Sanitary system

Abstract

A toilet system according to an embodiment includes a first detection sensor provided to a toilet device to detect a non-odor gas, a second detection sensor provided to the toilet device to detect a malodor gas, and an estimation mechanism that estimates at least one of a bacterium in the intestines of a user using the toilet device, a metabolite of the bacterium in the intestines, and a pH based on detection results of the first detection sensor and the second detection sensor.

Description

Technical Field

[0001] Embodiments of this disclosure relate to a toilet system. Background Technology

[0002] Conventionally, a technology has been provided for collecting data in water-using spaces such as toilets through sensor detection. For example, a toilet seat device equipped with a gas composition sensor (see, for example, Patent Document 1 and Patent Document 2) has been provided, which can detect the excrement gas (such as farts) emitted by the toilet user (hereinafter referred to as "user") when defecating.

[0003] Existing technical documents Patent documents Patent Document 1: Japanese Patent Application Publication No. 2005-315836; Patent Document 2: Japanese Patent Application Publication No. 2016-145806. Summary of the Invention

[0004] The problem that the invention aims to solve However, the aforementioned prior art still has room for improvement. For example, in Patent Document 1, data obtained from a sensor that reacts with hydrogen is used for processing; in Patent Document 2, data obtained from a sensor that reacts with an odorous gas containing sulfur is used for processing. However, because it is difficult to make inferences from a comprehensive perspective, it is sometimes impossible to properly infer information related to human health. Therefore, it is desirable to be able to properly infer information related to human health, for example, based on the detection results of multiple sensors that detect different components.

[0005] The purpose of this disclosure is to provide a toilet system that can appropriately estimate information related to human health.

[0006] means for solving problems One embodiment of the toilet system is characterized by comprising: a first detection sensor disposed in the toilet device for detecting odorless gases; a second detection sensor disposed in the toilet device for detecting malodorous gases; and an estimation mechanism that, based on the detection results of the first and second detection sensors, estimates at least one of the following: intestinal bacteria, intestinal bacterial metabolites, and pH of a user of the toilet device.

[0007] According to one embodiment of the toilet system, based on the detection results of two sensors—a first sensor for detecting odorless gases and a second sensor for detecting malodorous gases—at least one of the following—intestinal bacteria, intestinal bacterial metabolites, and pH—of the user using the toilet device. Thus, according to the present invention, the toilet system can more accurately estimate the user's intestinal environment by estimating intestinal bacteria, intestinal bacterial metabolites, or pH (Potential Hydrogen). Therefore, the toilet system can appropriately estimate information related to human health.

[0008] According to the inventors' research, it has been discovered that the ratio of odorless gases (composed of hydrogen, methane, and carbon dioxide) to foul-smelling gases (composed of hydrogen sulfide and methanethiol) in flatulence (defecation gas) expelled during defecation can indirectly reflect changes in the intestinal environment over time. It is known that the intestinal environment changes according to factors such as diet and physical activity. Accurately estimating the state of the intestinal environment can improve the quality of life for toilet users. Therefore, more accurate measurement of the composition of defecation gas is important, and the inventors are continuously developing and improving this technology through various hardware improvements. It should be noted that the intestinal environment refers to the environment created by intestinal bacteria residing in the intestines. Intestinal bacteria are generally classified into beneficial bacteria, harmful bacteria, and opportunistic bacteria. Recent studies have shown that short-chain fatty acids produced by beneficial bacteria have a positive impact on physical and mental health, and it is known that the intestinal pH tends to be acidic in an environment rich in short-chain fatty acids. Therefore, to accurately estimate the intestinal environment, intestinal bacteria, their metabolites, and the intestinal pH are crucial. However, previous techniques for measuring excremental gas have struggled to estimate these indicators.

[0009] Therefore, a toilet system according to one embodiment, based on the detection results of two sensors—a first sensor for detecting odorless gases and a second sensor for detecting malodorous gases—that is, based on information about the different gas components, infers at least one of the following: the intestinal bacteria, intestinal bacterial metabolites, and pH of the user using the toilet device. Thus, the toilet system can accurately infer intestinal bacteria, intestinal bacterial metabolites, or pH, and therefore can appropriately infer information related to human health.

[0010] In one embodiment of the toilet system, the estimation mechanism estimates at least one of the following: intestinal bacteria, intestinal bacterial metabolites, and pH of the user of the toilet device, based on the composition ratio of excrement gas obtained from the detection results of the first detection sensor and the second detection sensor.

[0011] According to one embodiment, a toilet system can appropriately eliminate deviations caused by the amount of excreted gas by using the composition ratio of excreted gas obtained based on the detection results of a first detection sensor and a second detection sensor. Therefore, the toilet system can appropriately estimate information related to human health.

[0012] One embodiment of the toilet system further includes a third detection sensor for detecting stool characteristics, and based on the detection results of the first detection sensor, the second detection sensor, and the third detection sensor, presuming at least one of the intestinal bacteria, intestinal bacterial metabolites, and pH of the user of the toilet device.

[0013] According to one embodiment of the toilet system, by further incorporating a third detection sensor that detects stool characteristics, the user's intestinal environment can be more accurately estimated by inferring intestinal bacteria, intestinal bacterial metabolites, or pH. For example, by combining data on stool characteristics such as volume, shape, and color, the toilet system can more accurately estimate the intestinal environment. Therefore, the toilet system can appropriately estimate information related to a person's health.

[0014] One embodiment of the toilet system includes: a first detection unit for detecting feces; a second detection unit having at least one of the first detection sensor and the second detection sensor; and a control device that, based on the detection results of the first detection unit and the detection results of the second detection unit, performs a presumption process to presume at least one of providing information or a score related to the user's health, and performs control to output the result of the presumption process to an external source.

[0015] According to one embodiment, a toilet system can appropriately estimate information related to the user's health by estimating at least one of the provided information or score related to the user's health based on the detection results of a first detection unit and a second detection unit. Therefore, the toilet system can appropriately estimate information related to a person's health.

[0016] The health of the gut is primarily determined by the peristaltic movements of the intestinal wall and the intestinal environment. Peristaltic movements are mainly influenced by stress, sleep, and infections, while the intestinal environment is primarily affected by diet. Furthermore, information about stool (stool characteristics) is one indicator of peristalsis, and information about fecal gas (amount or concentration) is one indicator of the intestinal environment. Therefore, according to one embodiment of the toilet system, based on stool information related to the user's peristaltic movements and fecal gas information related to the user's intestinal environment, it can output health-related information or scores that take into account both the state of the intestinal wall and the state of the intestinal lining, thereby enabling more precise implementation of health support for the user.

[0017] One embodiment of the toilet system includes: a gas composition detection device comprising a first gas composition sensor (i.e., the first detection sensor) that reacts with hydrogen contained in the gas, and a second gas composition sensor (i.e., the second detection sensor) that reacts with an odorous gas containing sulfur and hydrogen; and a control device that controls the gas composition detection device, wherein the control device calculates a second calculated value corresponding to hydrogen from the second gas composition sensor based on a plurality of calculated values ​​corresponding to hydrogen contained in the gas, and calculates a third calculated value corresponding to the odorous gas based on the detection result of the second gas composition sensor and the second calculated value, the toilet system being a system for estimating the health status of the user or information related to the health status based on the third calculated value, wherein the plurality of calculated values ​​includes the first calculated value corresponding to hydrogen obtained based on the detection result of the first gas composition sensor.

[0018] According to one embodiment of the toilet system, a second calculated value is obtained based on an estimated value derived from multiple calculated values ​​of hydrogen, including a first calculated value derived from hydrogen, obtained from the detection results of a first gas composition sensor. Therefore, the influence of measurement deviation of the hydrogen sensor (corresponding to the first gas composition sensor) can be mitigated, and the amount of hydrogen more closely approximating the true value can be calculated. Thus, in the toilet system according to one embodiment, in a structure that calculates the amount of odorous gas based on the amount of hydrogen detected by the hydrogen sensor by removing the influence of hydrogen from the detection value of a gas composition sensor (also called an "odorous gas sensor") used to detect odorous gases (malodorous gases, etc.), even if the amount of hydrogen detected changes due to the measurement deviation of the hydrogen sensor, the detection amount of odorous gas caused by hydrogen can be suppressed from falling below zero. For example, the health status, such as the state of the intestinal environment, can be calculated with higher accuracy. Therefore, the toilet system can appropriately perform gas measurement-based processing. Consequently, the toilet system can appropriately estimate information related to human health.

[0019] One embodiment of the toilet system includes: a gas composition detection device comprising a first gas composition sensor (i.e., the first detection sensor) that reacts with hydrogen contained in the gas, and a second gas composition sensor (i.e., the second detection sensor) that reacts with an odorous gas containing sulfur and hydrogen; and a control device that controls the gas composition detection device, wherein the control device calculates a first calculated value corresponding to hydrogen based on the detection result of the first gas composition sensor, calculates a second calculated value corresponding to hydrogen based on the first calculated value, and calculates a third calculated value corresponding to the odorous gas based on the detection result of the second gas composition sensor and the second calculated value, wherein the toilet system is a system for estimating the health status of the user or information related to the health status based on the third calculated value, and the control device corrects for at least one of the zeroth calculated value, the second calculated value, or the third calculated value corresponding to the odorous gas and hydrogen calculated based on the detection result of the second gas composition sensor.

[0020] According to one embodiment, a toilet system calculates the amount of odorous gas based on the amount of hydrogen detected by a hydrogen sensor (corresponding to a first gas component sensor) by removing the influence of hydrogen from the detection value of an odorous gas sensor (corresponding to a second gas component sensor). Even if the hydrogen detection amount fluctuates due to measurement deviation of the hydrogen sensor, it can suppress the detection amount of odorous gas from falling below zero due to hydrogen. For example, it can calculate health status, such as the state of the intestinal environment, with higher accuracy. Therefore, the toilet system can appropriately perform gas-based measurement processing. Consequently, the toilet system can appropriately estimate information related to human health.

[0021] One embodiment of the toilet system includes: a gas composition detection device comprising a first gas composition sensor (i.e., the first detection sensor) that reacts with hydrogen contained in the gas, and a second gas composition sensor (i.e., the second detection sensor) that reacts with an odorous gas containing sulfur and hydrogen; a control device that controls the gas composition detection device; and an output mechanism that outputs information related to the processing result of the control device. The control device calculates a first calculated value corresponding to hydrogen based on the detection result of the first gas composition sensor, calculates a second calculated value corresponding to hydrogen based on the first calculated value, and calculates a third calculated value corresponding to an odorous gas based on the detection result of the second gas composition sensor and the second calculated value. The toilet system is a system for estimating the health status of the user or information related to the health status based on the third calculated value. The control device performs control when at least one of the first calculated value, the second calculated value, and the third calculated value meets a predetermined condition, such that the first information output by the output mechanism as information about the user's health status or related to the health status is not changed based on the third calculated value.

[0022] According to one embodiment, the toilet system can suppress the amount of odorous gases from falling below zero even if the hydrogen detection value measured by the hydrogen sensor (corresponding to the first gas composition sensor) deviates. For example, when displaying the user's health status, such as the state of the intestinal environment, the system can improve ease of use by avoiding data gaps. Therefore, the toilet system can appropriately perform processing based on gas composition measurement. As a result, the toilet system can appropriately infer information related to a person's health.

[0023] One embodiment of the toilet system includes: a gas composition detection device comprising at least one of a first detection sensor and a second detection sensor that reacts with gaseous components contained in the gas; and a control device that controls the gas composition detection device, the gas composition sensor being configured with a sensor element and a measuring resistive element, the control device controlling the gas composition detection device such that the measured value of the gas composition sensor becomes a value within a preset range when the user is not using the toilet, and performing a reference value control to control the measured value as a reference value to a predetermined value, the gas composition detection device using the reference value controlled by the reference value control to perform processing related to the measurement of defecation gases.

[0024] According to one embodiment of the toilet system, even if the detection value of the gas composition sensor changes due to temperature, humidity conditions, and air fresheners in the toilet space during non-defecation periods, the system can suppress resolution variations during each measurement by controlling the reference value of the gas composition sensor within a specified range at predetermined times. Therefore, according to this embodiment, the toilet system can accurately detect defecation gases even when the environmental conditions of the toilet space differ each time a measurement is performed during defecation, ensuring consistent measurement conditions for defecation gases daily. This allows for high-precision capture of changes in the user's physical condition over time, derived from defecation gases. Thus, the toilet system can appropriately perform processing related to gas composition measurement. Consequently, the toilet system can appropriately infer information related to human health.

[0025] One embodiment of the toilet system includes: a suction device that draws gas from the basin of a toilet bowl; a gas flow path through which the gas drawn by the suction device passes; a gas composition detection device comprising at least one of a first detection sensor and a second detection sensor that reacts with gas components contained in the gas passing through the gas flow path; a control device that controls the suction device and the gas composition detection device; and a pressure loss generating unit that increases the pressure loss generated when gas passes through in the direction of gas travel through the gas flow path, further downstream than the location of the gas composition sensor.

[0026] According to one embodiment of the toilet system, a pressure loss generating unit creates a pressure loss downstream of the gas composition sensor. This pressure loss causes turbulence to form downstream of the sensor, allowing the gas composition, after its concentration gradient has been homogenized, to contact the sensor. Therefore, the toilet system can appropriately measure the gas composition. Consequently, the toilet system can appropriately estimate information related to human health.

[0027] One embodiment of the toilet system includes: a gas flow path that draws in and passes through gas from the basin of a toilet bowl; and a sensor that reacts with gaseous components contained in the gas passing through the gas flow path, the gas flow path including a main flow path and a secondary flow path, the secondary flow path being disposed within the main flow path, gas flowing in from the main flow path passing through the secondary flow path at a slower flow rate than in the main flow path, and the sensor being disposed within the secondary flow path.

[0028] According to one embodiment of the toilet system, the sensor sensing unit is isolated by a secondary flow path. By temporarily retaining the defecation gas around the sensor sensing unit, the concentration gradient is homogenized, thereby obtaining a more accurate signal from the gas composition sensor. Furthermore, the main flow path can handle most of the flow, thus allowing the gas from the basin to be recovered to the gas flow path. Therefore, the toilet system can appropriately measure the gas composition. Consequently, the toilet system can appropriately infer information related to human health.

[0029] One embodiment of the toilet system includes: a suction device for drawing gas from the basin of a toilet; a gas flow path through which the drawn gas passes via the suction device; a gas composition detection device comprising at least one of a first detection sensor and a second detection sensor that reacts with gas components contained in the gas passing through the gas flow path; a deodorizing component disposed in the gas flow path for deodorizing the odorous components of the gas; and a control device for controlling the suction device and the gas composition detection device. The gas flow path includes an inlet portion for drawing gas into the gas flow path and an outlet portion disposed downstream of the inlet portion for discharging gas from the gas flow path to the outside of the gas flow path. The gas composition sensor is disposed between the inlet portion and the outlet portion. The inlet portion is disposed at a position capable of collecting defecation gas from the basin, and the outlet portion is configured to discharge gas from the gas flow path from a position further rearward than the user's seating position on the toilet to the outside of the toilet.

[0030] According to one embodiment, a toilet system has an exhaust section in the gas flow path that discharges gas components from a position further back than the user's seat on the toilet bowl. This prevents odor components accumulated in deodorizing components such as deodorizing filters from circulating in the flow path. By immediately discharging the gas into the toilet space, the accuracy of the gas component sensor can be stabilized. Therefore, the toilet system can suppress the decrease in the accuracy of gas component measurement. Furthermore, by discharging the gas further back than the user (body) while seated on the toilet seat, the toilet system suppresses the backflow of gas components discharged into the basin. Moreover, by discharging the gas components towards the rear of the body, the user is less likely to notice the odor. Thus, the toilet system can appropriately estimate information related to human health.

[0031] One embodiment of the toilet system includes: a suction device that draws in defecation gas discharged into the basin of a toilet bowl; a gas flow path through which the drawn gas passes via the suction device; a gas composition detection device comprising at least one of a first detection sensor and a second detection sensor that reacts with a predetermined gas composition contained in the gas passing through the gas flow path; and a control device that controls the suction flow rate of the suction device such that, when the suction flow rate of the suction device is set to x (L / min), 10 ≤ x ≤ 200 is satisfied.

[0032] According to one embodiment, a toilet system can control the suction flow rate (hereinafter also referred to as "flow rate") of the suction device within an appropriate range by setting it to x (L (liters) min (minutes)) and controlling it to satisfy 10 ≤ x ≤ 200. For example, by controlling the suction flow rate of the suction device within the aforementioned range, the toilet system can suppress the increased possibility of inaccurate measurement due to a sharp increase or decrease in concentration caused by an increased suction flow rate to the gas flow path equipped with a gas composition sensor, and also suppress the increased possibility of inaccurate measurement due to a decreased suction flow rate to the gas flow path equipped with a gas composition sensor affecting the measurement of the next user. Thus, the toilet system can suppress the increased possibility of inaccurate measurement. Therefore, the toilet system can appropriately estimate information related to human health.

[0033] For example, if the suction flow rate is greater than 200 L / min, the size of the suction device becomes larger, or it produces undesirable effects such as driving noise. Furthermore, if the suction flow rate is less than 10 L / min, defecation gas will leak outside the basin, making proper detection impossible. As described above, as long as the flow rate is below 200 L / min, the toilet system can suction defecation gas from the basin without affecting measurement accuracy. Furthermore, as described above, as long as the flow rate is above 10 L / min, the toilet system can control the time it takes for the sensor signal to recover to the baseline from its peak value without affecting the measurement of the next user.

[0034] For example, the suction device described in Patent Document 2 can prevent fecal gas from diffusing into the basin while supplying more fecal gas to the gas composition sensor. On the other hand, considering the relationship between suction flow rate and measurement accuracy, as the suction flow rate increases, there are problems such as the gas composition sensor's resolution not keeping up with the rapid changes in concentration, making it impossible to accurately read the peak value (making it difficult to obtain an accurate sensor signal). Similarly, it has been recognized that as the suction flow rate decreases, the time it takes for the sensor signal to recover to the baseline after reaching the peak value becomes longer, which will affect the measurement of the next user.

[0035] Therefore, by controlling the suction flow rate of the suction device within the aforementioned range, the aforementioned problem is solved, and the possibility of inaccurate measurement being impossible is suppressed. Thus, the toilet system can appropriately estimate information related to human health.

[0036] In one embodiment of the toilet system, the control device controls the suction flow rate to be above 50 L / min and below 170 L / min.

[0037] For example, as long as the flow rate is below 170 L / min, defecation gas in the basin can be drawn away without affecting the measurement accuracy. Furthermore, as long as the flow rate is above 50 L / min, the time it takes for the sensor signal to recover to the baseline after reaching its peak value can be controlled within a range that does not affect the measurement of the next user. Therefore, according to one embodiment of the toilet system, by controlling the suction flow rate to be above 50 L / min and below 170 L / min, the suction flow rate of defecation gas discharged into the toilet basin can be controlled within an appropriate range. Thus, the toilet system can appropriately estimate information related to human health.

[0038] One embodiment of the toilet system includes: a suction device that draws in defecation gas discharged into the basin of a toilet bowl; a gas flow path through which the drawn gas passes via the suction device; a gas composition detection device comprising at least one of a first detection sensor and a second detection sensor that reacts with a predetermined gas composition contained in the gas passing through the gas flow path; and a control device that controls the suction flow rate of the suction device, setting the driving setting condition of the gas composition detection device as y, setting the suction flow rate of the suction device as x1 (L / min), and setting the number of signal processing steps for converting the electrical signal detected by the gas composition detection device into a digital signal as x2 (Hz). In the following equation 1, when variables α, β, and b are set to 0.025≤α≤0.045, -11≤β≤-7, and 1.5≤b≤3.0 respectively, 0≤y≤500 is satisfied. (Equation 1) .

[0039] According to one embodiment of the toilet system, the suction flow rate (hereinafter also referred to as "flow rate") of the suction device is set to x1 (L (liters) / min (minutes)), and the number of signal processing operations (sampling rate) when the electrical signal detected by the gas composition detection device is converted into a digital signal is set to x2 (Hz). In the above formula, when the variables α, β, and b are set to 0.025≤α≤0.045, -11≤β≤-7, and 1.5≤b≤3.0 respectively, by controlling the setting condition y of the gas composition detection device to satisfy 0≤y≤500, the suction flow rate of the defecation gas discharged into the toilet bowl and the number of signal processing operations (sampling rate) when the electrical signal detected by the gas composition detection device is converted into a digital signal can be controlled within an appropriate range. In this way, by controlling the above conditions to satisfy the above conditions, the toilet system can make the setting condition of the gas composition detection device meet the reference value, thereby controlling the flow rate of defecation gas and the number of processing operations of the gas composition sensor within an appropriate range. As a result, the toilet system can appropriately estimate information related to human health.

[0040] One embodiment of the toilet system includes: a suction device that draws gas from the basin of a toilet; a gas flow path through which the drawn gas passes via the suction device; a gas composition detection device comprising at least one of a first detection sensor and a second detection sensor that reacts with a predetermined gas composition contained in the gas passing through the gas flow path; a status detection mechanism that detects changes in the status of the toilet where the toilet is located; and a standby time setting mechanism that sets a standby time from the end of the measurement of the previous user to the time when the measurement of the next user can be performed, based on a historical record of the toilet status detected by the gas composition detection device or the status detection mechanism.

[0041] According to one embodiment, a toilet system sets a standby time for measuring defecation gases based on historical toilet status records. This avoids interference from various factors within the toilet space, enabling accurate measurements. Therefore, the toilet system can suppress interference in the processing of defecation gas information. Consequently, the toilet system can appropriately estimate information related to human health.

[0042] For example, according to the method described in Patent Document 2, if only odor interference is considered, it is sometimes possible to take countermeasures such as having the subject wait until the odor interference stabilizes before defecation, or showing an error even if the subject is affected by interference during defecation. However, if the noise generated by the sterilization function or automatic washing operation of a regular toilet is not considered in addition to odor interference, it is difficult to accurately measure the defecation gas.

[0043] Therefore, in one embodiment of the toilet system, by setting an appropriate standby time for the location capable of measuring defecation gas based on the toilet's historical status, accurate signals from the gas composition sensor used to measure the composition of defecation gas can be obtained. This allows the user to avoid the influence of various disturbances within the toilet space and perform accurate measurements. Consequently, the toilet system can appropriately estimate information related to human health.

[0044] One embodiment of the toilet system includes: a suction device that draws gas from the basin of a toilet bowl; a gas flow path through which the drawn gas passes via the suction device; a gas composition detection device comprising at least one of a first detection sensor and a second detection sensor that reacts with a predetermined gas composition contained in the gas passing through the gas flow path; a state detection mechanism that detects state changes within the toilet where the toilet is located; and a data acquisition range setting mechanism that sets the acquisition range of data parsed by a data analysis mechanism from the gas composition measurement data based on a historical record of the toilet state detected by the gas composition detection device or the state detection mechanism.

[0045] According to one embodiment of the toilet system, the data acquisition range required for gas composition analysis is set to the minimum required range based on the toilet's historical status records, avoiding the influence of various interferences in the toilet space, thereby enabling accurate measurement. Therefore, the toilet system can suppress the influence of interference in processing using gas composition information. Furthermore, according to the toilet system, by setting the data capacity required for gas composition analysis to the minimum required range, the storage capacity and data communication capacity used for analysis can be reduced. Thus, the toilet system can appropriately infer information related to human health.

[0046] As shown above, apart from odor interference, it is difficult to accurately measure excrement gases if the interference caused by the sterilization function or automatic washing action of ordinary toilets is not considered.

[0047] Therefore, in one embodiment of the toilet system, by setting the data acquisition range required for gas composition analysis to the minimum required range, accurate signals from the gas composition sensor used to measure the composition of fecal gases can be obtained, avoiding the influence of various interferences in the toilet space on the user, and enabling accurate measurement. Thus, the toilet system can appropriately infer information related to human health.

[0048] Invention Effects According to one method of implementation, information related to a person's health can be appropriately inferred. Attached Figure Description

[0049] Figure 1 This is a perspective view showing an example of the structure of the bathroom according to the first embodiment; Figure 2 This is a plan view showing an example of the structure of the measuring device according to the first embodiment; Figure 3 This is a diagram illustrating an example of the overall outline of the toilet system according to the first embodiment; Figure 4 This is a diagram illustrating a structural example of the toilet system according to the first embodiment; Figure 5 This is a block diagram illustrating an example of the structure of the toilet seat device according to the first embodiment; Figure 6 This is a block diagram illustrating an example of the structure of the control device according to the first embodiment; Figure 7 This is a diagram showing an outline of the processing in the first embodiment; Figure 8 This is a diagram illustrating an example of the mechanism by which gas components are generated; Figure 9 This diagram illustrates an example of the processing performed by the toilet system according to the first embodiment; Figure 10 This is a diagram illustrating an example of the structure of a gas composition sensor; Figure 11 This is a graph representing an example of a second evaluation based on multiple data points; Figure 12 This diagram illustrates an example of a presumption of the first evaluation of the first embodiment; Figure 13 This diagram illustrates an example of a presumption of the second evaluation of the first embodiment; Figure 14 This is a diagram illustrating an example of the provision of information in the first embodiment; Figure 15 This is a diagram illustrating an example of information used to estimate scores in the first embodiment; Figure 16 This is a diagram illustrating an example of presumed information used to provide information in the first embodiment; Figure 17 This is a diagram illustrating an example of presumed information used to provide information in the first embodiment; Figure 18 This is a diagram illustrating an example of information provided by a bathroom system; Figure 19 This is a diagram illustrating an example of information provided by a bathroom system; Figure 20 This is a diagram illustrating an example of information provided by a bathroom system; Figure 21 This is a diagram illustrating an example of information derived from an inference. Figure 22 This is a diagram illustrating an example of information derived from an inference. Figure 23 This is an example of a diagram representing information related to a user's gut health; Figure 24 This is an example of a recommendation message presented to a user; Figure 25 This is an example of a recommendation message presented to a user; Figure 26 This is a diagram representing an example of information provided to the user by other users; Figure 27 This is an example of a display style for information provided to the user; Figure 28 This is an example of a recommendation message presented to a user; Figure 29 This is a diagram representing an example of information provided to the user by other users; Figure 30 This is a diagram representing an example of information about a user who is related to that user; Figure 31 This is a diagram illustrating an example of user-related hierarchy information; Figure 32 This is an example of an information notification sent to a user; Figure 33 This diagram illustrates an example of information notifications based on user usage patterns. Figure 34 This diagram illustrates an example of information notifications based on user usage patterns. Figure 35 This is a perspective view showing an example of the structure of the bathroom according to the second embodiment; Figure 36 This is a plan view showing an example of the structure of the measuring device according to the second embodiment; Figure 37 This is a diagram illustrating an example of the overall outline of the toilet system according to the second embodiment; Figure 38 This is a diagram illustrating an example of the relationship between a user's actions and the system's actions; Figure 39 This is a block diagram illustrating an example of the structure of the toilet seat device according to the second embodiment; Figure 40 This is a graph illustrating an example of the relationship between values ​​obtained from measurements based on a gas composition sensor and the amount of gas composition. Figure 41 This is a diagram illustrating an example of a gas composition sensor and reactants; Figure 42 This is a diagram illustrating an example of the calculation and processing of odorous gases; Figure 43 It is a diagram summarizing the calculation of the amount of odorous gases; Figure 44 This is a graph illustrating an example of how the measurement bias of a gas composition sensor affects calculations; Figure 45 This is a graph illustrating an example of how the measurement bias of a gas composition sensor affects calculations; Figure 46 This is a diagram showing the first measurement example using a gas composition sensor; Figure 47 This is a diagram showing the second measurement example using a gas composition sensor; Figure 48 This is a diagram showing the third measurement example using a gas composition sensor; Figure 49 This is a diagram representing the fourth measurement example using a gas composition sensor; Figure 50 This is a diagram showing the 5th and 6th measurements taken by the gas composition sensor; Figure 51 This is a graph representing the 7th measurement example using a gas composition sensor; Figure 52 This is a diagram showing the structure corresponding to the third test case and an example of the control; Figure 53 This is a diagram showing the structure corresponding to the fourth test case and a control case; Figure 54 This is a diagram showing the structure corresponding to the fourth test case and a control case; Figure 55 This is a diagram showing the structure corresponding to the fourth test case and a control case; Figure 56 This is a diagram representing the first change to information made in the bathroom system; Figure 57 This is a diagram showing an example of information regarding changes made to the bathroom system; Figure 58 This is a diagram showing examples of the scores corrected for the toilet system; Figure 59 This is a diagram showing the second change to the information made in the bathroom system; Figure 60 This is a diagram representing the third change in information regarding the bathroom system; Figure 61 This is a diagram illustrating an example of baseline control; Figure 62 This is a diagram illustrating an example of when to implement baseline value control; Figure 63 This is a diagram representing feedback control based on a reference value; Figure 64 This is a diagram illustrating an example of actions preceding defecation. Figure 65 This is a perspective view showing an example of the structure of the bathroom according to the third embodiment; Figure 66This is a plan view showing an example of the structure of the measuring device according to the third embodiment; Figure 67 This is a diagram illustrating an example of the overall outline of the toilet system according to the third embodiment; Figure 68 This is a diagram illustrating an example of the effect of concentration gradients in a measurement; Figure 69 This is a diagram illustrating an example of the first structure; Figure 70 This is a diagram illustrating an example of turbulence generation in the first structure; Figure 71 This is a diagram illustrating an example of turbulence generation in the first structure; Figure 72 This is a diagram illustrating other ways of representing the first structure; Figure 73 This is a diagram illustrating an example of the second structure; Figure 74 This is a diagram illustrating an example of turbulence generation in the second structure; Figure 75 This is a diagram illustrating an example of turbulence generation in the second structure; Figure 76 This is a diagram illustrating other ways of representing the second structure; Figure 77 This is a diagram illustrating an example of the third structure; Figure 78 This is a diagram illustrating an example of the fourth structure; Figure 79 This is a diagram illustrating an example of attracting less traffic; Figure 80 This is a diagram illustrating an example of a control mode; Figure 81 This is a graph representing an example of sampling rate; Figure 82 This is a diagram illustrating an example of flow calculation; Figure 83 This is a diagram showing an example of the measurement results; Figure 84 This is a diagram showing an example of the measurement results; Figure 85 This is a diagram illustrating a processing example for setting standby time; Figure 86 This is an example of a diagram representing a historical record of bathroom status; Figure 87 This is a diagram illustrating a processing example for setting standby time; Figure 88 This is a diagram illustrating a processing example for setting standby time; Figure 89 This is a diagram illustrating a processing example for setting standby time; Figure 90 This is a graph illustrating an example of variations in standby time; Figure 91 This is a diagram illustrating a processing example of setting the acquisition range; Figure 92 This is a diagram illustrating a processing example of setting the acquisition range; Figure 93 This is a diagram illustrating a processing example of setting the acquisition range; Figure 94 This is a diagram illustrating a processing example of setting the acquisition range. Detailed Implementation

[0050] (Implementation Method) The embodiments of the toilet system disclosed in this application will now be described in detail with reference to the accompanying drawings. Furthermore, the present invention is not limited to the following embodiments.

[0051] <1. First Implementation> The toilet system according to the first embodiment described below manages health-related information inferred from the detection results of the first detection unit 21 (which is a detection unit for detecting feces) and the detection results of the second detection unit 22 (which is a detection unit for detecting excrement gases).

[0052] In the following example, the second detection unit 22 is described as having a first gas composition sensor 401 for detecting odorless gases as the first detection sensor and a second gas composition sensor 402 for detecting malodorous gases as the second detection sensor. For example, odorless gases include hydrogen (H2), methane (CH4), carbon dioxide (CO2), etc. Furthermore, malodorous gases include, for example, hydrogen sulfide (H2S), methanethiol (CH3SH), etc. It should be noted that the above is only one example, and odorless and malodorous gases will be described in detail later. Furthermore, in the following description, without distinguishing between the first gas composition sensor 401 and the second gas composition sensor 402, it is sometimes referred to as "gas composition sensor 40".

[0053] Furthermore, in the following example, we will describe a case where the first detection unit 21 has a line sensor as a structure for detecting stool characteristics. Any structure can be used as long as the first detection unit 21 and the second detection unit 22 can acquire (detect) the desired information, which will be detailed later. Furthermore, the stool gas mentioned here refers to the gaseous components expelled from the intestines; for example, stool gas also includes gaseous components expelled simultaneously with defecation and gaseous components not expelled simultaneously with defecation. Moreover, if the toilet system 1 does not use information about stool characteristics to infer health-related information, the toilet system 1 may not have the first detection unit 21.

[0054] Next, after providing an overview of the toilet system 1, the various processes performed by the toilet system 1 and the structures used to perform these processes will be described.

[0055] <1-1. Example of a bathroom structure> First, refer to Figure 1 The structure of the toilet system according to the first embodiment will be described. Figure 1 This is a perspective view showing an example of the structure of the toilet system according to the first embodiment. It should be noted that... Figure 1 In order to illustrate the structure of the measuring device 4 and the first detection unit 21, the illustration is shown through the toilet seat 5 and the toilet lid 9.

[0056] like Figure 1 As shown, a toilet 7 is installed on the floor F of the toilet R. It should be noted that, hereinafter, the orientation of the space from the floor F towards the toilet R is sometimes referred to as "up". The toilet R is equipped with the structural elements of a toilet system 1, including a suction device 10, a measuring device 4 for detecting gas composition as a second detection unit 22, and a first detection unit 21, etc.

[0057] The toilet 7 is a toilet bowl, and it has a basin 8. The basin 8 is a downwardly recessed shape and is the part that receives the user's excrement. It should be noted that the toilet 7 is not limited to the floor-standing type shown in the figure; it can be of any form as long as it is suitable for the bathroom system 1, such as a wall-mounted type. An inner edge is provided on the toilet 7, extending along the entire outer periphery of the end of the opening opposite the basin 8. For example, in the bathroom R, a flushing water tank for storing flushing water can be provided near the toilet 7, or a flushing water tank can be omitted, i.e., it can be a tankless type.

[0058] For example, if a user operates the washing control unit (not shown) installed in the bathroom R, washing water is supplied to the basin 8 of the toilet 7 to wash the toilet. The washing control unit can be a lever or a touch operation on the object to be washed displayed on the operating device 30. It should be noted that the washing control unit is not limited to manual toilet washing by the user via a lever or the like; it can also be used to wash the toilet by detecting the human body through a sensor such as a seat sensor.

[0059] The toilet seat assembly 2 is an example of a toilet unit, installed on the upper part of the toilet bowl 7, and includes a main body 3, a measuring device 4, a toilet seat 5, a washing nozzle 6, and a first detection unit 21. It should be noted that the measuring device 4 and the first detection unit 21 can be configured as separate devices from the toilet seat assembly 2, as will be described later. The toilet seat assembly 2 is installed on the upper part of the toilet bowl 7, which has a basin 8 for receiving excrement. The toilet seat assembly 2 is installed on the upper part of the toilet bowl 7 such that the washing nozzle 6 enters or exits the basin 8 before spraying washing water. It should be noted that the toilet seat assembly 2 can be installed in a way that allows it to be detached from the toilet bowl 7, or it can be integrated with the toilet bowl 7. For example, when the toilet seat assembly 2 is integrated with the toilet bowl 7, the toilet unit can have both a toilet seat assembly 2 and a toilet bowl 7.

[0060] The toilet seat device 2, through a structure including a measuring device 4, measures the biometric information of the user of the toilet R based on the excrement gas discharged into the basin 8 of the toilet seat 7 installed in the toilet R. The measuring device 4 includes a suction device 10 and a second detection unit 22. The toilet seat device 2, through a structure including a first detection unit 21, measures the biometric information of the user of the toilet R based on the feces discharged into the basin 8 of the toilet seat 7 installed in the toilet R. It should be noted that the measuring device 4 and the first detection unit 21 will be determined by... Figure 2 Please provide a detailed explanation.

[0061] like Figure 1 As shown, the toilet seat 5 is formed in a ring shape and is positioned at a location that overlaps with the opening of the toilet bowl 7 along the end (inner edge) of the basin portion 8. The toilet seat 5 is used for a user to sit on. The toilet seat 5 functions as a seating part that supports the buttocks of the user. In addition, if necessary, a toilet cover 9 can be installed on the toilet seat assembly 2, or the toilet seat assembly 2 may not have a toilet cover 9.

[0062] The cleaning nozzle 6 is a nozzle used to dispense cleaning water. The cleaning nozzle 6 is configured to be driven by a motor or other power source (…). Figure 5 Driven by a nozzle motor 61 (or similar device), it can enter or exit relative to the outer casing of the main body 3. Furthermore, the cleaning nozzle 6 is connected to a water source such as a tap water pipe (not shown). And, as... Figure 1 As shown, when the cleaning nozzle 6 is in the position of entering relative to the outer casing of the main body 3 (also known as the "entry position"), it sprays water from the water source onto the user's body to perform local cleaning.

[0063] exist Figure 1The image shows the washing nozzle 6 in the engaged position. It should be noted that the washing nozzle 6 can also be used for washing within the toilet bowl 7 (basin 8, etc.). The washing nozzle 6 can be used to switch between a localized washing mode (washing only a portion of the user's body) and a general toilet washing mode (spraying water into the toilet bowl 7). For example, the washing nozzle 6 can switch between the localized washing mode and the general toilet washing mode based on control of the toilet seat assembly 2.

[0064] The operating device 30 is located inside the toilet R. The operating device 30 is positioned where it can be operated by the user. The operating device 30 is positioned where it can be operated when the user is seated on the toilet seat 5. Figure 1 In this configuration, the operating device 30 is positioned on the left-hand wall W when viewed from the perspective of a user seated on the toilet seat 5. It should be noted that the operating device 30 is not limited to being positioned on the wall, as long as it is usable by the user seated on the toilet seat 5; it can be configured in various ways. For example, the operating device 30 can be integrally integrated into the toilet seat assembly 2.

[0065] The operating device 30 can communicate with the toilet seat device 2 via a predefined network in a wired or wireless manner. For example, as long as the toilet seat device 2 and the operating device 30 can send and receive information, they can be connected in any way, either wired or wirelessly.

[0066] For example, the operating device 30 receives various operations from the user via a display surface (e.g., display screen 31) through a touchscreen function. Furthermore, the operating device 30 may include switches or buttons to receive various operations. For example, the display screen 31 is a display screen of a flat panel terminal, such as a liquid crystal display or an organic EL (Electro-Luminescence) display, serving as a display device for displaying various information. That is, the operating device 30 receives user input through the display screen 31 and can also output to the user. The display screen 31 is a display device for displaying various information.

[0067] The operating device 30 accepts operations performed by the user (the user) to control various functions provided within the toilet R. The operating device 30 also accepts operations performed by the user to control the partial washing performed by the toilet seat device 2. For example, the operating device 30 may have a switch or button for accepting the aforementioned user operations, and various processes are performed based on the user's contact with the switch or button. It should be noted that the above is just one example; the operating device 30 can accept operations performed by the user to perform various processes. Furthermore, the user's smartphone or other user terminal (equivalent to...) Figure 3 The display device 300 can have the same functions as the operating device 30.

[0068] The toilet system 1, through various structures or processes described later, measures the biometric information of the user of the toilet R based on feces and excrement gases discharged into the basin 8 of the toilet bowl 7 installed in the toilet R. The toilet system 1 performs controls to appropriately measure feces and excrement gases. Based on the information collected through measurement, the toilet system 1 sends data to the user's smartphone or other user terminal (equivalent to...). Figure 3 Information is provided to the display device 300 in the toilet. In addition, the toilet system 1 can provide information to the operation device 30 (or display screen 31) of the toilet R based on information collected through measurements, etc.

[0069] <1-2. Structure of the measuring device> Next, refer to Figure 2 The structure of measuring device 4 will be described. Figure 2 This is a plan view showing an example of the structure of the measuring device according to the first embodiment. Figure 2 In the example shown, the measuring device 4 and the first detection unit 21 are arranged within the main body 3. Figure 2 In the middle, the outer shell (cover) of the main body 3, where the measuring device 4 and the first detection unit 21 are arranged, is removed to show the structure of the measuring device 4 and the first detection unit 21.

[0070] The measuring device 4 has an aspiration device 10 for aspirating gas components from the basin 8 of the toilet 7 and a second detection unit 22 for detecting the components of the aspirated gas.

[0071] The suction device 10 has a fan for drawing gas from the basin 8 of the toilet seat 7. The suction device 10 is connected to a pipe 101 that communicates with the basin 8 of the toilet seat 7. The pipe 101 functions as a flow path for the gas components in the basin 8 to flow into the measuring device 4. The suction device 10 draws gas components from the basin 8 by driving the fan and using the pipe 101 as a flow path. For example, the suction device 10 performs suction-related processes under the control of the control device 100. It should be noted that when the suction device 10 is shared with a deodorizing device built into the toilet seat device 2, the suction device 10 can be controlled by a different control mechanism (device) than the control device 100.

[0072] The second detection unit 22 detects fecal gas. The second detection unit 22 detects information related to the intestinal environment. The second detection unit 22 performs processing related to the detection of the composition of the gas attracted by the suction device 10. Figure 2 In this configuration, the second detection unit 22 is positioned at the rear of the suction device 10 when viewed from the basin side 8. It should be noted that... Figure 2As an example only, the second detection unit 22 can be positioned anywhere as long as the gas components attracted by the suction device 10 can be introduced. The second detection unit 22 is connected to the pipe 102, which communicates with the outside of the main body 3. The pipe 102 functions as a flow path for the gas components in the second detection unit 22 to flow out of the measuring device 4. For example, driven by the suction device 10, the gas components in the second detection unit 22 are discharged to the outside of the measuring device 4 through the pipe 102.

[0073] For example, the second detection unit 22 performs processing related to the detection of gas components under the control of the control device 100. The second detection unit 22 has two gas component sensors 40: a first gas component sensor 401 and a second gas component sensor 402. The first gas component sensor 401 is installed in the toilet seat device 2 and functions as a first detection sensor for detecting odorless gases. The second gas component sensor 402 is installed in the toilet seat device 2 and functions as a second detection sensor for detecting malodorous gases. It should be noted that the term "odorless" does not mean completely odorless; odorless gases can also be gas components other than malodorous gases, such as gas components originating from bacteria (also known as "harmful bacteria") that have adverse effects on the human body, which are gas components that do not have a malodorous smell.

[0074] As described above, for example, the first gas composition sensor 401 is a gas composition sensor 40 that detects odorless gases such as hydrogen, methane, and carbon dioxide. The first gas composition sensor 401 detects odorless gases originating from so-called beneficial bacteria (also called "Type I bacteria") such as lactic acid bacteria and bifidobacteria. For example, Type I bacteria are bacteria that produce (generate) short-chain fatty acids as a type of metabolite (also called "short-chain fatty acid producing bacteria"). It should be noted that Type I bacteria are not limited to lactic acid bacteria and bifidobacteria, but can be various types of bacteria, as long as they are beneficial to the human body. For example, the first gas composition sensor 401 detects odorless gases as an example of gaseous components originating from fermentation in the intestines and indicating high health (also called "health-related gases"). For example, health-related gases can be gaseous components originating from fermentation in the intestines, and the higher the health of the intestines, the more abundant they are. As examples, health-related gases include hydrogen, methane, carbon dioxide, acetic acid, ethanol, and water.

[0075] As shown above, the second gas composition sensor 402 is, for example, a gas composition sensor 40 that detects malodorous gases such as hydrogen sulfide and methanethiol. The second gas composition sensor 402 detects malodorous gases originating from so-called harmful bacteria (also known as "Class II bacteria") such as Clostridium perfringens and Staphylococcus aureus. For example, Class II bacteria are bacteria that produce putrefactive products (also known as "putrefactive product-producing bacteria") as examples of metabolites. It should be noted that Class II bacteria are not limited to Clostridium perfringens and Staphylococcus aureus, but can be any type of bacteria that has adverse effects on the human body. For example, the second gas composition sensor 402 detects malodorous gases as examples of gaseous components originating from putrefaction in the intestines and indicating low health (also known as "odorous gases"). For example, odorous gases can be sulfur-containing gaseous components found in fecal matter. Examples of odorous gases include hydrogen sulfide, methanethiol, ammonia, trimethylamine, indole, and skatole.

[0076] Thus, the second detection unit 22 includes a gas composition sensor 40 that reacts with the gaseous components contained in the gas. The gas composition sensor 40 detects specific components of the gas. For example, the gas composition sensor 40 may be a semiconductor gas composition sensor. It should be noted that the above is only one example, and the gas composition sensor is not limited to a semiconductor gas composition sensor 40; various types of sensors can be used. It should also be noted that the gas composition sensor 40 may be used as a unit combining multiple sensor principles such as semiconductor and infrared absorption. For example, the gas composition sensor 40 may detect fecal gas, which represents the intestinal environment of the user, as will be described later.

[0077] The first detection unit 21 functions as a third detection sensor for detecting the characteristics of feces. The first detection unit 21 detects feces. It should be noted that this explanation uses the case where the first detection unit 21 detects information related to the characteristics of feces as a detection image, but as long as the characteristics of feces can be detected, the third detection sensor, such as the first detection unit 21, can have any structure. The first detection unit 21 detects the feces excreted by the user by photographing the inside of the bowl 8 of the toilet 7. For example, the first detection unit 21 detects information related to intestinal peristalsis. For example, the first detection unit 21 has the structure of an image sensor. The first detection unit 21 has a light-receiving unit 210. For example, the light-receiving element is a line sensor consisting of a CCD (Charge Coupled Device) sensor or a CMOS (Complementary Metal Oxide Semiconductor) sensor arranged in a row. It should be noted that the light-receiving element is not limited to a line sensor (one-dimensional image sensor), and various sensors such as area sensors (two-dimensional image sensors) can also be used. It should be noted that the first detection unit 21 may have a structure that emits light of a specified wavelength. Figure 9 (Light-emitting elements such as 220), which will be discussed later.

[0078] exist Figure 2 In this process, the light-receiving unit 210 of the first detection unit 21 is configured to receive light from region AD1 within the basin 8 of the toilet 7. For example, the light-receiving unit 210 is configured to receive reflected light from feces falling into the basin 8 of the toilet 7. Regarding the first detection unit 21's imaging of the falling feces, [further details will be provided]. Figure 9 The following explanation will be provided. It should be noted that the light-receiving part 210 of the first detection unit 21 is not limited to photographing falling feces. As long as the information required to estimate (determine) the nature of the feces can be obtained, feces in any state, such as feces after they have come into contact with water, can be detected.

[0079] For example, the light-receiving unit 210 can detect feces after it has fallen into the toilet. In this case, the light-receiving unit 210 can be configured to photograph the water seal portion of the toilet 7 (e.g., the water seal portion of the accumulator basin 8). The light-receiving unit 210 can be a surface sensor (two-dimensional image sensor). Thus, the first detection unit 21 can detect feces excreted into the toilet 7, and can be configured in any position and can employ any image sensor as long as the desired information can be estimated.

[0080] <1-3. Overall Overview Example of a Toilet System> Next, refer to Figure 3 as well as Figure 4 An example of the overall overview of toilet system 1 is described. Figure 3This is a diagram illustrating an example of the overall outline of the toilet system according to the first embodiment. Figure 4 This is a diagram illustrating a structural example of the toilet system according to the first embodiment. It should be noted that... Figure 3 as well as Figure 4 The diagram only illustrates a portion of the structure of the toilet system 1 that requires explanation; points identical to those described above are omitted from the explanation.

[0081] like Figure 4 As shown, the toilet system 1 includes: a toilet seat 2, which has a first detection unit 21, a second detection unit 22, and a control device 100; a display device 300; and a server device 400. The toilet system 1 may also include multiple toilet seat 2, multiple display devices 300, and multiple server devices 400.

[0082] The toilet seat device 2 is a device installed within the toilet R. The toilet seat device 2 communicates with other devices such as the display device 300 and the server device 400. It should be noted that the toilet seat device 2 can perform processing (personal identification) to acquire information for identifying the user using the toilet seat 7 within the toilet R. The toilet seat device 2 uses personal identification to differentiate and collect information from multiple users, such as family members. For example, the toilet seat device 2 acquires information for identifying the user using the toilet seat 7 through communication with the user's display device 300 or through the user's operation of the operating device 30, thus performing personal identification of the user. For example, the toilet seat device 2 communicates with the user's display device 300 and receives a user ID (also simply "ID") as user identification information from the display device 300. It should be noted that the toilet seat device 2 can identify the user using the toilet seat 7 in the toilet R by any method, as long as it can determine the user.

[0083] like Figure 3As shown, the first detection unit 21 has a light-receiving unit 210 for detecting stool. Furthermore, the second detection unit 22 has a gas composition sensor 40 for detecting fecal gases. Additionally, the control device 100 is a computer (information processing device) that performs presumption and health-related information processing (also called "presumption processing"). The control device 100 functions as a presumption mechanism that, based on the detection results of the first gas composition sensor 401 and the second gas composition sensor 402, performs presumption processing (also called "intestinal environment-related presumption processing") to presume at least one of the following for the user of the toilet seat device 2: intestinal bacteria, intestinal bacterial metabolites, and pH. Thus, the control device 100 presumes information related to the human intestinal environment (also called "intestinal environment information"), including intestinal bacteria, intestinal bacterial metabolites, and pH. It should be noted that intestinal environment information, as long as it is related to the human intestinal environment, is not limited to intestinal bacteria, intestinal bacterial metabolites, and pH; it can include various types of information. For example, based on the detection results of the first detection unit 21 and the detection results of the second detection unit 22, the control device 100 estimates at least one of the intestinal environment information, including the user's intestinal bacteria, intestinal bacterial metabolites, and pH.

[0084] Furthermore, the control device 100 communicates with devices displaying information to the user, such as the display device 300, via short-range wireless communication functions such as Bluetooth (registered trademark), BLE (Bluetooth Low Energy), and infrared. The control device 100 can communicate with the display device 300 without using the network N. It should be noted that as long as the control device 100 can transmit and receive information, it can be connected to devices such as the display device 300 in any way, for example, via a predefined network (network N, etc.) such as the Internet, through wired or wireless means.

[0085] It should be noted that, in Figure 4 The illustration shows a toilet seat device 2 having a first detection unit 21, a second detection unit 22, and a control device 100, but it is not limited to this. For example, the control device 100 may be provided separately from the first detection unit 21 and the second detection unit 22, and may control the first detection unit 21 and the second detection unit 22 and acquire various information by communicating wirelessly or wiredly with them.

[0086] Furthermore, the control device 100 may be a device located outside the toilet R. In this case, the control device 100 may be connected to devices such as the toilet seat device 2, the first detection unit 21, and the second detection unit 22 located inside the toilet R via a network (such as the Internet, Network N) in a wired or wireless manner, so as to obtain the desired information.

[0087] Furthermore, the first detection unit 21 and the second detection unit 22 can be controlled by a control mechanism different from the control device 100. In this case, the control device 100 is a first control device that performs various information processing such as estimation processing, and the toilet system 1 can have a second control device, which is different from the control device 100, as a device to control the first detection unit 21 and the second detection unit 22. Thus, the toilet system 1 can have a control device 100 as a first control device that performs various information processing such as estimation processing using information from the first detection unit 21 and the second detection unit 22, and a detection unit control device as a second control device that controls the first detection unit 21 and the second detection unit 22.

[0088] For example, the first control device and the second control device are connected via a wired or wireless communication network (such as the Internet, Network N, etc.), and the first control device can use the detection results from the first detection unit 21 and the detection result information from the second detection unit 22 received from the second control device to perform estimation processing. For example, the first control device can be a portable terminal (device) such as a smartphone or laptop computer that can be carried by the manager of the toilet system 1.

[0089] Display device 300 is a display device (computer) that displays information provided to the user. For example, display device 300 may be a user terminal (portable terminal) owned by the user. In this case, display device 300 may be implemented, for example, via a smartphone, mobile phone, PDA (Personal Digital Assistant), tablet terminal, or laptop PC (Personal Computer). For example, display device 300 may be communicatively connected to devices included in the bathroom system 1, such as control device 100, via a predetermined network (network N, etc.) through wired or wireless means.

[0090] The display device 300 and the control device 100 exchange and receive information. The display device 300 receives information provided to the user from the control device 100. The display device 300 receives health-related information estimated by the control device 100 through estimation processing. The display device 300 receives health-related information or scores (hereinafter also referred to as "gut score") as health-related information to the user. The display device 300 displays the health-related information received from the control device 100. It should be noted that examples of the information displayed by the display device 300 will be described later.

[0091] like Figure 3 As shown, server device 400 is a computer such as a cloud server. Server device 400 and devices such as display device 300 are connected to each other via a predetermined network (network N, etc.) such as the Internet, either wired or wirelessly. It should be noted that as long as server device 400 can transmit and receive information, it can be connected to devices such as display device 300 in any way, whether wired or wireless. Furthermore, server device 400 can be communicatively connected to control device 100, etc., to acquire raw data through measurement, process the data, and send the results to display device 300.

[0092] The server device 400 stores the information collected from the display device 300 in a storage unit. The server device 400 collects health-related information for each user and stores it in the storage unit. For example, the server device 400 stores the user's health-related information in the storage unit in correspondence with the user's identification information (ID, etc.).

[0093] <1-3-1. Other Structural Examples of Toilet Systems> It should be noted that the above is only one example; as long as the desired treatment can be achieved, the toilet system 1 can adopt any device structure. Regarding this, several examples of system structures other than those described above are provided below.

[0094] Furthermore, the toilet system 1 may have at least one of a first detection unit for detecting feces and a second detection unit for detecting fecal gases. In this case, the control device 100 receives information from the detection unit not included in the toilet system 1 and uses the received information to perform various information processing such as estimation processing. For example, the toilet system 1 may have at least one of a measuring device 4 and a first detection unit 21.

[0095] For example, if the toilet system 1 does not have a measuring device 4, the control device 100 of the toilet system 1 is communicatively connected to the measuring device 4 via a wired or wireless connection, and receives information related to defecation gas detected by the second detection unit 22 from the measuring device 4. In this case, the control device 100 of the toilet system 1 performs estimation processing using the information related to defecation gas received from the measuring device 4, which is not included in the toilet system 1.

[0096] For example, if the toilet system 1 does not have a first detection unit 21, the control device 100 of the toilet system 1 is communicatively connected to the first detection unit 21 via a wired or wireless connection, and receives information related to feces detected by the first detection unit 21. In this case, the control device 100 of the toilet system 1 performs estimation processing using the information related to defecation gases received from the first detection unit 21, which is not included in the toilet system 1.

[0097] It should be noted that, in the absence of both the measuring device 4 and the first detection unit 21 in the toilet system 1, the control device 100 of the toilet system 1 uses the information related to defecation gas received from the measuring device 4 and the information related to feces received from the first detection unit 21 to perform the estimation process.

[0098] Furthermore, the toilet system 1 may only have a structure that acquires detected information and performs estimation processing using the acquired information. In this case, the toilet system 1 only has a control device 100, which communicates with other devices to acquire (receive) desired information, performs various information processing such as estimation processing using the acquired information, and sends the desired information to other devices.

[0099] Furthermore, the display device 300 may or may not be included within the toilet system 1. For example, if the display device 300 is the operating device 30 of the toilet R, the display device 300 may be included within the toilet system 1. In this case, the operating device 30 has the function of displaying information related to the user's health.

[0100] Furthermore, the structure and configuration of the server device 400 in the toilet system 1 can be arbitrary as long as it can communicate with and process devices such as the display device 300. For example, the server device 400 in Figure 3 In the cloud-based configuration shown, it can be composed of multiple computers (servers). For example, server device 400 can be a portable terminal (device) such as a laptop computer that can be carried by the administrator of toilet system 1. Furthermore, server device 400 can be configured within toilet R.

[0101] <1-4. Functional Structure of Toilet Seat Device> Next, refer to Figure 5 The functional structure of the toilet seat device 2 is explained. Figure 5 This is a block diagram illustrating an example of the structure of the toilet seat device according to the first embodiment. For example... Figure 5 As shown, the toilet seat device 2 includes a human body sensor 32, a seating sensor 33, an illuminance sensor 34, a control device 100, a nozzle motor 61, and a cleaning nozzle 6.

[0102] It should be noted that, Figure 5 The structure of the toilet seat device 2 shown is only one example. If the various components are arranged separately, the toilet seat device 2 may only have a toilet seat 5. Thus, Figure 5 The structure of the toilet seat device 2 shown is only one example; the toilet seat device 2 can adopt any structure. The human body sensor 32, the seating sensor 33, the illuminance sensor 34, etc., can be positioned arbitrarily, as long as they can perform the desired sensing. Furthermore, the toilet seat device 2 only needs to be able to detect the user's seating on the toilet seat 5, and only needs to have at least one of the human body sensor 32, the seating sensor 33, and the illuminance sensor 34. The toilet seat device 2 communicates via a communication device (e.g., Figure 6 The communication unit 110 of the control device 100 (or similar device) transmits and receives information with the information processing devices such as the display device 300 and the server device 400 via a predetermined network (such as the Internet) through wired or wireless means.

[0103] The human body sensor 32 has the function of detecting human bodies. For example, the human body sensor 32 is used as a seating detection mechanism to detect when a user sits on the toilet seat 5. For example, the human body sensor 32 is implemented by using a thermoelectric sensor or the like that that emits infrared signals. For example, the human body sensor 32 can be implemented by a micro (micro) wave sensor or the like. For example, the human body sensor 32 is an infrared light-emitting distance sensor that can detect human bodies that are near the toilet seat 5 before a person (user) sits on the toilet seat 5 or the user sitting on the toilet seat 5.

[0104] The human body sensor 32 also functions as an off-seat detection sensor to detect when a user leaves the toilet seat 5. The human body sensor 32 detects the user's seated state relative to the toilet seat 5. The human body sensor 32 outputs a detection signal to the control device 100. It should be noted that the above is only one example, and the human body sensor 32 is not limited to the above; it can detect the human body through various mechanisms. For example, the human body sensor 32 detects a person (such as a user) approaching the toilet seat 5.

[0105] The seating sensor 33 has the function of detecting when a person sits on the toilet seat device 2. For example, the seating sensor 33 is used as a seating detection mechanism to detect when a user sits on the toilet seat 5. For example, the seating sensor 33 is implemented by a load sensor or the like. The seating sensor 33 detects when a user sits on the toilet seat 5. The seating sensor 33 is capable of detecting when a user sits on the toilet seat 5.

[0106] The seating sensor 33 also functions as a sensor to detect when the user leaves the toilet seat 5. The seating sensor 33 detects the user's seated position relative to the toilet seat 5. It should be noted that the above is only one example; the seating sensor 33 is not limited to the above and can detect a person's seating position on the toilet seat device 2 through various mechanisms. The seating sensor 33 outputs a seating detection signal to the control device 100.

[0107] The illuminance sensor 34 is a sensor for detecting illuminance. For example, the illuminance sensor 34 is used as a seating detection mechanism to detect when a user sits on the toilet seat 5. For example, the illuminance sensor 34 is positioned facing the basin 8 to detect the illuminance inside the basin 8.

[0108] The illuminance sensor 34 also functions as an occupancy detection sensor to detect when a user leaves the toilet seat 5. The illuminance sensor 34 detects the user's seated position relative to the toilet seat 5. It should be noted that the above is only one example; the illuminance sensor 34 can be configured in any position as long as it can detect the user's seated position on the toilet seat 5 through illuminance detection.

[0109] The control device 100 controls various structures and processes. The control device 100 is a computer (information processing device) that performs various information processing related to the detection (determination) of fecal matter and gas composition. The control device 100 can be any device as long as it has the necessary control structure; for example, it can be a microcomputer.

[0110] The control device 100 controls various structures used for detecting (measuring) stool. The control device 100 controls the first detection unit 21. The control device 100 sends control information for controlling the electronic shutter function of the light-receiving unit 210 to the first detection unit 21. It should be noted that the shutter function of the light-receiving unit 210 is not limited to an electronic shutter; any method, such as a mechanical shutter, can be used as long as the desired detection can be performed. Furthermore, the control device 100 has [specific features] in the first detection unit 21. Figure 9 In the case of a light-emitting element 220 or other light-emitting part, control information for controlling the lighting and extinguishing of the light-emitting part is sent to the first detection unit 21.

[0111] For example, the control device 100 causes the first detection unit 21 to emit light and receive light. The control device 100 controls the first detection unit 21 to emit light and the light-receiving unit 210 to receive light. When the control device 100 detects that the user is sitting on the toilet seat 5 through the seating sensor 33, the first detection unit 21 emits light and receives light.

[0112] The control device 100 controls various structures used for detecting (measuring) gas components. The control device 100 controls the second detection unit 22. The control device 100 transmits control information to the second detection unit 22 via a wired connection. It should be noted that the control device 100 can also transmit control information to the second detection unit 22 wirelessly. For example, if the control device 100 is configured as a separate device from the toilet seat device 2, control information for the second detection unit 22 can be transmitted wirelessly to the toilet seat device 2. In this case, the control device of the toilet seat device 2 can also control the second detection unit 22 based on the received control information.

[0113] For example, the control device 100 can control the second detection unit 22 during periods other than when the user is using the toilet 7, so that the measured value of the gas composition sensor 40 becomes a value within a predetermined range, and perform reference value control to control the measured value used as a reference value (baseline) to a specified value. The control device 100 can perform reference value control to control the reference value to a specified value by changing the resistance value of the resistive element of the gas composition sensor 40.

[0114] Furthermore, the control device 100 can also control the suction device 10. For example, the control device 100 controls the start and stop of the suction of the suction device 10. The control device 100 sends control information to the suction device 10 via a wired connection. It should be noted that the control device 100 can also send control information to the suction device 10 wirelessly. For example, if the control device 100 is configured as a separate device from the toilet seat device 2, it can send control information of the suction device 10 to the toilet seat device 2 wirelessly. In this case, the control device of the toilet seat device 2 can control the suction device 10 based on the received control information.

[0115] In addition to the above, the control device 100 can also control various structures of the toilet system 1. The control device 100 controls the nozzle motor 61, etc. The control device 100 controls the nozzle motor 61, etc., based on signals sent from the operating device 30.

[0116] The control device 100 controls the nozzle motor 61 based on control instructions related to localized cleaning sent from the operating device 30. The control device 100 controls the nozzle motor 61 to cause the cleaning nozzle 6 to enter or exit. It should be noted that the control device 100 is not limited to controlling the nozzle motor 61; it can also control various mechanisms. For example, the control device 100 controls the opening and closing of a solenoid valve, which functions as a valve that controls the flow of fluid electromagnetically. For example, the control device 100 controls the solenoid valve to, for instance, switch the supply and stop of tap water from the water supply pipe.

[0117] The control device 100 transmits control information to the nozzle motor 61 and the like via a wired connection. It should be noted that the control device 100 can also transmit control information wirelessly to the nozzle motor 61 and the like. For example, if the control device 100 is configured as a separate unit from the toilet seat 2, it can wirelessly transmit control information to the nozzle motor 61 and the like to the toilet seat 2. In this case, the control device of the toilet seat 2 can control the nozzle motor 61 and the like based on the received control information.

[0118] The nozzle motor 61 is a drive source (motor) that drives the cleaning nozzle 6 to enter or exit. The nozzle motor 61 performs control to move the cleaning nozzle 6 into or out of the main body 3. The nozzle motor 61 performs control to move the cleaning nozzle 6 into or out according to the instructions from the control device 100.

[0119] Furthermore, the control device 100 can control, for example... Figure 1 The toilet seat 9 and toilet seat 5 are shown in this configuration. In this case, the control device 100 controls the toilet seat 9 and toilet seat 5 based on signals sent from the operating device 30. The control device 100 controls the toilet seat 9 based on control instructions related to the opening and closing of the toilet seat sent from the operating device 30. The control device 100 controls the toilet seat 5 based on control instructions related to the opening and closing of the seat area sent from the operating device 30. The control device 100 sends control information to the toilet seat 9 and toilet seat 5 via a wired connection. It should be noted that the control device 100 can also send control information to the toilet seat 9 and toilet seat 5 wirelessly.

[0120] The control device 100 determines whether the seating detection mechanism, such as the human body sensor 32, the seating sensor 33, and the illuminance sensor 34, has detected that the user is seated. For example, the control device 100 determines whether the user's seat has been detected by the seating sensor 33.

[0121] exist Figure 5In the structure shown, the toilet seat device 2 including the control device 100 is illustrated as an example. The control device 100, the human body sensor 32, the seating sensor 33, and the illuminance sensor 34 can also be configured as separate devices from the toilet seat device 2. For example, the control device 100 can also be configured as a separate device from the toilet seat device 2. For example, the control device 100 can be a server device, located at a position spaced apart from the toilet seat device 2. In this case, the control device 100 communicates with each of the toilet seat device 2, the human body sensor 32, the seating sensor 33, and the illuminance sensor 34, and receives various information from each device. Furthermore, in this case, the toilet seat device 2 can have various structures (control circuits, etc.) for controlling the nozzle motor 61, etc. It should be noted that the above is only one example, and the toilet system 1 can adopt any device structure as long as it can perform the desired processing.

[0122] <1-5. Functional Structure of the Control Device> The following is for reference Figure 6 The functional structure of the control device is explained. Figure 6 This is a block diagram illustrating an example of the structure of the control device according to the first embodiment. For example... Figure 6 As shown, the control device 100 includes a communication unit 110, a storage unit 120, and a control unit 130. It should be noted that the structure of the control device 100 is not limited to... Figure 6 The structure shown can be any other structure as long as it can achieve the desired processing. For example, the control device 100 may not have a communication unit 110.

[0123] The communication unit 110 is implemented, for example, through a communication circuit. The communication unit 110 is connected to a designated network via wired or wireless means to transmit and receive information with external information processing devices. For example, the communication unit 110 is connected to a designated network via wired or wireless means to transmit and receive information with other devices such as the operating device 30. It should be noted that the communication unit 110 can be configured as a separate device (communication device) from the control device 100, and can be included in the toilet seat device 2.

[0124] The storage unit 120 is implemented, for example, by a semiconductor storage element such as RAM (Random Access Memory) or flash memory, or a storage device such as a hard disk or optical disk. For example, the storage unit 120 is a storage medium that non-temporarily stores data such as programs used for various information processing and can be read by a computer.

[0125] The storage unit 120 of the first embodiment stores various information required for processing. The storage unit 120 stores various information acquired from other devices such as various sensors. The storage unit 120 stores various information for various information processing tasks. The storage unit 120 stores information for inference processing related to the intestinal environment. For example, the storage unit 120 stores functions (calculation formulas) for calculating intestinal environment information.

[0126] For example, storage unit 120 stores a function (also called an "intestinal estimation function") that takes the detection results of odorless gases (e.g., a value representing the amount of odorless gases) and the detection results of malodorous gases (e.g., a value representing the amount of malodorous gases) as input and outputs a value describing the intestinal environment. For example, storage unit 120 stores information for estimating at least one of the user's intestinal bacteria, intestinal bacterial metabolites, and pH. Storage unit 120 stores a function (also called an "intestinal bacteria estimation function") used to calculate values ​​related to the amount of intestinal bacteria as an intestinal estimation function.

[0127] For example, the storage unit 120 stores an intestinal bacteria estimation function, i.e., a first-class bacteria estimation function, which takes the value obtained based on the detection of the first gas composition sensor 401 (also called "gas composition value") as input (independent variable) and outputs a value representing the amount of first-class bacteria in the intestine (dependent variable). The gas composition value mentioned here can be any value obtained based on the detection of the gas composition sensor 40, and can be various values ​​such as the value output by the gas composition sensor 40 (measured voltage value, etc.), the calculated resistance value of the sensor element, the estimated amount of gas, etc. For example, the value (gas composition value) obtained based on the detection of the first gas composition sensor 401 can be various values ​​such as the value output by the first gas composition sensor 401 (measured voltage value, etc.), the calculated resistance value of the sensor element, the estimated amount of gas components, etc. It should be noted that the first type of bacteria estimation function can also be a function that, in addition to the value obtained based on the detection of the first gas composition sensor 401, takes a value representing the stool characteristics (also called "stool characteristics value") as input (independent variable) and outputs a value representing the amount of first type of bacteria in the intestine (dependent variable). Thus, each intestinal estimation function can be a function that, in addition to the gas composition value, takes the stool characteristics value obtained based on the detection of the third detection sensor such as the first detection unit 21 as input.

[0128] For example, the storage unit 120 stores an intestinal bacteria estimation function, i.e., a second-type bacteria estimation function, which takes the value (gas composition value) obtained based on the detection of the second gas composition sensor 402 as input (independent variable) and outputs a value representing the amount of second-type bacteria in the intestine (dependent variable). For example, the value (gas composition value) obtained based on the detection of the second gas composition sensor 402 can be various values ​​such as the value output by the second gas composition sensor 402 (measured voltage value, etc.), the calculated resistance value of the sensor element, and the estimated amount of gas composition. It should be noted that the second-type bacteria estimation function can be a function that, in addition to the gas composition value, i.e., the value obtained based on the detection of the second gas composition sensor 402, also takes into account fecal characteristics such as the value representing the type of feces obtained based on the detection of the first detection unit 21 as input.

[0129] The storage unit 120 stores a function (also called a "metabolite estimation function") used to calculate values ​​related to metabolites of intestinal bacteria as an intestinal estimation function. For example, the storage unit 120 stores a metabolite estimation function, namely a short-chain fatty acid estimation function, which takes the value obtained based on the detection of the first gas composition sensor 401 as input (independent variable) and outputs a value representing the amount of fermentation-derived metabolites, namely short-chain fatty acids, as a dependent variable. It should be noted that the short-chain fatty acid estimation function may be a function that, in addition to the gas composition value, i.e., the value obtained based on the detection of the first gas composition sensor 401, also takes into account fecal characteristics such as the value representing the type of feces obtained based on the detection of the first detection unit 21 as input.

[0130] For example, the storage unit 120 stores a metabolite estimation function, i.e., a putrefaction product estimation function, which takes the value obtained based on the detection of the second gas composition sensor 402 as input (independent variable) and outputs the value representing the amount of metabolites, i.e., putrefaction products, which are the source of putrefaction. It should be noted that the putrefaction product estimation function can be a function that takes, in addition to the gas composition value, i.e., the value obtained based on the detection of the second gas composition sensor 402, also as input values ​​representing the type of feces, such as the value obtained based on the detection of the first detection unit 21.

[0131] The storage unit 120 stores a function (also called a "pH estimation function") used to calculate pH-related values ​​as an intestinal estimation function. For example, the storage unit 120 stores a pH estimation function that takes the values ​​obtained based on the detection of the first gas component sensor 401 and the values ​​obtained based on the detection of the second gas component sensor 402 as inputs (independent variables) and outputs a pH value representing the pH value in the intestine (dependent variable). It should be noted that the pH estimation function can be a function that takes only one of the values ​​obtained based on the detection of the first gas component sensor 401 and the values ​​obtained based on the detection of the second gas component sensor 402 as independent variables and outputs a pH value representing the pH value in the intestine (dependent variable).

[0132] For example, the pH estimation function can be a function that takes into account, in addition to at least one of the values ​​obtained from the detection of the first gas composition sensor 401 and the detection of the second gas composition sensor 402, a stool characteristic value, such as a value indicating the type of stool obtained from the detection of the first detection unit 21. It should be noted that the stool characteristic value is not limited to the type of stool, but can also be a value calculated based on various factors such as the color and quantity of stool. The stool characteristic value can be calculated using a stool characteristic value estimation function that takes the values ​​indicating the type of stool, the color of stool, and the quantity of stool as inputs (independent variables) and outputs a value indicating the stool characteristic (dependent variable). In this case, the storage unit 120 stores the stool characteristic value estimation function, the control device 100 uses the stool characteristic value estimation function to calculate the stool characteristic value, and uses the calculated stool characteristic value to perform estimation processing.

[0133] It should be noted that the above is only one example, and the storage unit 120 may also store any information used to estimate the intestinal environment. The storage unit 120 may store a machine learning model (also simply referred to as a "model") that outputs information representing the intestinal environment as the output of at least one of the first gas composition sensor 401 and the second gas composition sensor 402. In this case, the functions described above can be understood as machine learning models (models). Furthermore, for example, the storage unit 120 stores information related to baseline value control such as target values.

[0134] The storage unit 120 stores information such as the characteristics of stool and various information used for stool-related estimation processing. For example, the storage unit 120 stores thresholds used for stool-related estimation processing. For example, the storage unit 120 stores various models (also called "estimation models") used for estimations related to the characteristics of stool. For example, the storage unit 120 stores various estimation models for estimating the shape, color, amount, etc. of stool. It should be noted that the above is only one example; the storage unit 120 stores various information related to stool detection.

[0135] Storage unit 120 stores various information, such as the amount or concentration of gas components, used for estimation processing related to gas components. For example, storage unit 120 stores thresholds used for estimation processing related to gas components. For example, storage unit 120 stores various functions (also called "gas component estimation functions") used for estimating (calculating) the amount or concentration of gas components. For example, storage unit 120 stores various gas component estimation functions used for estimating the amount or concentration of gases of a specified composition. For example, storage unit 120 stores a function (also called an "evaluation estimation function") used to calculate (estimate) the evaluation of defecation gas based on the amount or concentration of gas components of a specified composition. It should be noted that the above is only one example; storage unit 120 stores various information related to the detection of gas components.

[0136] return Figure 6 Continuing with the description, the control unit 130 is implemented, for example, by an MPU (Micro Processing Unit) or CPU (Central Processing Unit) using RAM or the like as its working area to execute programs stored within the control device 100 (e.g., various information processing programs disclosed herein). Alternatively, the control unit 130 may be implemented, for example, by an integrated circuit such as an ASIC (Application Specific Integrated Circuit) or a FPGA (Field Programmable Gate Array).

[0137] like Figure 6 As shown, the control unit 130 includes an acquisition unit 131, a processing unit 132, and an output unit 133, and performs the information processing functions and actions described below. It should be noted that the internal structure of the control unit 130 is not limited to... Figure 6 The structure shown can be any other structure, as long as it is the structure for information processing described later.

[0138] The acquisition unit 131 of the first embodiment acquires various types of information. The acquisition unit 131 acquires various types of information from the storage unit 120. The acquisition unit 131 receives information from other devices. The acquisition unit 131 receives information (detection information, etc.) detected by various sensors from various sensors.

[0139] The acquisition unit 131 acquires information (detection information, etc.) detected by the seating detection mechanism. The acquisition unit 131 receives information (detection information, etc.) detected by at least one of the human body sensor 32, seating sensor 33 and illuminance sensor 34 from the sensor.

[0140] Acquisition unit 131 acquires the detection results of the first detection unit 21 for detecting stool. Acquisition unit 131 acquires the detection results of the first detection unit 21 for detecting information related to intestinal peristalsis. Acquisition unit 131 acquires the detection results of the second detection unit 22 for detecting fecal gas. Acquisition unit 131 acquires the detection results of the second detection unit 22 for detecting information related to the intestinal environment.

[0141] The acquisition unit 131 receives information from the first detection unit 21 based on the detection results of the first detection unit 21. The acquisition unit 131 receives information indicating the stool-related detection results detected by the first detection unit 21. Furthermore, the acquisition unit 131 receives information from the second detection unit 22 based on the detection results of the second detection unit 22. The acquisition unit 131 receives information indicating the fecal gas-related detection results detected by the second detection unit 22.

[0142] The acquisition unit 131 acquires information used to estimate intestinal environment information. For example, the acquisition unit 131 acquires information such as functions or models used to estimate intestinal environment information. For example, the acquisition unit 131 acquires a function (intestinal estimation function) that takes the detection results of odorless gas (e.g., a value representing the amount of odorless gas) and the detection results of malodorous gas (e.g., a value representing the amount of malodorous gas) as input and outputs a value representing the intestinal environment.

[0143] For example, the acquisition unit 131 acquires information for estimating at least one of the user's intestinal bacteria, intestinal bacterial metabolites, and pH. The acquisition unit 131 acquires a function (intestinal estimation function) for calculating values ​​related to the amount of intestinal bacteria. The acquisition unit 131 acquires a function (intestinal estimation function) for calculating values ​​related to intestinal bacterial metabolites. The acquisition unit 131 acquires a function (intestinal estimation function) for calculating pH-related values.

[0144] The processing unit 132 of the first embodiment performs various processes. The processing unit 132 uses information stored in the storage unit 120 to perform various processes. The processing unit 132 performs estimation processing, such as intestinal environment-related estimation processing. The processing unit 132 uses various information stored in the storage unit 120 to perform estimation processing.

[0145] The processing unit 132 performs presumption processing to estimate information related to the user's health based on the detection results of the gas composition sensor 40. For example, the processing unit 132 uses information obtained from the toilet system 1 to perform presumption processing.

[0146] Based on the detection results of the first gas composition sensor 401 and the second gas composition sensor 402, the processing unit 132 estimates the intestinal bacteria of the user of the toilet seat device 2. Based on the detection results of the first gas composition sensor 401 and the second gas composition sensor 402, the processing unit 132 estimates the metabolites of the intestinal bacteria of the user of the toilet seat device 2. Based on the detection results of the first gas composition sensor 401 and the second gas composition sensor 402, the processing unit 132 estimates the (intestinal) pH of the user of the toilet seat device 2. For example, based on the composition ratio of excrement obtained by the detection results of the first gas composition sensor 401 and the second gas composition sensor 402, the processing unit 132 estimates at least one of the intestinal bacteria, intestinal bacterial metabolites, and pH of the user of the toilet seat device 2.

[0147] Based on the detection results of the first detection unit 21 and the detection results of the second detection unit 22, which has a first gas composition sensor 401 and a second gas composition sensor 402, the processing unit 132 estimates at least one of the following: intestinal bacteria, intestinal bacterial metabolites, and pH of the user using the toilet seat device 2. The processing unit 132 can use the information detected by the first detection unit 21 to perform the estimation process. For example, the processing unit 132 uses an image captured by the first detection unit 21 to perform the estimation process. The processing unit 132 estimates (determines) whether the image captured by the first detection unit 21 contains feces. The processing unit 132 uses image recognition-related techniques to estimate whether the image contains feces.

[0148] Processing unit 132 calculates various gas-related information. Processing unit 132 calculates numerical values ​​based on the measured values ​​obtained by the second detection unit 22. Processing unit 132 calculates the resistance value of the sensor element based on the voltage value measured by the gas composition sensor 40. Processing unit 132 calculates the resistance value of the sensor element based on the voltage value measured by the first gas composition sensor 401. Processing unit 132 calculates the resistance value of the sensor element based on the voltage value measured by the second gas composition sensor 402. For example, processing unit 132 uses a function representing the relationship between the voltage value and the resistance value of the sensor element to calculate the resistance value of the sensor element based on the measured voltage value. Processing unit 132 calculates the resistance value of the sensor element using equation (1) described later.

[0149] The processing unit 132 calculates the amount of gas based on the calculated resistance value of the sensor element. The processing unit 132 uses a function (gas composition estimation function) that represents the relationship between the resistance value and the amount of gas to estimate (calculate) the amount of gas components based on the calculated resistance value. For example, the processing unit 132 uses a gas composition estimation function that represents the relationship between the sensor element and the amount of gas components calculated based on the voltage value measured by the first gas composition sensor 401 to calculate the amount of odorless gas based on the calculated resistance value. For example, the processing unit 132 uses a gas composition estimation function that represents the relationship between the sensor element and the amount of gas components calculated based on the voltage value measured by the second gas composition sensor 402 to calculate the amount of malodorous gas based on the calculated resistance value.

[0150] It should be noted that the above is only one example. The processing unit 132 can use various information to estimate the amount of gas components such as odorless gas and malodorous gas. For example, the processing unit 132 can calculate the amount of gas components related to the intestinal environment based on the change in the baseline of the sensor data detected by the second detection unit 22 (e.g., the voltage value before the measurement of defecation gas).

[0151] For example, the processing unit 132 uses a model (feces estimation model) that takes an image as input and outputs information (a score) indicating whether the input image contains feces to estimate whether the image contains feces. In this case, the processing unit 132 compares the score output by the feces estimation model with the input image with a threshold (first threshold). If the score is above the first threshold, it is estimated that the image contains feces. Alternatively, the processing unit 132 compares the score output by the feces estimation model with the input image with the first threshold. If the score is below the first threshold, it is estimated that the image does not contain feces. It should be noted that the above is only one example, and the processing unit 132 may also use various other information to estimate whether an image contains feces.

[0152] The processing unit 132 infers (classifies) the characteristics of the stool based on the detection results of the first detection unit 21. The processing unit 132 classifies the characteristics of the stool as first biological information. Based on an image containing stool (also called a "stool image") captured by the first detection unit 21, the processing unit 132 classifies the characteristics of the stool corresponding to that image. For example, the processing unit 132 uses the stool image to classify the shape (also simply called "form") of the stool corresponding to that image. For example, the processing unit 132 uses the stool image to classify the shape of the stool corresponding to that image into multiple categories (7 levels) based on the Bristol Stool Classification System. In this case, the processing unit 132 classifies the stool in a manner that corresponds to the central level (i.e., level 4), i.e., banana-shaped (normal stool), as the optimal state, and the state corresponding to the state further away from that level is worse.

[0153] The processing unit 132 uses a stool image to classify the shape of the stool corresponding to the stool image into one of several shape-based levels. For example, the processing unit 132 uses a stool image to classify the shape of the stool corresponding to the stool image into one of the following: nut-shaped, hard, wrinkled, banana-shaped, slightly soft (semi-paste-like), mud-like, and watery (watery). For example, the processing unit 132 can classify (determine) the shape of the stool based on various information (features) such as the length in the falling direction of the stool image and the number of stool (lumps).

[0154] Processing unit 132 can classify the shape of feces using AI (artificial intelligence) related technologies. For example, processing unit 132 can use a learning model (shape estimation model) generated through machine learning to classify the shape of feces. In this case, the shape estimation model is pre-learned using training data representing classification judgments. This training data includes a combination of multiple feces images and labels (correct information) representing the shape of the blocks (feces) contained in the feces image (any one of nut-shaped, hard, wrinkled, banana-shaped, slightly soft, muddy, and watery). For example, the shape estimation model is a model that takes a feces image as input and outputs information representing the shape of the blocks (feces) contained in the input feces image. For example, the shape estimation model learns in a way that, given a feces image as input, it outputs information about the label (shape of feces) corresponding to the input feces image. The learning of the shape estimation model appropriately uses various methods related to so-called supervised learning.

[0155] In this case, the shape estimation model is stored in the storage unit 120, and the processing unit 132 uses the shape estimation model stored in the storage unit 120 to classify the shape of the stool. It should be noted that the control device 100 can perform learning processing to generate various estimation models, and the control device 100 can obtain various estimation models from external devices such as the server device 400. Furthermore, the above is only one example; the processing unit 132 can appropriately use various information to classify the shape of the stool. Moreover, the seven levels of nut-like, hard, wrinkled, banana-like, slightly soft, muddy, and watery are only one example of shapes; the processing unit 132 can classify other shapes, including shapes at level 6 or lower. Furthermore, this example shows the classification of stool shape into one of multiple levels, but it is not limited to this; in cases where multiple stool shapes are present in a single excretion, multiple stool shapes can be classified.

[0156] Furthermore, the processing unit 132 can infer various information other than the shape of the stool. For example, the processing unit 132 can infer (classify) the amount of stool based on an image captured by the first detection unit 21. For example, the processing unit 132 can classify the amount of stool based on the proportion of stool in the image. For example, the processing unit 132 can classify the amount of stool using a score output by a stool estimation model. The processing unit 132 can classify the amount of stool as "very little" when the score output by the stool estimation model with an input image is above a first threshold and below a second threshold. The second threshold is set to a value larger than the first threshold. Furthermore, the processing unit 132 can classify the amount of stool as "little" when the score output by the stool estimation model with an input image is above the second threshold and below a third threshold. The third threshold is set to a value larger than the second threshold.

[0157] Furthermore, the processing unit 132 can classify the amount of stool as "moderate" when the score output by the stool estimation model with the input image is above the third threshold and below the fourth threshold. The fourth threshold is set to a value larger than the third threshold. Furthermore, the processing unit 132 can classify the amount of stool as "excessive" when the score output by the stool estimation model with the input image is above the fourth threshold and below the fifth threshold. The fifth threshold is set to a value larger than the fourth threshold. Furthermore, the processing unit 132 can classify the amount of stool as "very excessive" when the score output by the stool estimation model with the input image is above the fifth threshold. It should be noted that the above five-level classification is only one example, and the processing unit 132 can appropriately use various information to classify the amount of stool. For example, the processing unit 132 can use four thresholds for a three-level classification.

[0158] Furthermore, for example, the processing unit 132 can use a stool image to estimate (classify) the color of the stool corresponding to the stool image. The processing unit 132 uses the stool image to classify the color of the stool corresponding to the stool image into one of several color-based level categories. For example, the processing unit 132 uses the stool image to classify the color of the stool corresponding to the stool image into one of the following: yellow, light yellowish-brown, yellowish-brown, brown, dark brown, and deep dark brown.

[0159] The processing unit 132 classifies the color of the feces based on the detection results of the first detection unit 21. The processing unit 132 appropriately uses various techniques related to feces color classification to classify the feces color into one of the following: yellow, light yellowish-brown, yellowish-brown, brown, dark brown, and deep dark brown. For example, the processing unit 132 classifies (determines) the color of the feces based on various information (features) such as brightness and luminance of a color image (RGB). For example, the processing unit 132 can use a learning model (color estimation model) generated through machine learning to classify the color of the feces.

[0160] Here, use Figure 7 and Figure 8 Examples of presumed treatments related to the gut microbiome are illustrated. Figure 7 This is a diagram showing an outline of the processing in the first embodiment. Figure 8 This is a diagram illustrating an example of the mechanism by which gas components are generated. Hereinafter, the case where the processing unit 132 (control device 100) performs the presumed processing will be described as an example, but the presumed processing can also be performed by any structure (device) included in the toilet system 1.

[0161] like Figure 7 As shown, the processing unit 132 functions as an estimation mechanism EM, performing estimation processing using the detection results of the first gas composition sensor 401 (which is the first detection sensor), i.e., the first detection result FD related to odorless gases. Furthermore, the processing unit 132 functions as an estimation mechanism EM, performing estimation processing using the detection results of the second gas composition sensor 402 (which is the second detection sensor), i.e., the second detection result SD related to malodorous gases. Additionally, the processing unit 132 functions as an estimation mechanism EM, performing estimation processing using the detection results of the first detection unit 21 (which is the third detection sensor), i.e., the third detection result TD related to stool characteristics. Furthermore, the processing unit 132 functions as an estimation mechanism EM, performing estimation processing to generate an estimation result ERS representing information related to at least one of the user's intestinal bacteria, intestinal bacterial metabolites, and pH.

[0162] pass Figure 8 The digestive process shown in the diagram generates gaseous components and metabolites in the human body. For example, nutrients such as proteins, carbohydrates, and lipids ingested through diet undergo fermentation and putrefaction processes in the intestinal environment, including beneficial bacteria (Class I bacteria), harmful bacteria (Class II bacteria), and opportunistic bacteria (Class III bacteria), generating fecal gases and metabolites in the human body. As described above, fermentation by Class I bacteria in the human body produces short-chain fatty acids such as acetic acid, butyric acid, and propionic acid, as well as odorless gases (also called "fermentation-derived components"). Furthermore, putrefaction by Class II bacteria in the human body produces putrefactive products such as indole and skatole, as well as foul-smelling gases (also called "putrefaction-derived components"). For example, the more fermentation-derived components there are, the more acidic (weakly acidic) the pH (in the intestines) becomes; the more putrefaction-derived components there are, the more alkaline (weakly alkaline) the pH (in the intestines) becomes. The processing unit 132 estimates information representing the user's intestinal environment through the following processes.

[0163] For example, processing unit 132 uses an intestinal estimation function to estimate information related to at least one of the user's intestinal bacteria, intestinal bacterial metabolites, and pH. Processing unit 132 uses an intestinal estimation function to estimate information related to the user's intestinal bacteria. Processing unit 132 uses a metabolite estimation function to estimate information related to the metabolites of the user's intestinal bacteria. Processing unit 132 uses a pH estimation function to estimate information related to the user's (intestinal) pH.

[0164] For example, the processing unit 132 uses a first-type bacteria estimation function to estimate the amount of first-type bacteria in the user's intestinal flora. The processing unit 132 inputs the value obtained based on the detection of the first gas composition sensor 401 into the first-type bacteria estimation function, and estimates the amount of first-type bacteria in the intestinal flora based on the value output by the first-type bacteria estimation function. It should be noted that when using information about stool characteristics, the processing unit 132 inputs the value obtained based on the detection of the first gas composition sensor 401 and the stool characteristic value obtained based on the detection of the first detection unit 21 into the first-type bacteria estimation function, and estimates the amount of first-type bacteria in the intestinal flora based on the value output by the first-type bacteria estimation function.

[0165] For example, the processing unit 132 uses a type II bacteria estimation function to estimate the amount of type II bacteria in the user's gut. The processing unit 132 inputs the value obtained based on the detection of the second gas composition sensor 402 into the type II bacteria estimation function, and estimates the amount of type II bacteria in the gut based on the value output by the type II bacteria estimation function. It should be noted that when using information about stool characteristics, the processing unit 132 inputs the value obtained based on the detection of the second gas composition sensor 402 and the stool characteristic value obtained based on the detection of the first detection unit 21 into the type II bacteria estimation function, and estimates the amount of type II bacteria in the gut based on the value output by the type II bacteria estimation function.

[0166] Furthermore, the processing unit 132 can estimate information representing the intestinal bacterial balance based on the amount of the first type of bacteria and the amount of the second type of bacteria. The processing unit 132 can estimate a value representing the intestinal bacterial balance (also called an "intestinal bacterial balance value") based on the amount of the first type of bacteria and the amount of the second type of bacteria. In this case, the processing unit 132 can calculate the intestinal bacterial balance value using the ratio of the amount of the first type of bacteria to the amount of the second type of bacteria. It should be noted that the processing unit 132 uses a function (intestinal bacterial balance estimation function) that takes as input the value obtained based on the detection of the first gas composition sensor 401 and the value obtained based on the detection of the second gas composition sensor 402, and outputs the intestinal bacterial balance value as output to calculate the intestinal bacterial balance value.

[0167] For example, processing unit 132 uses a short-chain fatty acid estimation function to estimate the amount of short-chain fatty acids in the metabolites of bacteria in the user's intestines. Processing unit 132 inputs the value obtained based on the detection of the first gas composition sensor 401 into the short-chain fatty acid estimation function, and estimates the amount of short-chain fatty acids in the metabolites of bacteria in the intestines based on the value output by the short-chain fatty acid estimation function. It should be noted that when using information about stool characteristics, processing unit 132 inputs the value obtained based on the detection of the first gas composition sensor 401 and the stool characteristic value obtained based on the detection of the first detection unit 21 into the short-chain fatty acid estimation function, and estimates the amount of short-chain fatty acids in the metabolites of bacteria in the intestines based on the value output by the short-chain fatty acid estimation function.

[0168] For example, the processing unit 132 uses a putrefaction product estimation function to estimate the amount of putrefaction products in the metabolites of bacteria in the user's intestines. The processing unit 132 inputs the value obtained based on the detection of the second gas composition sensor 402 into the putrefaction product estimation function, and estimates the amount of putrefaction products in the metabolites of bacteria in the intestines based on the value output by the putrefaction product estimation function. It should be noted that when using information about stool characteristics, the processing unit 132 inputs the value obtained based on the detection of the second gas composition sensor 402 and the stool characteristic value obtained based on the detection of the first detection unit 21 into the putrefaction product estimation function, and estimates the amount of putrefaction products in the metabolites of bacteria in the intestines based on the value output by the putrefaction product estimation function.

[0169] Furthermore, the processing unit 132 can estimate information representing the balance of bacterial metabolites in the intestine based on the amount of short-chain fatty acids and the amount of putrefactive products. The processing unit 132 can estimate a value representing the balance of bacterial metabolites in the intestine (also called a "metabolite balance value") based on the amount of short-chain fatty acids and the amount of putrefactive products. In this case, the processing unit 132 can calculate the metabolite balance value using the ratio of the amount of short-chain fatty acids to the amount of putrefactive products as the metabolite balance value. It should be noted that the processing unit 132 uses a function (metabolite balance estimation function) that takes as input the value obtained based on the detection of the first gas component sensor 401 and the value obtained based on the detection of the second gas component sensor 402, and outputs the metabolite balance value as output to calculate the metabolite balance value.

[0170] For example, the processing unit 132 uses a pH estimation function to estimate the pH value in the user's intestines. The processing unit 132 inputs the values ​​obtained based on the detection of the first gas component sensor 401 and the values ​​obtained based on the detection of the second gas component sensor 402 into the pH estimation function, and estimates the pH value output by the pH estimation function as the pH value in the intestines. It should be noted that when using information about stool characteristics, the processing unit 132 inputs the values ​​obtained based on the detection of the first gas component sensor 401, the values ​​obtained based on the detection of the second gas component sensor 402, and the stool characteristic value obtained based on the detection of the first detection unit 21 into the pH estimation function, and estimates the pH value output by the pH estimation function as the pH value in the intestines.

[0171] Furthermore, the processing unit 132 can use values ​​related to odorless gases obtained based on the values ​​detected by the first gas composition sensor 401, and values ​​related to malodorous gases obtained based on the values ​​detected by the second gas composition sensor 402, to estimate the intestinal environment. For example, the processing unit 132 can... Figure 7 As shown in the estimation example EX, the intestinal environment is estimated using a value obtained based on the component ratio of the amount of odorless gas and the amount of malodorous gas. In this case, the processing unit 132 can estimate the intestinal environment using the value obtained by dividing the amount of odorless gas by the amount of malodorous gas (also known as the "gas component ratio").

[0172] For example, the processing unit 132 can use a function (intestinal bacterial balance estimation function) that takes the gas component ratio as input and outputs an intestinal bacterial balance value to estimate the user's intestinal bacteria. Furthermore, for example, the processing unit 132 can use a function (metabolite balance estimation function) that takes the gas component ratio as input and outputs a metabolite balance value to estimate the metabolites of the user's intestinal bacteria. Furthermore, for example, the processing unit 132 can use a function (pH estimation function) that takes the gas component ratio as input and outputs a pH value to estimate the pH of the user's intestines.

[0173] It should be noted that the above processing is only one example, and the processing unit 132 can perform various processing. Several processing examples are described below.

[0174] For example, based on the detection results of the first detection unit 21 and the second detection unit 22, the processing unit 132 performs a presumption process that presumes at least one of the information provided or the score related to the user's health. The processing unit 132 performs control to output the result of the presumption process to an external device. The processing unit 132 performs control to output the result of the presumption process to the output unit 133 by issuing an instruction to the output unit 133.

[0175] Processing unit 132 deduces first biological information based on the characteristics of feces according to the detection results of first detection unit 21. Processing unit 132 deduces second biological information based on the amount or concentration of fecal gas according to the detection results of second detection unit 22. Processing unit 132 performs control to output the first biological information and the second biological information.

[0176] Processing unit 132 performs estimation processing such that the number of samples from the detection results of the second detection unit 22 is greater than the number of samples from the detection results of the first detection unit 21. Processing unit 132 uses data obtained from a single toilet visit, i.e., the detection results of the first detection unit 21, to estimate first biometric information. Processing unit 132 uses data obtained from multiple toilet visits, i.e., the detection results of the second detection unit 22, to estimate second biometric information.

[0177] The processing unit 132 estimates a higher score based on the higher the first evaluation corresponding to the classification of stool characteristics obtained based on the detection results of the first detection unit 21. The processing unit 132 also estimates a higher score based on the higher second evaluation obtained based on the detection results of the second detection unit 22.

[0178] The processing unit 132 uses the characteristics of the stool obtained from the detection by the first detection unit 21 to estimate an evaluation related to peristaltic movement (also known as the "first evaluation"). Here, the case where the stool characteristics are based on the 7-level classification of the Bristol stool classification system will be used as an example for explanation.

[0179] When the stool's consistency is at the middle level (level 4) of the 7-level scale, i.e., banana-shaped (normal stool), the processing unit 132 estimates the first evaluation to be the highest. Furthermore, the further the stool's consistency deviates from the middle level of the 7-level scale, the lower the processing unit 132 estimates the first evaluation. When the stool's consistency is at the lowest level (level 1) of the 7-level scale, i.e., nut-shaped (nut-shaped stool), the processing unit 132 estimates the first evaluation to be low. Furthermore, when the stool's consistency is at the highest level (level 7) of the 7-level scale, i.e., watery (watery stool), the processing unit 132 estimates the first evaluation to be low.

[0180] It should be noted that the above is only one example, and the processing unit 132 can estimate the first evaluation in any way. For example, in the Bristol stool classification method described above, the center corresponds to the best state, and the extreme grades correspond to the poor state. Depending on the classification method, there are also cases where the lowest grade (classification) corresponds to the worst state, and the grades (classifications) further up correspond to the better state, and there are also cases where the highest grade (classification) corresponds to the worst state. In this case, if the stool characteristics are at the lowest grade (classification), the processing unit 132 will estimate the first evaluation as the lowest, and if the stool characteristics are at the highest grade (classification), the first evaluation will be estimated as the highest. Thus, the processing unit 132 estimates the first evaluation by adopting a method corresponding to the classification method of the stool characteristics.

[0181] Furthermore, the processing unit 132 performs estimation processing using the information detected by the second detection unit 22. The processing unit 132 performs estimation processing using information about the gas composition detected by the second detection unit 22. The processing unit 132 performs calculation processing. The processing unit 132 performs calculation processing using various information stored in the storage unit 120. The processing unit 132 performs calculation processing using various information acquired by the acquisition unit 131.

[0182] The processing unit 132 can calculate various information based on the calculated resistance value of the sensor element, and is not limited to calculating the amount of gas components. For example, the processing unit 132 calculates the concentration of gas components based on the calculated resistance value of the sensor element. The processing unit 132 uses a function (gas component estimation function) that represents the relationship between the resistance value and the concentration of the gas components to estimate (calculate) the concentration of the gas components based on the calculated resistance value. For example, the processing unit 132 can calculate the concentration of gas components corresponding to the intestinal environment based on the amount of change in the baseline of the sensor data detected by the second detection unit 22 (e.g., the voltage value before the measurement of defecation gas). For example, the processing unit 132 can convert the amount or concentration of gas components into their respective scores (e.g., intestinal environment score) and generate information representing changes over time.

[0183] Processing unit 132 uses the amount or concentration of gas components obtained based on the detection by second detection unit 22 to estimate an evaluation related to the intestinal environment (also called a "second evaluation"). For example, processing unit 132 estimates the second evaluation based on the amount or concentration of fecal gas as second biological information. For example, processing unit 132 uses an evaluation estimation function that takes the amount or concentration of gas components obtained based on the detection by second detection unit 22 as input and outputs an evaluation of fecal gas (second evaluation) to estimate the second biological information.

[0184] When processing unit 132 detects a gaseous component (health-related gas) whose higher quantity or concentration generally indicates a better intestinal environment, it uses the quantity or concentration of the health-related gas to estimate a second evaluation. For example, processing unit 132 estimates the second evaluation based on the principle that a higher quantity or concentration of the health-related gas generally indicates a higher second evaluation. Processing unit 132 uses a gaseous component estimation function where a higher quantity or concentration of the health-related gas generally indicates a higher value for the second evaluation to estimate (calculate) the second evaluation.

[0185] When processing unit 132 can detect a gaseous component (odorous gas) whose higher quantity or concentration indicates a more likely deterioration of the intestinal environment, it uses the quantity or concentration of the odorous gas to estimate a second evaluation. For example, processing unit 132 estimates the second evaluation in a manner where a higher quantity or concentration of the odorous gas results in a lower second evaluation. Processing unit 132 uses a gaseous component estimation function where a higher quantity or concentration of the odorous gas results in a lower value for the second evaluation to estimate the second evaluation.

[0186] It should be noted that the above is only one example, and the processing unit 132 can estimate the second evaluation in any way. For example, the processing unit 132 can estimate the second evaluation based on the ratio of the amount or concentration of healthy gases to the amount or concentration of odorous gases in the user's defecation gas. The processing unit 132 estimates the second evaluation based on the calculated ratio, in a way that the more healthy gases there are than odorous gases in the user's defecation gas, the higher the second evaluation. The control device 100 estimates the second evaluation based on the calculated ratio, in a way that the more odorous gases there are than healthy gases in the user's defecation gas, the lower the second evaluation. It should be noted that the above is only one example, and the control device 100 can make arbitrary estimations based on calculated scores. Furthermore, as in the second or third embodiment, when the toilet system 1 includes an estimation mechanism 200, the estimation process can be performed by the estimation mechanism 200.

[0187] The processing unit 132 controls the structures performing various detections. For example, the processing unit 132 controls the first detection unit 21. Furthermore, for example, the processing unit 132 controls the second detection unit 22. The processing unit 132 controls the second detection unit 22 to ensure that the measured value of the gas composition sensor 40 is within a predetermined range during periods other than when the user is using the toilet 7, and performs reference value control to control the measured value used as a reference value to a predetermined value. The processing unit 132 performs reference value control to control the reference value to a predetermined value by changing the resistance value of the resistive element of the gas composition sensor 40. The processing unit 132 performs reference value control whenever the defecation gas measurement ends. The processing unit 132 performs reference value control by processing the feedback of the measured value of the gas composition sensor 40.

[0188] The output unit 133 of the first embodiment performs output processing for outputting various types of information. The output unit 133 functions as a transmitter of various types of information. The output unit 133 performs output processing by sending information to an external information processing device. For example, the output unit 133 sends various types of information to the display device 300. For example, the output unit 133 sends various types of information to a management device such as a computer or smartphone used by a manager. Furthermore, the output unit 133 can perform output processing by sending information to the operation device 30 (or the display screen 31).

[0189] Output unit 133 sends information indicating the processing result of processing unit 132. Output unit 133 sends various information for display device 300 to display device 300. Output unit 133 outputs the estimated processing result to the outside. Output unit 133 sends the estimated processing result to the outside via communication unit 110. Output unit 133 controls display device 300 to output the estimated processing result by sending the estimated processing result to display device 300. For example, output unit 133 sends the estimated processing result to display device 300, and the estimated processing result is displayed on display device 300.

[0190] <1-6. Processing Examples> Next, various treatment examples based on the structure of the aforementioned toilet system 1 will be described. It should be noted that explanations of points that are the same as those described above will be omitted.

[0191] <1-6-1. Processing Summary> First, use Figure 9 This provides an overall overview of the process. Figure 9 This diagram illustrates an example of the processing performed by the toilet system according to the first embodiment.

[0192] The following description, following an example of the fecal detection performed by the first detection unit 21 and the fecal gas detection performed by the second detection unit 22, outlines the presumption process based on this detection. It should be noted that the fecal detection performed by the first detection unit 21 and the fecal gas detection performed by the second detection unit 22 are merely examples; any detection method can be used for the first detection unit 21 and the second detection unit 22 as long as the desired information can be detected.

[0193] <1-6-1-1. Stool Test> First, an example of stool detection performed by the first detection unit 21 will be explained. The specific operation of the method for acquiring stool images (data) performed by the first detection unit 21 will be described below, referring to... Figure 9 The first detection process, MS1, will be explained. Figure 9The first detection process MS1 is a diagram representing an example of a data acquisition method.

[0194] right Figure 9 The elements shown in the first detection process MS1 will be explained. Objects OB1 and OB2 schematically illustrate the falling images of feces (excrement) as the detection (measurement) object. Specifically, object OB1, shown as a solid line, schematically shows the position of feces falling at a certain moment (moment 1), and object OB2, shown as a dashed line, schematically shows the position of feces falling at a later moment (moment 2). That is, Figure 9 Objects OB1 and OB2 in the diagram represent the same feces falling at different times, with object OB2 corresponding to feces falling at a later time than object OB1. Hereinafter, without distinguishing between object OB1 and object OB2, they will be referred to as object OB.

[0195] exist Figure 9 In the first detection process MS1, the first detection unit 21 is shown to have three light-emitting elements 220a, 220b, and 220c. For example, light-emitting elements 220a, 220b, and 220c are LEDs (Light Emitting Diodes) that emit light of different wavelengths. It should be noted that, in the description without distinguishing between light-emitting elements 220a, 220b, and 220c, they are referred to as light-emitting element 220.

[0196] exist Figure 9 The first detection process MS1 conceptually illustrates the following process: Light from the light-emitting element 220 is irradiated onto a falling object OB, and a two-dimensional image is acquired (generated) based on the light-receiving result of the light-receiving unit 210. A dashed line extending from the light-emitting element 220 to the object OB schematically represents the irradiation of the object OB by light from the light-emitting element 220, and a dashed line extending from the object OB to the light-receiving unit 210 schematically represents the reflected light from the object OB received by the light-receiving unit 210. That is, in... Figure 9 In the first detection process MS1, the data (one-dimensional image) of the lower end (front end in the falling direction) of the object OB is schematically shown when the object OB corresponding to the object OB1 is detected, and the data (one-dimensional image) of the upper end (rear end in the falling direction) of the object OB is detected when the object OB corresponding to the object OB2 is detected.

[0197] exist Figure 9In the first detection process MS1, the first detection unit 21 arranges the data (one-dimensional image) acquired by causing the light-emitting elements 220a, 220b, and 220c to emit light respectively, according to each wavelength over time, into a time sequence to generate information (two-dimensional image). It should be noted that various processing methods can be performed for color images generated using light-emitting elements and line sensors with different wavelengths. Detailed descriptions are omitted here, but an example of a simple processing method will be described.

[0198] The first detection unit 21 generates a two-dimensional image corresponding to the first light-emitting element (light-emitting element 220a) by arranging the light-receiving data (one-dimensional image) obtained by emitting light of the first light-emitting element (light-emitting element 220a) in chronological order. For example, the first detection unit 21 generates information (first two-dimensional image) corresponding to the first wavelength by arranging the light-receiving data (one-dimensional image) obtained by emitting light of the first wavelength, such as 590nm, in a chronological sequence.

[0199] Furthermore, the first detection unit 21 generates a two-dimensional image corresponding to the second light-emitting element (light-emitting element 220b) by arranging the light-receiving data (one-dimensional image) obtained by emitting light of the second light-emitting element (light-emitting element 220b) in chronological order. For example, the first detection unit 21 obtains information (second two-dimensional image) corresponding to the second wavelength by arranging the light-receiving data (one-dimensional image) obtained by emitting light of the second wavelength, such as 670nm, in a chronological sequence.

[0200] Furthermore, the first detection unit 21 generates a two-dimensional image corresponding to the third light-emitting element (light-emitting element 220c) by arranging the light-receiving data (one-dimensional image) obtained by emitting light of the third light-emitting element (light-emitting element 220c) in chronological order. For example, the first detection unit 21 obtains information (third two-dimensional image) corresponding to the third wavelength by arranging the light-receiving data (one-dimensional image) obtained by emitting light of the third wavelength, such as 870nm, in a chronological sequence.

[0201] Thus, the first detection unit 21 can acquire a color image by generating two-dimensional images of three wavelengths corresponding to the first, second, and third light-emitting elements, respectively. For example, the first detection unit 21 can generate a color image by synthesizing the aforementioned first, second, and third two-dimensional images. Furthermore, by setting the light-receiving element of the line sensor or the like in the light-receiving unit 210 to a color-type light-receiving element, multiple color light-emitting elements are simultaneously illuminated, and the color of the reflected light is detected by the light-receiving unit, thereby generating a color image.

[0202] Furthermore, the first detection unit 21 can generate a two-dimensional image for the camera taking the picture. For example, the first detection unit 21 can have a surface sensor (two-dimensional image sensor) with CCD sensors or CMOS sensors arranged in a planar (two-dimensional) manner as the light receiving unit 210.

[0203] <1-6-1-2. Detection of fecal gas> Next, we will describe an example of detecting fecal gas in the second detection unit 22. See below for reference. Figure 9 The second detection process MS2 describes the specific operation of the method for acquiring gas composition information performed by the second detection unit 22. Figure 9 The second detection process, MS2, is a diagram illustrating an example of a data acquisition method. Explanations for points identical to those described above are omitted as appropriate.

[0204] like Figure 9 As shown in the second detection process MS2, the gas composition sensor 40 of the second detection unit 22 detects information such as the amount of fecal gas in the gas composition to be detected. It should be noted that the second detection unit 22 may have three or more gas composition sensors 40. For example, the second detection unit 22 may have three or more gas composition sensors 40, such as a hydrogen sensor, an odorous gas sensor, and a methane gas composition sensor.

[0205] For example, the toilet system can detect (estimate) the amount or concentration of odorous gas by removing the influence of the amount or concentration of healthy gas detected by the second detection unit 22 from the amount or concentration of the gas component detected by the odorous gas detected by the second detection unit 22.

[0206] Next, use Figure 10 An example of the structure of a gas composition sensor will be described. Figure 10 This is a diagram illustrating an example of the structure of a gas composition sensor. Specifically, Figure 10 This is a diagram illustrating an example of the circuit structure CR of a semiconductor-type gas composition sensor 40.

[0207] The gas composition sensor 40 is equipped with a sensor element and a resistive element for measurement. Figure 10 In the middle, the gas composition sensor 40 has a sensor element (corresponding to Figure 10 The sensor resistor RS) and the measuring resistor element (corresponding to Figure 10 The circuit structure CR in which the resistive element RL is connected in series.

[0208] The value related to the gas quantity is calculated using the following equation (1) via a semiconductor gas composition sensor 40. Equation (1) corresponds to... Figure 10The circuit structure CR shown is related to Figure 10 The same formula as function FC1 in the text.

[0209] RS=((Vc-Vout) / Vout)×RL…(1) In Equation (1), “RS” represents the resistance value of the sensor element. For example, in Equation (1), “RS” represents the resistance value of the sensor resistor RS, which is calculated as an example of the value based on the measurement of the gas composition sensor 40. Thus, Equation (1) is the formula for calculating the resistance value.

[0210] In Equation (1), “RL” represents the resistance value of the resistive element RL. In Equation (1), “Vc” represents the voltage value of the circuit voltage Vc. In Equation (1), “Vout” represents the voltage value of the output voltage Vout of the resistive element. For example, in Equation (1), “Vout” represents the voltage value of the resistive element RL, which is an example of a measured value determined by the gas composition sensor 40.

[0211] The resistance value of the sensor resistor RS in equation (1) is an indicator related to the amount or concentration of defecation gas. The toilet system 1 calculates the indicator (resistance value) related to the amount or concentration of defecation gas based on the measured value (voltage value), and calculates the gas volume based on the calculated resistance value. It should be noted that a detailed explanation of the principle of semiconductor-type gas composition sensors is omitted, but for example only... Figure 10 The circuit structure CR shows "RH" corresponding to the heater (resistor) used to heat the sensor element, "V" H "Corresponds to the voltage of the heater. It should be noted that the gas composition sensor in this invention is not limited to a semiconductor sensor; any sensor that satisfies the above formula (1) can be used instead."

[0212] It should be noted that the above is only one example, and the second detection unit 22 is not limited to the above, and can have any type of gas composition sensor. For example, the second detection unit 22 can have an infrared CO2 sensor (carbon dioxide concentration analyzer) or other gas composition sensors. Furthermore, the second detection unit 22 can have multiple or more types of gas composition sensors. For example, the second detection unit 22 can have an electrochemical gas composition sensor in addition to an infrared CO2 sensor.

[0213] <1-6-1-3. Presumed Treatment> Next, an example of the estimation process based on the detection of feces by the first detection unit 21 and the detection of fecal gas by the second detection unit 22 will be described. The control device 100 performs the estimation process as shown below. It should be noted that points that are the same as those described above are omitted from the description.

[0214] The control device 100, based on the detection of stool by the first detection unit 21, classifies the characteristics of the stool as first biological information. Figure 9 In the first biological information DT1 obtained based on stool detection information, the control device 100 presumes the detected stool to be banana-shaped, corresponding to the central grade of the 7 grades in the Bristol Stool Classification. For example, the first biological information DT1 is information related to peristaltic movement (first information).

[0215] Furthermore, the control device 100 uses the estimated characteristics of the stool to estimate a first evaluation related to peristaltic movement. It should be noted that the processing example for this is through... Figure 12 To be discussed later.

[0216] The control device 100 estimates the amount or concentration of the estimated gas components used for the second evaluation based on the detection of fecal gas by the second detection unit 22. Figure 9 In the second evaluation, the control device 100 uses a function (gas composition estimation function) that represents the relationship between the resistance value and its quantity or concentration to estimate the quantity or concentration of the gas composition X (e.g., odorous gas) for the second evaluation.

[0217] Furthermore, the control device 100 uses the estimated amount or concentration of gas component X as input and outputs an evaluation estimation function for the second evaluation, estimating the second evaluation as second biological information. Figure 9 In this process, the control device 100 estimates the value (also known as the "intestinal environment score") shown by the black dot (●) in the time series data DT2 obtained based on fecal gas detection information as a second evaluation, based on the estimated amount or concentration of gas component X. For example, the time series data DT2 is time series data from one month ago (1M ago) to the day of treatment, and is information related to the intestinal environment (second evaluation). It should be noted that the second evaluation can be calculated based on multiple intestinal environment scores, which will be described later.

[0218] The control device 100, based on the first evaluation and the second evaluation, presumes at least one of the provided information or scores related to the user's health. It should be noted that... Figure 9 The diagram illustrates both an example of a health-related score for the user, namely intestinal score information INF1 related to intestinal score or intestinal grade, and an example of information provided, namely recommendation information INF2. However, the control device 100 may also presume one of them. For example, in Figure 10 In this study, since both the first and second evaluations were good, as shown in the intestinal score information INF1, the control device 100 presumed a high intestinal score or intestinal grade. It should be noted that in... Figure 10For ease of explanation, the diagram shows both an estimated intestinal score of "90 points" and an intestinal grade of "A" as scores. However, the control device 100 may estimate only one of "90 points" and "A" depending on the scoring method. It should be noted that when intestinal score and intestinal grade are not distinguished, they are collectively referred to as intestinal score.

[0219] Furthermore, if the second evaluation in the first and second evaluations deteriorates, the control device 100 provides information suggesting a deterioration in the intestinal environment, as indicated by recommendation information INF2. Additionally, the control device 100 provides information suggesting recommended actions to address the deterioration of the intestinal environment.

[0220] Thus, the toilet system 1 can infer the user's intestinal health status based on information from both peristaltic movement and the intestinal environment, thereby appropriately estimating the user's overall health. In other words, the toilet system 1 can infer the user's intestinal health status based on information from both the movement of the intestines themselves and the balance of intestinal bacteria residing in the intestines.

[0221] In the above example, toilet system 1 infers the characteristics of stool based on the detection of linear sensors and infers the intestinal environment based on the detection of gas composition sensors. For example, toilet system 1 visualizes peristalsis and the intestinal environment as the state of the intestine by sensing the shape, quantity, color, and gas (such as flatulence) of stool. By understanding the state of the intestine, it can inform about the state of the intestine in relation to physical and mental health. In this way, toilet system 1 infers and informs about the daily state and changes of the intestine based on stool and gas, thereby providing insights for improving lifestyle.

[0222] Furthermore, in the above example, the toilet system 1 can infer the overall state and changes of the user's intestines based on the user's daily excretory behavior, and provide information based on the inference results to the user. For example, regarding peristaltic movements, the toilet system 1 can display information about the inferred characteristics of the stool (color, shape, quantity). The toilet system 1 displays the first biological information indicating the characteristics of the stool on the user's display device 300. Figure 9 In this system, the toilet system 1 displays first biometric information DT1 on the user's display device 300. For example, the toilet system 1 displays information about the presumed characteristics of feces shown by the first biometric information DT1 on the user's display device 300.

[0223] Regarding the intestinal environment, the toilet system 1 can acquire the composition of fecal gas using a gas composition sensor and display changes over time. For example, the toilet system 1 displays second biological information, such as a second evaluation, on the user's display device 300. Figure 9In this system, the toilet system 1 displays time-series data DT2 on the user's display device 300. For example, the toilet system 1 displays information such as the inferred gut environment score shown in the time-series data DT2 on the user's display device 300.

[0224] In addition, Figure 9 In this system, the toilet system 1 displays intestinal score information (INF1) or recommendation information (INF2) on the user's display device 300. Thus, the toilet system 1 can display information combining peristaltic movement and the intestinal environment. Therefore, the toilet system 1 can infer and display health-related information appropriately based on information such as stool characteristics and defecation gases.

[0225] <1-6-2. Presumed Example> Figure 9 The information or score provided related to the user's health shown is only one example; the toilet system 1 may appropriately use various information to presume the provision of information and scores. Several examples are documented regarding this. It should be noted that, for... Figure 9 Points with identical content should have their descriptions omitted appropriately.

[0226] <1-6-2-1. Evaluation Presumption Example> First, use Figures 11-13 An example of an evaluation of toilet system 1 is explained below. For example, toilet system 1 may use a larger sample size for the second evaluation than the sample size used for the first evaluation. For example, toilet system 1 may use data obtained from multiple toilet visits for the second evaluation. For example, the data used for the first evaluation may be data obtained from a single toilet visit, and the data used for the second evaluation may be data obtained from multiple toilet visits. For example, the number of toilet visits mentioned here may be the number of times toilet R is used, counted as one visit, from the time a user enters toilet R until they leave. In this case, if multiple pieces of information are obtained through a single toilet visit, their average or representative value can be used as the information for that single visit. For example, if toilet system 1 detects the amount or concentration of a gas component twice during a single visit, the average of the two detected amounts or concentrations can be used as the amount or concentration of the gas component for that single visit. It should be noted that the number of toilet visits can use any information, such as the number of times a user defecates.

[0227] For example, such as Figure 11 As shown, the toilet system 1 makes a second evaluation based on multiple data. Figure 11 This is a diagram representing an example of a second evaluation obtained based on multiple data points. Figure 11 In the DT21 time-series data, the white circles representing points PT indicate gut environment scores corresponding to each toilet visit, while the black dots representing calculated values ​​CV indicate a second evaluation calculated using information from multiple points PT. Figure 11 In the example of time series data DT21, the toilet system 1 uses information calculated from multiple data such as moving averages, i.e., the calculated value CV, to estimate the second evaluation. Figure 11 The curve in the time series data DT21 represents the time series change of the second evaluation calculated using multiple data such as moving averages.

[0228] Thus, the toilet system 1 can appropriately estimate a second evaluation related to the intestinal environment by inferring a second evaluation based on multiple test results.

[0229] Next, use Figure 12 This section explains the presumption of the first evaluation. Figure 12 This diagram illustrates an example of a presumption of the first evaluation of the first embodiment. For example... Figure 12 As shown, the toilet system 1 presumes the central (central value) level of the 7-level stool characteristics as optimal, and presumes that the first evaluation decreases as it moves away from the central level. In this case, the toilet system 1 makes a presumption based on the fact that the closer the stool characteristics are to the central value, the better the first evaluation. The toilet system 1 presumes (determines) that the stool characteristics are better the closer they are to the central level. For example, when the stool characteristics are at the central level of the 7-level stool characteristics, i.e., banana-shaped, the control device 100 presumes the first evaluation as the highest. For example, when the first evaluation is between 0 and 100 points, the control device 100 can presumes the first evaluation as 100 points when the stool characteristics are banana-shaped. Furthermore, when the first evaluation is in the 7-level (level) system of S, A to F, and S is the optimal evaluation, and the evaluation deteriorates from A to F, the control device 100 can presumes the first evaluation as S when the stool characteristics are banana-shaped. It should be noted that, as mentioned above, the classification and evaluation based on the Bristol stool classification system is only one example. For instance, any classification such as "hard," "ideal," or "slightly soft" can be used, and any method such as resolution (number of categories) or labeling can be employed. Furthermore, as mentioned above, it is not limited to the case where the central value corresponds to the best state and the extreme values ​​correspond to the worst state. As long as there is a corresponding evaluation for each category, any correspondence can be used, and the central value does not necessarily correspond to the highest evaluation.

[0230] Next, use Figure 13 The presumptions for the second evaluation are explained. Figure 13 This diagram illustrates an example of a presumption of the second evaluation of the first embodiment. (See diagram for example.) Figure 13As shown, toilet system 1 makes an inference based on the intestinal environment score and other values ​​estimated from fecal gas, with a higher value indicating a better second evaluation. In this case, toilet system 1 makes an inference based on the inference results from fecal gas, with a higher value indicating a better second evaluation. Thus, toilet system 1 infers (determines) that the higher the score of the information (odor information) obtained based on fecal gas, the better.

[0231] <1-6-2-2. Information Output Example> The toilet system 1 outputs information corresponding to the results of the presumption processing. For example, if the user's stool characteristics (evaluation, etc.) are unfavorable, the toilet system 1 provides the user with suggestions related to stress or sleep. For example, the toilet system 1 generates information including "Peristalsis has worsened. Get some sleep." and provides this information to the user.

[0232] Furthermore, if the odor of a user's stool is unpleasant, the toilet system 1 can provide dietary-related suggestions. For example, the toilet system 1 can generate information such as "Your gut environment has deteriorated. Eat some yogurt," and provide this information to the user.

[0233] For example, regarding users, toilet system 1 obtains... Figure 14 Using time-series data DT12 on stool characteristics and time-series data DT22 on intestinal environment scores, the toilet system 1 generates provisioning information INF22 based on the time-series data DT12 and DT22. The toilet system 1 then displays the generated provisioning information INF22 on the display device 300 of the user. Figure 14 This is a diagram illustrating an example of the information provided in the first embodiment. For example, time series data DT12 and time series data DT22 are time series data from one month ago (1M ago) to the date of processing.

[0234] exist Figure 14 As shown in time series data DT12 and DT22, the gut environment score decreased. Therefore, the toilet system 1 generates information INF22 containing the message "The gut environment has deteriorated. Start taking healthy actions." and provides this information to the user. In this way, the toilet system 1 compares the information on the changes in stool characteristics over time shown in time series data DT12 with the information on the changes in odor over time shown in time series data DT22. It prioritizes the suggestion related to the information showing the more significant deterioration trend and generates and displays the generated information.

[0235] <1-6-2-3. Information Provided and Score Estimation Examples> Next, use Figures 15-17 An example of presumed information or scores provided for toilet system 1 will be explained. Figure 15 This is a diagram illustrating an example of information used to estimate scores in the first embodiment. Figure 16 This is a diagram illustrating an example of presumed information used to provide information in the first embodiment. Figure 17 This is a diagram illustrating an example of presumed information used to provide information in the first embodiment.

[0236] Figure 15 The estimated table MT1 shown is a list of estimated intestinal grades obtained by combining the classification shown in the "stool characteristics" item and the intestinal environment grades A to D obtained based on the intestinal environment score shown in the "odor" item. The "stool characteristics" item corresponds to the estimation based on the detection results (stool detection) of the first detection unit 21. In addition, the "odor" item corresponds to the estimation based on the detection results (fecal gas detection) of the second detection unit 22.

[0237] exist Figure 15 In this system, the intestinal health is graded on a 7-level scale (S, A to F), with S representing the best intestinal condition, A being slightly better, and F representing the worst. It's important to note that health-related scores are not limited to intestinal health grades and can utilize any information (e.g., numerical values ​​from 0 to 100). For example, toilet system 1 can combine information about stool characteristics (evaluation, etc.) with odor information to output an intestinal score.

[0238] exist Figure 15 In this system, the "odor" category includes four levels (A to D) of the gut microenvironment. For example, the gut microenvironment level represents a four-level evaluation, with A being the best and D the worst. For instance, toilet system 1 estimates the four levels of gut microenvironment by converting the estimated gut microenvironment score. In this case, for example, toilet system 1 can estimate the gut microenvironment level as A if the gut microenvironment score is 75 or higher.

[0239] Furthermore, for Toilet System 1, a gut environment score between 50 and 75 is presumed to be grade B; a score between 25 and 50 is presumed to be grade C; and a score below 25 is presumed to be grade D. It should be noted that... Figure 15The gut microbiome level shown is just one example; the "odor" item can use any information (such as gut microbiome score, secondary evaluation, etc.).

[0240] exist Figure 15 The category of "stool characteristics" includes four subgroups: Group 1 ("banana-shaped"), Group 2 ("wrinkled" and "slightly soft"), Group 3 ("dry and hard" and "muddy"), and Group 4 ("nut-shaped" and "watery"). It should be noted that... Figure 15 The taxonomic group shown is just one example; any combination of taxonomic groups can be used. Furthermore, the "fecal characteristics" item can use any information (e.g., first assessment).

[0241] exist Figure 15 In this system, toilet system 1 uses the user's gut microbiome level, stool characteristics classification, and presumption form MT1 to presume a score for the user's gut microbiome level. For example, if the user's gut microbiome level is A and the stool characteristics are banana-shaped, toilet system 1 will presume the user's gut microbiome level to be S based on the corresponding elements in presumption form MT1.

[0242] Figure 16 The estimated table MT2 shown is a summary table providing information based on a combination of two categories (good and bad) for the "stool characteristics" item and the "odor" item. Figure 16 The document shows four possible combinations of two categories for "stool characteristics" and two categories for "odor." It should be noted that... Figure 16 The information provided shown is only one example, and each piece of information can be any content corresponding to the state of the stool's characteristics or odor.

[0243] exist Figure 16 In the text, the "odor" category includes two subcategories: "good" and "bad." It should be noted that... Figure 16 The classification of "odor" shown is just one example; any combination of classifications can be used.

[0244] For example, if toilet system 1 is rated above the specified value in the second evaluation, it is classified as... Figure 16 The "Good" rating shown is classified as follows when the second evaluation is less than the specified value. Figure 16 The result is "not good". It should be noted that the above classification is just one example; the bathroom system 1 can use various information to classify odors as... Figure 16 Either "good" or "bad" is indicated. For example, toilet system 1 is classified as such if its intestinal environment score is above a specified value. Figure 16The "good" rating shown indicates a gut microbiome score below a specified value, which is classified as... Figure 16 The word "bad" is shown.

[0245] exist Figure 16 The section on "Stool Characteristics" includes two categories: "Good" and "Bad." It should be noted that... Figure 16 The classification of "fecal characteristics" shown is just one example; any combination of classifications can be used.

[0246] For example, if the first evaluation of toilet system 1 is above the specified value, it is classified as... Figure 16 The "Good" rating shown is classified as follows when the first evaluation is less than the specified value. Figure 16 The label "not good" is shown. It should be noted that the above classification is only one example; toilet system 1 can use various information to classify the characteristics of stool as... Figure 16 Either "good" or "bad" is indicated. For example, in toilet system 1, if the stool has a "banana-like" consistency, it is classified as... Figure 16 The "good" label indicates that the stool's consistency is not "banana-shaped," and is therefore classified as... Figure 1 The word "bad" is shown.

[0247] exist Figure 16 In this system, the toilet system 1 uses the user's odor classification, stool characteristics classification, and presumption form MT2 to presume the information to be provided to the user. For example, if the user's "odor" and "stool characteristics" are both "bad," the toilet system 1, based on the corresponding elements in the presumption form MT2, presumes that the information provided, such as "Your intestinal condition is trending very bad. Perhaps you should adjust your diet?" is suitable as information to provide to the user.

[0248] Figure 17 The estimated table MT3 shown is a summary table providing information based on a combination of the three categories indicated by the "Stool Characteristics" item and the three categories indicated by the "Odor" item. Figure 17 The document shows nine combinations of information corresponding to the three categories of "stool characteristics" and the three categories of "odor". It should be noted that... Figure 17 The information provided shown is only one example, and each piece of information may include any content corresponding to the state of the stool's texture or odor.

[0249] exist Figure 17 The "odor" category includes three subcategories: "good," "normal," and "bad." It should be noted that... Figure 17 The classification of "odor" shown is just one example; any combination of classifications can be used.

[0250] For example, if the second evaluation of toilet system 1 is above the first threshold, it is classified as... Figure 17 The "Good" rating shown is classified as follows: if the second evaluation is less than the first threshold but greater than the second threshold. Figure 17 The "normal" rating shown is classified as follows when the second evaluation is less than the second threshold. Figure 17 The "bad" rating is shown. The first threshold can be any value, and the second threshold can be any value less than the first threshold. It should be noted that for the "odor" item, the toilet system 1 can use the intestinal environment score and classify it into any of the three categories: "good", "normal", and "bad".

[0251] exist Figure 17 In the classification of stool characteristics, there are three groups: Group 1 ("muddy" and "watery"), Group 2 ("wrinkled," "banana-like," and "slightly soft"), and Group 3 ("nut-like" and "hard"). It should be noted that... Figure 17 The taxonomic group shown is just one example; any combination of taxonomic groups can be used.

[0252] exist Figure 17 In this system, the toilet system 1 uses the user's odor classification, stool consistency classification, and the presumption form MT3 to presume the information to be provided to the user. For example, if the user's "odor" is "normal" and their "stool consistency" is "nut-like" and "hard," belonging to the third category, the toilet system 1, based on the corresponding elements in the presumption form MT3, presumes that information such as "Start exercising twice a week" is suitable to be provided to that user.

[0253] <1-6-3. Example of Information Provision> Next, based on the above processing, an example of information provision to users by the toilet system 1 will be described. For example, the toilet system 1 generates various types of information as shown below, and displays the generated information on the corresponding display device 300 used by the user. For example, the display device 300 used by the user has a health management application (also called a "gut health application") installed to display health-related information about the user, and displays various health-related information about the user through the gut health application.

[0254] It should be noted that as long as the display device 300 can display information related to the user's health, it can do so through applications other than gut health applications. The following explanation will take the case where the user of the display device 300 displaying the information is user U as an example.

[0255] First, use Figure 18 This illustrates an example of information related to the basic functions of a gut health application provided by the toilet system 1 and displayed by the display device 300. Figure 18 This is a diagram illustrating an example of information provided by a bathroom system.

[0256] Figure 18 The content CT1, for example, corresponds to the home screen of a gut health application displayed on the display device 300. Content CT1 includes a display area AR1 that displays various information. Figure 18 In the display area AR1, intestinal status information IM1 is displayed, including the intestinal level at the time of display, a diagram of the human body, and an intestinal illustration corresponding to the intestinal level at the time of display. Figure 18 The image shows a case where user U's gut health level is A at the time of display.

[0257] Content CT1 includes a button IC1 labeled "Recommended Information for You". Additionally, Content CT1 includes a button IC2 labeled "What is Gut Health?". Content CT1 includes a button IC3 labeled "Gut Motility". Content CT1 includes a button IC4 labeled "Gut Environment". Content CT1 includes a button IC5 labeled "Gut Health Diary". Content CT1 includes a button IC6 labeled with information related to the user U's family. It should be noted that examples of the information displayed when buttons IC1 through IC6 are selected (assigned) will be described later.

[0258] For example, when button IC3 in the content CT1 displayed on display device 300 is selected, display device 300 displays... Figure 19 The content shown is information like CT2. Figure 19 This is a diagram illustrating an example of information provided by a bathroom system.

[0259] Figure 19 The content CT2 corresponds, for example, to a display image related to intestinal movement shown on the display device 300. Content CT2 includes feature information FT1 to FT3 representing information related to the user U's stool at the time of display. It should be noted that... Figure 19 The example shows the case where the feature information FT1 to FT3 is text information. However, as long as the feature information FT1 to FT3 can display the corresponding information, it can be displayed in any way, such as as an icon.

[0260] For example, the feature information FT1 represents the amount of stool of user U at the time of display. Figure 19 This shows cases with large amounts of stool. For example, feature information FT2 indicates the color of user U's stool at the time of display. Figure 19The text shows the stool color as brown. For example, feature information FT3 indicates the characteristics of user U's stool at the time of display. Figure 19 The stool's appearance is shown to be normal (banana-shaped, etc.). In Figure 19 The text describes the characteristics of stool as soft, normal, or hard, but the Bristol Stool Classification also allows for different stool shapes, such as banana-shaped or nut-shaped.

[0261] Content CT2 includes a button IC11 labeled "Calendar View". For example, when button IC11 in Content CT2 displayed on display device 300 is selected, display device 300 displays a calendar related to the measurement results of intestinal motility (e.g., stool characteristics).

[0262] Content CT2 includes a button IC12 labeled "Today's One-Sentence Explanation of Peristalsis". For example, when button IC12 in content CT2 displayed on display device 300 is selected, display device 300 displays a one-sentence explanation of today's peristalsis.

[0263] Content CT2 includes a button IC13 labeled "Related Information". For example, when button IC13 in Content CT2 displayed on display device 300 is selected, display device 300 displays related information related to intestinal motility (e.g., stool characteristics).

[0264] Content CT2 includes a button IC14 labeled "Score". For example, when button IC14 in Content CT2 displayed on display device 300 is selected, display device 300 displays information related to scores (e.g., first evaluation) associated with intestinal motility.

[0265] For example, when button IC4 in the content CT1 displayed on display device 300 is selected, display device 300 displays... Figure 20 The content shown is information like CT3. Figure 20 This is a diagram illustrating an example of information provided by a bathroom system.

[0266] Figure 20 Content CT3, for example, corresponds to the image related to the intestinal environment displayed on the display device 300. Content CT3 includes time-series data on changes in intestinal environment indicators representing the user U's intestinal environment up to the display time, and information provided based on these changes. It should be noted that in... Figure 20 In this context, intestinal environment indicators can be any indicators such as intestinal environment score or intestinal environment level.

[0267] Content CT3 includes a button IC21 labeled "Today's One-Sentence Explanation of the Gut Environment". For example, when button IC21 in Content CT3 displayed on display device 300 is selected, display device 300 displays a one-sentence explanation of the day related to the gut environment.

[0268] Content CT3 includes a button IC22 labeled "Related Information". For example, when button IC22 in Content CT3 displayed on display device 300 is selected, display device 300 displays related information related to the intestinal environment.

[0269] Content CT3 includes a button IC23 labeled "Explanation of Intestinal Environment Indicators". For example, when button IC23 in Content CT3 displayed on display device 300 is selected, display device 300 displays the explanation related to intestinal environment indicators represented by Content CT3.

[0270] Furthermore, the information displayed in display area AR1 is not limited to... Figure 18 The information shown can be any kind of information. Regarding this, use... Figure 21 and Figure 22 Example of recording. Figure 21 and Figure 22 This is a diagram illustrating an example of information obtained based on a presumed result.

[0271] like Figure 21 As shown, the display device 300 can display intestinal status information IM2 in the display area AR1, including information suggesting maintaining the status quo such as "Good condition!", a diagram of the human body, and an intestinal illustration corresponding to the intestinal level at the time of display. In this case, the display device 300... Figure 18 The displayed area AR1 in CT1 shows intestinal status information IM2, replacing intestinal status information IM1. Furthermore, as... Figure 22 As shown, the display device 300 can display intestinal status information IM3 in the display area AR1, including information such as "It's getting better!" suggesting an improvement in the status and recommending maintaining the status quo, and intestinal illustrations indicating changes in intestinal status. In this case, the display device 300... Figure 18 The content shown in CT1 is displayed in the AR1 area, which displays the intestinal status information IM3 instead of the intestinal status information IM1.

[0272] Thus, the display device 300 displays arbitrary information in the display area AR1. For example, the display device 300 displays information in the display area AR1 such as scores or grades representing the overall state of the gut, judgments of good or bad, and changes relative to the past. In this way, the display device 300 can display indicators representing the state of the gut in a way that includes scores or grades. Furthermore, the display device 300 can display the above information together with the aforementioned charts showing trends over time.

[0273] For example, when button IC5 in the content CT1 displayed on display device 300 is selected, display device 300 displays... Figure 23 The content shown is information like CT4. Figure 23 This is an example of a diagram representing information related to a user's gut health.

[0274] Figure 23 The content CT4 corresponds, for example, to the screen related to gut health displayed on the display device 300. Content CT4 includes calendar information such as a gut health diary showing the user U's gut health up to the time of display.

[0275] Content CT4 includes a button IC31 labeled "Information related to gut health". For example, when button IC31 in content CT4 displayed on display device 300 is selected, display device 300 displays information related to gut health.

[0276] Content CT4 includes a button IC32 labeled “Everyone’s Gut Health”. For example, when button IC32 in content CT4 displayed on display device 300 is selected, display device 300 displays information related to the gut health of users other than user U, but an example of this will be described later.

[0277] Content CT4 includes a button IC33 labeled "Review Past Gut Health". For example, when button IC33 in Content CT4 displayed on display device 300 is selected, display device 300 displays the user U's historical gut health information, etc.

[0278] For example, when button IC1 in the content CT1 displayed on the display device 300 is selected, the display device 300 displays... Figure 24 The content shown is information like CT5. Figure 24 This is an example of a recommendation message presented to a user. Figure 24 As an example, the text shows a scenario where user U has a poor gut condition (e.g., a gut grade of D).

[0279] Figure 24The content CT5 corresponds to information provided by the display device 300, such as recommendation information. Content CT5 includes information provided corresponding to the user U's intestinal state up to the display time. Content CT5 includes information INF5, which includes suggestions to improve dietary habits, such as "Your physical condition has deteriorated compared to the past" or "Your intestinal environment indicators are showing a worsening trend. Is your diet irregular?".

[0280] Content CT5 includes a button IC41 labeled "Recommended Information". For example, when button IC41 in content CT5 displayed on display device 300 is selected, display device 300 displays... Figure 25 The content shown is CT6 and other information. Figure 25 This is an example of a recommendation message presented to a user.

[0281] Figure 25 The content CT6 corresponds to screens displaying information such as recommendations shown on the display device 300. Content CT6 includes information INF6, which includes suggestions on foods to consume, such as, "The gut environment is affected by diet and lifestyle. It's said that soluble dietary fiber is good for the gut!" Furthermore, content CT6 includes information on relevant websites, animations, recommended products, etc., that can help improve dietary habits.

[0282] For example, when button IC32 in the content CT4 displayed on display device 300 is selected, display device 300 displays... Figure 26 The content shown is information like CT7. Figure 26 This is a diagram representing an example of information provided to other users of the user.

[0283] Figure 26 The content CT7 corresponds, for example, to a screen displaying information about other users shown on the display device 300. Content CT7 includes information related to the gut health of other users similar to user U in terms of age, gender, physique, or lifestyle. Figure 26 The content CT7 includes information related to gut health recorded by other users in the gut health application or posted on SNS (Social Networking Service) that is similar to the user U attribute of the 30-year-old group.

[0284] It should be noted that other users similar to user U are not limited to attributes, but can be users with similar evaluations such as gut health scores. For example, if user U's gut health score is B, other users similar to user U could be users with a gut health score of B. Thus, the display device 300 displays a method for showing the gut health of people with similar attributes or gut health scores. For example, the toilet system 1 can acquire records or SNS information of other people associated with attributes or gut health scores related to user identification information (ID, etc.), and use this information for gut health-related information of other users similar to user U.

[0285] Figure 27 For example, the screen in content CT8 corresponds to the screen in which a specific date in the calendar of the gut health diary contained in content CT4 is selected. Figure 27 This is an example of a display style for information provided to the user. Content CT8 includes calendar information of the user U's gut health diary up to a certain point in time, as well as gut scores or icons corresponding to the selected date.

[0286] For example, for the user's haptic information, the display device 300 prompts manual input or icon selection. Figure 27 In this system, the display device 300 can prompt the user U to select a comprehensive evaluation from A to E based on their own subjective sensory experience. Furthermore, the display device 300 can display subjectively related icons based on the user's sensory experience, prompting the user U to select from these icons. Then, the restroom system 1 can use the sensory information obtained from the user's selection to perform re-evaluation and correction of the user U's score, or to set the score value that the user U should strive for.

[0287] Furthermore, for each of multiple items, the display device 300 can prompt manual input or icon selection. Figure 27 In this system, for various items such as diet, lifestyle habits, and exercise, the display device 300 displays icons, prompting the user U to select from these icons. In addition, the display device 300 can obtain information by linking with other applications on a smartphone or a smartwatch. Thus, the bathroom system 1 collects information to decide whether to exclude a point (outlier, etc.) when abnormal values ​​are found in intestinal health information or measurements. Then, the bathroom system 1 can manage the information obtained from the user's selection as information representing intestinal health activity or status, in association with the presumed results. Furthermore, the bathroom system 1 can perform outlier exclusion processing based on the information obtained from the user.

[0288] Furthermore, the display device 300 can provide information based on changes in the user's intestinal condition. Figure 28In the middle, the display device 300 displays content CT9, which includes information provided corresponding to changes in the user U's score (gut score), diet, and exercise, and a button IC91 for prompting the purchase of goods corresponding to the provided information. Figure 28 This is an example of a recommendation message presented to a user.

[0289] exist Figure 28 Since user U's score was good when consuming ○○ yogurt, the restroom system 1 presumes that ○○ yogurt is suitable for user U. Therefore, the restroom system 1 generates content CT9, which includes information that ○○ yogurt is suitable for user U and recommends its consumption, as well as a button IC91 for purchasing ○○ yogurt, and displays it on the display device 300.

[0290] When button IC91 in the content CT9 displayed on display device 300 is selected, display device 300 displays information such as EC (e-commerce) websites selling ○○ yogurt. Thus, when the score deteriorates, display device 300 displays information recommending products related to previously effective gut health tools.

[0291] It should be noted that when button IC32 in the content CT4 displayed on the display device 300 is selected, the display device 300 can display... Figure 26 Information other than the content CT7 shown. For example, if button IC32 in the content CT4 displayed on the display device 300 is selected, the display device 300 can display information other than the content CT7 shown. Figure 29 The content shown is information like CT10. Figure 29 This is a diagram representing an example of information provided to other users of the user.

[0292] Figure 29 The content CT10 corresponds, for example, to a screen displaying information about other users shown on the display device 300. Content CT10 includes information related to the gut health of other users similar to user U. Figure 29 In the CT10 content, information related to the gut health records of other users in the same 30-year-old population as the user U attribute is included.

[0293] For example, display device 300 outputs a group of information associated with attributes or gut health scores related to information (ID, etc.) that identifies other users similar to user U. For instance, display device 300 displays content CT10, which includes gut health records representing the individual gut health content or period of other users, and information such as changes in gut health scores during that period. In this way, display device 300 allows the user to view information related to the gut health activities and results of other users (persons).

[0294] For example, when button IC6 in the content CT1 displayed on display device 300 is selected, display device 300 displays... Figure 30 The content shown is information like CT11. Figure 30 This is a diagram representing an example of user information related to the user. In Figure 30 As an example, the display shows the situation where user U's family consists of four people: user U (I), spouse (father), and two children (son A and son B). For example, for each user, the toilet system 1 manages the information (ID, etc.) used to identify users associated with that user in association with the information (ID, etc.) used to identify that user, thereby enabling the identification of users associated with each user.

[0295] Figure 30 The content CT11 corresponds, for example, to a screen displaying a user overview related to the user shown on the display device 300. Content CT11 includes four gut microbiota levels for each family member of user U up to the current time of display. Content CT11 includes a button IC101 labeled "→See Details" for the spouse (father) at level B. Furthermore, content CT11 includes a button IC102 labeled "→See Details" for user U (me) at level A. Furthermore, content CT11 includes a button IC103 labeled "→See Details" for the child (son A) at level B. Furthermore, content CT11 includes a button IC104 labeled "→See Details" for the child (male B) at level C.

[0296] When the "→See details" button corresponding to each user is selected, the display device 300 jumps to display information indicating the individual results for that user. For example, when button IC101 is selected, the display device 300 displays information indicating the presumed results for the spouse (father). For example, the display device 300 switches the information displayed in the display area AR1 from the user U (me)'s information to the spouse (father)'s information.

[0297] Thus, the restroom system 1 can display the results of other people in conjunction with their IDs. When the specified conditions, such as ID linkage, are met, the restroom system 1 sends data to a third party. It should be noted that in the above example, the user related to the user is illustrated using a family member as an example. However, users related to the user are not limited to family members; they can be users shared with the user's institution, such as hospitals or gyms, or individuals who provide support for the user's health management, such as doctors or gym instructors.

[0298] For example, when button IC2 in the content CT1 displayed on display device 300 is selected, display device 300 displays... Figure 31 The content shown is information like CT12. Figure 31 This is a diagram representing an example of user-related hierarchy information.

[0299] Figure 31 The content CT12 corresponds, for example, to a screen related to gut health displayed on the display device 300. Content CT12 includes level information such as the gut health level of the user U at the time of display. Figure 31 In this context, content CT12 includes information related to the distribution of gut health grades among users aged 30, whose attributes are similar to those of users U (who are also 30-year-olds). In this case, the toilet system 1 extracts information about users aged 30 from the information of the managed users, and generates information representing the distribution of gut health grades among the 30-year-old users based on the extracted information. Content CT12 is generated by appending information representing the grade corresponding to user U to the generated distribution information. Figure 31 In this context, the display device 300 displays content CT12, which indicates that user U's gut health grade is C, and that gut health grade C is the average level among users aged 30. Thus, the display device 300 allows the user to compare their grade with other users (people) with similar attributes (e.g., attributes). It should be noted that, in addition to this, CT12 can also be an evaluation of the grade related to gut motility (e.g., first-class rating) or an evaluation of the grade of gut environment indicators.

[0300] In addition, toilet system 1 can notify users at designated times. Toilet system 1 can... Figure 32 The display area AR11 of the display device 300 shown displays arbitrary information. Figure 32 This is an example of an information notification sent to a user.

[0301] For example, if the user (the user) using display device 300 experiences a worsening score, display device 300 displays content CT13 in display area AR11, including information such as "Attention! Intestinal grade has decreased!". For example, if user U's intestinal grade deteriorates, user U's display device 300 displays content CT13 in display area AR11. In this way, display device 300 prompts attention when the user's score deteriorates.

[0302] Furthermore, if the scores of others (other users) related to the user of display device 300 (the user themselves) deteriorate, display device 300 displays content CT14 in display area AR11, including information such as "Attention! Male B's intestinal score has decreased! Please check." For example, if the intestinal score of user U's child, male B, deteriorates, user U's display device 300 will display content CT14 in display area AR11. In this way, display device 300 prompts attention when the scores of others related to the user deteriorate.

[0303] Furthermore, the toilet system 1 can notify users based on their usage patterns. Regarding this, the user... Figure 33 and Figure 34 Please provide an explanation. Figure 33 and Figure 34 This diagram illustrates an example of information notifications based on user usage patterns. For instance, bathroom system 1 can send push notifications based on a user's login history to a gut health application. Furthermore, bathroom system 1 can assess a user's level of concern for gut health based on the number of times they log into the gut health application or the frequency of their gut health input.

[0304] For example, toilet system 1 can assess a user's level of concern for gut health based on their active participation in activities, and then notify the user of a message praising their positive action. Figure 33 In this system, if a user logs into the gut health application for ten consecutive days, the bathroom system 1 will notify the user of information such as content CT15. For example, if user U logs into the gut health application for ten consecutive days, user U's display device 300 will display content CT15. For example, when user U logs into the gut health application on the tenth day, display device 300 will overlay content CT15 (pop-up display) on content CT1.

[0305] For example, if a user hasn't logged into the gut health application for a period of time (e.g., a week), and the system assesses that the user has a low level of concern for gut health, it can send the user information to encourage active participation. When the user is assessed as having a low level of concern for gut health, the system 1 can... Figure 34 The display area AR12 of the display device 300 shown displays information that encourages the user to take active actions.

[0306] For example, if a user using display device 300 has not logged into the gut health application for a specified period (e.g., 7 days), display device 300 displays content CT16 in display area AR12, including information such as "Not logged in for ○○ days. Want to restart your gut health journey?". For example, if user U has not logged into the gut health application for ○○ days, user U's display device 300 will display content CT16 in display area AR12. In this way, the toilet system 1 can notify users based on their level of concern for gut health in order to maintain user engagement.

[0307] As described above, the toilet system 1 provides various information to enable users to obtain the information they desire. For example, when a user's bowel condition is poor, the toilet system 1 can encourage the user to improve their condition. The toilet system 1 can meet the user's need to know when their health-related condition deteriorates, by notifying them of a decline in their score, thereby giving the user the motivation to re-evaluate their life.

[0308] Furthermore, the toilet system 1 encourages users to maintain a healthy bowel condition. It fulfills users' needs for health management linked to their lifestyle habits, providing reassurance through notifications of good scores. In addition, the toilet system 1 addresses users' needs not only for their own health but also for the health management of their family members or patients, such as allowing them to monitor the abdominal condition of family members and plan menus accordingly.

[0309] Furthermore, the Toilet System 1 can encourage users to further improve their gut health when it shows signs of improvement. The Toilet System 1 meets users' needs for finding suitable gut health methods and utilizing gut health indicators effectively. It allows users to recognize the effects of foods like yogurt they've started consuming and encourages them to continue eating them. In addition, as a tool for maintaining gut health, the Toilet System 1 provides users with the motivation to strive towards their goals.

[0310] Furthermore, the toilet system 1 can compare data with past data and issue an alarm if the score falls below a predetermined value. Additionally, the toilet system 1 can report the completion of the measurement at stages such as when the first biological information, second biological information, provided information, or scores are all collected. Furthermore, the toilet system 1 can prioritize outputting information from the first and second biological information sets that is unfavorable and supported by evidence. Furthermore, the toilet system 1 can update recommended information or scores with each excretion. In addition, the toilet system 1 detects the characteristics of stool using an image sensor and detects excrement gas using a gas composition sensor.

[0311] In addition, the toilet system 1 can detect not only the characteristics of the stool, but also the amount, color, density, and user's subjective information such as the feeling of cleanliness after defecation, as information related to intestinal peristalsis. It can also detect not only the gas in the stool, but also the color and odor of the stool, as subjective information related to the user, as information related to the intestinal environment.

[0312] The toilet system 1 generates various types of content as described above and provides this content to the user. For example, the control device 100 of the toilet system 1 generates various information such as content CT1 to CT16. Then, the control device 100 sends the generated information such as content CT1 to CT16 to the user's display device 300. The display device 300, having received the information such as content CT1 to CT16, displays the received information such as content CT1 to CT16.

[0313] In this case, for example, the processing unit 132 of the control device 100 functions as a generation unit that performs various information generation processes. The processing unit 132 generates various information, such as content, for the display device 300 to display. Figures 18-34 The displayed image (content). For example, the processing unit 132 appropriately uses various related technologies such as image generation or image processing to generate content (image information) to be provided to the display device 300. For example, the processing unit 132 appropriately uses various technologies such as Java (registered trademark) to generate the image (image information) to be provided to the display device 300. It should be noted that the processing unit 132 can generate the content (image information) to be provided to the display device 300 based on CSS (Cascading Style Sheets), JavaScript (registered trademark), or HTML (Hypertext Markup Language). In addition, for example, the processing unit 132 can generate content in various formats such as JPEG (Joint Photographic Experts Group), GIF (Graphics Interchange Format), or PNG (Portable Network Graphics).

[0314] <2. Second Implementation> It should be noted that the above processing is only one example, and the toilet system can perform various processing to appropriately estimate health-related information. For one example, the second embodiment will be described below. After describing the overview of the toilet system 1A, the various processing performed by the toilet system 1A and the structure used to perform this processing will be described below. It should be noted that descriptions of points identical to those in the first embodiment will be appropriately omitted. Furthermore, the toilet system of the second embodiment can be combined with the toilet system of the first embodiment. For example, the correction of gas component values ​​described below can be performed in the toilet system of the first embodiment. Furthermore, when the toilet system of the second embodiment uses fecal information for estimation processing, it can have a third detection sensor (e.g., the first detection unit 21, etc.) for detecting fecal characteristics. In addition, sometimes gas components originating from fermentation sources in the intestines and indicating high health (odorless gases, etc.) are considered healthy gases, while gas components originating from putrefaction in the intestines and indicating low health (malodorous gases, etc.) are considered odorous gases.

[0315] For example, health-related gases are gaseous components produced through fermentation by beneficial bacteria in the gut. For instance, health-related gases can be gaseous components originating from gut fermentation, with higher concentrations observed as gut health increases. Specific examples of health-related gases include hydrogen, carbon dioxide, acetic acid, methane, ethanol, and water. It should be noted that the above is just one example; health-related gases can be various gaseous components. For example, health-related gases can be odorless gases, or they can be understood as simply odorless gases.

[0316] Furthermore, for example, odorous gases are gaseous components produced by the fermentation of harmful bacteria in the intestines. For instance, odorous gases can be gaseous components containing sulfur in fecal matter. Examples of odorous gases include ammonia, trimethylamine, hydrogen sulfide, methanethiol, indole, and skatole. It should be noted that the above is only one example; odorous gases can be various gaseous components. For example, odorous gases can be foul-smelling gases, and the term "odorous gas" can also be interpreted as "foul-smelling gas."

[0317] <2-1. Example of a bathroom structure> First, refer to Figure 35 The structure of the toilet system according to the second embodiment will be described. Figure 35 This is a perspective view showing an example of the structure of the toilet system according to the second embodiment. It should be noted that this is an example of the toilet system according to the second embodiment. Figure 35 The toilet system 1A shown is similar to the one that lacks a first detection unit 21. Figure 1The toilet system 1 shown has the same structure, so further explanation is omitted. It should be noted that the toilet system of the second embodiment, when processing information about waste disposal, may include a first detection unit 21.

[0318] The toilet system 1A performs control to appropriately measure excrement gas. Based on the information collected through measurements, the toilet system 1A can transmit data to the user's smartphone or other user terminal (equivalent to...). Figure 37 Information is provided to the display device 300 in the toilet. In addition, the toilet system 1A can provide information to the operation device 30 (or display screen 31) of the toilet R based on information collected by measurement and the like.

[0319] <2-2. Structure of the measuring device> Next, refer to Figure 36 The structure of measuring device 4 will be described. Figure 36 This is a plan view showing an example of the structure of the measuring device according to the second embodiment. Figure 36 In the example shown, the measuring device 4 is arranged inside the main body 3. It should be noted that, for... Figure 2 Points that have been explained before and are identical to those already explained should be omitted from the explanation as appropriate.

[0320] The measuring device 4 has an aspiration device 10 for aspirating gas components from the basin 8 of the toilet 7 and a gas component detection device 20 for detecting the components of the aspirated gas.

[0321] The gas composition detection device 20 performs processing related to the detection of the composition of the gas attracted by the suction device 10. Figure 36 In this configuration, the gas composition detection device 20 is positioned at the rear of the suction device 10 when viewed from the side of the basin 8. It should be noted that... Figure 36 As an example only, the gas composition detection device 20 can be positioned anywhere as long as it is located where the gas composition attracted by the suction device 10 can be introduced. The gas composition detection device 20 is connected to a pipe 102 that communicates with the outside of the main body 3. The pipe 102 functions as a flow path for the gas composition inside the gas composition detection device 20 to flow out of the measuring device 4. For example, driven by the suction device 10, the gas composition inside the gas composition detection device 20 is discharged to the outside of the measuring device 4 through the pipe 102.

[0322] For example, the gas composition detection device 20 performs processing related to the detection of gas composition under the control of the control device 100. The gas composition detection device 20 includes a gas composition sensor 40 that reacts with the gaseous components contained in the gas. The gas composition sensor 40 detects specific components of the gas. For example, the gas composition detection device 20 includes at least one of a first gas composition sensor 401 and a second gas composition sensor 402 that react with the gaseous components contained in the gas. The gas composition detection device 20 includes a first gas composition sensor 401 that reacts with hydrogen contained in the gas, and a second gas composition sensor 402 that reacts with an odorous gas (malodorous gas) containing sulfur and hydrogen.

[0323] For example, the gas composition sensor 40 may be a semiconductor gas composition sensor. The gas composition sensor 40 may be a hydrogen sensor capable of detecting hydrogen. The gas composition sensor 40 may be an odorous gas sensor capable of detecting odorous gases. The gas composition sensor 40 may be a methane gas composition sensor capable of detecting methane. For example, the gas composition detection device 20 has multiple gas composition sensors 40. The multiple gas composition sensors 40 may include a gas composition sensor 40a as a hydrogen sensor, a gas composition sensor 40b as an odorous gas sensor, and a gas composition sensor 40c as a methane gas composition sensor. Without specifically distinguishing between gas composition sensors 40a to 40c, gas composition sensor 40 will be described as gas composition sensor 40. For example, gas composition sensor 40a may be used as a first gas composition sensor 401. Furthermore, gas composition sensor 40b may be used as a second gas composition sensor 402. Furthermore, gas composition sensor 40c may be used as the first gas composition sensor 401.

[0324] It should be noted that the above is only one example, and the gas composition sensor is not limited to the semiconductor-type gas composition sensor 40; any type of sensor can be used. For example, the gas composition detection device 20 can have any single or multiple gas composition sensors, such as an infrared-type carbon dioxide concentration meter or a CO2 sensor.

[0325] <2-3. Overall Overview Example of a Toilet System> Next, refer to Figure 37 An example of the overall overview of the toilet system 1A is described. Figure 37 This is a diagram illustrating an example of the overall outline of the toilet system according to the second embodiment. It should be noted that, compared to... Figure 35 and Figure 36 Points that are already explained in the same way should be omitted from the explanation as appropriate.

[0326] exist Figure 37 In this system, the toilet system 1A includes an aspiration device 10, a gas composition detection device 20, a control device 100, and a estimation mechanism 200. Figure 35 and Figure 36 The illustration shows a toilet seat device 2 comprising a suction device 10, a gas composition detection device 20, and a control device 100, but is not limited to this. For example, the control device 100 may be separately installed from the suction device 10 and the gas composition detection device 20, communicating with and controlling them wirelessly or via wired connection. Furthermore, the suction device 10 may be controlled by a different control mechanism than the control device 100.

[0327] The estimation mechanism 200 is a computer (information processing device) that has the function of performing estimation processing based on information obtained from the detection by the gas composition detection device 20. For example, the estimation mechanism 200 can be a cloud server (server device) located outside the toilet R. In this case, the estimation mechanism 200 is communicatively connected via a estimation network such as the Internet to a device (also called a "toilet-in-place device") such as the toilet seat device 2 or the gas composition detection device 20 located in the toilet R via wired or wireless means.

[0328] Furthermore, the estimation mechanism 200 is connected via a predetermined network such as the Internet to a device such as the display device 300 that displays information to the user, and can be connected via wired or wireless means. It should be noted that the estimation mechanism 200, as long as it can transmit and receive information, can be connected to devices such as the toilet installation and the display device 300 in any way, whether via wired or wireless means. It should also be noted that the estimation mechanism 200 can communicate with the control device 100.

[0329] The estimation mechanism 200 uses information received from the device in the restroom to perform estimation processing related to the user's health status. It should be noted that the data acquired so far can be stored in the estimation mechanism 200 or in the display device 300. The estimation mechanism 200 generates information (also called "health estimation information") or related information for estimating the user's health status based on the amount of healthy gases (odorless gases) and odorous gases (malodorous gases) in the user's defecation gas. The estimation mechanism 200 calculates a score based on the ratio of the amount of healthy gases (odorless gases) to the amount of odorous gases (malodorous gases) in the user's defecation gas, as the user's health estimation information. For example, the estimation mechanism 200 can use any information such as ratios or odorous components. It should be noted that the above is only one example; the estimation mechanism 200 can generate any information as the user's health estimation information. For example, the estimation mechanism 200 can generate information such as the following as the user's health estimation information, and can generate health estimation information based on the following processing results.

[0330] For example, the estimation mechanism 200 can estimate information related to the user's intestinal state based on the measured values. For example, the estimation mechanism 200 can estimate information related to the state of bacteria. In this case, for example, the estimation mechanism 200 can estimate the occupancy rate of a certain bacterium, the amount and ratio of beneficial and harmful bacteria, etc. Furthermore, for example, the estimation mechanism 200 can estimate the state of metabolites. In this case, for example, the estimation mechanism 200 can estimate the amount or ratio of beneficial and harmful substances, etc. For example, the estimation mechanism 200 can estimate the pH state in the intestine. Furthermore, the estimation mechanism 200 can generate information that scores or evaluates the quality of such information. For example, the estimation mechanism 200 can generate such information as the user's health estimation information.

[0331] Furthermore, for example, the presumption agency 200 can generate information related to the user's health status based on the measured values. In this case, for example, the presumption agency 200 can generate information for evaluating the user's gut environment, such as scores or conditions. For example, the presumption agency 200 can generate information related to the user's gut environment. For example, the presumption agency 200 can generate information related to the user's immunity. For example, the presumption agency 200 can generate information related to the user's ease of weight loss. For example, the presumption agency 200 can generate information related to cholesterol levels. For example, the presumption agency 200 can generate information related to metabolic scores. For example, the presumption agency 200 can generate such information as presumed health information for the user. It should be noted that the above examples are merely illustrations, and the presumption agency 200 may generate various types of information related to the user's health status, not limited to the examples described above.

[0332] Based on a calculated ratio, the estimation agency 200 estimates that the user is healthier if their defecation contains more healthy gases (odorless gases) than odorous gases (malodorous gases). Conversely, the estimation agency 200 estimates that the user is less healthy if their defecation contains more odorous gases (malodorous gases) than healthy gases (odorless gases). It should be noted that this is only one example; the estimation agency 200 can make arbitrary estimations based on the calculated score. The estimation agency 200 sends the information to be provided to the user to the display device 300. The estimation agency 200 sends the calculated score as the user's health estimation information to the display device 300 used by that user.

[0333] The presupposition mechanism 200 is not limited to a cloud server (server device) and can be any device. That is, the device structure and configuration of the presupposition mechanism 200 can be adopted in any way as long as the desired processing can be achieved. For example, the presupposition mechanism 200 can be a portable terminal (device) such as a laptop computer carried by the administrator of the toilet system 1A. In addition, the presupposition mechanism 200 can be configured within the toilet R. For example, the presupposition mechanism 200 can be a structure configured within the toilet R. For example, the function of the presupposition mechanism 200 can be the same as that of the toilet seat device 2. In this case, the control device 100 can have the function of the presupposition mechanism 200.

[0334] Display device 300 is a display device (computer) that displays information provided to the user. For example, display device 300 can be a user terminal (portable terminal) owned by the user. In this case, display device 300 is implemented, for example, via a smartphone, mobile phone, PDA (Personal Digital Assistant), tablet terminal, or laptop PC (Personal Computer). For example, display device 300 is communicatively connected via a presumption network to devices included in the toilet system 1A of the presumption agency 200, etc., through wired or wireless means.

[0335] The display device 300 sends and receives information with the estimation mechanism 200. The display device 300 sends and receives information provided to the user from the estimation mechanism 200. The display device 300 receives a score calculated as a health estimation information of the user from the estimation mechanism 200. The display device 300 displays information including the score calculated as a health estimation information of the user.

[0336] exist Figure 37In this system, the display device 300 displays a score calculated as presumed health information for the user, as the user's gut environment score. For example, the display device 300 displays the user's gut environment score in a time-series manner according to the date and time of excretion. The display device 300 displays the target value of the score, information indicating the change of the user's gut environment score over time, and textual information representing the evaluation. For example, the display device 300 may request information from the presumption agency 200 and display information obtained from the presumption agency 200.

[0337] It should be noted that the above is only one example, and the toilet system 1A can adopt any device structure as long as it can achieve the desired treatment. In the toilet system 1A, the toilet seat device 2 may have a structure other than the display device 300. For example, the toilet seat device 2 may have a measuring device 4, a control device 100, and a estimation mechanism 200. Furthermore, for example, the display device 300 may or may not be included in the toilet system 1A. For example, if the display device 300 is the operating device 30 of the toilet R, the display device 300 may be included in the toilet system 1A. In this case, the operating device 30 has the function of displaying the user's estimated health information.

[0338] <2-4. User Actions and System Actions> Next, use Figure 38 An example illustrating the relationship between the behavior (actions) of users of toilet system 1A and the operation (movements) of toilet system 1A. Figure 38 This is an example of how a user's actions relate to the system's actions.

[0339] First, refer to Figure 38 This describes the procedure for users of toilet R to defecate. Users of toilet R perform the following steps: Figure 38 The actions in stages 1 through 7 are shown in the diagram.

[0340] First, as the first stage of the action, the user enters the toilet R. As the second stage of the action, the user inside the toilet R undresses. As the third stage of the action, the user, after undressing, sits on the toilet seat 5 in the toilet R. As the fourth stage of the action, the user sitting on the toilet seat 5 defecates in the basin 8 of the toilet 7.

[0341] As the fifth stage of the action, the user finishes their bowel movement by using the toilet seat device 2 for local cleaning or using toilet paper. As the sixth stage of the action, the user, having finished their bowel movement, stands up and gets off the toilet seat 5. As the seventh stage of the action, the user, after getting off the toilet, cleans the toilet 7, leaves the toilet R, and checks the results of the defecation gas analysis performed by the toilet system 1A.

[0342] Next, the procedure for the operation of the toilet system 1A corresponding to the actions of the user described above will be explained. Toilet system 1A begins to draw in gas components until the user entering toilet R begins to defecate. Figure 38 In this system, the toilet system 1A begins to attract gas components during stages 1 to 3. Thus, the toilet system 1A completes measurement preparation before the user defecates. For example, the toilet system 1A attracts gas (gas components) from the basin 8 before the user defecates, thereby attracting gas that serves as a reference (baseline) for comparison with the gas components after the user's defecation. For example, the toilet system 1A calculates the increment (increment) relative to the baseline to estimate (calculate) the amount of components contained in the defecation gas.

[0343] Toilet system 1A measures fecal gases during the period from when a seated user defecates until they leave their seat. Figure 38 In this system, the toilet system 1A measures the user's excretory gases before entering stage 4 and during stage 5. Thus, the toilet system 1A continuously draws in gases and collects data while the user is seated.

[0344] After completing the measurement of excrement gas, toilet system 1A performs analysis of the excrement gas. Figure 38 In this process, the toilet system 1A analyzes the user's excrement gas during stages 6 and 7. Therefore, after the user finishes defecating, the toilet system 1A analyzes the information obtained from the user's excrement gas (result) and calculates a score. The toilet system 1A analyzes the user's excrement gas and provides the analysis results to the user. It should be noted that the analysis and provision of results are not limited to stages 6 and 7; they can be performed at any time whenever the information can be provided. For example, the toilet system 1A can perform the analysis and provide various information such as results at any time during or after the measurement.

[0345] <2-5. Functional Structure of Toilet Seat Device> Next, refer to Figure 39 The functional structure of the toilet seat device 2 is explained. Figure 39 This is a block diagram illustrating an example of the structure of the toilet seat device according to the second embodiment. For example... Figure 39 As shown, the toilet seat device 2 includes a human body sensor 32, a seating sensor 33, an illuminance sensor 34, a control device 100, a nozzle motor 61, and a cleaning nozzle 6.

[0346] It should be noted that one example of the toilet seat device in the second embodiment is... Figure 35 The toilet seat device 2 shown, except that it does not have a first detection unit 21, is designed to be compatible with... Figure 1 The toilet seat device 2 shown has a substantially the same structure, so a description of this is omitted. It should be noted that the toilet seat device of the second embodiment may not have a first detection unit 21 when processing information about feces.

[0347] The control device 100 of the second embodiment controls various structures used for measuring gas composition. For example, the control device 100 controls various valves such as switching valves and shut-off valves. For example, the control device 100 controls the flow path of the gas composition by controlling the switching valve. For example, the control device 100 switches the flow path of the gas composition by switching the switching valve. The control device 100 controls the gas composition detection device 20.

[0348] The control device 100 controls the gas composition detection device 20 to start or stop measuring defecation gas based on the user's use of the toilet R. For example, the control device 100 instructs the gas composition detection device 20 to start measuring defecation gas when the user sits on the toilet seat 5, and instructs the gas composition detection device 20 to stop measuring defecation gas when the user leaves the toilet seat 5.

[0349] The control device 100 transmits control information to the gas composition detection device 20 via a wired connection. It should be noted that the control device 100 can also transmit control information to the gas composition detection device 20 wirelessly. For example, if the control device 100 is configured as a different device from the toilet seat device 2, the control information of the gas composition detection device 20 can be transmitted to the toilet seat device 2 wirelessly. In this case, the control device of the toilet seat device 2 can control the gas composition detection device 20 based on the received control information.

[0350] <2-6. Functional Structure of the Control Device> The functional structure of the control device will be described below. It should be noted that the block diagram of the control device in the second embodiment is different from that in the first embodiment. Figure 6 The block diagram of the control device 100 (e.g., the control device 100 of the second embodiment) is the same, so the illustration is omitted. Descriptions of points identical to those of the control device in the first embodiment are appropriately omitted. The control device 100 of the second embodiment includes a communication unit 110, a storage unit 120, and a control unit 130.

[0351] The storage unit 120 of the second embodiment stores various information required for processing in the same way as the storage unit 120 of the first embodiment. The storage unit 120 stores various information acquired from various sensors and other devices. The storage unit 120 stores various information used in various information processing processes. The storage unit 120 stores information used in various processes. For example, the storage unit 120 stores threshold-related information used in processing, such as a first threshold and a second threshold.

[0352] Storage unit 120 stores information indicating predetermined conditions for determining whether information can be changed. Storage unit 120 stores information indicating predetermined conditions, which include at least one of the following: a first calculated value or a second calculated value exceeds a first threshold, or a third calculated value is lower than a second threshold smaller than the first threshold. Storage unit 120 stores information indicating predetermined conditions, which include: a second calculated value exceeding a zeroth calculated value corresponding to odorous gases (malodorous gases) and hydrogen, calculated based on the detection results of a second gas composition sensor. Storage unit 120 functions as a storage mechanism for storing past first information. Storage unit 120 stores various historical information such as the results of past presumed processing and past output information.

[0353] The acquisition unit 131 in the second embodiment acquires various information in the same way as the acquisition unit 131 in the first embodiment. The acquisition unit 131 acquires defecation behavior prediction information obtained based on the detection of the seating detection mechanism. For example, the acquisition unit 131 acquires defecation behavior prediction information indicating the user's seating position.

[0354] The processing unit 132 of the second embodiment performs various processes in the same way as the processing unit 132 of the first embodiment. The processing unit 132 uses information stored in the storage unit 120 to perform various processes. The processing unit 132 controls the gas composition detection device 20.

[0355] The processing unit 132 performs a decision-making process. The processing unit 132 uses various information stored in the storage unit 120 to perform the decision-making process. The processing unit 132 uses various information acquired by the acquisition unit 131 to determine whether to perform reference value control.

[0356] The processing unit 132 performs calculations. The processing unit 132 performs calculations using various information stored in the storage unit 120. The processing unit 132 performs calculations using various information acquired by the acquisition unit 131.

[0357] Processing unit 132 calculates various information related to gas composition. Processing unit 132 calculates values ​​based on the measured values ​​measured by gas composition detection device 20. Processing unit 132 calculates the resistance value of sensor element based on the voltage value measured by gas composition sensor 40. For example, processing unit 132 uses a function representing the relationship between voltage value and resistance value of sensor element, and calculates the resistance value of sensor element based on the measured voltage value. Processing unit 132 uses equation (1) to calculate the reciprocal of the resistance value of sensor element (also called "calculated value").

[0358] The processing unit 132 can calculate the concentration of the gas component based on the calculated resistance value of the sensor element. In this case, the processing unit 132 uses a function that represents the relationship between the resistance value and the concentration of the gas component to calculate the concentration of the gas component based on the calculated resistance value.

[0359] The processing unit 132 performs estimation processing to estimate information related to the user's health based on the detection results of the gas composition sensor 40. For example, the processing unit 132 estimates information (health estimation information) or related information for estimating the user's health status based on a first calculated value corresponding to hydrogen and a third calculated value corresponding to odorous gases (malodorous gases) obtained from the detection results of the first gas composition sensor, i.e., gas composition sensor 40a). Thus, when the processing unit 132 performs health-related estimation processing, the processing unit 132 uses information obtained from the toilet system 1A to perform estimation processing. For example, the processing unit 132 uses information measured through measurement processing to estimate information related to the user's health. For example, the processing unit 132 uses corrected information to estimate information related to the user's health. Thus, the processing unit 132 of the second embodiment can perform estimation processing in the same way as the processing unit 132 of the first embodiment. It should be noted that when the estimation mechanism 200 performs processing, the processing unit 132 may not perform estimation processing.

[0360] Processing unit 132 calculates a second calculated value corresponding to hydrogen in the gas composition sensor 40b based on multiple calculated values ​​corresponding to hydrogen contained in the gas. Processing unit 132 calculates a third calculated value corresponding to the odorous gas (malodorous gas) based on the detection result of gas composition sensor 40b and the second calculated value. Processing unit 132 calculates a second calculated value using multiple calculated values, including a first calculated value corresponding to hydrogen obtained based on the detection result of gas composition sensor 40a.

[0361] The processing unit 132 calculates a second calculated value, which is a statistical value of multiple calculated values ​​obtained by the gas composition sensor 40a from multiple measurements of the gas composition in the sealed space. The processing unit 132 also calculates a second calculated value, which is a statistical value of multiple calculated values ​​obtained by the processing unit 132 from multiple measurements of the gas composition in the storage section. Finally, the processing unit 132 calculates a second calculated value, which is a statistical value of one or more calculated values ​​obtained by the processing unit 132 from one or more measurements of the gas composition in the flow path.

[0362] The processing unit 132 calculates a second calculated value using multiple calculated values, including a fourth calculated value corresponding to hydrogen obtained based on a first calculated value and the detection result of the gas composition sensor 40c. For example, if the difference between the amount or concentration calculated based on the first calculated value and the amount or concentration calculated based on the fourth calculated value is less than or equal to a predetermined value, the processing unit 132 determines that methane gas is not present in the user's defecation gas. For example, if the difference between the amount or concentration calculated based on the first calculated value and the amount or concentration calculated based on the fourth calculated value is greater than a predetermined value, the processing unit 132 determines that methane gas is present in the user's defecation gas.

[0363] If the processing unit 132 determines that the user's defecation gas contains methane gas, it uses the gas composition sensor 40c as a methane gas detection sensor. If the processing unit 132 determines that the user's defecation gas does not contain methane gas, it uses the gas composition sensor 40c as a hydrogen gas detection sensor.

[0364] The processing unit 132 calculates a first calculated value corresponding to hydrogen based on the detection result of the first gas composition sensor, i.e., gas composition sensor 40a. Based on the first calculated value, the processing unit 132 calculates a second calculated value corresponding to hydrogen based on the second gas composition sensor, i.e., gas composition sensor 40b. When gas composition sensor 40c is used as a hydrogen detection sensor, the processing unit 132 calculates the second calculated value based on the first and fourth calculated values. Based on the detection result of gas composition sensor 40b and the second calculated value, the processing unit 132 calculates a third calculated value corresponding to odorous gases (malodorous gases).

[0365] The processing unit 132 corrects for at least one of the odorous gas (malodorous gas) calculated based on the detection results of the gas composition sensor 40b and the zeroth calculated value, the second calculated value, or the third calculated value corresponding to hydrogen. As a correction, the processing unit 132 performs a correction that decreases the second calculated value or increases the zeroth calculated value.

[0366] The processing unit 132 performs correction when the first calculated value or the second calculated value exceeds a first threshold, or when the third calculated value is lower than a second threshold that is smaller than the first threshold. The processing unit 132 also performs correction when the second calculated value exceeds a zeroth calculated value. The processing unit 132 has a preset correction value corresponding to a value associated with an odorous gas (malodorous gas), and as a correction, replaces the third calculated value with the correction value when the third calculated value is lower than a third threshold.

[0367] If at least one of the first calculated value, the second calculated value, and the third calculated value meets a predetermined condition, the processing unit 132 performs the following control: It does not change the first information, which is information output by the output mechanism related to the user's health status, based on the third calculated value. If the predetermined condition is met, the processing unit 132 changes the value contained in the first information to a preset value.

[0368] The processing unit 132 modifies the first information based on the first information stored in the storage mechanism. If predetermined conditions are met, the processing unit 132 determines and outputs second information related to measurement accuracy. If predetermined conditions are met, the processing unit 132 determines and outputs third information related to measurement error.

[0369] The output unit 133 of the second embodiment performs output processing of various information in the same way as the output unit 133 of the first embodiment. When the estimation mechanism 200 performs estimation processing, the output unit 133 sends various information used by the estimation mechanism 200 for estimation processing to the estimation mechanism 200. The output unit 133 sends information representing the measured values ​​determined by the gas composition detection device 20. The output unit 133 sends information representing the calculated values ​​calculated by the processing unit 132.

[0370] The output unit 133 outputs various information, including content. When specified conditions are met, the output unit 133 outputs information including the original data, i.e., the modified score. When specified conditions are not met, the output unit 133 outputs information including the original data, i.e., the score. When specified conditions are met, the output unit 133 outputs second information related to measurement accuracy. When specified conditions are met, the output unit 133 outputs third information related to measurement errors.

[0371] <2-7. Gas Composition Sensor> Here, an example of the structure of a gas composition sensor will be described. For example, in the toilet system 1A of the second embodiment, a sensor with a structure already described is used. Figure 10 The circuit structure of the gas composition sensor 40 CR is described in the diagram. It should be noted that details regarding the structure of the gas composition sensor 40 are not provided in the original text. Figure 10The content is the same, so detailed explanations are omitted.

[0372] <2-8. Overview of Treatment in Toilet Systems> Next, a processing example based on the structure of the aforementioned toilet system 1A will be described. First, before describing the various processes in toilet system 1A, the gas composition sensor and the relationship between the values ​​obtained from the gas composition sensor and the amount of gas composition will be explained. It should be noted that points identical to those described above will be omitted as appropriate.

[0373] <2-8-1. Example of the relationship between the measurement and quantity of gas composition by gas composition sensors> First, use Figure 40 The relationship between the measurement and quantity of gas composition by the gas composition sensor is explained. Figure 40 This is a graph illustrating an example of the relationship between values ​​obtained from sensor measurements and the amount of gas components.

[0374] For example, Figure 40 Chart GR11 shows two logarithmic graphs, namely, the calculated value of component A (the reciprocal of the resistance value) obtained from measurements by a gas composition sensor, versus the gas quantity of component A (also simply referred to as "quantity"). Specifically, Figure 40 In the chart GR11, the vertical axis represents the calculated value of component A (1 / kΩ), and the horizontal axis represents the amount of component A (mL).

[0375] The points (○) in Chart GR11 correspond, for example, to actual measurement results taken to derive the relationship between the gas composition sensor's measurements and quantities. They show the calculated value of component A obtained from actual measurements by the gas composition sensor, taking the gas composition including the quantity corresponding to the horizontal axis. It should be noted that... Figure 40 For illustrative purposes, only 5 points (measured results) are shown in the figure, but the actual measurement results can be more than 6 points or less than 4 points.

[0376] Line LN1 in chart GR11 represents the relationship between the calculated value of component A and the amount of gas containing component A. The formula (function) corresponding to line LN1 is a regression equation used to calculate (estimate) the amount of gas containing component A based on the calculated value. For example, the formula (function) corresponding to line LN1 is derived by performing regression analysis on the points (measured results) in chart GR11.

[0377] Thus, the calculated value of component A obtained from the measurement by the gas composition sensor and the quantity of component A have a linear correlation on a double logarithmic scale. Therefore, the gas quantity of component A can be calculated based on the calculated values ​​such as the peak value of the gas composition sensor. That is, the gas quantity of each component can be calculated based on the calculated values ​​obtained from the measurements of multiple gas composition sensors. By calculating the gas quantity of each component, the toilet system 1A can calculate the ratio between the quantity of healthy gases (odorless gases) that sum up the quantities of gases belonging to the healthy category (odorless gases) and the quantity of odorous gases (malodorous gases) that sum up the quantities of gases belonging to the odorous category (malodorous gases).

[0378] <2-8-2. Gas Composition Sensor and Reaction Component Example> Next, use Figure 41 The gas composition sensor and reaction component examples are explained. Figure 41 This is a diagram illustrating an example of a gas composition sensor and reactants. (Through...) Figure 41 Table TB11 shows the correspondence between each gas component sensor and the components it detects. Figure 41 In the diagram, "○" indicates that the gas composition sensor reacts to the component, and "×" indicates that the gas composition sensor does not react to the component. For example... Figure 41 As shown in Table TB11, the components detected by each gas component sensor are different.

[0379] exist Figure 41 In this context, the first gas composition sensor (hydrogen sensor) is a sensor that reacts only to hydrogen (H2). For example, gas composition sensor 40a is the first gas composition sensor (hydrogen sensor). For example, gas composition sensor 40a of toilet system 1A is a gas composition sensor that reacts only to hydrogen, that is, the resistance value of the sensor resistor RS in equation (1) changes according to the change in the amount of hydrogen.

[0380] In such Figure 41 In the case of the first gas composition sensor shown, which only reacts to hydrogen, the calculated value of hydrogen-derived gas in the user's excrement is expressed by the following formula (2).

[0381] 1 / R S＿1 =1 / R air +1 / R H2＿1 …(2) In equation (2), “R” S＿1 "This corresponds to the resistance value of the sensor resistor RS of the first gas composition sensor. For example, "R" in equation (2) S＿1 "This is the resistance value of the sensor resistor RS, calculated based on the measured value of the first gas composition sensor."

[0382] In equation (2), “R” air "Corresponds to the resistance value derived from the baseline in the first gas composition sensor. For example, "R" in equation (2) air "It is the resistance value of the air originating from the pelvic cavity 8 before the user's defecation gas is expelled."

[0383] Furthermore, in equation (2), “R” H2＿1 "Corresponds to the resistance value originating from hydrogen in the first gas composition sensor. For example, "R" in equation (2) H2＿1 "It is the resistance value of hydrogen-derived gas contained in the fecal gas expelled by the user."

[0384] In equation (2), “R” S＿1 "and R in formula (2) air "R" is a calculated value obtained based on the measurement of the first gas composition sensor, and is detected (acquired) through the measurement of the first gas composition sensor. Therefore, the toilet system 1A calculates "R" in equation (2) by substituting the value obtained from the measurement of the first gas composition sensor into equation (2). H2＿1 ".

[0385] On the other hand, Figure 41 In this embodiment, the second gas composition sensor (odor gas sensor) is a sensor that reacts to odorous gases (such as H2S) but also to hydrogen (H2). For example, the gas composition sensor 40b of the toilet system 1A is a second gas composition sensor (odor gas sensor). For example, the gas composition sensor 40b reacts to both odorous gases and hydrogen, that is, the resistance value of the sensor resistor RS in equation (1) changes with the change in the amount of odorous gas and hydrogen. It should be noted that in this second embodiment, the detection unit for the hydrogen sensor is adjusted to react strongly to hydrogen, and the detection unit for the odor gas sensor is adjusted to react strongly to odorous gases, and the composition of each detection unit is adjusted accordingly.

[0386] like Figure 41 As shown in the second gas composition sensor, when reacting to odorous gases and hydrogen, the calculated value of the odorous gas contained in the user's excrement is expressed by the following formula (3).

[0387] 1 / R S＿2 =1 / R air +1 / R H2＿2 +1 / R H2S＿2 …(3) In equation (3), “R” S＿2"This corresponds to the resistance value of the sensor resistor RS of the second gas composition sensor. For example, "R" in equation (3) S＿2 "The resistance value of the sensor resistor RS is calculated based on the measured value of the second gas composition sensor."

[0388] In equation (3), “R” air "This corresponds to the resistance value derived from the baseline in the second gas composition sensor. For example, "R" in equation (3) air "The resistance value of the air originating from the pelvic cavity 8 before the user's defecation gas is expelled."

[0389] Furthermore, in equation (3), “R” H2＿2 "This corresponds to the resistance value derived from hydrogen in the second gas composition sensor. For example, "R" in equation (3) H2＿2 "It is the resistance value of hydrogen-derived gas contained in the fecal gas expelled by the user."

[0390] Furthermore, in equation (3), “R” H2S＿2 "This corresponds to the resistance value originating from the odorous gas in the second gas composition sensor. For example, "R" in equation (3) H2S＿2 "It is the resistance value of the hydrogen sulfide and other odorous gases contained in the fecal gas expelled by the user."

[0391] In equation (3), “R” S＿2 "and R in formula (3) air "R" is a calculated value obtained based on the measurement of the second gas composition sensor. It is detected (obtained) through the measurement of the second gas composition sensor. However, in equation (3), "R" H2＿2 "and "R H2S＿2 "These two variables have not yet been determined. Therefore, the toilet system 1A cannot determine "R" in equation (3) solely through equation (3)." H2S＿2 The value of “”. Therefore, the toilet system 1A uses information from gas composition sensors other than the second gas composition sensor to calculate (estimate) the amount of odorous gas, but this will be discussed later.

[0392] In addition, Figure 41 In this context, the third gas composition sensor (methane gas composition sensor) is a sensor that reacts to methane (CH4, etc.) but also to hydrogen (H2). For example, the gas composition sensor 40c in the bathroom system 1A is a third gas composition sensor (methane gas composition sensor). For example, the gas composition sensor 40c is a gas composition sensor that reacts to both methane and hydrogen, that is, the resistance value of the sensor resistor RS in equation (1) changes with the change in the amount of methane and hydrogen.

[0393] In addition, Figure 41In this context, the fourth gas composition sensor (carbon dioxide gas composition sensor) is a sensor that reacts only to carbon dioxide (CO2). For example, the CO2 sensor in bathroom system 1A is a fourth gas composition sensor (carbon dioxide gas composition sensor). Alternatively, the CO2 sensor could be an infrared gas composition sensor that reacts only to carbon dioxide.

[0394] <2-8-3. Example of calculating the amount of odorous gases> As described above, the calculated value (reciprocal of the resistance value) of the second gas composition sensor (odor gas sensor) is the sum of the values ​​of several components. For example, the calculated value (reciprocal of the resistance value) of the second gas composition sensor is the sum of the calculated value (reciprocal of the resistance value) of the baseline and the calculated values ​​(reciprocal of the resistance values) of the components originating from the reactants (odor gas + health-related gas). Therefore, the toilet system 1A derives this by using simultaneous equations corresponding to the calculated values ​​of each component and the equations corresponding to multiple gas composition sensors. Regarding this, using... Figure 42 Please provide an explanation. Figure 42 This is a diagram illustrating an example of the calculation and processing of the amount of odorous gases.

[0395] Figure 42 The function group FG11 in the table represents equations (2) to (6) and their corresponding relationships for the calculation (estimation) of the amount of odorous gases.

[0396] Equations (2) and (4) detail the calculated values ​​of the first gas composition sensor (hydrogen sensor) and the relationship between the calculated values ​​and the gas quantity. It should be noted that... Figure 42 Equation (2) in this context is the same as Equation (2) above, and detailed explanation is omitted.

[0397] LogH2 quantity = Log(1 / R) H2＿1 )*CE1+CS1 …(4) In equation (4), “H2 amount” is the amount of hydrogen calculated (estimated) by the measurement of the first gas composition sensor, and “LogH2 amount” corresponds to the logarithmic representation (logarithmic value) of the amount of hydrogen.

[0398] In equation (4), “R” H2＿1 "The resistance value derived from hydrogen gas is obtained based on the measurement of the first gas composition sensor, "Log(1 / R H2＿1 ")" corresponds to the logarithmic representation (logarithmic value) of the calculated value (the reciprocal of the resistance value) derived from hydrogen.

[0399] In equation (4), “CE1” is related to “Log(1 / R”. H2＿1 The coefficients related to ")" can be set to any value such as "-0.4...". Furthermore, "CS1" in equation (4) is added to "Log(1 / R H2＿1The constant on “CE1” can be set to any value such as “1.1…”. For example, the manager of the toilet system 1A can derive the coefficient “CE1” or the constant “CS1” contained in the formula (4) through actual measurement, and set the formula (4).

[0400] Equations (3), (5), and (6) detail the calculated values ​​of the second gas composition sensor (odorous gas sensor) and the relationship between the calculated values ​​and the gas quantity. It should be noted that... Figure 42 Equation (3) in this context is the same as Equation (3) above, and detailed explanation is omitted.

[0401] LogH2 quantity = Log(1 / R) H2＿2 )*CE2+CS2 …(5) In Equation (5), “H2 amount” is the amount of hydrogen calculated (estimated) by measurement of the second gas composition sensor, and “LogH2 amount” corresponds to the logarithmic representation (logarithmic value) of the amount of hydrogen.

[0402] In equation (5), “R” H2＿2 "The resistance value derived from hydrogen gas is obtained based on the measurement of the second gas composition sensor, "Log(1 / R H2＿2 ")" corresponds to the logarithmic representation (logarithmic value) of the calculated value (the reciprocal of the resistance value) derived from hydrogen.

[0403] In equation (5), “CE2” is related to “Log(1 / R”. H2＿2 The coefficients related to ")" can be set to any value such as "-0.8...". Furthermore, "CS2" in equation (5) is added to "Log(1 / R H2＿2 The constant on “CE2” can be set to any value such as “2.0…”. For example, the manager of the toilet system 1A can derive the coefficient “CE2” or constant “CS2” contained in formula (5) through actual measurement and set formula (5).

[0404] LogH2S quantity = Log(1 / R) H2S＿2 )*CE3+CS3 … (6) In Equation (6), “H2S amount” is the amount of odorous gas calculated (estimated) by the measurement of the second gas composition sensor, and “LogH2S amount” corresponds to the logarithmic representation (logarithmic value) of the amount of odorous gas.

[0405] In equation (6), “R” H2S＿2 "The resistance value derived from odorous gases is obtained based on the measurement of the second gas composition sensor," Log(1 / R H2S＿2 ")" corresponds to the logarithmic representation (logarithmic value) of the calculated value (the reciprocal of the resistance value) derived from odorous gases.

[0406] In equation (6), “CE3” is related to “Log(1 / R”. H2S＿2 The coefficients related to ")" can be set to any value such as "-0.7...". Furthermore, "CS3" in equation (6) is added to "Log(1 / R H2S＿2 The constant on “CE3” can be set to any value such as “1.8…”. For example, the manager of the toilet system 1A can derive the coefficient “CE3” or constant “CS3” contained in formula (6) through actual measurement and set formula (6).

[0407] Next, an example of the treatment of calculating (estimating) the amount of odorous gas in toilet system 1A will be explained using equations (2) to (6).

[0408] First, the toilet system 1A uses equations (2) and (4) to calculate the value related to the amount of hydrogen obtained based on the measurement of the first gas composition sensor. For example, the toilet system 1A uses the value obtained by the measurement of the gas composition sensor 40a ("R" in equation (2)). S＿1 The value of "R" and "R" air Find the value of "LogH2" in equation (4) using equation (2) and equation (4).

[0409] Then, the toilet system 1A substitutes the value of "LogH2" in equation (4) into equation (5) to calculate "R" in equation (5). H2＿2 The value of "".

[0410] Then, the toilet system 1A will calculate "R" in equation (5). H2＿2 Substitute the value of " into equation (3) to find "R" in equation (3). H2S＿2 The value of "". For example, the toilet system 1A will calculate "R" in equation (5). H2＿2 The value of “R” and the value obtained by measuring the gas composition sensor 40b (in Equation (3)). S＿2 The value of "R" and "R" air Substitute the value of "" into equation (3) to find "R" in equation (3). H2S＿2 The value of "". For example, the toilet system 1A will calculate "R" in equation (3). H2S＿2 Substitute the value of “H2S” into equation (6) to find the value of “H2S” in equation (6).

[0411] Thus, the toilet system 1A calculates (estimates) the amount of odorous gas by subtracting the influence from hydrogen from the output of the second gas composition sensor (odorous gas sensor). It should be noted that the above process is only one example; as long as the toilet system 1A can calculate (estimate) the amount of odorous gas, it can perform any process.

[0412] <2-8-4. Problems in calculating the amount of odorous gases> Next, regarding the problem of calculating the amount of odorous gases using the hydrogen sensor described above, we will use... Figure 43 Please provide an explanation. Figure 43 It is a diagram summarizing the calculation of the amount of odorous gases.

[0413] Figure 43 The measurement MS2 corresponds to the measurement of the second gas component sensor (odor gas sensor). For example, the waveform in measurement MS2 represents the sensor output (e.g., voltage value). In the odor gas calculation process, the zeroth calculated value ZV1 is calculated as the zeroth calculated value corresponding to hydrogen and odor gas obtained based on the detection result of measurement MS2 of the second gas component sensor (step S10). For example, the length of the zeroth calculated value ZV1 represents the magnitude of the value of the zeroth calculated value ZV1. Figure 43 The zeroth calculated value ZV1 includes the value corresponding to hydrogen, namely the first mixture value HV1, and the value corresponding to odorous gases, namely the second mixture value OV1.

[0414] For example, the first mixed value HV1 is the "R" of equation (3). H2＿2 "Corresponding to the correct value (accurate value). Furthermore, for example, the second mixed value OV1 is the "R" of equation (3). H2S＿2 "The corresponding correct value (exact value). It should be noted that in..." Figure 43 For illustration, the details of the first mixed value HV1 and the second mixed value OV1 in the zeroth calculated value ZV1 are shown in the figure, but they are actually inferred based on the third calculated value described later.

[0415] Figure 43 The measurement MS1 in the figure corresponds to the measurement of the first gas composition sensor (hydrogen sensor). For example, the waveform in the measurement MS1 represents the sensor output (e.g., voltage value). In the odorous gas calculation process, the first calculated value FV1 is calculated as the first calculated value corresponding to hydrogen obtained based on the detection result of the measurement MS1 of the first gas composition sensor (step S11). For example, the first calculated value FV1 corresponds to "R" in equation (2). H2＿1 "Refer to the calculated value related to the amount of hydrogen based on the measurement value of the first gas composition sensor. It should be noted that the numbering of steps S11, etc., is a symbol used to distinguish each process and does not indicate the order. For example, step S11 may be performed before step S10."

[0416] In the calculation of odorous gases, the second calculated value SV1 is calculated based on the first calculated value FV1, as the second calculated value corresponding to hydrogen from the second gas component sensor (step S12). For example, the second calculated value SV1 is the "R" value in equation (3). H2＿2 The corresponding estimated value. A second calculated value for hydrogen gas contained in the second gas composition sensor is calculated based on the first calculated value FV1. For example, if the processing unit 132 determines that the user's defecation gas does not contain methane gas, it calculates a second calculated value SV1 corresponding to hydrogen gas from the second gas composition sensor based on the first calculated value FV1.

[0417] In the calculation of odorous gases, the third calculated value TV1 is calculated based on the zero calculated value ZV1 and the second calculated value SV1 as the third calculated value corresponding to the odorous gas (step S13). The third calculated value TV1 is calculated by subtracting the second calculated value SV1 from the zero calculated value ZV1, thereby obtaining the third calculated value corresponding to the odorous gas. For example, the third calculated value TV1 is the same as "R" in equation (3). H2S＿2 The corresponding estimated value. In the odorous gas calculation process, the amount of odorous gas is calculated (estimated) using the third calculated value TV1 obtained through the process described above. Therefore, the measurement deviation of the first gas component sensor will affect the final calculation (estimated) amount of odorous gas. Therefore, problems may arise in the calculation of the amount of odorous gas in the odorous gas calculation process.

[0418] Next, use Figure 44 and Figure 45 This section provides specific examples illustrating the problems in calculating the amount of odorous gases. Figure 44 and Figure 45 This is a graph illustrating an example of how the measurement bias of a gas composition sensor affects calculations. It should be noted that, for calculations already performed using... Figure 43 Points with identical content should be omitted from the description appropriately.

[0419] First, use Figure 44 This section explains the problems that may arise even when there is a certain amount of odorous gas present.

[0420] The zero-value ZV2 is the zero-value corresponding to hydrogen and odorous gases obtained based on the detection results measured by the second gas composition sensor. Figure 44 The zeroth calculated value ZV2 includes the value corresponding to hydrogen, namely the first mixture value HV2, and the value corresponding to odorous gases, namely the second mixture value OV2.

[0421] For example, the first mixed value HV2 is the "R" of equation (3). H2＿2"Corresponding to the correct value (accurate value). Furthermore, for example, the second mixed value OV2 is the "R" of equation (3). H2S＿2 "The corresponding correct value (exact value). It should be noted that in..." Figure 44 For illustration purposes, the diagram shows the details of the first mixed value HV2 and the second mixed value OV2 in the zeroth calculated value ZV2, but in reality, it is inferred based on the third calculated value described later.

[0422] Figure 44 The second calculated value SV2 is calculated based on the first calculated value corresponding to hydrogen obtained from the detection result measured by the first gas composition sensor. The second calculated value SV2 is the second calculated value corresponding to hydrogen from the second gas composition sensor. For example, the second calculated value SV2 is the value corresponding to "R" in equation (3). H2＿2 "The corresponding estimated value."

[0423] Here, in the presence of a measurement deviation from the first gas composition sensor, this deviation is also reflected in the second calculated value SV2. Figure 44 In the example shown, the measurement error of the second calculated value SV2 caused by the measurement deviation of the first gas composition sensor is ±20%. Figure 44 The measurement error ME2 is obtained by visualizing the measurement error ±20% in the second calculated value SV2. The value of the second calculated value SV2 may vary between the upper and lower ends of the measurement error ME2.

[0424] The second calculated value SV2 corresponds to the length to the upper end of the measurement error ME2 when the measurement error of the first gas composition sensor is at its maximum in the negative direction (e.g., the measurement error is -20%). In this case, the second calculated value SV2 is the minimum, thus estimating a low amount of hydrogen.

[0425] The second calculated value SV2 corresponds to the length to the lower end of the measurement error ME2 when the measurement error of the first gas composition sensor is at its maximum in the positive direction (e.g., +20%). In this case, the second calculated value SV2 is the maximum, thus estimating a larger amount of hydrogen.

[0426] The third calculated value TV2 is calculated based on the zeroth calculated value ZV2 and the second calculated value SV2, as a third calculated value corresponding to the odorous gas (step S21). For example, the third calculated value TV2 is the "R" of equation (3). H2S＿2 "The corresponding estimated value."

[0427] Here, the measurement error in the second calculated value SV2 is ±20%, but it affects the third calculated value TV2. Figure 44The error range ER2 is obtained by visualizing the error caused by the measurement error in the second calculated value SV2 in the third calculated value TV2. When the measurement error in the second calculated value SV2 is ±20%, the value of the third calculated value TV2 varies between the upper and lower ends of the error range ER2.

[0428] The third calculated value TV2 is the value corresponding to the length to the upper end of the error range ER2 when the measurement error in the second calculated value SV2 is -20%. In this case, the third calculated value TV2 is the largest, thus presuming a larger amount of odorous gas.

[0429] The third calculated value TV2 is the value corresponding to the length to the lower end of the error range ER2 when the measurement error in the second calculated value SV2 is +20%. In this case, the third calculated value TV2 is the minimum value, thus presuming that the amount of odorous gas is small.

[0430] Next, use Figure 45 This section explains the problems that arise when there is a large amount of hydrogen and a small amount of odorous gases.

[0431] The zero-value ZV3 is obtained based on the detection results measured by the second gas composition sensor, corresponding to the zero-value for hydrogen and odorous gases. Figure 45 The zeroth calculated value ZV3 includes the value corresponding to hydrogen, namely the first mixture value HV3, and the value corresponding to odorous gases, namely the second mixture value OV3.

[0432] For example, the first mixed value HV3 is the "R" of equation (3). H2＿2 "Corresponding to the correct value (accurate value). Furthermore, for example, the second mixed value OV3 is the "R" of equation (3). H2S＿2 "The corresponding correct value (exact value). It should be noted that in..." Figure 45 For illustration purposes, the diagram shows the details of the first mixed value HV3 and the second mixed value OV3 in the zeroth calculated value ZV3, but it is actually inferred based on the third calculated value described later.

[0433] Figure 45 The second calculated value SV3 is derived from the first calculated value corresponding to hydrogen obtained based on the detection result measured by the first gas composition sensor. The second calculated value SV3 is the second calculated value corresponding to hydrogen from the second gas composition sensor. For example, the second calculated value SV3 is the value corresponding to "R" in equation (3). H2＿2 "The corresponding estimated value."

[0434] Here, in the event of a measurement deviation from the first gas composition sensor, this deviation is also reflected in the second calculated value SV3. Figure 45The example shown is that the measurement error of the second calculated value SV3, caused by the measurement deviation of the first gas composition sensor, is ±20%. Figure 45 The measurement error ME3 is obtained by visualizing the measurement error of ±20% in the second calculated value SV3. The value of the second calculated value SV3 can vary between the upper and lower ends of the measurement error ME3.

[0435] The second calculated value SV3 is the value corresponding to the length to the upper end of the measurement error ME3 when the measurement error of the first gas composition sensor is at its maximum in the negative direction (e.g., the measurement error is -20%). In this case, the second calculated value SV3 is at its minimum, thus estimating a low amount of hydrogen.

[0436] The second calculated value SV3 is the value corresponding to the length to the lower end of the measurement error ME3 when the measurement error of the first gas composition sensor is at its maximum in the positive direction (e.g., +20%). In this case, the second calculated value SV3 is at its maximum, thus inferring a larger amount of hydrogen. Figure 45 In the example, the amount of hydrogen is relatively large, while the amount of odorous gas is relatively small. Therefore, when the measurement error of the first gas component sensor is in the positive direction, the second calculated value SV3 will be larger than the zero calculated value ZV3.

[0437] The third calculated value TV3 is calculated based on the zeroth calculated value ZV3 and the second calculated value SV3, as a third calculated value corresponding to the odorous gas (step S22). For example, the third calculated value TV3 is the "R" of equation (3). H2S＿2 "The corresponding estimated value."

[0438] Here, the measurement error in the second calculated value SV3 is ±20%, but it affects the third calculated value TV3. Figure 45 The error range ER3 is obtained by visualizing the error caused by the measurement error in the second calculated value SV3 in the third calculated value TV3. When the measurement error in the second calculated value SV3 is ±20%, the value of the third calculated value TV3 can vary between the upper and lower ends of the error range ER3.

[0439] The third calculated value TV3 is the value corresponding to the length to the upper end of the error range ER3 when the measurement error in the second calculated value SV3 is -20%. In this case, the third calculated value TV3 is the largest value, thus presuming a larger amount of odorous gas.

[0440] The third calculated value TV3 is the value corresponding to the length to the lower end of the error range ER3 when the measurement error in the second calculated value SV3 is +20%. In this case, the third calculated value TV3 will make the second calculated value SV3 greater than the zeroth calculated value ZV3, thereby causing the amount of odorous gas to become below 0.

[0441] Thus, if the first gas composition sensor has a measurement deviation, this deviation will affect the estimated value of the amount of odorous gas. For example, in the above... Figure 45 In such cases, due to the significant deviation in the hydrogen quantity measurement by the first gas composition sensor, the calculated value of odorous gases may fall below 0 in some situations, making proper calculation impossible. In such cases, it is difficult to properly perform processing based on gas composition measurement. Therefore, the toilet system 1A can solve the above problem by performing any of the following first, second, and third processes, thereby properly performing processing based on gas composition measurement.

[0442] <2-9. First Treatment (Multiple Measurements)> To suppress the influence of the measurement deviation of the first gas composition sensor, the toilet system 1A performs a first process using multiple pieces of information. Specifically, the toilet system 1A calculates a second calculated value corresponding to hydrogen from the second gas composition sensor based on multiple calculated values, including a first calculated value, obtained from the detection results of the first gas composition sensor.

[0443] Therefore, the toilet system 1A can suppress the influence of measurement bias by performing multiple measurements. An example of the structure and measurement of the toilet system 1A in such a case of performing multiple measurements will be described below.

[0444] <2-9-1. First Test Case> First, use Figure 46 The first test case will be explained. Figure 46 This is a graph representing the first measurement example using a gas composition sensor. Specifically, Figure 46 This is a graph representing a summary of the processing using multiple data points, i.e., the first measurement example. Figure 46 In order to illustrate the processed image, only a portion of the structure of the toilet system 1A is shown. Figure 46 In this device, the gas composition detection apparatus 20 includes a gas composition sensor 40a serving as a hydrogen sensor and a gas composition sensor 40b serving as an odorous gas sensor. It should be noted that details identical to those described above are omitted.

[0445] Figure 46Measurement MS11 in the diagram corresponds to the measurement of gas composition sensor 40a. For example, line LN11 in measurement MS11 represents the sensor output as the measured value (voltage value) of gas composition sensor 40a. The shaded portion in measurement MS11 corresponds to one measurement process, representing the change in sensor output caused by fecal gas expelled during one defecation or flatulence. For example, toilet system 1A uses the maximum value (peak value) of one measurement process as the measured value (voltage value) to calculate a calculated value.

[0446] In the first measurement example, the toilet system 1A acquires multiple data points for the excrement gas produced by one bowel movement or flatulence using the gas composition sensor 40a, and averages them. For example, the toilet system 1A performs measurements by the gas composition sensor 40a multiple times, acquires multiple measured values, and averages the calculated values. For example, the value obtained by averaging the corresponding calculated values ​​measured by the gas composition sensor 40a multiple times by the toilet system 1A is used as "R" in equation (2). H2＿1 The value of '(also called the 'determining value') is used to calculate the second calculated value.

[0447] In this way, the toilet system 1A calculates a second calculated value, which is a statistical value of multiple calculated values ​​obtained from multiple measurements of the gas composition by the gas composition sensor 40a. In this case, the second calculated value is the statistical value of multiple calculated values ​​obtained from multiple measurements of the gas composition by the gas composition sensor 40a. Thus, the toilet system 1A can average multiple data points, reduce measurement bias, and calculate (estimate) a hydrogen quantity close to the true value. It should be noted that the above example illustrates the case where the average value is used as the determining value for calculating the second calculated value, but it is not limited to the average value; the determining value used for calculating the second calculated value can be any value, such as the median value.

[0448] <2-9-2. Second Test Case> Next, use Figure 47 The second test case will be explained. Figure 47 This is a graph representing the second measurement example using a gas composition sensor. Specifically, Figure 47 This is a diagram showing a summary of the second measurement example performed by a toilet system 1A with a specific structure for acquiring multiple data points. It should be noted that descriptions of points identical to those described above have been appropriately omitted.

[0449] exist Figure 47 In the toilet system 1A, there is a mechanism for trapping gas components, namely a sealing mechanism 500. The sealing mechanism 500 has a sealed space inside, capable of trapping gas components within that sealed space. Figure 47In this configuration, a sealing mechanism 500 is disposed between the gas composition detection device 20 and the suction device 10. That is, the sealing mechanism 500 is disposed in the flow path between the gas composition detection device 20 and the suction device 10. The sealing mechanism 500 retains the gas composition attracted by the suction device 10 in a sealed space. For example, the sealing mechanism 500 has a storage section for storing the gas composition attracted by the suction device 10. The storage section has a sealed space inside, enabling the gas composition to be stored in the sealed space.

[0450] In the second measurement example, the toilet system 1A stores the excrement gas in a sealed mechanism 500 for multiple measurements. In this case, the toilet system 1A performs multiple measurements on the retained gas components using a mechanism such as the sealed mechanism 500 that retains the gas components. For example, if the toilet system 1A detects an output value above a predetermined value by the gas component sensor, it closes the shut-off valve (not shown). Then, the toilet system 1A stops the suction device 10, retaining the gas components in the storage section. Then, the toilet system 1A brings the retained gas components into contact with the gas component sensor 40a.

[0451] Figure 47 The measurement MS12 in the measurement corresponds to the measurement of the gas composition sensor 40a. For example, line LN12 in measurement MS12 represents the measured value (voltage value) of the gas composition sensor 40a, i.e., the sensor output. The shaded portion in measurement MS12 corresponds to a measurement process, representing the change in sensor output caused by fecal gas expelled during a defecation or flatulence. For example, the toilet system 1A uses the maximum value (peak value) of a measurement process as the measured value (voltage value) to calculate the calculated value.

[0452] In the second measurement example, after the toilet system 1A retains the excrement gas in the flow path (such as the sealing mechanism 500), it obtains multiple calculated values ​​through multiple measurements by the hydrogen sensor, i.e., the gas composition sensor 40a, and then averages them.

[0453] Thus, the toilet system 1A calculates the second calculated value as a statistical value of multiple calculated values ​​obtained by repeatedly measuring the gas composition in the enclosed space using the gas composition sensor 40a. Specifically, the toilet system 1A calculates the second calculated value as a statistical value of multiple calculated values ​​obtained by repeatedly measuring the gas composition in the storage compartment.

[0454] Therefore, by retaining excrement gas within the flow path, the toilet system 1A can perform multiple measurements of the excrement gas using a hydrogen sensor, and average these multiple signals to reduce the impact of sensor measurement bias. The processing after calculating the second value is the same as in the first measurement example, so detailed explanation is omitted.

[0455] <2-9-3. Third Test Case> Next, use Figure 48 The third test case will be explained. Figure 48 This is a graph representing the third measurement example using a gas composition sensor. Specifically, Figure 48 This is a diagram showing a summary of a third measurement performed in a toilet system 1A with the gas composition detection device 20 installed within a sealed mechanism 500. It should be noted that descriptions of points identical to those described above have been appropriately omitted.

[0456] exist Figure 48 In the toilet system 1A, there is a sealing mechanism 500. Figure 48 In the example, the sealed mechanism 500 houses the gas composition detection device 20. In the toilet system 1A of the third test example, the gas composition detection device 20 is housed within the sealed mechanism 500. The sealed mechanism 500 has a flow path that can be switched to a closed flow path via an opening and closing mechanism (e.g., a switching valve), which will be described later.

[0457] In the third measurement example, the toilet system 1A repeatedly measures the gas composition remaining within the sealed mechanism 500 using the gas composition detection device 20 within the sealed mechanism 500. In this case, the toilet system 1A acquires data multiple times after the gas composition has filled the sensor surface and the sensor output has stabilized.

[0458] Figure 48 Measurement MS13 in the diagram corresponds to the measurement of gas composition sensor 40a. For example, line LN13 in measurement MS13 represents the measured value (voltage value) of gas composition sensor 40a, i.e., the sensor output. The shaded portion in measurement MS13 corresponds to a portion of the range where the sensor output (power value) reaches its peak (maximum) due to the gas composition remaining within the sealed mechanism 500. For example, the toilet system 1A performs multiple measurements within the range where the sensor output (power value) reaches its peak (maximum). Figure 48 The three determinations (represented by the dashed circle ○) were performed, and the corresponding calculated values ​​were obtained for each determination.

[0459] In the third measurement example, the toilet system 1A stores the excrement gas in a sealed mechanism 500 for multiple measurements. The toilet system 1A retains the excrement gas in the sealed mechanism 500, which houses the gas composition detection device 20, and obtains multiple calculated values ​​through multiple measurements by the hydrogen sensor, i.e., the gas composition sensor 40a, and then averages them.

[0460] Thus, the toilet system 1A calculates the second calculated value, which is a statistical value of multiple calculated values ​​obtained by the gas composition sensor 40a measuring the gas composition within the sealed mechanism 500 multiple times.

[0461] Therefore, by continuously excrement gas coming into contact with the hydrogen sensor, the toilet system 1A can acquire a signal when the sensor output is stable. Furthermore, through averaging, it can further reduce the impact of measurement deviations caused by the gas composition sensor. The processing after calculating the second value is the same as in the first measurement example, so detailed explanation is omitted.

[0462] <2-9-4. Fourth Test Case> Next, use Figure 49 The fourth test case will be explained. Figure 49 This is a graph representing the fourth measurement example of the gas composition sensor. Figure 49 It means having the same as Figure 47 A schematic diagram of the fourth test case performed on a toilet system 1A with the same structure as the second test case shown. Specifically, Figure 49 This is a diagram illustrating a fourth measurement example of measuring the gas composition that is retained in the flow path between the gas composition detection device 20 and the suction device 10 after passing through the sealing mechanism 500. It should be noted that explanations of points identical to those described above have been appropriately omitted.

[0463] The structure of the toilet system 1A in the fourth test case is the same as that in the toilet system 1A in the second test case, so the illustrations and detailed descriptions are omitted.

[0464] Figure 49 The measurement MS14 in the diagram corresponds to the measurement of gas composition sensor 40a. For example, line LN14 in MS14 represents the measured value (voltage value) of gas composition sensor 40a, i.e., the sensor output. For example, in bathroom system 1A... Figure 49 The dashed line ○ in the figure indicates the time when the sensor output (power value) reaches its peak (maximum). The data is then processed. Figure 49 (This is repeated three times), calculating the values ​​corresponding to each measurement process. For example, in bathroom system 1A, the gas components are repeatedly exposed to the sensor to obtain multiple data points corresponding to the peak values ​​output by the sensor.

[0465] In the fourth measurement example, the toilet system 1A, in order to perform multiple measurements, stores the excrement gas through a sealed mechanism 500 in the flow path between the gas composition detection device 20 and the suction device 10. The toilet system 1A retains the excrement gas in the flow path between the gas composition detection device 20 and the suction device 10, and obtains multiple calculated values ​​through multiple measurements by the hydrogen sensor, i.e., the gas composition sensor 40a, and then averages them. That is, after retaining the excrement gas in a location different from that within the gas composition detection device 20, the toilet system 1A repeatedly contacts the hydrogen sensor, i.e., the gas composition sensor 40a, with the gas composition, and obtains multiple calculated values ​​through multiple measurements by the gas composition sensor 40a, and then averages them.

[0466] Thus, the second calculated value is the statistical value obtained from multiple measurements of the gas composition within the flow path in the bathroom system 1A.

[0467] Therefore, the toilet system 1A reduces the impact of measurement errors by repeatedly measuring the retained excrement gas using a hydrogen sensor, obtaining a first calculated value, and averaging it. The processing after calculating the second calculated value is the same as in the first measurement example, so detailed explanation is omitted.

[0468] <2-9-5. Fifth Test Case> Next, use Figure 50 The fifth test case will be explained. Figure 50 This is a graph representing the fifth measurement example using a gas composition sensor. Specifically, Figure 50 This is a schematic diagram of the fifth measurement example performed by a toilet system 1A equipped with a gas composition detection device 20 (referred to as "gas composition detection device 20A") having a third gas composition sensor, namely gas composition sensor 40c. It should be noted that descriptions of points identical to those described above have been appropriately omitted.

[0469] exist Figure 50 In this example, the toilet system 1A includes a gas composition detection device 20A equipped with a gas composition sensor 40c. For instance, the third gas composition sensor, namely gas composition sensor 40c, is a gas composition sensor that is more sensitive to hydrogen and less sensitive to odorous gases compared to gas composition sensor 40b. In the fifth measurement example, gas composition sensor 40c may be a hydrogen sensor.

[0470] In the fifth measurement example, the toilet system 1A obtains values ​​(e.g., calculated values) representing the amount and concentration of hydrogen from the hydrogen sensor, i.e., gas composition sensor 40a, and the third gas composition sensor, i.e., gas composition sensor 40c, and averages them.

[0471] The toilet system 1A calculates the corresponding value for hydrogen (also known as the "fourth calculated value") based on the detection results of the gas composition sensor 40c. For example, the toilet system 1A uses equation (1) to calculate the fourth calculated value corresponding to hydrogen from the gas composition sensor 40c based on the measured value (voltage value) of the gas composition sensor 40c. The toilet system 1A substitutes the fourth calculated value into a regression equation to calculate (estimate) the amount of hydrogen obtained based on the measurement of the gas composition sensor 40c.

[0472] Figure 50 Measurement MS15 in the diagram corresponds to the measurements of gas composition sensors 40a and 40c. For example, line LN151 in measurement MS15 represents the measured value (voltage value) of gas composition sensor 40a, i.e., the sensor output. For example, line LN152 in measurement MS15 represents the measured value (voltage value) of gas composition sensor 40c, i.e., the sensor output.

[0473] For example, bathroom system 1A in Figure 50 The dashed line ○ on line LN151 in the diagram indicates the moment when the sensor output (power value) of gas composition sensor 40a reaches its peak (maximum), and the measurement process is performed to calculate the first calculated value corresponding to the measurement process. Furthermore, for example, in the toilet system 1A... Figure 50 The dashed line ○ on line LN152 indicates the moment when the sensor output (power value) of gas composition sensor 40c reaches its peak (maximum), and the corresponding fourth calculated value is calculated. For example, bathroom system 1A acquires data corresponding to the peak values ​​of the hydrogen sensor and the third gas composition sensor.

[0474] In the fifth measurement example, the toilet system 1A uses the first calculation obtained based on the measurement of gas composition sensor 40a and the fourth calculation obtained based on the measurement of gas composition sensor 40c to calculate the second calculated value.

[0475] Thus, the toilet system 1A calculates a statistical value, namely the second calculated value, which is obtained by using multiple calculated values ​​obtained from multiple gas composition sensors 40 (gas composition sensor 40a and gas composition sensor 40c). The second calculated value is derived from the statistical value of the multiple calculated values ​​obtained from the multiple gas composition sensors 40.

[0476] Therefore, the toilet system 1A reduces individual variability bias and calculates a more accurate amount of hydrogen by calculating values ​​(e.g., calculated values) from multiple gas composition sensors and averaging them. The processing after calculating the second value is the same as in the first measurement example, so detailed explanation is omitted.

[0477] <2-9-6. Sixth Test Case> Next, the sixth test example will be described. The toilet system 1A in the sixth test example differs from the toilet system 1A in the fifth test example in that the gas composition detection device 20A has a different type of gas composition sensor than the hydrogen sensor, namely, the gas composition sensor 40c, which serves as the third gas composition sensor. It should be noted that points identical to those described above have been appropriately omitted.

[0478] In the sixth measurement example, the toilet system 1A includes a gas composition detection device 20A, which has a methane gas composition sensor 40c, different from the hydrogen sensor 40a. For example, the methane gas composition sensor 40c is a gas composition sensor that reacts more readily with hydrogen and methane but less readily with odorous gases. For example, the methane gas composition sensor 40c is mounted on the gas composition detection device 20A to measure methane gas in fecal matter.

[0479] Here, fewer people carry methanogenic bacteria, and a smaller proportion of people have methane gas in their fecal matter. For example, for people who do not produce methane gas, the amount of hydrogen gas can be calculated using a methane gas composition sensor.

[0480] Therefore, in the sixth measurement example, when the person producing methane gas is the subject of the measurement in the toilet system 1A, the gas composition sensor 40c is used as a methane gas detection sensor. When the toilet system 1A determines that the user's excrement gas contains methane gas, the gas composition sensor 40c is used as a methane gas detection sensor.

[0481] On the other hand, when the toilet system 1A uses a person who does not produce methane gas as the subject of measurement, the gas composition sensor 40c is used as a hydrogen gas detection sensor. When the toilet system 1A determines that the user's excrement gas does not contain methane gas, the gas composition sensor 40c is used as a hydrogen gas detection sensor.

[0482] As described above, even a gas composition sensor intended for measuring other components can be adapted for use as a hydrogen measurement sensor when it is not used for its original purpose, thereby further improving the measurement accuracy o...

Claims

1. A toilet system, characterized in that, have: The first detection sensor, which is installed in the bathroom fixture, is used to detect odorless gases; The second detection sensor is installed in the toilet device to detect malodorous gases; as well as An estimating mechanism, based on the detection results of the first and second detection sensors, estimates at least one of the intestinal bacteria, intestinal bacterial metabolites, and pH of a user using the toilet device.

2. The toilet system according to claim 1, characterized in that, The estimation mechanism estimates at least one of the following: intestinal bacteria, intestinal bacterial metabolites, and pH of the user of the toilet device, based on the composition ratio of the excreted gas obtained from the detection results of the first detection sensor and the second detection sensor.

3. The toilet system according to claim 1, characterized in that, The toilet system also has a third detection sensor for detecting the characteristics of feces. Based on the detection results of the first detection sensor, the second detection sensor, and the third detection sensor, at least one of the following—intestinal bacteria, intestinal bacterial metabolites, and pH—of the user using the toilet device is estimated.

4. The toilet system according to claim 3, characterized in that, The toilet system has the following features: The first testing section is used to test stool. The second detection unit has at least one of the first detection sensor and the second detection sensor; as well as The control device performs a presumption process based on the detection results of the first detection unit and the detection results of the second detection unit, presuming at least one of the information provided or the score related to the user's health, and performs control to output the result of the presumption process to the outside.

5. The toilet system according to claim 1, characterized in that, The toilet system has the following features: A gas composition detection device, comprising a first gas composition sensor, namely the first detection sensor, which reacts with hydrogen contained in the gas, and a second gas composition sensor, namely the second detection sensor, which reacts with an odorous gas containing sulfur and hydrogen. and Control device, which controls the gas composition detection device, The control device calculates a second calculated value corresponding to hydrogen from the second gas composition sensor based on multiple calculated values ​​corresponding to hydrogen contained in the gas. Based on the detection result of the second gas composition sensor and the second calculated value, it calculates a third calculated value corresponding to the odorous gas. The restroom system is a system that estimates the user's health status or information related to the health status based on the third calculated value. The plurality of calculated values ​​includes a first calculated value corresponding to hydrogen, obtained based on the detection results of the first gas composition sensor.

6. The toilet system according to claim 1, characterized in that, The toilet system has the following features: A gas composition detection device, comprising a first gas composition sensor, namely the first detection sensor, which reacts with hydrogen contained in the gas, and a second gas composition sensor, namely the second detection sensor, which reacts with an odorous gas containing sulfur and hydrogen. and Control device, which controls the gas composition detection device, The control device calculates a first value corresponding to hydrogen based on the detection result of the first gas composition sensor, calculates a second value corresponding to hydrogen based on the first value, and calculates a third value corresponding to the odorous gas based on the detection result of the second gas composition sensor and the second value. The restroom system is a system that estimates the user's health status or information related to the health status based on the third calculated value. The control device will use at least one of the zero calculated value, the second calculated value, or the third calculated value corresponding to the odorous gas and hydrogen, calculated based on the detection results of the second gas component sensor, as the object for correction.

7. The toilet system according to claim 1, characterized in that, The toilet system has the following features: A gas composition detection device, comprising a first gas composition sensor, namely the first detection sensor, which reacts with hydrogen contained in the gas, and a second gas composition sensor, namely the second detection sensor, which reacts with an odorous gas containing sulfur and hydrogen. Control device, which controls the gas composition detection device; as well as The output mechanism outputs information related to the processing result of the control device. The control device calculates a first value corresponding to hydrogen based on the detection result of the first gas composition sensor, calculates a second value corresponding to hydrogen based on the first value, and calculates a third value corresponding to the odorous gas based on the detection result of the second gas composition sensor and the second value. The restroom system is a system that estimates the user's health status or information related to the health status based on the third calculated value. The control device performs control when at least one of the first calculated value, the second calculated value, and the third calculated value meets a predetermined condition, so that the first information output by the output mechanism as the user's health status or information related to the health status is not changed based on the third calculated value.

8. The toilet system according to claim 1, characterized in that, The toilet system has the following features: A gas composition detection device, comprising at least one of a first detection sensor and a second detection sensor, which react with the gaseous components contained in the gas; and Control device, which controls the gas composition detection device, The gas composition sensor is equipped with a sensor element and a measuring resistor element. The control device controls the gas composition detection device such that the measured value of the gas composition sensor becomes a value within a preset range when the user is not using the toilet, and performs reference value control to control the measured value, which is used as a reference value, to a predetermined value. The gas detection and analysis device uses the reference value, which is controlled by the reference value, to perform processing related to the measurement of fecal gas.

9. The toilet system according to claim 1, characterized in that, The toilet system has the following features: A suction device that draws in gas from the basin of the toilet. A gas flow path through which the gas attracted by the suction device passes; A gas composition detection device comprising at least one of a first detection sensor and a second detection sensor, which react with the gas composition contained in the gas passing through the gas flow path. A control device that controls the suction device and the gas composition detection device; and The pressure loss generating unit increases the pressure loss generated when gas passes through in the direction of gas travel through the gas flow path, which is located further downstream than the gas composition sensor.

10. The toilet system according to claim 1, characterized in that, The toilet system has the following features: A gas flow path, which draws in gas from the toilet bowl and allows it to pass through; and The sensor sensing unit reacts with the gas components contained in the gas passing through the gas flow path. The gas flow path includes a main flow path and a secondary flow path, wherein the secondary flow path is disposed within the main flow path, and gas flowing in from the main flow path passes through the secondary flow path at a slower flow rate than in the main flow path. The sensor sensing element is disposed within the secondary flow path.

11. The toilet system according to claim 1, characterized in that, The toilet system has the following features: A suction device that draws in gas from the basin of the toilet. A gas flow path through which the attracted gas passes via the suction device; A gas composition detection device comprising at least one of a first detection sensor and a second detection sensor, which react with the gas composition contained in the gas passing through the gas flow path. A deodorizing component is disposed in the gas flow path and is used to deodorize the odorous components of the gas; as well as A control device that controls the suction device and the gas composition detection device. The gas flow path includes an inlet section for allowing gas to flow into the gas flow path, and an outlet section disposed downstream of the inlet section for discharging gas from the gas flow path to the outside of the gas flow path. The gas composition sensor is disposed between the inlet and the outlet. The inlet is positioned to collect the excrement gas within the basin. The discharge section is configured to discharge gas in the gas flow path from a position further back than the user's seating position on the toilet to the outside of the toilet.

12. The toilet system according to claim 1, characterized in that, The toilet system has the following features: A suction device that draws in the expelled gas from the toilet bowl; A gas flow path through which the attracted gas passes via the suction device; A gas composition detection device comprising at least one of a first detection sensor and a second detection sensor, which reacts with a specified gas composition contained in the gas passing through the gas flow path. as well as A control device that controls the suction flow rate of the suction device. When the suction flow rate of the suction device is set to x (L / min), 10≤x≤200 is satisfied.

13. The toilet system according to claim 12, characterized in that, The control device controls the suction flow rate to be above 50 L / min and below 170 L / min.

14. The toilet system according to claim 1, characterized in that, The toilet system has the following features: A suction device that draws in the expelled gas from the toilet bowl; A gas flow path through which the attracted gas passes via the suction device; A gas composition detection device comprising at least one of a first detection sensor and a second detection sensor, wherein the gas composition sensor reacts with a predetermined gas composition contained in the gas flowing through the gas path; and A control device that controls the suction flow rate of the suction device. Set the driving conditions of the gas composition detection device to y. Set the suction flow rate of the suction device to x1 (L / min). The number of signal processing steps when converting the electrical signal detected by the gas composition detection device into a digital signal is set to x2 (Hz). In the following equation 1, when variables α, β, and b are set to 0.025≤α≤0.045, -11≤β≤-7, and 1.5≤b≤3.0 respectively, 0≤y≤500 is satisfied. (Equation 1) 。 15. The toilet system according to claim 1, characterized in that, The toilet system has the following features: A suction device that draws in gas from the basin of the toilet. A gas flow path through which the attracted gas passes via the suction device; A gas composition detection device comprising at least one of a first detection sensor and a second detection sensor, which reacts with a specified gas composition contained in the gas passing through the gas flow path. A status detection mechanism detects changes in the status of the toilet equipped with the toilet; and The standby time setting mechanism sets the standby time from the end of the measurement of the previous user to the time when the measurement of the next user can be performed, based on the historical records of the toilet status detected by the gas composition detection device or the status detection mechanism.

16. The toilet system according to claim 1, characterized in that, The toilet system has the following features: A suction device that draws in gas from the basin of the toilet. A gas flow path through which the attracted gas passes via the suction device; A gas composition detection device comprising at least one of a first detection sensor and a second detection sensor, which reacts with a specified gas composition contained in the gas passing through the gas flow path. A status detection mechanism detects changes in the status of the toilet equipped with the toilet; and The data acquisition range setting mechanism sets the acquisition range of the data parsed by the data parsing mechanism in the gas composition measurement data based on the historical records of the toilet status detected by the gas composition detection device or the status detection mechanism.

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