attribute information. In this second example, the user inputs preference information by selecting from the five options from "sensitive to heat" to "sensitive to cold" as described above, as well as by selecting whether the user prefers a cool or warm environment from three options ("prefers cold / cool," "neither," or "prefers hot / warm") as preference information. For example, the storage unit 130 pre-stores table data for determining thermal sensation groups as shown in FIG. 3. This determination table data associates the user's preference for heat and cold with the thermal sensation group. The arithmetic processing unit 120 then refers to the table data for determining thermal sensation groups stored in the storage unit 130 and identifies the thermal sensation group to which the user's thermal sensation belongs according to the combination of the user's preference information.

[0024] A third example will be described with reference to FIG. 4. In this third example, a thermal sensation group to which a user's thermal sensation belongs is identified based on biological information among the user's attribute information. In this third example, the user operates the attribute information input unit 21 to input their own height, weight, age, and gender as biological information. For example, table data for determining a thermal sensation group as shown in FIG. 4 is stored in advance in the storage unit 130. This determination table data associates a metabolic rate index value calculated from the user's biological information and gender with a thermal sensation group. Here, the metabolic rate index value is an index that serves as a guide for the user's metabolic rate, and is calculated, for example, by the following formula (1). Note that X, Y, and Z are preset coefficients.

[0025] (Metabolic index value) = (height) × X + (weight) × Y - (age) × Z (1)

[0026] The arithmetic processing unit 120 calculates the metabolic rate index value of the user from the biological information of the user using equation (1). Then, the arithmetic processing unit 120 refers to the table data for determining the thermal sensation group stored in the storage unit 130, and identifies the thermal sensation group to which the thermal sensation of the user belongs according to the metabolic rate index value and gender of the user.

[0027] A fourth example will be described with reference to Fig. 5. Like the third example, this fourth example also identifies the thermal sensation group to which the user's thermal sensation belongs based on biological information among the user's attribute information. In this fourth example, the user's body surface temperature detected by the biological sensor 12 is used as the biological information. For example, the storage unit 130 stores in advance table data for determining thermal sensation groups as shown in Fig. 5. This determination table data associates the user's body surface temperature and the user's ambient temperature with the thermal sensation group.

[0028] The arithmetic processing unit 120 then refers to the table data for determining thermal sensation groups stored in the storage unit 130, and identifies the thermal sensation group to which the user's thermal sensation belongs, based on the combination of the user's body surface temperature and the user's ambient temperature. If an infrared sensor is provided as the biosensor 12, the user's ambient temperature can be obtained from the detection results of the infrared sensor. Alternatively, the detection results of the temperature sensor 11 may be used as the user's ambient temperature. In this case, it is more preferable to detect the temperatures of the user's hands and feet and use the values ​​obtained as the body surface temperature. Alternatively, the difference or ratio between the temperatures of the user's hands and feet and the temperature of the user's face may be used as the body surface temperature. This reduces the variation in absolute values ​​of the hand and foot temperatures among individual users.

[0029] In this way, the arithmetic processing unit 120 identifies the thermal sensation group to which the user's thermal sensation belongs. Then, the arithmetic processing unit 120 determines a combination of the room temperature at the time of sleeping and the thermal insulation performance of the bedding and nightwear according to the thermal sensation group to which the user's thermal sensation belongs, as identified in this way. For example, the storage unit 130 pre-stores table data for determining a combination of the room temperature at the time of sleeping and the thermal insulation performance of the bedding and nightwear, as shown in FIG. 6. This determination table data associates the thermal sensation group to which the user's thermal sensation belongs with a combination of the room temperature at the time of sleeping and the thermal insulation performance of the bedding and nightwear. The arithmetic processing unit 120 refers to the determination table data for combinations of the room temperature at the time of sleeping and the thermal insulation performance of the bedding and nightwear stored in the storage unit 130, and determines a combination of the room temperature at the time of sleeping and the thermal insulation performance of the bedding and nightwear according to the thermal sensation group to which the user's thermal sensation belongs.

[0030] In the example shown in Figure 6, the sum of the clo (clo) values ​​is used to represent the thermal performance of bedding and sleepwear. The clo value is a value that represents the thermal resistance that blocks heat loss from the body surface to the outside world. However, the thermal performance of bedding and sleepwear may be represented using an index other than the clo value. The calculation processing unit 120 may calculate the thermal performance of bedding and sleepwear using a human body thermal model analysis, rather than referring to the determination table data stored in the memory unit 130 to determine the combination of the room temperature when the user is sleeping and the thermal performance of the bedding and sleepwear.

[0031] 6, two combinations of room temperature and the thermal insulation performance of bedding and sleepwear are set for one thermal sensation group. However, the number of combinations of room temperature and the thermal insulation performance of bedding and sleepwear associated with one thermal sensation group is not limited to two. That is, only one combination of room temperature and the thermal insulation performance of bedding and sleepwear may be set for one thermal sensation group, or three or more combinations of room temperature and the thermal insulation performance of bedding and sleepwear may be set.

[0032] If two or more combinations of room temperature and thermal insulation performance of bedding and sleepwear are set for one thermal sensation group, the calculation processing unit 120 may determine these two or more combinations of room temperature and thermal insulation performance of bedding and sleepwear as the combination of room temperature at the time the user goes to bed and thermal insulation performance of bedding and sleepwear, or may determine one selected from these as the combination of room temperature at the time the user goes to bed and thermal insulation performance of bedding and sleepwear.

[0033] An example of selecting one from two or more combinations of room temperature and heat retention performance of bedding and sleepwear will be described below. In this case, for example, as shown in FIG. 1, the information source from which the data acquisition unit 110 of the information processing device 100 acquires data further includes a sleep detection means 13. The sleep detection means 13 is for detecting the user's sleep state. The sleep detection means 13 includes a sensor for detecting biometric information such as the user's body movement, pulse rate, and respiratory rate. Note that the biometric information such as body movement, pulse rate, and respiratory rate can be detected using, for example, a wearable sensor, a Doppler sensor, or the like. The sleep detection means 13 uses the detection results of the user's biometric information to determine a sleep index indicating the user's sleep quality. Specific examples of sleep indexes indicating sleep quality include the following:

[0034] Sleep efficiency: The percentage of time during sleep (from falling asleep to waking up) that is determined to be deep sleep, light sleep, or REM sleep. Awake ratio: The percentage of time during sleep determined to be awake. The smaller the value, the fewer awakenings there are during sleep, and the better the quality of sleep. Time to fall asleep: The time it takes to fall asleep after getting into bed. The shorter this time is, the faster you fall asleep and the better the quality of your sleep. Deep sleep ratio: The percentage of time determined as deep sleep during sleep. The higher the value, the more deep sleep you have and the better the quality of your sleep. Sleep Score: A score calculated by weighting each of the above sleep indicators and sleep time. The higher the value, the better the quality of sleep.

[0035] The input unit 20 may further include a subjective information input unit 22. The subjective information input unit 22 is used by the user to input subjective information such as sleep sensation, thermal sensation, and comfort sensation during sleep. The sleep sensation is the user's subjective evaluation of sleep, such as whether they slept well or not, and whether they woke up feeling good or bad. The user operates the subjective information input unit 22 of the input unit 20 to input subjective information, for example, when waking up.

[0036] The data acquisition unit 110 acquires sleep index data from the sleep detection means 13 and acquires subjective information data from the subjective information input unit 22. The storage unit 130 then stores these data acquired by the data acquisition unit 110. That is, the storage unit 130 further stores sleep indexes indicating the quality of the user's sleep during past bedtimes. Note that the sleep index indicating the user's sleep quality may also include subjective information about the feeling of sleep. The storage unit 130 also stores past sleep indexes in association with combinations of room temperature at that time and the thermal insulation performance of bedding and sleepwear.

[0037] When two or more combinations of room temperature and the thermal insulation performance of bedding and sleepwear are set for one thermal sensation group, the calculation processing unit 120 determines the combination of room temperature and the thermal insulation performance of bedding and sleepwear that resulted in the best past sleep index among these two or more combinations of room temperature and the thermal insulation performance of bedding and sleepwear as the combination of room temperature and the thermal insulation performance of bedding and sleepwear when the user goes to bed. That is, the calculation processing unit 120 determines the combination of room temperature and the thermal insulation performance of bedding and sleepwear when the user goes to bed based on the thermal sensation group to which the user's thermal sensation belongs and the user's past sleep index. In this way, the calculation processing unit 120 can determine the best combination of room temperature and the thermal insulation performance of bedding and sleepwear when the user goes to bed, taking into account the user's past sleep history.

[0038] The user may also be able to input desired conditions regarding the bedroom room temperature or the bedding and sleepwear. In this case, as shown in FIG. 1, the input unit 20 further includes a condition information input unit 23. The condition information input unit 23 is used by the user to input desired conditions regarding the bedroom room temperature or the bedding and sleepwear. FIG. 7 shows an example of a display screen of the condition information input unit 23. In the example shown in FIG. 7, the user can input the desired bedroom room temperature, the desired bedding, and sleepwear. The user can also input a planned bedtime and whether or not to control the air conditioner 220. When controlling the air conditioner 220, the user may be able to select whether or not to set the operating mode of the air conditioner 220 to an energy-saving mode, rather than the desired bedroom room temperature itself. The energy-saving mode is an operating mode in which the temperature setting of the air conditioner 220 is increased to reduce the amount of power consumed by the air conditioner 220. In other words, the user can select the energy-saving mode instead of inputting a higher room temperature as the desired condition.

[0039] The data acquisition unit 110 acquires data relating to the conditions desired by the user from the condition information input unit 23. Then, the storage unit 130 stores the data acquired by the data acquisition unit 110. That is, the storage unit 130 further stores information relating to the conditions desired by the user regarding the room temperature in the bedroom or the bedding and sleepwear.

[0040] When two or more combinations of room temperature and the thermal performance of bedding and sleepwear are set for one thermal sensation group, the arithmetic processing unit 120 determines, from among these two or more combinations of room temperature and the thermal performance of bedding and sleepwear, the combination that best matches the user's desired conditions for the room temperature in the bedroom or the bedding and sleepwear, as the combination of room temperature and the thermal performance of bedding and sleepwear at the time of the user going to bed. That is, the arithmetic processing unit 120 determines the combination of room temperature and the thermal performance of bedding and sleepwear at the time of the user going to bed, depending on the thermal sensation group to which the user's thermal sensation belongs and the user's desired conditions for the room temperature and the thermal performance of bedding and sleepwear at the time of bed.

[0041] For example, if there is one combination of two or more room temperatures and the thermal insulation performance of bedding and sleepwear that matches the room temperature desired by the user, that combination is selected. Also, if the user selects the energy-saving mode of the air conditioner 220, the combination of two or more room temperatures and the thermal insulation performance of bedding and sleepwear, for example, the one with the highest room temperature is selected. In this way, the combination of room temperature and the thermal insulation performance of bedding and sleepwear at bedtime can be selected to reflect the user's wishes.

[0042] The user's desired conditions for bedding and nightwear may specify the bedding and nightwear to be used by the user. Alternatively, the user's desired conditions may specify only one of the bedding and nightwear. In this case, the information specifying the bedding and nightwear to be used may be input automatically, rather than the user operating the condition information input unit 23 to input the information specifying the bedding and nightwear to be used. In this case, the type and heat retention performance of the bedding and nightwear to be used may be estimated and used based on, for example, images captured by a camera, detection results from an infrared sensor, purchase or rental information for the bedding and nightwear, and a comparison of the detection results from a temperature sensor measuring the temperature inside the bed with the room temperature.

[0043] The information processing device 100 further includes an output unit 140. The output unit 140 outputs the calculation results of the arithmetic processing unit 120. That is, the output unit 140 outputs the combination of the room temperature at the time of the user's sleep and the heat retention performance of the bedding and sleepwear determined by the arithmetic processing unit 120.

[0044] The proposed air conditioning system according to this embodiment further includes a display unit 210. The output unit 140 of the information processing device 100 transmits to the display unit 210 the combination of the room temperature at the time the user goes to bed and the thermal insulation performance of the bedding and sleepwear determined by the arithmetic processing unit 120. The display unit 210 then displays the combination of the room temperature at the time the user goes to bed and the thermal insulation performance of the bedding and sleepwear output from the output unit 140. The display unit 210 is, for example, a liquid crystal display, an organic EL display, or the like. Alternatively, for example, a display provided in a terminal device such as the above-mentioned smartphone, tablet terminal, personal computer, or smartwatch, or an information appliance such as a smart TV, may be used as the display unit 210.

[0045] In this case, the display unit 210 may display the specific type of bedding and sleepwear that has the desired thermal performance, rather than the actual thermal performance of the bedding and sleepwear, such as the clo value. For bedding, this may be a comforter, a blanket, or a throw. For sleepwear, this may be short-sleeved, long-sleeved, shorts, or long pants. In cooperation with rental and retail companies of bedding and sleepwear, products with the desired thermal performance may be displayed as recommendations. Furthermore, a list of bedding and sleepwear owned or rented by the user may be stored in the storage unit 130, and bedding and sleepwear with the desired thermal performance may be displayed.

[0046] The proposed air conditioning system according to this embodiment further includes an air conditioner 220. The air conditioner 220 is a device that adjusts the room temperature in the bedroom where the user sleeps. Specific examples of the air conditioner 220 include an air conditioner and a heater. The air conditioner 220 may include not only a device that directly adjusts the room temperature in the bedroom, such as an air conditioner, but also a device that indirectly adjusts the room temperature in the bedroom, such as a ventilation device, a window opening / closing device, or a curtain opening / closing device. The output unit 140 of the information processing device 100 transmits the room temperature at the time of the user sleeping, determined by the arithmetic processing unit 120, to the air conditioner 220. The air conditioner 220 then adjusts the room temperature in the bedroom according to the room temperature at the time of the user sleeping, output from the output unit 140.

[0047] An infrared sensor or the like may be used to detect changes in the thermal insulation performance of the sleeping user's bedding and sleepwear. The thermal insulation performance of the sleeping user's bedding and sleepwear may change, for example, when the sleeping user gets out of bed. In such a case, the room temperature in the user's bedroom at the time of sleep may be changed to match the changed thermal insulation performance. The control of the air conditioner 220 may then be changed according to the changed bedroom room temperature. In this way, even if the thermal insulation performance of the bedding and sleepwear changes, a thermal environment suitable for sleep can be maintained by changing the room temperature.

[0048] Next, an example of the operation of the proposed air-conditioning system equipped with the information processing device 100 configured as described above will be described with reference to the flow diagram of FIG. 8. First, in step S11, the data acquisition unit 110 of the information processing device 100 acquires necessary data such as user attribute information. The acquired data is stored in the memory unit 130 of the information processing device 100. In the following step S12, the calculation processing unit 120 of the information processing device 100 uses the user attribute information to identify to which of multiple pre-set thermal sensation groups the user's thermal sensation belongs. Then, the calculation processing unit 120 determines a combination of the room temperature at the time of sleep of the user and the thermal insulation performance of the bedding and sleepwear according to the thermal sensation group to which the user's thermal sensation belongs. After step S12, the process proceeds to step S13.

[0049] In step S13, the output unit 140 of the information processing device 100 transmits the combination of the room temperature at the time of the user going to bed and the thermal insulation performance of the bedding and nightwear determined in step S12 to the display unit 210. The display unit 210 then displays the combination of the room temperature at the time of the user going to bed and the thermal insulation performance of the bedding and nightwear. After step S13, the process proceeds to step S14. In step S14, the calculation processing unit 120 of the information processing device 100 determines whether the user has gone to sleep based on the detection result of the sleep detection means 13, etc. If the user has gone to sleep, the process proceeds to step S15.

[0050] In step S15, the output unit 140 of the information processing device 100 transmits the room temperature at the time of the user's sleep, determined in step S12, to the air conditioner 220. The air conditioner 220 is then controlled so that the bedroom temperature becomes the determined room temperature. In the following step S16, the calculation processing unit 120 of the information processing device 100 determines whether the user has finished sleeping based on the detection result of the sleep detection means 13, etc. If the user has finished sleeping, the process proceeds to step S17. In step S17, the air conditioner 220 enters normal operation. When the process of step S17 is completed, the series of operations ends.

[0051] According to the proposed air conditioning system equipped with the information processing device 100 configured as described above, the thermal sensation group to which the user's thermal sensation belongs is identified using the user's attribute information, and the combination of the room temperature at the time of sleep and the thermal insulation performance of the bedding and sleepwear is determined according to the thermal sensation group to which the user's thermal sensation belongs, thereby helping to create a thermal environment in both the bedroom and bed that is suitable for the individual user.

[0052] In this embodiment, multiple users sleeping in the same bedroom may be targeted. In this case, the biometric sensor 12 detects biometric information of each of the multiple users sleeping in the same bedroom. Furthermore, attribute information of each user is input to the attribute information input unit 21 of the input unit 20. The data acquisition unit 110 acquires this data, and the data acquired by the data acquisition unit 110 is stored in the storage unit 130. That is, the storage unit 130 stores the attribute information of the multiple users.

[0053] The arithmetic processing unit 120 uses the attribute information of the multiple users stored in the storage unit 130 to identify to which of the above-mentioned thermal sensation groups the thermal sensation of each of the multiple users belongs. Then, the arithmetic processing unit 120 determines a combination of the room temperature of the bedroom in which the multiple users sleep and the thermal performance of the bedding and sleepwear of each of the multiple users, depending on the thermal sensation group to which the thermal sensation of each of the multiple users belongs. Here, when the thermal sensations of multiple users sleeping in the same bedroom all belong to the same thermal sensation group, the arithmetic processing unit 120 can determine a combination of the room temperature of the bedroom and the thermal performance of the bedding and sleepwear of each of the multiple users in the same way as when there is only one subject person.

[0054] On the other hand, when the thermal sensations of multiple users sleeping in the same bedroom belong to different thermal sensation groups, the combinations of the room temperature of the bedroom and the thermal insulation performance of the bedding and nightwear of each of the multiple users are determined, for example, as follows. First, the arithmetic processing unit 120 identifies two or more combinations of the room temperature and the thermal insulation performance of the bedding and nightwear of each of the multiple users for each thermal sensation group. Next, the arithmetic processing unit 120 extracts combinations of the room temperature and the thermal insulation performance identified for each thermal sensation group that have the same (common) room temperature. Then, the arithmetic processing unit 120 determines combinations of the room temperature of the bedroom and the thermal insulation performance of the bedding and nightwear of each of the multiple users from the extracted combinations that have the same room temperature.

[0055] In this way, the arithmetic processing unit 120 determines a combination of one room temperature at the time of sleep of multiple users and the heat retention performance of each of the multiple users' bedding and sleepwear, according to the thermal sensation group to which each of the multiple users' thermal sensations belongs. This makes it possible to support the creation of thermal environments in both the bedroom and bed that are suited to each individual user, even when multiple users sleep in the same bedroom.

[0056] Embodiment 2 A second embodiment of the present disclosure will be described with reference to Figs. 9 to 13. Fig. 9 is a diagram illustrating the overall configuration of the system. Fig. 10 is a block diagram illustrating the configuration of a learning device. Fig. 11 is a diagram illustrating an example of a neural network in the learning device. Fig. 12 is a flow diagram illustrating an example of the operation of the learning device. Fig. 13 is a flow diagram illustrating an example of the operation of the system.

[0057] The second embodiment described here uses machine learning in the configuration of the first embodiment described above to infer the combination of the room temperature at the time of a user's sleep and the thermal insulation performance of bedding and sleepwear. The information processing device, learning device, proposed system, and air conditioning system according to the second embodiment will be described below, focusing on the differences from the first embodiment. Configurations whose description is omitted are basically the same as those of the first embodiment. In the following description, configurations that are the same as or correspond to those of the first embodiment will be described with the same reference numerals as those used in the description of the first embodiment.

[0058] The proposed air-conditioning system according to this embodiment includes an information processing device 100, as shown in FIG. 9. Similar to the first embodiment, the information processing device 100 includes a computer having a calculation processing unit 120 and a storage unit 130. The proposed air-conditioning system according to this embodiment further includes a trained model storage unit 400. A trained model is stored in the trained model storage unit 400. The trained model is used to infer, from the user's attribute information, the combination of the room temperature at the time of sleeping and the thermal insulation performance of the bedding and sleepwear of the user. The trained model stored in the trained model storage unit 400 is generated, for example, by a learning device 300, which will be described later. The trained model storage unit 400 may be provided in a server device or the like that is capable of communicating with the information processing device 100, or may be provided in the information processing device 100 itself.

[0059] The arithmetic processing unit 120 performs a process of inferring a combination of the room temperature at the time of sleeping and the thermal insulation performance of the bedding and nightwear from the user's attribute information acquired by the data acquisition unit 110, i.e., information including one or both of the user's biometric information and preference information regarding cold and heat stored in the memory unit 130, using the trained model stored in the trained model storage unit 400. The arithmetic processing unit 120 inputs the input data acquired by the data acquisition unit 110, i.e., the data stored in the memory unit 130, into the trained model, thereby outputting a combination of the room temperature at the time of sleeping and the thermal insulation performance of the bedding and nightwear inferred from the input data. In this way, the arithmetic processing unit 120 uses the trained model for inferring a combination of the room temperature at the time of sleeping and the thermal insulation performance of the bedding and nightwear from the input data acquired by the data acquisition unit 110, and outputs a combination of the room temperature at the time of sleeping and the thermal insulation performance of the bedding and nightwear from the input data.

[0060] As described above, the calculation processing unit 120 in this embodiment functions as an inference unit that infers, from the user's attribute information, the combination of the room temperature at the time of sleeping and the thermal insulation performance of the bedding and sleepwear. The information processing device 100 functions as an inference device that infers, from the user's attribute information, the combination of the room temperature at the time of sleeping and the thermal insulation performance of the bedding and sleepwear.

[0061] The proposed air conditioning system according to this embodiment may further include a learning device 300 as shown in FIG. 10. The learning device 300 according to this embodiment learns the combination of the room temperature when the user sleeps and the thermal insulation performance of the bedding and sleepwear. Next, the configuration of the learning device 300 according to this embodiment will be described with reference to FIG. 10. As shown in the figure, the learning device 300 includes a learning data acquisition unit 310 and a model generation unit 320.

[0062] The learning data acquisition unit 310 acquires learning data. The learning data includes user attribute information and a combination of the room temperature at the time of the user's sleep and the thermal insulation performance of the bedding and sleepwear. As described above, the user attribute information includes one or both of the user's biometric information and preference information regarding cold and heat. The learning data is data that associates the user attribute information with a combination of the room temperature at the time of the user's sleep and the thermal insulation performance of the bedding and sleepwear.

[0063] The model generation unit 320 learns the combination of the room temperature when the user goes to bed and the thermal insulation performance of the bedding and sleepwear from the above-mentioned learning data created based on the association between the user's attribute information and the combination of the room temperature when the user goes to bed and the thermal insulation performance of the bedding and sleepwear. That is, the model generation unit 320 uses the learning data acquired by the learning data acquisition unit 310 to generate a learned model that infers the combination of the room temperature when the user goes to bed and the thermal insulation performance of the bedding and sleepwear from the user's attribute information.

[0064] The learning algorithm used by the model generation unit 320 may be a known algorithm such as supervised learning, semi-supervised learning, or reinforcement learning. An example will be described in which a neural network is used. The model generation unit 320 learns the combination of the room temperature at the time of the user's sleep and the thermal insulation performance of the bedding and sleepwear, for example, by so-called supervised learning according to a neural network model. Here, supervised learning refers to a technique in which pairs of input and result (label) data are provided to the learning device 300, which learns the features of the learning data and infers the result from the input.

[0065] A neural network is composed of an input layer consisting of multiple neurons, an intermediate layer (hidden layer) consisting of multiple neurons, and an output layer consisting of multiple neurons. The intermediate layer may be one layer or two or more layers. For example, in a three-layer neural network as shown in Figure 11, when multiple inputs are input to the input layer (X1-X3), the values ​​are multiplied by weight W1 (w11-w16) and input to the intermediate layer (Y1-Y2), and the result is further multiplied by weight W2 (w21-w26) and output from the output layer (Z1-Z3). This output result varies depending on the values ​​of weights W1 and W2.

[0066] In the present disclosure, the neural network learns combinations of room temperature at the time of sleeping and the thermal insulation performance of bedding and sleepwear through so-called supervised learning based on the above-mentioned learning data created based on the association between the user's attribute information acquired by the learning data acquisition unit 310 and combinations of room temperature at the time of sleeping and the thermal insulation performance of bedding and sleepwear. That is, the neural network learns by inputting the user's attribute information into the input layer and adjusting the weights W1 and W2 so that the result output from the output layer is close to the combination of room temperature at the time of sleeping and the thermal insulation performance of bedding and sleepwear.

[0067] The model generation unit 320 generates and outputs a trained model by performing the above-described learning. The trained model storage unit 400 stores the trained model output from the model generation unit 320. As described above, the trained model storage unit 400 may be provided in, for example, a server device or the like that is capable of communicating with the information processing device 100, or may be provided in the information processing device 100 itself. The trained model storage unit 400 may also be provided in the learning device 300. Note that the learning algorithm used in the learning device 300 may be deep learning, which learns to extract features themselves, or other known methods.

[0068] The learning data may be updated each time the user finishes sleeping, and the learning device 300 may use the updated learning data to update the trained model. In this case, for example, a sleep index indicating the quality of the user's sleep may be used, and the correct answer data may be overwritten with the combination of the room temperature and the thermal insulation performance of the bedding and sleepwear when the sleep index improves. This improves the trained model and increases the accuracy of inferring the combination of the room temperature and the thermal insulation performance of the bedding and sleepwear when the user goes to sleep.

[0069] Next, an example of the operation of the learning device 300 configured as described above will be described with reference to the flow diagram of Fig. 12. First, in step S21, the learning data acquisition unit 310 acquires learning data. Note that the user's attribute information included in the learning data and data on the combination of the room temperature at the time of sleeping and the thermal insulation performance of the bedding and sleepwear are acquired simultaneously, but it is sufficient if these data are input in association with each other, and the user's attribute information and data on the combination of the room temperature at the time of sleeping and the thermal insulation performance of the bedding and sleepwear may be acquired at different times.

[0070] After step S21, the learning device 300 then performs the process of step S22. In step S22, the model generation unit 320 uses the learning data acquired in step S21 to learn the combination of the room temperature at the time of the user's sleep and the thermal insulation performance of the bedding and sleepwear through so-called supervised learning, and generates a trained model. In the following step S23, the trained model storage unit 400 stores the trained model generated in step S22. When the process of step S23 is completed, the series of operations ends.

[0071] Next, an example of the operation of the proposed air conditioning system equipped with the information processing device 100 configured as described above will be described with reference to the flow diagram in FIG. 13. First, in step S31, the data acquisition unit 110 of the information processing device 100 acquires necessary data such as user attribute information. The acquired data is stored in the memory unit 130 of the information processing device 100. In the following step S32, the calculation processing unit 120 of the information processing device 100 inputs the user attribute information into the trained model stored in the trained model storage unit 400. In further following step S33, the calculation processing unit 120 outputs an inference result of the combination of the room temperature when the user goes to bed and the thermal insulation performance of the bedding and sleepwear. After step S33, the process proceeds to step S34.

[0072] In step S34, the output unit 140 of the information processing device 100 transmits the combination of the room temperature at the time of the user's sleep and the thermal insulation performance of the bedding and nightwear, which was inferred in step S32, to the display unit 210. The display unit 210 then displays the combination of the room temperature at the time of the user's sleep and the thermal insulation performance of the bedding and nightwear. After step S34, the process proceeds to step S35. In step S35, the calculation processing unit 120 of the information processing device 100 determines whether the user has fallen asleep based on the detection result of the sleep detection means 13, etc. If the user has fallen asleep, the process proceeds to step S36.

[0073] In step S36, the output unit 140 of the information processing device 100 transmits the room temperature at the time of the user's sleep, which was inferred in step S33, to the air conditioner 220. The air conditioner 220 is then controlled so that the room temperature in the bedroom becomes the inferred room temperature. In the following step S37, the calculation processing unit 120 of the information processing device 100 determines whether the user's sleep has ended based on the detection result of the sleep detection means 13, etc. If the user's sleep has ended, the process proceeds to step S38. In step S38, the learning device 300 updates the trained model using the sleep indices, etc., for this sleep. When the process of step S38 is completed, the series of operations ends.

[0074] In the proposed air conditioning system equipped with the information processing device 100 configured as described above, by inferring the combination of the room temperature at the time of the user's sleep and the thermal insulation performance of the bedding and sleepwear from the user's attribute information, it is possible to support the creation of thermal environments in both the bedroom and bed that are suitable for the individual user, as in embodiment 1.

[0075] FIG. 14 is a diagram showing an example of a configuration for realizing the functions of the information processing device 100 and the learning device 300 of the present disclosure. The functions of the information processing device 100 and the learning device 300 are realized by, for example, a processing circuit. The processing circuit may include a processor 101 and a memory 102. The processing circuit may be dedicated hardware 103. A portion of the processing circuit may be formed as dedicated hardware 103, and the processing circuit may further include a processor 101 and a memory 102. In the example shown in the figure, a portion of the processing circuit is formed as dedicated hardware 103. Furthermore, in the example shown in the figure, the processing circuit further includes a processor 101 and a memory 102.

[0076] The processing circuit, part of which is at least one dedicated hardware 103, may be, for example, a single circuit, a composite circuit, a programmed processor, a parallel programmed processor, an ASIC, an FPGA, or a combination thereof. When the processing circuit includes at least one processor 101 and at least one memory 102, the functions of the information processing device 100 and the learning device 300 are realized by software, firmware, or a combination of software and firmware.

[0077] The software and firmware are written as programs and stored in memory 102. Processor 101 realizes the functions of each unit by reading and executing the programs stored in memory 102. Processor 101 is also called a CPU (Central Processing Unit), central processing unit, processing unit, arithmetic unit, microprocessor, microcomputer, or DSP. Memory 102 may include, for example, non-volatile or volatile semiconductor memory such as RAM, ROM, flash memory, EPROM, and EEPROM, or a magnetic disk, flexible disk, optical disk, compact disk, minidisk, DVD, etc.

[0078] In this way, the processing circuits of the information processing device 100 and the learning device 300 can realize each function of the information processing device 100 and the learning device 300 by hardware, software, firmware, or a combination of these. When the processing circuits of the information processing device 100 and the learning device 300 include at least a processor 101 and a memory 102, the processor 101 executes a program stored in the memory 102 in the information processing device 100 and the learning device 300, and the hardware and software of the information processing device 100 and the learning device 300 work together to realize the functions of each unit of the information processing device 100 and the learning device 300. Note that the proposed system and air conditioning system are not limited to a configuration in which operation is controlled by a single information processing device 100. The proposed system and air conditioning system may also be controlled by multiple devices working together.

[0079] In the present disclosure, the embodiments, configuration examples, modifications, etc. may be combined in any manner without departing from the spirit of the present disclosure. Examples of various aspects of the present disclosure are summarized below as appendices. (Appendix 1) An information processing device including a computer having a storage unit and an arithmetic processing unit, the storage unit stores attribute information including one or both of biometric information and preference information regarding cold and heat of the user; The arithmetic processing unit Using the attribute information of the user stored in the storage unit, it is determined to which of a plurality of preset thermal sensation groups the thermal sensation of the user belongs; determining a combination of a room temperature at the time of sleeping of the user and the heat retention performance of bedding and sleepwear according to the thermal sensation group to which the thermal sensation of the user belongs; The information processing device further comprises an output unit that outputs a combination of the room temperature when the user is sleeping and the heat retention performance of the bedding and sleepwear. (Appendix 2) the storage unit stores the attribute information of a plurality of the users; The arithmetic processing unit Using the attribute information of the users stored in the storage unit, it is determined to which of the thermal sensation groups the thermal sensation of each of the users belongs; An information processing device as described in Appendix 1, which determines a combination of one room temperature at the time of sleeping of the multiple users and the thermal insulation performance of bedding and sleepwear of each of the multiple users according to the thermal sensation group to which each of the multiple users' thermal sensations belongs. (Appendix 3) The storage unit further stores a sleep index indicating the quality of sleep of the user when he or she went to bed in the past; The arithmetic processing unit An information processing device according to claim 1 or 2, which determines a combination of room temperature at the time of bedtime of the user and the thermal insulation performance of bedding and sleepwear based on the thermal sensation group to which the user's thermal sensation belongs and the sleep index of the user at previous bedtimes of the user. (Appendix 4) The information processing device described in Appendix 1 or Appendix 2, wherein the calculation processing unit determines a combination of the room temperature at the time of sleeping and the thermal insulation performance of the bedding and sleepwear, based on the thermal sensation group to which the user's thermal sensation belongs and the user's desired conditions for the room temperature at the time of sleeping and the thermal insulation performance of the bedding and sleepwear. (Appendix 5) An information processing device including a computer having a storage unit and an arithmetic processing unit, the storage unit stores attribute information including one or both of biometric information and preference information regarding cold and heat of the user; the calculation processing unit infers a combination of the room temperature at the time of sleeping of the user and the thermal insulation performance of the bedding and sleeping clothes from the attribute information of the user stored in the storage unit, using a trained model for inferring a combination of the room temperature at the time of sleeping of the user and the thermal insulation performance of the bedding and sleeping clothes from the attribute information of the user; The information processing device further comprises an output unit that outputs a combination of the room temperature when the user is sleeping and the heat retention performance of the bedding and sleepwear. (Appendix 6) An information processing device according to any one of Supplementary Note 1 to Supplementary Note 5; A suggestion system comprising: a display unit that displays the combination of the room temperature at the time of the user's sleep and the thermal insulation performance of the bedding and sleepwear output from the output unit. (Appendix 7) An information processing device according to any one of Supplementary Note 1 to Supplementary Note 6; an air conditioning device that adjusts the room temperature of the bedroom where the user sleeps, The air conditioning device is an air conditioning system that adjusts the room temperature of the bedroom in accordance with the room temperature at the time of the user's sleep output from the output unit. (Appendix 8) a data acquisition unit that acquires learning data including attribute information including one or both of a user's biological information and preference information regarding cold and heat, and a combination of the user's room temperature when sleeping and the thermal insulation performance of the user's bedding and sleepwear; and a model generation unit that generates a trained model for inferring a combination of the room temperature at the time of sleeping of the user and the thermal insulation performance of the bedding and sleepwear from the attribute information of the user, using the learning data acquired by the data acquisition unit. [Explanation of symbols]

[0080] 11 Temperature Sensor 12 Biometric sensors 13 Sleep detection method 20 Input section 21 Attribute information input section 22 Subjective information input section 23 Condition information input section 100 Information processing device 101 processors 102 memory 103 Dedicated Hardware 110 Data Acquisition Unit 120 Processing unit 130 Storage section 140 Output section 210 Display section 220 Air conditioning equipment 300 Learning Device 310 Learning data acquisition unit 320 Model Generation Unit 400 Trained model memory

Claims

1. An information processing device including a computer having a storage unit and an arithmetic processing unit, the storage unit stores attribute information including one or both of biometric information and preference information regarding cold and heat of the user; The arithmetic processing unit Using the attribute information of the user stored in the storage unit, it is determined to which of a plurality of preset thermal sensation groups the thermal sensation of the user belongs; determining a combination of a room temperature at the time of sleeping of the user and the heat retention performance of bedding and sleepwear according to the thermal sensation group to which the thermal sensation of the user belongs; The information processing device further comprises an output unit that outputs a combination of the room temperature when the user is sleeping and the heat retention performance of the bedding and sleepwear.

2. the storage unit stores the attribute information of a plurality of the users; The arithmetic processing unit Using the attribute information of the users stored in the storage unit, it is determined to which of the thermal sensation groups the thermal sensation of each of the users belongs; The information processing device according to claim 1, wherein a combination of one room temperature at the time of sleeping of the plurality of users and the heat retention performance of bedding and sleepwear of each of the plurality of users is determined according to the thermal sensation group to which the thermal sensation of each of the plurality of users belongs.

3. The storage unit further stores a sleep index indicating the quality of sleep of the user when he or she went to bed in the past; The arithmetic processing unit 3. The information processing device according to claim 1, wherein a combination of room temperature at the time of bedtime of the user and the thermal insulation performance of bedding and sleepwear is determined based on the thermal sensation group to which the user's thermal sensation belongs and the sleep index of the user at the time of bedtime in the past.

4. The information processing device described in claim 1 or claim 2, wherein the calculation processing unit determines a combination of the room temperature at the time of sleeping and the thermal insulation performance of the bedding and sleepwear based on the thermal sensation group to which the user's thermal sensation belongs and the conditions desired by the user regarding the room temperature at the time of sleeping and the thermal insulation performance of the bedding and sleepwear.

5. An information processing device including a computer having a storage unit and an arithmetic processing unit, the storage unit stores attribute information including one or both of biometric information and preference information regarding cold and heat of the user; the calculation processing unit infers a combination of the room temperature at the time of sleeping of the user and the thermal insulation performance of the bedding and sleeping clothes from the attribute information of the user stored in the storage unit, using a trained model for inferring a combination of the room temperature at the time of sleeping of the user and the thermal insulation performance of the bedding and sleeping clothes from the attribute information of the user; The information processing device further comprises an output unit that outputs a combination of the room temperature when the user is sleeping and the heat retention performance of the bedding and sleepwear.

6. An information processing device according to any one of claims 1 to 5; A suggestion system comprising: a display unit that displays the combination of the room temperature at the time of the user's sleep and the thermal insulation performance of the bedding and sleepwear output from the output unit.

7. An information processing device according to any one of claims 1 to 5; an air conditioning device that adjusts the room temperature of the bedroom where the user sleeps, The air conditioning device is an air conditioning system that adjusts the room temperature of the bedroom in accordance with the room temperature at the time of the user's sleep output from the output unit.

8. a data acquisition unit that acquires learning data including attribute information including one or both of a user's biological information and preference information regarding cold and heat, and a combination of the user's room temperature when sleeping and the thermal insulation performance of the user's bedding and sleepwear; and a model generation unit that generates a trained model for inferring a combination of the room temperature at the time of sleeping of the user and the thermal insulation performance of the bedding and sleepwear from the attribute information of the user, using the learning data acquired by the data acquisition unit.

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