Information processing method, information processing apparatus, and recording medium

Abstract

The present application provides an information processing method, an information processing device, and a recording medium, which can accurately predict the timing of excretion. The information processing device according to the present application is characterized by comprising: an acquisition unit that acquires in-vivo information and in-vitro information, the in-vivo information being information related to excretion in a body, the in-vitro information being information different from the in-vivo information and information related to outside the body; and a prediction unit that predicts, based on the in-vivo information and the in-vitro information, the timing of excretion by a wearer of an absorbent article in the future.

Description

Technical Field

[0001] This invention relates to an information processing method, an information processing apparatus, and a recording medium. Background Technology

[0002] Previously, techniques were known for providing users with various information related to absorbent articles. As an example of such techniques, there is a known technique that suggests a method for placing the absorbent article based on the posture at the time of leakage, using measurements from sensors installed on the absorbent article. Additionally, there is a known technique that suggests appropriate absorbent articles and replacement timing based on the amount of urine absorbed by the absorbent article.

[0003] Existing technical documents

[0004] Patent documents

[0005] Patent Document 1: Japanese Patent Application Publication No. 2018-202154

[0006] Patent Document 2: Japanese Patent Application Publication No. 2018-206381 Summary of the Invention

[0007] The problem the invention aims to solve

[0008] However, the aforementioned existing technologies may not be able to predict the timing of excretion with high precision.

[0009] For example, in the prior art, only the measurement results of sensors installed on absorbent articles are used, so it may not be possible to predict the timing of excretion with high accuracy.

[0010] This application was made in view of the above circumstances, and its purpose is to predict the timing of excretion with high accuracy.

[0011] Solution for solving the problem

[0012] The information processing apparatus of this application is characterized by having: an acquisition unit that acquires internal information and external information, wherein the internal information is information related to excretion within the body and the external information is information different from the internal information and is related to the external body; and a prediction unit that predicts the excretion timing of a wearer of an absorbent material based on the internal information and the external information.

[0013] The effects of the invention

[0014] According to one method of implementation, the timing of excretion can be predicted with high accuracy. Attached Figure Description

[0015] Figure 1 This is a diagram illustrating the overall situation of information processing involved in the implementation method.

[0016] Figure 2 This is a diagram illustrating an example of noise detection involved in the implementation method.

[0017] Figure 3 This is a diagram illustrating a structural example of the information processing apparatus involved in the implementation method.

[0018] Figure 4 This diagram illustrates an example of a wearer information storage unit according to an embodiment.

[0019] Figure 5 This diagram illustrates an example of an in-body or out-of-body information storage unit involved in the implementation method.

[0020] Figure 6 This diagram illustrates an example of a time schedule information storage unit involved in an implementation method.

[0021] Figure 7A This is an illustrative diagram explaining the decision-making process for guiding people to the toilet at a specific time.

[0022] Figure 7B This is an illustrative diagram explaining the decision-making process for guiding people to the toilet at a specific time.

[0023] Figure 7C This is an illustrative diagram explaining the decision-making process for guiding people to the toilet at a specific time.

[0024] Figure 8 This is a flowchart illustrating the decision-making process used to determine whether a pad should be used together.

[0025] Figure 9 This is a flowchart illustrating the learning process involved in the implementation method.

[0026] Figure 10 This is a flowchart illustrating the prediction processing involved in the implementation method.

[0027] Figure 11 This is a diagram illustrating an example of a hardware structure.

[0028] Explanation of reference numerals in the attached figures

[0029] 1: Information processing system; 30: User device; 100: Information processing device; 120: Storage unit; 121: Wearer information storage unit; 122: In-body and out-of-body information storage unit; 123: Schedule information storage unit; 124: Guidance timing storage unit; 130: Control unit; 131: Acquisition unit; 132: First determination unit; 133: Threshold determination unit; 134: Generation unit; 135: Prediction unit; 136: Information control unit; 137: First decision unit; 138: Second decision unit; 139: Proposal unit; 140: Second determination unit; SN1: First sensor; SN2: Second sensor. Detailed Implementation

[0030] From the description in this specification and the accompanying drawings, at least the following matters become clear.

[0031] An information processing method, executed by an information processing device, is characterized by comprising the following steps: an acquisition step, acquiring internal information and external information, wherein the internal information is information related to excretion within the body, and the external information is information different from the internal information and is information related to the external body; and a prediction step, based on the internal information and the external information, predicting the excretion timing of a wearer of an absorbent material in the future.

[0032] According to this information processing method, the information processing device can predict the timing of excretion with high accuracy. Furthermore, by improving prediction accuracy, for example, the waiting time for excretion can be effectively reduced for those receiving care. Therefore, according to this information processing device, efficient excretion care can be achieved. In other words, according to this information processing method, the information processing device can more accurately provide advanced excretion care.

[0033] In addition, the information processing device acquires information related to the amount of excrement accumulated in the body as the body information.

[0034] According to such an information processing device, information related to the amount of excrement accumulated in the body is obtained as internal information, so it is possible to determine how much excrement is accumulated in the body and the tendency to excrete.

[0035] In addition, the information processing device acquires information related to excrement excreted from the body to the outside as the external information.

[0036] According to such an information processing device, information related to excrement excreted from the body to the outside is acquired as external information. Therefore, by combining information related to excretion within the body with information related to excrement excreted from the body to the outside, the timing of excretion can be predicted with higher accuracy.

[0037] In addition, the information processing device acquires excretion information indicating that excrement has been excreted from the body to the outside as the external information.

[0038] According to such an information processing device, excretion information indicating that excrement has been excreted from the body to the outside is acquired as external information, so it is possible to determine how much excrement is accumulated in the body when there is a tendency to excrete.

[0039] In addition, the information processing device predicts the excretion timing of the wearer after the current time point based on the information obtained before the current time point, and uses this prediction as the excretion timing.

[0040] According to such an information processing device, based on the information obtained before the current time point, the excretion timing of the wearer after the current time point can be predicted, thus enabling the prediction of excretion timing that is consistent with the individual wearer.

[0041] In addition, the information processing device predicts the excretion timing based on information obtained before the current time point, which is information related to the amount of excrement accumulated in the body when the excrement is excreted from the body to the outside.

[0042] According to such an information processing device, the timing of excretion is predicted based on information obtained before the current time point from the acquired information, and is related to the amount of excrement accumulated in the body when excretion is excreted from the body to the outside. Therefore, by utilizing a tendency related to an excretion volume threshold, such as the tendency to excrete when a certain amount of excrement is accumulated in the body, the timing of excretion can be predicted with high accuracy.

[0043] In addition, the information processing device predicts the excretion timing based on trend information, which is information related to the accumulation amount, and the trend information represents a trend related to the accumulation amount.

[0044] According to such an information processing device, excretion timing is predicted based on tendency information, which is related to the amount of accumulation. The tendency information represents a tendency related to the amount of accumulation, and thus it is possible to predict excretion timing that is consistent with the individual wearer with high accuracy.

[0045] In addition, the information processing device predicts the timing of excretion based on information related to the amount of accumulation and the amount of excrement accumulated in the wearer's body at the current time.

[0046] Based on such an information processing device, the timing of excretion can be predicted based on information related to the amount of accumulated waste and the amount of waste accumulated in the wearer's body at the current time. Therefore, it is possible to predict the excretion timing that is consistent with the individual wearer with high accuracy.

[0047] In addition, the information processing device predicts the timing of excretion based on the correlation between dietary information related to the wearer's food and drink intake and the wearer's excretion status, the internal information, and the external information.

[0048] According to such an information processing device, the timing of excretion is predicted based on the correlation between dietary information related to the wearer's food intake and the wearer's excretion status, internal information, and external information. Therefore, the fact that the timing of excretion changes according to dietary status can be taken into account in the prediction processing, resulting in a more accurate prediction of the timing of excretion.

[0049] In addition, the information processing device predicts the timing of excretion based on the correlation between drug information related to the drug administered to the wearer and the wearer's excretion status, the internal information, and the external information.

[0050] According to such an information processing device, the timing of excretion is predicted based on the correlation between drug information related to the drug administered to the wearer and the wearer's excretion status, internal information and external information. Therefore, the fact that the excretion period changes according to the laxative status can be taken into account in the prediction process, resulting in a more accurate prediction of the timing of excretion.

[0051] In addition, the information processing device controls regulations related to the care of the wearer based on the correlation between dietary information and excretion status, as well as the correlation between medication information and excretion status.

[0052] Based on such an information processing device, the regulations related to the caregiver are controlled according to the correlation between dietary information and excretion status, as well as the correlation between medication information and excretion status, thus enabling more suitable care for the wearer.

[0053] In addition, the information processing device makes controls related to the food and drink provided to the wearer or the laxative administered to the wearer based on the correlation.

[0054] Based on such an information processing device, controls related to the food and drink provided to the wearer or the laxative administered to the wearer can be made based on correlation, thereby effectively improving the wearer's QOL.

[0055] In addition, the information processing device acquires the internal information detected by a first sensor worn on the wearer's body and the external information detected by a second sensor installed on the absorbent article.

[0056] According to such an information processing device, internal information detected by a first sensor worn on the wearer's body and external information detected by a second sensor installed on the absorbent material are acquired, thus enabling the acquisition of internal and external information at any time in the same environment.

[0057] In addition, the information processing device makes prescribed suggestions to the caregiver of the wearer based on the predicted excretion timing.

[0058] Based on this information processing device, a prescribed suggestion is made to the wearer based on the predicted excretion timing. Therefore, it is possible to suggest care at various precisely calculated timings, thereby improving the efficiency of the wearer's work. Furthermore, as a result, the quality of care received by the wearer can be improved.

[0059] In addition, the information processing device determines the timing of regulations related to the care of the wearer based on the predicted excretion timing.

[0060] Based on such an information processing device, the timing of the regulations related to the care of the wearer is determined based on the predicted excretion timing, thus enabling the timing of the regulations related to the care of the wearer to be determined with high precision.

[0061] In addition, the information processing device determines the replacement time of the absorbent material worn by the wearer based on the predicted excretion time, and proposes to replace the absorbent material at the replacement time.

[0062] Based on this information processing device, the timing for changing the absorbent clothing worn by the wearer is determined according to the predicted excretion time, and the replacement of the absorbent clothing is suggested at that time, thus effectively reducing the risk of urinary leakage.

[0063] In addition, if the information processing device determines that the predicted amount of excretion at the excretion timing exceeds the remaining amount of excrement that the absorbent article can absorb, it determines the timing specified before the excretion timing as the replacement timing.

[0064] According to such an information processing device, if the predicted amount of excretion at the time of excretion exceeds the amount of excrement that the absorbent material can absorb, the time before the time of excretion is determined as the replacement time. Therefore, if the risk of leakage is determined to be high when the next urination occurs without replacing the absorbent material, it can be suggested to replace the absorbent material at the time before the predicted time of the next urination.

[0065] In addition, the information processing device determines the guidance timing for guiding the wearer to the toilet based on the predicted excretion timing, and proposes to guide the wearer to the toilet at the guidance timing.

[0066] Based on this information processing device, the guidance time for guiding the wearer to the toilet is determined based on the predicted excretion time, and the wearer is guided to the toilet at that time. Therefore, the most practical time that can achieve efficient toilet guidance can be presented.

[0067] In addition, the information processing device determines the timing for guiding the wearer to the toilet based on the predicted excretion time and the subject's schedule or the wearer's schedule.

[0068] Based on such an information processing device, the timing for guiding the wearer to the toilet is determined according to the predicted excretion time and the target's or wearer's schedule, thus presenting the most practical timing that can achieve efficient toilet guidance.

[0069] In addition, the information processing device determines the timing for guiding the wearer to the toilet based on the timetable and the necessity of guiding each wearer to the toilet based on the excretion timing.

[0070] Based on this information processing device, the timing for guiding the wearer to the toilet is determined according to a schedule and the wearer's need to be guided to the toilet based on their elimination timing. This improves the success rate of toilet training and presents the most practical timing for efficient toilet guidance. Furthermore, the wearer (e.g., a young child) easily understands the importance of eliminating waste in the toilet, and the recipient (e.g., a caregiver) can effectively utilize the time within the schedule.

[0071] Furthermore, this information processing device can assist caregivers (such as caregivers) in working efficiently in stressful care settings, thus helping to create an environment more conducive to their work. As a result, the wearer (such as the person being cared for) receives appropriate care, leading to an increase in quality of life (QOL).

[0072] In addition, the information processing device determines whether to use the replacement absorbent pad together with the absorbent item worn by the wearer based on the predicted excretion timing, and makes a suggestion corresponding to the determination result.

[0073] Based on such an information processing device, it determines whether to use a replacement absorbent pad together with the absorbent item worn by the wearer based on the predicted excretion time, and makes a suggestion corresponding to the determination result, thus enabling suggestions for diaper care specifically for defecation.

[0074] In addition, the information processing device determines whether to use the replacement absorbent pad together with the absorbent item worn by the wearer, based on the type of excrement that may be excreted at the excretion time.

[0075] Based on this information processing device, it determines whether to use a replacement absorbent pad together with the absorbent item worn by the wearer, based on the type of excrement that may be excreted at the time of excretion. Therefore, it can make suggestions on diaper care corresponding to urination and defecation respectively.

[0076] In addition, the information processing device determines whether to use the replacement absorbent pad together with the absorbent item worn by the wearer based on the state of the excrement that may be excreted at the excretion time.

[0077] Based on this information processing device, it determines whether to use a replacement absorbent pad together with the absorbent item worn by the wearer, based on the state of the excrement that may be excreted at the scheduled time of excretion. Therefore, it is possible to make suggestions on diaper care that can effectively reduce the risk of leakage.

[0078] In addition, the information processing device determines whether to use the replacement absorbent pad together with the absorbent item worn by the wearer based on the predicted body movements of the wearer at the time of excretion.

[0079] Based on such an information processing device, it can determine whether to use a replacement absorbent pad together with the absorbent item worn by the wearer, based on the predicted body movements of the wearer at the time of excretion. Therefore, it is possible to make suggestions on diaper care that can effectively reduce the risk of leakage.

[0080] Hereinafter, an example of a mode (hereinafter referred to as "Employment") for implementing the information processing method, information processing apparatus, and information processing program will be described in detail with reference to the accompanying drawings. However, this Embodiment is not intended to limit the information processing method, information processing apparatus, and information processing program. Furthermore, in the following Embodiment, the same reference numerals will be used to mark the same parts, and repeated descriptions will be omitted.

[0081] [1. Overview of information processing involved in the implementation method]

[0082] First, an overview of the information processing involved in the implementation method will be explained according to the premise. Conventionally, there are known techniques that predict excretion using information obtained from sensors that detect the state inside the body, and techniques that predict excretion using information obtained from sensors that detect the state outside the body (e.g., whether or not excretion has occurred) by being installed in absorbent materials such as diapers. However, conventional methods predict excretion by separately using information representing the state inside the body and information representing the state outside the body, and therefore cannot be said to predict the timing of excretion with high accuracy.

[0083] Therefore, in this embodiment, it is conceivable to predict future excretion timing by combining in vivo information with external information, wherein the in vivo information is related to excretion within the body, and the external information is related to excrement excreted from the body to the outside. That is, in this embodiment, as the information processing involved in the implementation, the following information processing is performed.

[0084] Specifically, in this embodiment, internal and external information are acquired, and the timing of excretion by the wearer of the absorbent material is predicted based on the acquired internal and external information. The internal information is related to internal excretion, and the external information is related to excrement excreted from the body to the outside. For example, in this embodiment, internal information detected by a first sensor worn on the wearer's body is acquired, and external information detected by a second sensor installed on the absorbent material is acquired.

[0085] Furthermore, more specifically, in this embodiment, the timing of excretion is predicted based on information obtained from in vivo and external information acquired before the current time point, and this information relates to the amount of excrement accumulated in the body when the excrement is excreted from the body to the outside.

[0086] [2. Regarding the information processing system involved in the implementation method]

[0087] Next, use Figure 1 This describes the information processing system involved in the implementation method. Figure 1 This is a diagram illustrating the overall information processing involved in the implementation method. For example... Figure 1 As shown, the information processing system 1 according to the embodiment includes a first sensor SN1, a second sensor SN2, a target device 30, and an information processing device 100. The first sensor SN1, the second sensor SN2, the target device 30, and the information processing device 100 are connected in a wired or wireless communication manner via a network N (not shown). Furthermore, Figure 1The information processing system 1 shown may include multiple first sensors SN1, multiple second sensors SN2, multiple object devices 30, and multiple information processing devices 100. Furthermore, detection devices having the functions of first sensors SN1 and second sensors SN2 may be mounted on the same device. Specifically, the first sensors SN1 and second sensors SN2 may not be... Figure 1 As shown, the devices are independent of each other. The first sensor SN1 and the second sensor SN2 constitute a detection device with the detection functions of both.

[0088] [3. Regarding each device]

[0089] Next, the various devices included in the information processing system 1 according to the embodiment will be described. The first sensor SN1 is an example of a first sensor, used in a manner worn on the body of a wearer wearing absorbent materials. Furthermore, the first sensor SN1 detects internal information, which is information related to excretion within the body. For example, the first sensor SN1 detects information related to the amount of excrement accumulated in the body as internal information. For example, the first sensor SN1 uses ultrasound to measure changes in bladder distension, thereby measuring the amount of urine accumulated in the bladder at that time. Additionally, for example, the first sensor SN1 uses ultrasound to measure changes in rectal distension, thereby measuring the amount of stool accumulated in the rectum at that time. Furthermore, in addition to ultrasound, the detection method of the first sensor SN1 can also use other detection methods such as impedance detection, image analysis, optical sensors, and detection using non-visible light.

[0090] In addition, for example, the first sensor SN1 performs detection (measurement) as described above at predetermined intervals (e.g., every minute), thereby sending the detection results to the information processing device 100 at predetermined intervals (e.g., every minute).

[0091] In addition, the first sensor SN1 can also detect bladder status information, such as the bladder's expansion rate and size. Furthermore, the first sensor SN1 can also detect intestinal status information, such as intestinal movement (peristalsis).

[0092] The second sensor SN2 is an example of a second sensor used in a way that it is installed on an absorbent item worn by a wearer (a disposable "baby diaper" if the wearer is an infant, and a disposable "adult diaper" if the wearer is an adult). Furthermore, the second sensor SN2 detects external information, which is information related to excretions excreted from the body to the outside. For example, the second sensor SN2 detects the excretion of excrement from the body as external information. That is, the second sensor SN2 performs excretion detection.

[0093] For example, the second sensor SN2 detects the presence or absence of excretion based on impedance changes within the absorbent article. As an example, the second sensor SN2 detects the magnitude of the impedance between conductive components fitted into the absorbent article, and detects the presence or absence of excretion based on the pattern of impedance change over time. Furthermore, the second sensor SN2 determines whether the excrement is feces or urine based on the proportion of the impedance change relative to the change in time after a predetermined period from the detection of excretion. In addition to impedance, the second sensor SN2 can also use other detection methods such as conductive sensors, temperature, humidity, color, odor, and chemical sensors (detecting specific chemical substances).

[0094] The recipient device 30 is an information processing terminal used by the recipient to provide care to the wearer. The recipient device 30 can be, for example, a smartphone, tablet computer, laptop PC, desktop PC, mobile phone, PDA, etc. Alternatively, when the recipient is assumed to be an employee of a designated facility (e.g., a care facility), the recipient device 30 can be an information processing terminal with a so-called call bell function.

[0095] The information processing device 100 is an information processing device that performs the information processing described in the above-described embodiments, and is implemented by a server device, cloud system, or the like. In this embodiment, the information processing device 100 is assumed to be a server device.

[0096] [4. An example of information processing involved in the implementation]

[0097] From this point on, use Figure 1 This will illustrate an example of information processing involved in the implementation method. Figure 1 The following example illustrates this: Based on internal and external information obtained from a caregiver U11 (the person being cared for) who is staying in a designated care facility and wearing an adult diaper DP1 (hereinafter referred to as "diaper DP1"), the timing of the wearer U11's future excretion is predicted, and the prediction result is communicated. Furthermore, in this example, the caregiver U11 is cared for by care staff, etc.

[0098] On the other hand, in the information processing involved in the implementation method, not only those wearing adult diapers are considered as processing objects, but also toddlers wearing children's diapers. Furthermore, in this case, the object may be, for example, a childcare worker in a daycare center.

[0099] Here, based on Figure 1For example, wearer U11 wears the first sensor SN1 around the waist (e.g., near the lower abdomen) and wears a diaper DP1 with a second sensor SN2 installed.

[0100] In this state, the first sensor SN1 detects (measures) the amount of excrement (urine, feces) accumulated in the body (bladder, intestines) at predetermined intervals (e.g., every minute), and sends the measurement result to the information processing device 100 at these predetermined intervals. Therefore, the information processing device 100 acquires the amount of excrement (urine, feces) accumulated in the body (bladder, intestines) at predetermined intervals, i.e., the amount accumulated in the body (an example of body information) (step S11). More specifically, the information processing device 100 acquires at any time the combination of the amount of excrement (urine, feces) accumulated in the body (bladder, intestines), i.e., the amount accumulated in the body, and the date and time when the amount was measured.

[0101] Here, as in step S11, the information processing device 100 continuously acquires the combination of body accumulation and date / time, thus enabling it to store the body accumulation corresponding to each date / time as a historical record of body accumulation in the in-body / out-of-body information storage unit 122 (described later). Therefore, a curve representing the change in body accumulation over time is obtained based on the historical record of body accumulation. Figure 1 In the examples, curve CV11 corresponding to urine and curve CV12 corresponding to feces are shown for wearer U11. Curve CV11 is obtained by setting the horizontal axis to date and time (minutes) and the vertical axis to the amount of urine (ml) accumulated in wearer U11's bladder at the corresponding date and time (minutes). Curve CV12 is obtained by setting the horizontal axis to date and time (minutes) and the vertical axis to the amount of feces (g) accumulated in wearer U11's rectum at the corresponding date and time (minutes).

[0102] Furthermore, in the process of storing historical records of body accumulation, as explained above, the second sensor SN2 detects whether there is excretion based on impedance changes within the diaper DP1. If excretion is detected, excretion detection information (an example of external information) indicating that excretion has occurred is sent to the information processing device 100. Therefore, in the process of storing historical records of body accumulation, the information processing device 100 also acquires excretion detection information (an example of external information) indicating that excretion has occurred (step S12). The excretion detection information also includes date and time information indicating the date and time of the excretion.

[0103] Furthermore, as described above, the second sensor SN2 is a device capable of determining the type of excreted waste, such as whether the waste is feces or urine. However, in this embodiment, the second sensor SN2 is configured to only detect excretion and not to determine the type of excreted waste. Specifically, the second sensor SN2 is configured not to determine whether the waste is urine or feces, and the information processing device 100 determines the type of excrement based on internal information. On the other hand, the second sensor SN2 may also determine the type of excrement; in this case, the information processing device 100 can use the determination result of the second sensor SN2. In addition, the information processing device 100 can combine the determination result obtained based on internal information with the determination result of the second sensor SN2, thereby determining whether the waste is feces or urine with higher accuracy.

[0104] Furthermore, when excretion detection information indicating that excretion has occurred is obtained during the process of storing historical records of body accumulation (excretion detected), the information processing device 100 determines the type of excrement excreted based on the body information obtained from the first sensor SN1 when excretion occurs (step S13). Specifically, when excretion occurs, the information processing device 100 determines whether the wearer U11's excrement is urine or feces based on the body information obtained from the first sensor SN1. The first sensor SN1 also detects state information indicating bladder and bowel states, so the information processing device 100 determines whether the excrement is urine or feces based on the state information at the time of excretion. For example, if the information processing device 100 determines that the bladder moved during excretion based on the state information, it determines that the excrement is urine. On the other hand, if the information processing device 100 determines that the rectum moved during excretion based on the state information, it determines that the excrement is feces.

[0105] Furthermore, when the information processing device 100 obtains excretion detection information indicating that excretion has occurred (excretion detected), it performs excretion (step S14) when it determines how much excrement has accumulated in the body (bladder or intestine) according to the determination result in step S13. Specifically, the information processing device 100 determines how much excrement has accumulated in the body (bladder or intestine) according to the determination result based on the historical record of the amount of excrement accumulated in the body corresponding to the excrement indicated by the determination result stored before the time of excretion, and the date and time indicating the time of excretion.

[0106] That is, the information processing device 100 determines the amount of the excrement accumulated in the body when the excrement represented by the judgment result is excreted from the body to the outside, i.e., the accumulation threshold, based on the historical record of the amount of excrement in the body corresponding to the excrement represented by the judgment result stored before the time of excretion, and the date and time representing the time of excretion.

[0107] For example, suppose that the information processing device 100 determines in step S13 that urine has been excreted. In this case, the information processing device 100 determines the amount of urine accumulated in the bladder (inside the body) at the time of excretion from the bladder (inside the body) to the diaper DP1 (outside the body) based on the curve CV11 (historical record of internal accumulation) obtained before the time of urine excretion and the date and time of urine excretion. Here, in Figure 1 In the example, four peaks are circled in curve CV11, and the amount of accumulation in the body corresponding to each peak is the accumulation threshold.

[0108] based on Figure 1 For example, the volume of urine accumulated in the body corresponding to peak PK11 is "270ml". This example indicates that urination was detected at the date and time corresponding to peak PK11 on the horizontal axis (let's call it "date and time D11" for convenience), and the volume of urine accumulated in the bladder at the time of urination, i.e., the volume threshold, is "270ml". Alternatively, this example can also be described as the wearer U11 urinating at the time of date and time D11 when "270ml" of urine had accumulated in the bladder.

[0109] In addition, based on Figure 1 For example, the example shows a volume of urine accumulation corresponding to peak PK12 as "260ml". This example represents an instance where urination was detected at the date and time corresponding to peak PK12 on the horizontal axis (let's call it "date and time D12" for convenience), and the volume of urine accumulated in the bladder at that date and time D12, i.e., the accumulation threshold, is "260ml". Alternatively, this example can also be described as wearer U11 urinating at the date and time D12 when "260ml" of urine had accumulated in the bladder.

[0110] The same explanation can be given for peaks PK13 and PK14, so detailed explanations are omitted.

[0111] The example of determining urine as the result in step S13 has already been explained; the case of determining feces as the result will also be explained. For example, suppose that the information processing device 100 determines in step S13 that feces have been excreted. In this case, the information processing device 100 determines the amount of feces accumulated in the rectum (in the body) when the feces are excreted from the rectum (inside the body) to the diaper DP1 (outside the body) based on the curve CV12 (historical record of internal accumulation) obtained before the time point of feces excretion and the date and time of feces excretion. Here, in Figure 1 In the example, the curve CV12 shows three peaks circled, and the amount of accumulation in the body corresponding to each peak is the accumulation threshold.

[0112] based on Figure 1 For example, the amount of stool accumulated in the body corresponding to peak PK21 is "85g". This example indicates that a bowel movement was detected at the date and time corresponding to peak PK21 on the horizontal axis (let's call it "date and time D21" for convenience), and the amount of stool accumulated in the rectum at the time of the bowel movement, i.e., the accumulation threshold, is "85g". Alternatively, this example can also be described as the wearer U11 having a bowel movement at the time of the date and time D21 when "85g" of stool had accumulated in the rectum.

[0113] The same explanation can be given for peaks PK22 and PK23, so detailed explanations are omitted.

[0114] Furthermore, by repeating steps S11 to S14 in this way, learning data for obtaining the tendency related to the accumulation threshold is stored. Therefore, the information processing device 100 learns the model based on the historical record of the body's accumulation volume, that is, the historical record of the state at which the accumulation threshold is determined at any time (step S15). For example, the information processing device 100 learns the correlation between the tendency of the body's accumulation volume and time, where the body's accumulation volume is the amount of excrement accumulated in the body (bladder, intestines), and the time is the time from the point in time when such a body accumulation volume is reached until the accumulation threshold is reached. For example, the information processing device 100 generates a model that inputs the body's accumulation volume at the current time point into the model and outputs the time from the body's accumulation volume at the current time point until the accumulation threshold is reached (until excretion occurs).

[0115] Additionally, for example, the information processing device 100 updates the model based on the most recent historical record of the accumulation volume in the body, i.e., the historical record of the state in which the accumulation volume threshold is determined at any time, thereby repeating this process over time to generate the latest model. Furthermore, such a model corresponds to tendency information, which indicates a tendency related to the accumulation volume (accumulation volume threshold) of the excrement in the body when it is excreted from the body to the outside.

[0116] exist Figure 1 In the example, the information processing device 100 generates a prediction model MD11 for the wearer U11 based on the historical record (curve CV11) of the amount of urine accumulated in the body. In this model, the amount of urine accumulated at the current time is input, and the time from that accumulated amount until the accumulation threshold is reached (until urine is excreted) is output. Furthermore, in Figure 1 In the example, the information processing device 100 generates a predictive model MD12 for the wearer U11 based on the historical record (curve CV12) of the amount of feces accumulated in the body. In this model, the amount of feces accumulated in the body at the current time is input, and the time from that accumulated amount until the accumulation threshold is reached (until feces are excreted) is output. Furthermore, the information processing device 100 updates the predictive models MD11 and MD12 based on the historical record of the most recent specified period.

[0117] Furthermore, in this state, the information processing device 100 determines whether it has become a timing for predictive processing, which is the process of predicting the timing of excretion by the wearer U11 (step S16). For example, the information processing device 100 determines whether it has become a timing for predictive processing based on whether the second sensor SN2 detects excretion. For example, if the second sensor SN2 detects excretion, the information processing device 100 can determine that it has become a timing for predictive processing to predict the timing of excretion after the current time point when excretion has occurred. In addition, for example, if the information processing device 100 receives a request from a user (e.g., a caregiver of the wearer), it determines that it has become a timing for predictive processing to predict the timing of excretion after the current time point when the request is made.

[0118] Furthermore, during periods when the information processing device 100 determines that the time for prediction processing has not yet arrived (step S16; "No"), it remains in standby mode until the time for prediction processing can be determined. On the other hand, when the information processing device 100 determines that the time for prediction processing has arrived (step S16; "Yes"), it uses the latest prediction model generated so far to predict the excretion time of the wearer U11 after the current time point when the time for prediction processing has arrived (step S17). In other words, the information processing device 100 predicts the excretion time of the wearer U11 after the current time point based on tendency information, which represents a tendency related to an accumulation threshold obtained from historical records of body accumulation obtained in the period prior to the current time point when the time for prediction processing has arrived.

[0119] exist Figure 1 In the example, the information processing device 100 predicts the timing of excretion (urination) by the wearer U11 after the current time point based on the prediction model MD11 and the amount of urine accumulated in the wearer U11's body at the current time point, which is the timing for the prediction processing. Figure 1 The example curve CV11 shown indicates that the amount of urine accumulated in the wearer U11's body at the current time is "20ml". Therefore, the information processing device 100 applies the time output from the prediction model MD11 by inputting the urine volume "20ml" to the prediction model MD11 to the current time, thereby predicting the time period (the time period of urination) when the accumulation threshold is reached.

[0120] In addition, Figure 1 In the example, the information processing device 100 predicts the timing of excretion (defecation) of the wearer U11 after the current time point based on the prediction model MD12 and the amount of feces accumulated in the body of the wearer U11 at the current time point, which is the timing for the prediction processing. Figure 1 In the example of curve CV12 shown, the amount of feces accumulated in the body of wearer U11 at the current time is "30g". Therefore, the information processing device 100 applies the time output from the prediction model MD12 by inputting the amount of feces "30g" into the prediction model MD12 to the current time, thereby predicting the time period (the time period of defecation) when the accumulation threshold is reached.

[0121] Additionally, the information processing device 100 notifies the recipient of the prediction results to the care wearer U11 (step S18). Figure 1In the example, the recipient of the caregiver U11 is recipient T11. Therefore, the information processing device 100 sends the prediction result to the recipient device 30 of recipient T11, thereby notifying recipient T11 of the prediction result. For example, if the information processing device 100 predicts that the time period between 10:30 and 11:00 is the time period for urination, and the time period between 11:00 and 11:30 is the time period for defecation, the information processing device 100 sends the prediction result to the recipient device 30 of recipient T11.

[0122] If used Figure 1 As explained, the information processing device 100 of the embodiment continuously acquires the amount of excrement accumulated in the body (information from the first sensor), and acquires excretion detection information (information from the second sensor) indicating that excretion has been detected when excretion has occurred. Furthermore, the information processing device 100 combines the acquired information to determine how much excrement was accumulated in the body (bladder, intestines) before excretion occurred, i.e., the amount of excrement accumulated in the body at that time (i.e., the accumulation threshold).

[0123] Furthermore, the information processing device 100 uses tendency information (prediction model) to predict the timing of urination and defecation after the current time point. The tendency information (prediction model) is information calculated based on the historical record of body accumulation obtained before the current time point when the prediction processing is performed, and the accumulation threshold determined in the historical record of body accumulation, and is information indicating the tendency related to the accumulation threshold.

[0124] According to this information processing device 100, the timing of excretion is predicted by considering both information representing the state inside the body and information representing the state outside the body. Therefore, compared to existing technologies that only consider one aspect of this information, the timing of excretion can be predicted with high accuracy. Furthermore, by improving prediction accuracy, for example, the time spent waiting for excretion by the person receiving care can be effectively reduced, thus enabling efficient excretion care. In other words, the information processing device 100 according to this embodiment can achieve more accurate and advanced excretion care.

[0125] [5. Other implementation methods]

[0126] Information processing device 100 can also communicate with Figure 1 The information processing described herein is different in its method to predict excretion timing. Below, it will be compared with... Figure 1 The different information processing methods described are explained as other implementation methods.

[0127] [5-1. Predictions using propensity information other than those in the model]

[0128] exist Figure 1 The following example is illustrated: Information processing device 100 predicts excretion timing using a predictive model that represents tendency information related to a tendency threshold related to accumulation volume. This predictive model is generated based on historical records of accumulation volume and an accumulation threshold determined from those historical records. However, information processing device 100 does not necessarily need to use such a model as tendency information representing a tendency related to an accumulation threshold; various statistical information, such as [specific statistical information], can also be used as tendency information to predict excretion timing. Figure 1 Let's illustrate this point with an example.

[0129] For example, when the information processing device 100 determines the accumulation threshold through step S14, it skips the model generation process in step S15 and proceeds to step S16, thereby determining whether it has reached the timing for predictive processing to predict the timing of excretion for the wearer U11 to excrete in the future.

[0130] Furthermore, when the information processing device 100 determines that it is time to perform predictive processing (step S16; "Yes"), it calculates a tendency for the accumulation threshold using the accumulation threshold included in the historical records of the accumulation amount in the body obtained during a predetermined period prior to the current time (e.g., the historical records of the most recent predetermined period). Specifically, the information processing device 100 calculates the average of the accumulation thresholds during that period based on the accumulation thresholds included in the historical records of the accumulation amount in the body obtained during the predetermined period prior to the current time. Moreover, this average of the accumulation thresholds corresponds to tendency information, which indicates a tendency related to the accumulation amount (accumulation threshold) of the excrement in the body when it is excreted from the body to the outside.

[0131] exist Figure 1 In the example, the information processing device 100 calculates the average (urine) of the accumulation threshold by averaging the accumulation thresholds included in curve CV11 corresponding to the historical records of the accumulation of urine in the body during a specified period prior to the current time point. Additionally, in Figure 1 In the example, the information processing device 100 calculates the average of the accumulation threshold (feces) by averaging the accumulation thresholds included in the curve CV12 corresponding to the historical records of the accumulation of feces in the body during a specified period prior to the current time point.

[0132] Furthermore, the information processing device 100 predicts the excretion timing of the wearer U11 after the current time point based on the average of the accumulation threshold and the amount of excrement accumulated in the wearer U11's body at the current time point when the prediction processing is performed.

[0133] exist Figure 1 In the example, the information processing device 100 predicts the excretion timing of the wearer U11 after the current time point based on the average (urine) accumulation threshold and the amount of urine accumulated in the wearer U11's body at the current time point when the prediction processing is performed. Figure 1 The example curve CV11 shown indicates that the amount of urine accumulated in the wearer U11's body at the current time point is "20 ml". Therefore, the information processing device 100 predicts the time from the urine volume of "20 ml" until the average (urine) volume reaches the accumulation threshold.

[0134] For example, suppose the wearer U11 is "born in the 1980s, female, weighing 50 kg". Statistical values ​​are obtained to determine the rate at which urine accumulates, such as the rate at which urine accumulates in a "born in the 1980s, female, weighing 50 kg". In this case, the information processing device 100 predicts the time from the urine volume of "20 ml" until the average amount of urine reaches the accumulation threshold based on the urine volume of "20 ml" and the statistical value of the urine accumulation rate.

[0135] Furthermore, the information processing device 100 predicts the average time period (the time period of urination) to reach the accumulation threshold by applying the predicted time to the current moment.

[0136] Additionally, for example, let's say we obtain statistical values ​​for the urination intervals of a person who is "born in the 1980s, female, and weighs 50 kg," such as the interval between urinations (from urination to urination again). In this case, the information processing device 100 can predict the time from the urine volume of "20 ml" until the average (urine) volume reaches the accumulation threshold based on the statistical values ​​of the urine volume of "20 ml" and the urination interval.

[0137] The text also includes an explanation of feces. Figure 1 In the example, the information processing device 100 predicts the timing of the wearer U11's excretion (defecation) after the current time point based on the average (feces) accumulation threshold and the amount of feces accumulated in the wearer U11's body at the current time point when the prediction processing is performed. Figure 1 In the example of curve CV12 shown, the amount of feces accumulated in the body of wearer U11 at the current time point is "30g". Therefore, the information processing device 100 predicts the time from the amount of feces "30g" until the average amount (feces) reaches the accumulation threshold.

[0138] For example, suppose that a statistical value is obtained based on intestinal peristalsis to determine the rate at which stool accumulates, such as "a woman born in the 1980s, weighing 50 kg". In this case, the information processing device 100 predicts the time from the stool volume of "30 g" to the average (stool) volume reaching the accumulation threshold based on the statistical value of stool accumulation rate.

[0139] Furthermore, the information processing device 100 predicts the average time period (the time period of defecation) to reach the accumulation threshold by applying the predicted time to the current time.

[0140] Additionally, for example, let's say we obtain statistical values ​​for the defecation intervals of a person who is "born in the 1980s, female, and weighs 50 kg" and defecates at a certain interval (the interval between defecation and the next defecation). In this case, the information processing device 100 can predict the time from the amount of stool "30g" until the average amount of stool (stool) reaches the accumulation threshold based on the statistical values ​​of stool volume "30g" and defecation interval.

[0141] [5-2. Predictions using information from other wearers]

[0142] exist Figure 1 The following example is shown: Information processing device 100 is based on data from the wearer (in the context of processing) as the object of processing. Figure 1 The information processing device 100 uses internal and external information obtained from the wearer (U11) to predict the timing of excretion by the wearer. However, the information processing device 100 can also predict the timing of excretion by the wearer based on internal and external information obtained from other wearers different from the wearer being processed. For example, the information processing device 100 can predict the timing of excretion by the wearer being processed solely based on internal and external information obtained from other wearers different from the wearer being processed, or it can predict the timing of excretion by combining the internal and external information obtained from the wearer being processed with the internal and external information obtained from other wearers. Figure 1 Let's illustrate this point with an example.

[0143] For example, when generating a predictive model for wearer U11, in the initial stage where sufficient learning data has not been stored (e.g., the initial prediction of wearer U11's excretion timing), the information processing device 100 may not be able to generate a highly accurate predictive model. Therefore, in this case, the information processing device 100 predicts wearer U11's excretion timing based on preference information obtained from other users similar to wearer U11.

[0144] For example, the information processing device 100 predicts the excretion timing of the wearer U11 based on a prediction model obtained from other users whose attributes (age, gender, body information (weight, etc.)) are similar to those of the wearer U11, and the amount of excrement accumulated in the wearer U11's body at the current time. Alternatively, the information processing device 100 may also predict the excretion timing of the wearer U11 based on the tendency (average of accumulation thresholds) of accumulation thresholds obtained from other users whose attributes are similar to those of the wearer U11, and the amount of excrement accumulated in the wearer U11's body at the current time.

[0145] According to such an information processing device 100, even when the amount of information required for prediction is insufficient, the timing of discharge can be predicted with high accuracy.

[0146] [5-3. Noise detection combining in vivo and in vitro information]

[0147] according to Figure 1 For example, the first sensor SN1 detects (measures) the amount of excrement (urine, feces) accumulated in the body (bladder, intestines) at predetermined intervals (e.g., every minute), and sends the detection result to the information processing device 100 at the same predetermined intervals (e.g., every minute). Therefore, the information processing device 100 can obtain curves CV11 and CV12 representing the amount of excrement (urine, feces) accumulated in the body (bladder, intestines), i.e., the change in the amount accumulated in the body over time (historical record of the amount accumulated in the body).

[0148] Furthermore, as explained above, the second sensor SN2 detects the presence or absence of excretion based on impedance changes within the absorbent material, and thus can, for example, detect the magnitude of the impedance between the conductive components provided with the diaper DP1. Accordingly, the information processing device 100 can periodically acquire impedance values ​​from the second sensor SN2 in accordance with periodically acquiring the value of the amount of fluid accumulated in the body from the first sensor SN1.

[0149] Therefore, the information processing device 100 can obtain not only curves (curves CV11 and CV12) representing the change in volume over time (historical records of volume over time), but also curves representing the change in impedance over time (historical records of impedance). Thus, the information processing device 100 can detect noise included in the curves based on these two corresponding curves. Specifically, the information processing device 100 detects noise (noise peaks) included in these two curves by comparing the curve representing the change in volume over time with the curve representing the change in impedance over time. Figure 2 To illustrate this point.

[0150] Figure 2 This is a diagram illustrating an example of noise detection involved in the implementation method. Additionally, in Figure 2 In (a), curve CV111 based on the impedance value from the second sensor SN2 is shown as an example to represent the change of impedance value over time. Additionally, in Figure 2 In (b), curve CV121 based on the impedance value from the second sensor SN2 is shown as an example to represent the change of impedance value over time. For ease of explanation, curves CV111 and CV121 are assumed to have different shapes. The vertical axis of curves CV111 and CV121 represents the impedance value.

[0151] First, let me explain Figure 2 (a). In Figure 2 In example (a), the information processing device 100 transmits curve CV11 (according to...) Figure 1 The information processing device 100 compares the peaks in curve CV111 with the peaks in curve CV111 to determine whether a noise peak exists in at least one of the curves. For example, the information processing device 100 detects noise by targeting peaks in curve CV111 that exceed a predetermined threshold. Similarly, the information processing device 100 detects noise by targeting peaks in curve CV111 that exceed a predetermined threshold.

[0152] In this state, the information processing device 100 compares curve CV11 with curve CV111, thereby detecting noise peaks, for example, based on whether there are corresponding peaks in the two curves. Furthermore, the correspondence referred to here is the degree of similarity in peak position and peak shape.

[0153] exist Figure 2 In example (a), the information processing device 100 determines that the peak PK11 of curve CV11 corresponds to the peak PK111 of curve CV111. Furthermore, the information processing device 100 determines that the peak PK12 of curve CV11 corresponds to the peak PK121 of curve CV111. Additionally, the information processing device 100 determines that the peak PK14 of curve CV11 corresponds to the peak PK141 of curve CV111.

[0154] On the other hand, Figure 2 In example (a), the information processing device 100 determines that there is no peak on the side of curve CV111 that corresponds to peak PK13 of curve CV111. In this case, the information processing device 100 determines that peak PK13 of curve CV11 is a noise peak, and as a result, peak PK13 is detected as a noise peak.

[0155] Furthermore, when a noise peak is detected on the side of curve CV11 as shown in this example, the information processing device 100 is able to determine the accumulation threshold. Figure 1 In step S14), peak PK13 is excluded. With such an information processing device 100, it is possible to prevent the situation where a peak that is actually noise is mistakenly identified as a valid peak when determining the accumulation threshold. Therefore, it is possible to learn a more accurate model, resulting in improved prediction accuracy of discharge timing.

[0156] Next, regarding Figure 2 (b) will be explained. Figure 2 In example (b), the information processing device 100 transmits curve CV12 (according to...) Figure 1 The noise is determined by comparing the peaks in curve CV121 with the peaks in curve CV121 to determine whether a noise peak exists in at least one of the curves. For example, the information processing device 100 detects noise by targeting peaks in curve CV12 that exceed a predetermined threshold. Similarly, the information processing device 100 detects noise by targeting peaks in curve CV121 that exceed a predetermined threshold.

[0157] Moreover, with Figure 2 Similarly, in example (a), the information processing device 100 compares curve CV12 with curve CV121, thereby detecting noise peaks, for example, based on whether there are corresponding peaks in the two curves.

[0158] exist Figure 2 In example (b), the information processing device 100 determines that peak PK21 of curve CV12 corresponds to peak PK211 of curve CV121. Furthermore, the information processing device 100 determines that peak PK22 of curve CV12 corresponds to peak PK221 of curve CV121. Additionally, the information processing device 100 determines that peak PK23 of curve CV12 corresponds to peak PK231 of curve CV121.

[0159] On the other hand, Figure 2 In example (b), the information processing device 100 determines that there is no peak on the side of curve CV12 that corresponds to peak PK241 of curve CV121. In this case, the information processing device 100 determines that peak PK241 of curve CV121 is a noise peak, and as a result, peak PK241 is detected as a noise peak.

[0160] Furthermore, when the information processing device 100 detects a noise peak on the side of curve CV121 as in this example, it acquires discharge detection information from the first sensor SN1 ( Figure 1 Step S13) can determine the correctness of the detection result represented by the excretion detection information, thus improving the determination process for determining the accumulation threshold based on the excretion detection information. Figure 1The accuracy of step S14) is improved. Based on this information processing device 100, a more accurate model can be learned, thus improving the prediction accuracy of discharge timing.

[0161] In addition, Figure 2 The example shown illustrates how the information processing device 100 compares the amount of fluid accumulation in the body that changes over time with an impedance value. However, the comparison object for comparing the amount of fluid accumulation does not necessarily have to be an impedance value; it can be any value that serves as an indicator for detecting excretion. For example, the comparison object for comparing the amount of fluid accumulation could also be the concentration of odor components. Furthermore, in this case, the first sensor SN1 corresponds to an odor sensor that detects excretion based on changes in odor within the diaper DP1.

[0162] in addition, Figure 2 The noise detection processing described herein is performed, for example, by the threshold determination unit 133 described below. Alternatively, the information processing device 100 may also have a dedicated processing unit for performing noise detection processing.

[0163] [6. Structure of Information Processing Device]

[0164] Next, use Figure 3 The information processing apparatus 100 involved in the implementation method will be described below. Figure 3 This is a diagram illustrating a structural example of the information processing apparatus 100 according to the embodiment. For example... Figure 3 As shown, the information processing device 100 includes a communication unit 110, a storage unit 120, and a control unit 130.

[0165] (Regarding the Ministry of Communications' 110)

[0166] The communication unit 110 is implemented, for example, by a NIC (Network Interface Card). Furthermore, the communication unit 110 establishes a wired or wireless connection with the network N, for example, to transmit and receive information between the first sensor SN1, the second sensor SN2, and the target device 30.

[0167] (Regarding Storage Department 120)

[0168] The storage unit 120 is implemented, for example, by semiconductor memory elements such as RAM (Random Access Memory) or flash memory, or by storage devices such as hard disks or optical disks. The storage unit 120 includes a wearer information storage unit 121, an internal and external information storage unit 122, a schedule information storage unit 123, and a guidance timing storage unit 124.

[0169] (Regarding the wearer information storage department 121)

[0170] The wearer information storage unit 121 stores various information related to the wearer. Here, in Figure 4 The image shows an example of a wearer information storage unit 121 according to an embodiment. Figure 4 In the example, the wearer information storage unit 121 has items such as "facility ID", "wearer ID", "target ID", "absorbent item", "meal history", "medication history", "excretion history", "body movement history", "excretion time", and "replacement time".

[0171] "Facility ID" is identification information used to identify the facility (e.g., nursing home, daycare center, etc.) where the wearer identified by "Wearer ID" resides, and the facility (e.g., nursing home, daycare center, etc.) where the recipient identified by "Recipient ID" works. "Wearer ID" represents identification information used to identify the wearer of the absorbent item. This wearer could be, for example, an elderly person receiving care in a nursing home, or a young child attending a daycare center. "Recipient ID" is identification information used to identify the user who provides care to the wearer identified by "Wearer ID" and receives various notifications and suggestions using the information processing system 1 according to the implementation method.

[0172] "Absorbent articles" refers to information related to absorbent articles used by a wearer identified through a "wearer ID." Information such as the type of absorbent article, its product name, product number, and absorbency (capacity) is included. Figure 4 In the example, the wearer ID "U11" is mapped to the absorbent item "Absorbent Item #11". This example represents a wearer (wearer U11) identified by the wearer ID "U11" wearing the absorbent item represented by "Absorbent Item #11".

[0173] "Meal History" refers to information representing the historical eating habits of a wearer identified by their "wearer ID," such as "when the wearer ate (or drank) and in what quantity of what." The "Meal History" can be registered by the recipient or captured by photographic images of food or drink provided to the wearer. Furthermore, when a specified sensor (such as a camera) is installed in the container holding the food or drink, the information processing device 100 acquires the information detected by the sensor (e.g., photographic images) and stores it as a meal history in the wearer information storage unit 121. Figure 4In the example, the wearer ID "U11" is mapped to the dining history "Dinner History #11". This example indicates that the wearer identified by the wearer ID "U11" (wearer U11) consumed the food or drink indicated by "Dinner History #11".

[0174] "Drug Use History" refers to information representing the historical record of laxative administration to a wearer identified by their "Wearer ID," such as "when, what amount, and what type of laxative was administered to the wearer." The "Drug Use History" can be registered by the recipient, or, in cases where medication administration to a wearer is detected by a designated sensor (camera, etc.), the information processing device 100 acquires the information detected by that sensor (e.g., a camera image) and stores it as a drug use history in the wearer information storage unit 121. Figure 4 In the example, the wearer ID "U11" is mapped to the medication history "Medication History #11". This example indicates that the wearer identified by the wearer ID "U11" (wearer U11) was given a laxative as indicated by "Medication History #11".

[0175] "Excretion history record" refers to information representing the historical records of a wearer's bowel movements identified by their "wearer ID," such as "when the wearer excreted how much and what state (e.g., color, stool consistency) of stool." The "excretion history record" can be registered by the individual, or it can be recorded in the wearer's information storage unit 121 when a designated sensor (camera, etc.) detects the condition inside an absorbent material during defecation. The information processing device 100 then acquires the information detected by the sensor (e.g., a camera image) and stores it as a bowel movement history record. Figure 4 In the example, the wearer ID "U11" is mapped to the defecation history record "Defecation History Record #11". This example indicates that the wearer identified by the wearer ID "U11" (wearer U11) defecated as shown in "Defecation History Record #11".

[0176] "Body movement history" refers to the historical information of a wearer's body movements (postures) identified by their "wearer ID," such as "when and what body movements the wearer performed (e.g., in bed), and what posture they were in." The "body movement history" can be registered by the user, or it can be recorded in the wearer's information storage unit 121 when a specified sensor (camera, etc.) detects the wearer's movements. The information processing device 100 then acquires the information detected by the sensor (e.g., a camera image) and stores it as body movement history in the wearer's information storage unit 121. Figure 4In the example, the wearer ID "U11" is mapped to the body movement history "Body Movement History #11". This example indicates that the wearer identified by the wearer ID "U11" (wearer U11) performed the body movement and posture indicated by "Body Movement History #11".

[0177] "Excretion timing" refers to information indicating the timing of excretion. This excretion timing is predicted for the wearer identified through their "wearer ID," and is obtained through... Figure 1 The information processing described predicts the timing of excretion. Figure 4 In the example, the wearer ID "U11" is mapped to the excretion time "excretion time #11". This example indicates a prediction that the wearer (wearer U11) identified by the wearer ID "U11" will excrete at the time (e.g., time period) indicated by "excretion time #11".

[0178] "Replacement timing" refers to information indicating the replacement timing, which is determined for the wearer identified by the "wearer ID" and is determined by the first decision unit 137 described later. Figure 4 In the example, the wearer ID "U11" is mapped to the replacement time "Replacement Time #11". This example represents a decision to replace the absorbent item for the wearer (wearer U11) identified by the wearer ID "U11" at the time (e.g., time period) indicated by "Replacement Time #11".

[0179] In addition, Figure 4 Examples such as absorbent items #11, meal history #11, medication history #11, excretion history #11, body movement history #11, excretion timing #11, replacement timing #11, etc., use conceptual tags, but actually register appropriate numerical values, text, images (motion images), etc. to represent these contents.

[0180] (Regarding the in-body and external information storage unit 122)

[0181] The internal and external information storage unit 122 stores internal information and external information. The internal information relates to internal excretion, and the external information relates to excretions from the body to the outside. Figure 5 The text describes an example of an in-vivo or out-of-vivo information storage unit 122 as described in the embodiment. Figure 5 In the example, the internal and external information storage unit 122 has items such as "wearer ID", "excrement type", "date and time information", "internal accumulation", "status information", "detection presence", and "excretion volume".

[0182] "Wearer ID" represents identification information used to identify the wearer of the absorbent item. "Excrement Type" indicates whether the excrement is urine or feces.

[0183] "Date and time information" refers to the date and time when the amount of waste accumulated in the body (bladder, intestines), i.e., the "accumulated amount in the body," was detected (measured) through the first sensor SN1. For example... Figure 1 As explained, the first sensor SN1 detects (measures) the amount of excrement (urine, feces) accumulated in the body (bladder, intestines) at a predetermined interval (e.g., every minute) at that date and time, and sends the measurement result to the information processing device 100 at the same predetermined interval (e.g., every minute). Therefore, Figure 5 The "date and time information" corresponds to this example.

[0184] "Accumulated Amount in the Body" refers to the amount of excrement (urine, feces) accumulated in the wearer's body (bladder, intestines) at the date and time indicated by "Date and Time Information".

[0185] "State information" refers to bladder status, such as bladder expansion rate and bladder size. Additionally, "state information" refers to intestinal status, such as intestinal movement (peristalsis). The detection of "state information" is performed by the first sensor SN1.

[0186] "Detection presence or absence" indicates whether urine or feces have been excreted. For example... Figure 1 As explained, the second sensor SN2 detects whether there is discharge. If discharge is detected, it sends discharge detection information indicating that discharge has occurred to the information processing device 100. Therefore, upon receiving discharge detection information indicating that discharge has occurred (discharge detected), "○" is entered in the "Date and Time Information" field corresponding to that date and time, and in the field corresponding to "Detection Presence / Absence". Furthermore, the "Accumulated Amount in the Body" value corresponding to the "○" indicating detection presence / absence is a threshold value.

[0187] "Excretion volume" refers to the amount of excrement actually excreted into the absorbent material when excretion detection information indicating that excretion has occurred (excretion detected).

[0188] For example, in Figure 5In the example, the wearer ID "U11" was matched with the type of excrement "urine", the date and time information "February 15, 2020, 16:59", the accumulated volume in the body "250ml", the status information "bladder status #112", the presence or absence of a "○", and the excretion volume "200ml". This example indicates that the wearer identified by the wearer ID "U11" (wearer U11) urinated at the date and time "February 15, 2020, 16:59" when "250ml" of urine had accumulated in the bladder. Additionally, this example indicates that the actual amount of urine urinated by wearer U11 at that time was "200ml". Furthermore, this example indicates that at the date and time "February 15, 2020, 16:59", wearer U11's bladder was in a state like "bladder status #112".

[0189] Additionally, for example, in Figure 5 In the example, the wearer ID "U11" was matched with the type of excrement "stool", the date and time "February 15, 2020, 4:22 PM", the amount accumulated in the body "100g", the presence or absence of a "○", and the amount of excretion "90g". This example indicates that the wearer identified by the wearer ID "U11" (wearer U11) defecated at the date and time "February 15, 2020, 4:22 PM" when "100g" of stool had accumulated in the intestines. Additionally, this example indicates that the actual amount of stool excreted by wearer U11 at that time was "90g".

[0190] In addition, Figure 5 In the examples, such as bladder state #111 and intestinal state #111, conceptual labels are used, but the appropriate numerical values, text, etc., that represent these contents are actually registered.

[0191] (Regarding the Timetable Information Storage Department 123)

[0192] The timetable information is stored to access the user's timetable. Here, in Figure 6 The diagram shows an example of a time schedule information storage unit 123 involved in an implementation method. Figure 6 In the example, the timetable information storage unit 123 has items such as "facility ID (Identifier)", "user type", "ID", and "timetable".

[0193] “Facility ID” and Figure 4 The facility ID corresponds to this. "User Type" indicates which of the two, the object and the wearer, the corresponding "Schedule". "ID" represents the identification information for the user corresponding to the "User Type". For example, the "ID" corresponding to the user type "Object" represents the "Object ID" (…). Figure 4The "ID" corresponding to the user type "wearer" represents the "wearer ID" ( Figure 4 "Schedule" refers to the user's schedule within the facility represented by the "Facility ID," identified by the "ID."

[0194] exist Figure 6 In the example, the facility ID "FA1", user type "object user", ID "T11", and timetable "timetable#SK11" are mapped. This example shows that the timetable of object user "T11" in "facility FA1" is an example of the timetable represented by "timetable#SK11".

[0195] In addition, Figure 6 Examples such as timetable #SK11 use conceptual tags, but actually register the appropriate numerical values, text, timetables, etc. to represent these contents.

[0196] (Boot timing storage unit 124)

[0197] The guidance timing storage unit 124 stores information related to the guidance timing for guiding the wearer to the restroom. The guidance timing is determined by the second decision unit 138, which will be described later. The guidance timing stored in the guidance timing storage unit 124 will be explained in detail using FIG7.

[0198] (Regarding Control Department 130)

[0199] return Figure 3 The control unit 130 is implemented by executing various programs stored in the internal storage device of the information processing device 100 using RAM as the working area, such as a CPU (Central Processing Unit) or MPU (Micro Processing Unit). Alternatively, the control unit 130 may be implemented using integrated circuits such as ASIC (Application Specific Integrated Circuit) or FPGA (Field Programmable Gate Array).

[0200] like Figure 3 As shown, the control unit 130 includes an acquisition unit 131, a first determination unit 132, a threshold determination unit 133, a generation unit 134, a prediction unit 135, an information control unit 136, a first decision unit 137, a second decision unit 138, a suggestion unit 139, and a second determination unit 140, and performs or executes the information processing functions described below. Furthermore, the internal structure of the control unit 130 is not limited to... Figure 3The structure shown can be any other structure used for the information processing described later. Furthermore, the connection relationships between the various processing units within the control unit 130 are not limited to... Figure 3 The connection relationships shown can also be other connection relationships.

[0201] (Regarding Acquisition Department 131)

[0202] The acquisition unit 131 acquires internal information and external information. The internal information is related to excretion within the body, and the external information is different from the internal information but is related to external processes. For example, the acquisition unit 131 acquires information related to excrement excreted from the body to the outside as external information. For example, the acquisition unit 131 acquires information related to the amount of excrement accumulated in the body as internal information. Additionally, for example, the acquisition unit 131 acquires excretion information indicating the excretion of excrement from the body to the outside as external information. For example, the acquisition unit 131 acquires internal information detected by a first sensor worn on the wearer's body and external information detected by a second sensor installed on the absorbent material.

[0203] exist Figure 1 In the example, the first sensor SN1 detects (measures) the amount of excrement (urine, feces) accumulated in the body (bladder, intestines) at predetermined intervals (e.g., every minute), and sends the measurement results to the information processing device 100 at these predetermined intervals (e.g., every minute). Therefore, in Figure 1 In this example, the acquisition unit 131 acquires the amount of excrement (urine, feces) accumulated in the body (bladder, intestines) at predetermined intervals from the first sensor SN1, i.e., the amount of excrement (urine, feces) accumulated in the body at that time point (an example of body information). For example, the acquisition unit 131 acquires a combination of the amount of excrement (urine, feces) accumulated in the body (bladder, intestines), i.e., the "amount of excrement (urine, feces)," and the "date and time information" indicating the date and time when the amount was measured. In addition, the acquisition unit 131 stores the amount of excrement (urine, feces) corresponding to each date and time as a historical record of the amount of excrement (urine, feces) in the body in the body and external information storage unit 122.

[0204] In addition, Figure 1 In the example, the second sensor SN2 detects whether there is excretion based on the impedance change within the diaper DP1. Upon detecting excretion, it sends excretion detection information (an example of external information) to the information processing device 100. Therefore, in Figure 1In this example, the acquisition unit 131 acquires excretion detection information (an example of external information) indicating that excretion has occurred from the second sensor SN2. Furthermore, upon acquiring the excretion detection information, the acquisition unit 131 inputs "○" to the "detection presence or absence" field of the internal and external information storage unit 122 based on the determination result that either urine or feces has been excreted, and the date and time of acquiring the excretion detection information.

[0205] In addition, the first sensor can also detect state information indicating bladder status, such as bladder expansion rate and bladder size. Furthermore, the first sensor can also detect state information indicating intestinal movement (peristaltic motion). Therefore, the acquisition unit 131 also acquires state information indicating these states from the first sensor.

[0206] In addition, the acquisition unit 131 can also acquire various information related to the wearer, such as information about absorbent materials worn by the wearer, meal history (dietary information related to the food and drink consumed by the wearer), medication history (medication information related to the medication administered to the wearer), excretion history, body movement history, etc. Furthermore, the acquisition unit 131 stores this acquired information in the wearer information storage unit 121.

[0207] Furthermore, the acquisition unit 131 can also acquire urination prediction information indicating the timing of urination predicted for a person under care (e.g., a wearer of absorbent materials), and defecation prediction information indicating the timing of defecation predicted for a person under care. Therefore, the acquisition unit 131 is also a processing unit corresponding to the excretion information acquisition unit.

[0208] Here, the urination prediction information acquired by the acquisition unit 131, which is the excretion information acquisition unit, is, for example, the prediction result predicted by the prediction unit 135, which will be described later. Specifically, the excretion prediction information indicates the urination prediction result obtained by the acquisition unit 131, which is the excretion information acquisition unit. Figure 1 The information processing involved in the described embodiment includes the excretion timing (urination timing or defecation timing) predicted by the prediction unit 135.

[0209] On the other hand, the urination prediction information acquired by the acquisition unit 131, which is an excretion information acquisition unit, is not limited to information indicating the excretion timing predicted by the prediction unit 135 in the information processing involved in the embodiment. For example, the acquisition unit 131 may also acquire urination prediction information indicating the timing of urination predicted by any method. Similarly, the acquisition unit 131 may also acquire defecation prediction information indicating the timing of defecation predicted by any method.

[0210] For example, in Figure 1The example shown illustrates the prediction of excretion timing based on both in vivo and in vitro information. However, excretion timing can also be predicted based solely on in vivo information. In this case, the acquisition unit 131 acquires information representing the excretion timing predicted based solely on in vivo information. Alternatively, excretion timing can also be predicted based solely on in vitro information. In this case, the acquisition unit 131 acquires information representing the excretion timing predicted based solely on in vitro information.

[0211] (Regarding the First Judgment Section 132)

[0212] When excretion detection information is acquired by the acquisition unit 131 (excretion is detected), the first determination unit 132 determines the type of excrement excreted based on the body information obtained from the first sensor when excretion occurs. Specifically, when excretion occurs, the first determination unit 132 determines whether the excrement of the wearer being processed is urine or feces based on the body information obtained from the first sensor.

[0213] (Regarding the threshold determination section 133)

[0214] When the acquisition unit 131 acquires excretion detection information indicating that excretion has occurred (excretion detected), the threshold determination unit 133 determines the amount of excrement accumulated in the body at which excretion occurred, based on the determination result of the first determination unit 132. Specifically, the threshold determination unit 133 determines the amount of excrement accumulated in the body at which excretion occurred based on historical records of the amount of excrement in the body corresponding to the excrement indicated by the determination result stored before the time of excretion, and the date and time indicating the time of excretion. That is, the threshold determination unit 133 determines the amount of excrement accumulated in the body, i.e., the accumulation threshold, at the time of excretion when the excrement indicated by the determination result is excreted from the body to the outside.

[0215] (Regarding Generation Department 134)

[0216] The generation unit 134 learns the model based on the historical record of the amount of waste accumulated in the body, that is, the historical record of the state in which the accumulation threshold is determined at any time. For example, the generation unit 134 learns the correlation between the amount of waste accumulated in the body and time, where the amount of waste accumulated in the body (bladder, intestines) is the amount of waste accumulated in the body, and time is the time from the point in time when such a amount of waste is accumulated until the accumulation threshold is reached. For example, the generation unit 134 generates a model that takes the amount of waste accumulated at the current time as input and outputs the time from the amount of waste accumulated at the current time until the accumulation threshold is reached (until excretion occurs).

[0217] In addition, for example, the generation unit 134 updates the model based on the historical record of the body accumulation, that is, the historical record of the state in which the accumulation threshold is determined at any time, the most recent historical record of the specified period, thereby repeating the process of generating the latest model over time.

[0218] (Regarding Forecasting Department 135)

[0219] The prediction unit 135 predicts the excretion timing of the wearer of the absorbent material based on the internal and external information obtained by the acquisition unit 131. For example, the prediction unit 135 predicts the excretion timing of the wearer after the current time point based on the information obtained by the acquisition unit 131 that was obtained before the current time point, and uses this as the excretion timing.

[0220] More specifically, the prediction unit 135 predicts the excretion timing based on information obtained by the acquisition unit 131 prior to the current time point, specifically information related to the amount of excrement accumulated in the body when excretion occurs. For example, the prediction unit 135 predicts the excretion timing based on trend information related to the accumulation amount, where the trend information indicates a tendency related to the accumulation amount. For example, the prediction unit 135 predicts the excretion timing based on information related to the accumulation amount and the amount of excrement accumulated in the wearer's body at the current time point.

[0221] As an example, when the prediction unit 135 determines that it is time to perform prediction processing, it predicts the excretion timing of the wearer who is the subject of processing after the current time point, based on tendency information and the amount of excrement accumulated in the wearer's body at the current time point. The tendency information refers to a tendency related to an accumulation threshold obtained from historical records of the amount of excrement accumulated in the body in the period prior to the current time point when the prediction processing is scheduled. Specifically, the prediction unit 135 predicts the excretion timing based on a model (prediction model) generated by the generation unit 134 that serves as such tendency information, and the amount of excrement accumulated in the wearer's body at the current time point.

[0222] For example, the prediction unit 135 applies the time of the model output corresponding to urine, which is input as the amount of urine accumulated in the wearer's body at the current time point, to the current time point, thereby predicting the time period (the time period for urination) when the accumulated amount threshold is reached. Similarly, the prediction unit 135 applies the time of the model output corresponding to feces, which is input as the amount of feces accumulated in the wearer's body at the current time point, to the current time point, thereby predicting the time period (the time period for defecation) when the accumulated amount threshold is reached.

[0223] In addition, the prediction unit 135 can also notify the recipient of the prediction results. Specifically, the prediction unit 135 notifies the recipient of the prediction results to the wearer who is the recipient of the treatment. For example, the prediction unit 135 notifies the recipient of the prediction results by sending the prediction results to the recipient's device 30.

[0224] Alternatively, the prediction unit 135 can also use trend information other than the model to predict the timing of excretion. For example, the prediction unit 135 calculates the average of the accumulation thresholds during a specified period prior to the current time based on the accumulation thresholds included in the historical records of accumulation amounts obtained during that period. Furthermore, the prediction unit 135 predicts the timing of excretion based on the calculated average of the accumulation thresholds and various statistical values.

[0225] (Modification of the processing of prediction unit 135 (1))

[0226] The prediction unit 135 can predict the excretion timing based on the correlation between dietary information related to the food and drink consumed by the wearer (the subject of treatment) and the wearer's excretion status, internal information, and external information. For example, the prediction unit 135 corrects the excretion timing predicted based on internal and external information based on the correlation established between dietary information (dietary status) related to the food and drink consumed by the wearer (the subject of treatment) and the wearer's excretion status. Regarding this point, examples are given... Figure 1 The following explanation will be based on the example of wearer U11 (the wearer being processed).

[0227] For example, the learning generation unit 134 accesses the wearer information storage unit 121 to obtain the "meal history" and "excretion history" corresponding to the wearer U11. Figure 4 In this example, the generation unit 134 accesses the wearer information storage unit 121 to obtain "meal history #11" and "excretion history #11". Furthermore, the generation unit 134 learns the correlation between dietary and excretion patterns, such as "the tendency to excrete a certain amount of urine at what interval after consuming a certain amount of what (or drinking)." That is, the generation unit 134 learns the tendency of excretion time corresponding to dietary patterns.

[0228] In this state, when the prediction unit 135 determines that it is time to perform prediction processing, it applies the time output from the prediction model MD11 corresponding to the amount of urine accumulated in the wearer's body at the current time point, which is input as the processing object at the current time point, to the current time point, thereby predicting the time period when the accumulation threshold is reached (the time period of urination). Furthermore, the prediction unit 135 applies the tendency of excretion time corresponding to dietary status (the tendency learned by the generation unit 134) to the predicted time period, thereby correcting the predicted time period.

[0229] Alternatively, the generation unit 134 may learn the model by considering, for example, the tendency of excretion time corresponding to the aforementioned dietary situation, the tendency of the body's accumulated volume and the time from the point in time when such a volume is reached until the volume threshold is reached. For example, the generation unit 134 may generate a model that takes the body's accumulated volume at the current time, the most recent time when food was consumed, and the dietary situation (how much of what was eaten) as input, and outputs the time from the current body's accumulated volume until the volume threshold is reached (until excretion). In this case, the prediction unit 135 predicts the time period for urination by applying the time output from the model to the current time.

[0230] According to such an information processing device 100, dietary status can be combined with internal and external information, so the timing of excretion can be incorporated into the predictive processing based on changes in dietary status, resulting in a more accurate prediction of the timing of excretion.

[0231] Furthermore, while an example of urine as the excrement is shown here, the same applies when the excrement is feces; the information processing device 100 can learn the tendency in the same way.

[0232] (Modification (2) of the processing performed by the prediction unit 135)

[0233] Furthermore, the prediction unit 135 can predict the excretion timing based on the correlation between drug information related to the drug administered to the wearer as the treatment target and the wearer's excretion status, internal information, and external information. For example, the prediction unit 135 corrects the excretion timing predicted based on internal and external information based on the correlation established between the drug information (drug status) related to the drug administered to the wearer as the treatment target and the wearer's excretion status. Examples Figure 1 The point is illustrated using wearer U11 (the wearer being processed) as an example.

[0234] For example, the learning generation unit 134 accesses the wearer information storage unit 121 to obtain the "meal history," "medication history," and "excretion history" corresponding to the wearer U11. Figure 4 In the example, the generation unit 134 accesses the wearer information storage unit 121 to obtain "meal history #11", "medication history #11", and "excretion history #11". Furthermore, the generation unit 134 learns the correlation between laxative use and excretion time, such as "the tendency to excrete a certain amount of stool of a certain type and at what time and with what amount of laxative, given a specific time and dosage for a meal." In other words, the generation unit 134 learns the tendency for excretion time corresponding to laxative use, taking into account dietary habits.

[0235] In this state, when the prediction unit 135 determines that it is time to perform prediction processing, it applies the time output from the prediction model MD12 corresponding to the amount of feces accumulated in the wearer's body at the current time point, which is input as the processing target at the current time point, to the current time point, thereby predicting the time period when the accumulation threshold is reached (the time period for defecation). Furthermore, the prediction unit 135 applies a tendency for excretion time corresponding to the laxative situation, taking into account dietary conditions (the tendency for learning completion obtained by the generation unit 134), to the predicted time period, thereby correcting the predicted time period.

[0236] Alternatively, the generation unit 134 may learn the model by considering the tendency of the amount of bodily accumulation and the time from the point in time when such bodily accumulation occurs until the accumulation threshold is reached, for example, by also considering the tendency of excretion time corresponding to laxative use. For example, the generation unit 134 may generate a model that takes the current amount of bodily accumulation, dietary status, and laxative use status as input, and outputs the time from the current amount of bodily accumulation until the accumulation threshold is reached (until excretion occurs). In this case, the prediction unit 135 predicts the time period of defecation by applying the time output from the model to the current time.

[0237] According to such an information processing device 100, the laxative status can be combined with internal and external information, so the timing of excretion can be incorporated into the predictive processing based on the changes in the laxative status, resulting in a more accurate prediction of the timing of excretion.

[0238] (Regarding Information Control Department 136)

[0239] The information control unit 136 performs controls related to the food and drink provided to the wearer based on the correlation between dietary information related to the wearer's intake of food and drink and the wearer's excretion status. Additionally, the information control unit 136 performs controls related to the food and drink provided to the wearer based on the correlation between medication information related to the medication administered to the wearer and the wearer's excretion status.

[0240] For example, based on the learning performed by the generation unit 134 as described in the above-described modification, a correlation between dietary status and ease of defecation can sometimes be obtained, such as what kind of dietary status leads to a tendency to defecate easily without relying on laxatives. Therefore, based on the correlation between dietary status and ease of defecation, the information control unit 136 determines for the wearer U11 what kind of meal would facilitate defecation without relying on laxatives. Furthermore, the information control unit 136 proposes this determination result to the subject T11 as the most suitable meal for the wearer U11.

[0241] According to this information processing device 100, meal suggestions can be made with the goal of natural defecation, thus assisting the wearer in achieving independent excretion. Furthermore, the information processing device 100 can effectively improve the wearer's QOL (Quality of Life).

[0242] Furthermore, based on the learning performed by the generation unit 134 as described in the above-described modification, it is sometimes possible to obtain the correlation between the three factors: eating status, laxative status, and defecation status, such as how defecation status (e.g., stool characteristics, stool volume, defecation timing) changes depending on eating status and laxative status. Therefore, based on the correlation established between eating status, laxative status, and defecation status, the information control unit 136 determines the type, strength, amount, and timing of laxative administration for the wearer U11. Moreover, the information control unit 136 proposes the most suitable laxative administration method for the wearer U11 to the recipient T11 based on this determination result.

[0243] According to such an information processing device 100, laxatives can be administered in a manner that reduces the burden on the wearer and approximates natural defecation, thus effectively improving the wearer's QOL.

[0244] (Regarding the First Decision Section 137)

[0245] The first decision unit 137 determines the prescribed timing related to the care of the wearer of the needle as the treatment object based on the excretion timing predicted by the prediction unit 135. Specifically, the first decision unit 137 determines the replacement timing of the absorbent material worn by the wearer as the treatment object based on the excretion timing. For example, if it is determined that the actual excretion amount at the predicted excretion timing exceeds the remaining amount that the absorbent material can absorb, the first decision unit 137 determines the replacement timing as the time prescribed before that excretion timing. In addition, the replacement timing determined by the first decision unit 137 is suggested to the recipient by the suggestion unit 139, which will be described later. Figure 1 Let's illustrate this point with an example.

[0246] For example, the prediction unit 135 accesses the in-body and out-of-body information storage unit 122 and calculates the tendency of urine output (an example of output) based on the historical records of "excretion volume" (the amount of excretion excreted into the absorbent material) corresponding to the wearer U11, specifically the historical records showing that the excretion type is "urine". Furthermore, the prediction unit 135 predicts the urine output at the predicted excretion time based on the calculated urine output tendency and the predicted excretion time for the wearer U11. In this example, the prediction unit 135 predicts the urine output at the predicted excretion time to be "200ml".

[0247] Furthermore, in this state, the first decision unit 137 accesses the wearer information storage unit 121 to determine the absorbency (capacity) of the diaper DP1 used by the wearer U11. Moreover, based on the determined absorbency (capacity) and the historical records of "excretion volume" corresponding to the wearer U11, the first decision unit 137 calculates the remaining amount of urine that the diaper DP1 can absorb, i.e., the absorbable capacity. Here, let's assume that the first decision unit 137 calculates the remaining amount of urine that the diaper DP1 can absorb to be "150ml".

[0248] Moreover, based on the above example, the predicted actual urine output of "200ml" at the scheduled time of excretion exceeds "50ml" relative to the remaining amount of urine that the DP1 diaper can absorb (150ml).

[0249] Here, let's assume that the diaper DP1 is not changed when its absorbable capacity is "150ml", and urination occurs directly at the excretion time predicted by the prediction unit 135. As a result, an amount exceeding the absorbable capacity of "150ml" (50ml) will leak from the diaper DP1. Accordingly, in this case, where it is determined that the actual amount of excretion at the predicted excretion time exceeds the absorbable capacity, the first decision unit 137 determines the time specified before that excretion time as the time to change the diaper DP1.

[0250] For example, the first decision unit 137 determines the timing for changing diaper DP1 by making information related to the amount of urine accumulated in the bladder, i.e., the amount accumulated in the body, before the predicted excretion time a predetermined correlation is established with information related to the accumulation threshold. As an example, the first decision unit 137 determines the timing for changing diaper DP1 by predicting when the amount of urine accumulated in the bladder, i.e., the amount accumulated in the body, will reach a predetermined surplus before reaching the accumulation threshold.

[0251] Furthermore, the proposal unit 139 proposes to the recipient T11 that the replacement be performed at the replacement time determined by the first decision unit 137. Specifically, the proposal unit 139 sends the replacement time determined by the first decision unit 137 as proposal information to the recipient device 30 of the recipient T11.

[0252] Generally, absorbent pads are changed every time a bowel movement occurs. However, since absorbent pads have the ability to absorb moisture, they are usually changed around the time of multiple urinations. However, this absorption capacity has its limits, so if the pad is not changed after multiple urinations, unabsorbed urine will leak out. Therefore, according to this information processing device 100, if it is determined that the risk of leakage is high if the next urination occurs without changing the absorbent pad, it can suggest changing the pad before the predicted time of the next urination. As a result, the information processing device 100 can suggest an appropriate replacement time for each user, thus effectively reducing the risk of leakage.

[0253] (Regarding the Second Decision Section 138)

[0254] The second decision unit 138 determines the guidance time for the wearer to go to the toilet based on the excretion time predicted by the prediction unit 135. For example, the second decision unit 138 determines the guidance time for the wearer to go to the toilet based on the excretion time predicted by the prediction unit 135 and the wearer's schedule. For example, the second decision unit 138 determines the guidance time for the wearer to go to the toilet based on the aforementioned schedule and the necessity of guiding the wearer to the toilet based on the excretion time for each wearer. In addition, the suggestion unit 139 suggests to the wearer that they go to the toilet at the guidance time determined by the second decision unit 138.

[0255] Regarding this point, use Figures 7A to 7C Please provide an explanation. Figures 7A to 7C This is an explanatory diagram illustrating the decision-making process for timed guidance to the restroom. Below, without distinction... Figures 7A to 7C In such cases, it is simply recorded as "Figure 7". Furthermore, the timetable information indicating the recipient's schedule and the timetable information indicating the wearer's schedule are examples of information related to the person being cared for (e.g., the wearer). For example, the timetable information indicating the recipient's schedule and the timetable information indicating the wearer's schedule are examples of information related to the care of the person being cared for (e.g., the wearer).

[0256] In the example of Figure 7, the wearer being processed is exemplified by a child residing in daycare center FA2 (identified by facility ID "FA2"). Furthermore, accordingly, in Figure 7, the caregiver of the wearer being processed is a childcare worker belonging to daycare center FA2. Specifically, based on... Figure 6 For example, in a class of daycare FA2, there are ten children enrolled by wearers U21 to U30, and four caregivers T21 to T24 are providing care for these ten children. Furthermore, in the example in Figure 7, we assume that the timing (time period) of excretion of at least one of urine or feces is predicted for wearers U21 to U30.

[0257] Therefore, use Figures 7A to 7C Let's illustrate an example of the decision-making process for determining the timing of guiding someone to the restroom, using the second decision section 138. First, based on... Figure 7A The second decision unit 138 performs a reverse calculation based on the predicted excretion timing (time period) for urine or feces to determine the necessity of guiding someone to the toilet (level of urination / defecation urge) for each time period as low to high (step S61). For example, the second decision unit 138 determines that the necessity of guiding someone to the toilet (level of urination / defecation urge) is the lowest for time periods that are the excretion timing periods, based on the view that the time period that is the excretion timing period is the highest.

[0258] Regarding this point, let's take the example of a young child wearing the device, U21, to illustrate. Based on... Figure 7A For example, regarding wearer U21, the time period from 11:00 to 11:30 is predicted as the scheduled time for excretion. Therefore, the second decision unit 138 determines that the necessity (level of urination or defecation) for guiding wearer U21 to the toilet is highest during the time period from 11:00 to 11:30. Furthermore, based on this determination, the second decision unit 138 determines that the necessity (level of urination or defecation) for guiding to the toilet during the time period from 10:00 to 11:00, which is one stage earlier than the time period from 11:00 to 11:30, is of a moderate level. Additionally, the second decision unit 138 determines that the necessity (level of urination or defecation) for guiding to the toilet during the time period from 8:00 to 10:00, which is one stage earlier than the time period from 10:00 to 11:00, is of a low level.

[0259] In addition, Figure 7A The example shows how the necessity of guiding wearers U22 to U30 to the toilet was determined as "low", "medium", or "high" using the same method.

[0260] Next, the second decision unit 138, based on the premise that the wearer whose necessity to go to the toilet is "medium" or higher has a strong urge to urinate or defecate, performs the following processing based on the determination result in step S61. For example, the second decision unit 138 determines the time period during which more than half of the wearers U21 to U30 have a necessity to go to the toilet (feeling a certain degree of urge to urinate or defecate) (step S62).

[0261] based on Figure 7A For example, during the time period of "10:00 AM to 10:30 AM," the six wearers deemed to have a "medium" or higher necessity for being guided to the restroom were wearers U21, U23, U25, U26, U29, and U30. Therefore, in Figure 7A In the example, the second decision unit 138 determined the time period "10:00 to 10:30" as the time period during which more than half of the wearers would have a need to go to the toilet.

[0262] In addition, based on Figure 7A For example, during the time period of "10:30 AM to 11:00 AM," the ten wearers whose necessity for being guided to the restroom was determined to be "medium" or higher were wearers U21 to U30. Therefore, in Figure 7A In the example, the second decision section 138 also determined the time period "10:30 to 11:00" as the time period during which more than half of the wearers would have a need to go to the toilet.

[0263] In addition, based on Figure 7A For example, during the time period of "11:00 AM to 11:30 AM," the ten wearers whose necessity for being guided to the restroom was determined to be "medium" or higher were wearers U21 to U30. Therefore, in Figure 7A In the example, the second decision section 138 also determined the time period "11:00 to 11:30" as the time period during which more than half of the wearers would have a need to go to the toilet.

[0264] For example, guiding a child to the toilet when they feel the urge to urinate can lead to successful toilet training. However, if a child is taken to the toilet when they don't feel the urge, they may develop a dislike for going to the toilet, potentially resulting in toilet training failure. Furthermore, if a child is taken to the toilet but doesn't urinate, the caregiver's time is wasted. Therefore, guiding more children with the urge to urinate or defecate to the toilet at once can increase the success rate of toilet training and make toilet guidance more efficient, benefiting both the child and the caregiver. Accordingly, step S62 determines the time period that can further improve the success rate of toilet training and efficiently guide as many children as possible.

[0265] Furthermore, toilet training can be conducted during time periods that maximize the success rate of toilet training and efficiently guide as many people as possible to the toilet. However, since schedules are set for both caregivers and children, toilet training and elimination behaviors may not always be achievable within those time periods. Therefore, in the following approach, the individual schedules of caregivers and children will be considered to determine the most practical time period within the time frame for efficient toilet training that can result in toilet training and elimination behaviors.

[0266] Next, the explanation Figure 7B The second decision section 138 determines the time periods during which each of the wearers U21 to U30 can go to the toilet based on their daily schedules as young children (step S63). Figure 7B The example shows a daily schedule, or schedule #SK2, shared by all wearers U21 to U30. The second decision unit 138 can retrieve schedule #SK2 from the schedule information storage unit 123.

[0267] Furthermore, for example, the second decision section 138 determines the time periods in time slot #SK2 during which each wearer U21 to U30 can go to the restroom. For example, such as Figure 7BAs shown, the second decision unit 138 determines the time period for entering the park, "8:00 to 8:30", and the time periods for free movement, "9:00 to 9:30", "11:00 to 11:30", and "12:30 to 13:30", as the time periods during which each wearer U21 to U30 can go to the toilet.

[0268] Furthermore, the second decision-making unit 138 determines the time period during which the childcare worker can receive toilet guidance based on the schedules of each childcare worker (T21-T24). Figure 7B The table shows the daily schedule for each subject, T21 to T24. The second decision unit 138 can retrieve this schedule from the schedule information storage unit 123.

[0269] Moreover, based on Figure 7B For example, in the daily schedule of each subject from T21 to T24, tasks such as "entry into the park", "visit", "cleaning", "monitoring", "morning meeting", "outside the park", "lunch preparation", "lunch", "brushing teeth", "contacting the journal", and "responsible for the afternoon nap" are allocated according to time periods. On the other hand, there are also idle time periods in this schedule without any tasks.

[0270] Therefore, for example, the second decision unit 138 determines the time period in which more than half of the subjects T21 to T24 can be guided to the restroom as the time period in which the subject can be guided to the restroom. For example, the second decision unit 138 determines the time period in which more than half of the subjects can be guided to the restroom during idle time periods without business as the time period in which the subject can be guided to the restroom.

[0271] based on Figure 7B For example, during the time period from 9:00 AM to 9:30 AM, individuals T21, T22, and T24 (out of T21 to T24) have free time without any business. Therefore, during this time period, individuals T21, T22, and T24 can provide restroom guidance. In other words, during the time period from 9:00 AM to 9:30 AM, more than half of the individuals can provide restroom guidance. Therefore, in... Figure 7B In the example, the second decision unit 138 determined the time period "9:00 to 9:30" as the time period during which the target could be guided to the restroom.

[0272] In addition, based on Figure 7BFor example, during the time period from 11:00 AM to 11:30 AM, subjects T23 and T24 out of T21 to T24 have free time without any business. Therefore, subjects T23 and T24 can provide restroom guidance during this time period. In other words, during the time period from 11:00 AM to 11:30 AM, more than half of the subjects can provide restroom guidance. Therefore, in... Figure 7B In the example, the second decision unit 138 also determined the time period "11:00 to 11:30" as the time period during which the target could be guided to the restroom.

[0273] In addition, based on Figure 7B For example, during the time period from 12:30 PM to 1:00 PM, participants T21 to T24 are all free during this period, so they can provide restroom guidance. In other words, during the time period from 12:30 PM to 1:00 PM, more than half of the participants can be guided to the restroom. Therefore, in... Figure 7B In the example, the second decision unit 138 also determined the time period "12:30 to 13:00" as the time period during which the target could be guided to the restroom.

[0274] In addition, the time periods "13:30-14:00" and "14:00-14:30" can also be identified as the time periods during which the target can be guided to the restroom.

[0275] Next, the explanation Figure 7C The second decision unit 138 determines the time period that satisfies all the time period conditions as the guidance time for guiding the wearer to the toilet based on the time period corresponding to the determination results in S62 to S64 (step S65).

[0276] First, in step S62, the second decision unit 138 determines the time period that can further improve the success rate of toilet training and efficiently guide as many people as possible to the toilet (taking into account the time period during which toilet training can effectively guide people to the toilet). Figure 7A In the example, the second decision unit 138 determined that the time periods were "10:00 to 10:30", "10:30 to 11:00", and "11:00 to 11:30". Figure 7C The result of this determination is represented by a "double circle".

[0277] Additionally, in step S63, the second decision unit 138 determines the time period during which each wearer U21 to U30 can go to the restroom. Figure 7BIn the example, the second decision unit 138 determined that the time periods were "8:00-8:30", "9:00-9:30", "11:00-11:30", and "12:30-13:00". Figure 7C The result of the determination is indicated by a "circle mark".

[0278] Additionally, in step S64, the second decision unit 138 determines the time period during which the person can receive toilet guidance. Figure 7B In the example, the second decision unit 138 determined that the time periods were "9:30-10:30", "11:00-11:30", "12:30-13:00", "13:30-14:00", and "14:00-14:30". Figure 7C The result of the determination is indicated by a "circle mark".

[0279] In this state, the second decision unit 138 determines the time period corresponding to the "double circle" and the "circular mark," that is, the time period corresponding to the determination results in S62 to S64, as the time period condition. Furthermore, the second decision unit 138 determines the time period that satisfies all of these time period conditions as the guidance timing for guiding the wearer to the restroom. Based on... Figure 7C For example, the time period that satisfies all the time period conditions is the time period from "11:00 to 11:30".

[0280] In addition, based on Figure 7A For example, the necessity of guiding all wearers U21-U30 to the restroom during the time period "11:00-11:30" was determined to be "medium" or higher. This indicates that if wearers U21-U30 are guided to the restroom during this time period, toilet training is successful. Furthermore, based on... Figure 7B For example, during the time period "11:00 to 11:30", individuals T23 and T24 can be guided to the restroom. Therefore, the second decision unit 138 determines the time period "11:00 to 11:30", which satisfies all the time period conditions, as the guidance time for guiding the wearer to the restroom. In addition, the second decision unit 138 determines "individuals T23 and T24" as the personnel responsible for guiding them to the restroom during this time period, and determines "U21 to U30" as the wearers who should actually be guided to the restroom.

[0281] Additionally, the suggestion unit 139 suggests to the target that the wearer be guided to the restroom at the guidance time determined by the second decision unit 138 (S65). Specifically, the suggestion unit 139 sends suggestion information to a designated information processing terminal, such as that of the daycare center FA2, indicating the guidance time "11:00 to 11:30" determined by the second decision unit 138, the target "targets T23 and T24" responsible for guiding the restroom, and the wearers "U21 to U30" to be guided to the restroom. Furthermore, the suggestion unit 139 may also send suggestion information related to the target device 30 of each of the "targets T23 and T24" who are responsible for guiding the restroom. Additionally, the second decision unit 138, for example, stores this suggestion information and the facility ID "FA2" in the guidance time storage unit 124.

[0282] As illustrated in Figure 7, the information processing device 100 determines the timing for guiding the wearer to the toilet based on the subject's schedule, the wearer's schedule, and the necessity of guiding the wearer to the toilet based on the predicted elimination time for each wearer. According to this information processing device 100, the most practical timing that can improve the success rate of toilet training and achieve efficient toilet guidance can be presented. Furthermore, the wearer (infant) easily becomes aware of the importance of eliminating waste in the toilet, and the subject (caregiver) can effectively utilize the time in the schedule.

[0283] Furthermore, while Figure 7 illustrates the guidance of a young child as the person being guided to the toilet, this is not limited to children. The person being guided to the toilet can be any wearer who achieves a result through such guidance, such as a caregiver receiving care in a childcare facility.

[0284] For example, when the person being guided to the toilet is a person being cared for, proposing guidance timing using the information processing device 100 enables the caregiver (caregiver) to work efficiently in a stressful care setting, thus helping to create an environment conducive to their work. Furthermore, because this environment is conducive to the caregiver's work, the person being cared for receives appropriate care, resulting in improved quality of life (QOL). For example, the person being cared for receives appropriate care focused on independent bowel movements. Therefore, proposing guidance timing using the information processing device 100 benefits both the caregiver (caregiver) and the person being cared for.

[0285] Furthermore, the above example illustrates an instance where the second decision unit 138 determines the timing for guiding the wearer, the subject of treatment, to the toilet based on the excretion timing predicted based on internal and external information. However, the second decision unit 138 may also determine the timing for guiding the wearer, the subject of treatment, to the toilet based on urination prediction information and defecation prediction information obtained by the acquisition unit 131 (excretion information acquisition unit).

[0286] (Regarding Proposal Department 139)

[0287] The suggestion unit 139 makes a prescribed suggestion to the wearer, who is the recipient of the treatment, based on the excretion timing predicted by the prediction unit 135. For example, the suggestion unit 139 makes a suggestion related to the timing determined by the first decision unit 137 and the second decision unit 138.

[0288] For example, if the timing for replacing the absorbent material worn by the wearer to be treated is determined based on the excretion timing predicted by the prediction unit 135, the suggestion unit 139 suggests replacing the absorbent material at that timing.

[0289] Additionally, for example, if the timing for guiding the wearer to the toilet is determined based on the excretion timing predicted by the prediction unit 135, the suggestion unit 139 suggests guiding the wearer to the toilet at that timing.

[0290] Furthermore, the suggestion unit 139 can output suggestion information regarding suggestions for the excretion care of the person being cared for, based on urination prediction information and defecation prediction information acquired by the acquisition unit 131 (excretion information acquisition unit) and information related to the care of the person being cared for (wearer). Therefore, the suggestion unit 139 is also a processing unit corresponding to the output unit. In this regard, for example, urination prediction information and defecation prediction information are acquired by the acquisition unit 131, and after determining the guidance timing based on the urination prediction information and defecation prediction information, the suggestion unit 139 outputs information to the recipient device 30 indicating the guidance timing determined by the second determination unit 138 (suggestion information regarding suggestions for excretion care of the person being cared for).

[0291] Furthermore, for example, if the decision to use the replacement absorbent pad together with the absorbent item worn by the wearer is determined based on the excretion timing predicted by the prediction unit, the suggestion unit 139 can also make a suggestion corresponding to the decision result. The determination of whether to use the replacement absorbent pad together with the absorbent item worn by the wearer is made by the second determination unit 140. The second determination unit 140 will be described below.

[0292] (Regarding the second judgment section 140)

[0293] Before explaining the second determination unit 140, the topic of diaper pads (absorbent pads) will be explained. Diaper pads are auxiliary pads used together with diapers by being inserted into the outer layer of the diaper. Hereinafter, diaper pads will sometimes be simply referred to as "pads".

[0294] For example, in care settings, pads are often used in conjunction with diapers. By using pads together and only changing them when urinating, costs can be reduced and the burden of changing them lessened.

[0295] On the other hand, when pads are used together, the space inside the diaper to hold feces decreases, thus increasing the risk of feces leakage from the diaper compared to when pads are used alone. Therefore, from the viewpoint of reducing the risk of leakage, it is desirable to avoid using pads together.

[0296] Here, assuming the timing of bowel movements is known in advance, it would be possible to switch from using diapers alone to using pads together, for example, based on that timing. However, in the past, the timing of bowel movements could not be obtained with high precision, so the suggestion of diaper care centered on urination is important. Specifically, in the past, the timing of bowel movements could not be obtained with high precision, and ultimately, the timing of bowel movements was still unclear, so the suggestion of always using pads together is important.

[0297] To address the aforementioned issues and propose a method for diaper care oriented towards defecation, this embodiment proposes determining whether to use a replacement absorbent pad together with the absorbent item worn by the wearer (as an external adult diaper) based on the excretion timing (here, defecation timing) predicted by the prediction unit 135. As explained above, the defecation timing can be predicted with high accuracy through the prediction process performed by the prediction unit 135. Therefore, it is conceivable that if this defecation timing is used effectively, it is possible to effectively determine whether it is better to use a diaper alone or to use a pad together, thus enabling a method for diaper care oriented towards defecation.

[0298] Specifically, the second determination unit 140 determines whether to use the replacement absorbent pad together with the absorbent article worn by the wearer, based on the excretion timing predicted by the prediction unit 135. Furthermore, the suggestion unit 139 makes a suggestion corresponding to the determination result of the second determination unit 140. For example, the second determination unit 140 determines whether to use the replacement absorbent pad together with the absorbent article worn by the wearer based on the type of excrement that may be excreted at the excretion timing. Alternatively, the second determination unit 140 determines whether to use the replacement absorbent pad together with the absorbent article worn by the wearer based on the state of the excrement that may be excreted at the excretion timing. Additionally, the second determination unit 140 determines whether to use the replacement absorbent pad together with the absorbent article worn by the wearer based on the predicted body movements of the wearer at the excretion timing.

[0299] use Figure 8 This will explain the determination process performed using the second determination unit 140. Figure 8 This is a flowchart illustrating the decision-making process for determining whether to use a pad simultaneously. Additionally, in Figure 8 In this section, the wearer who is the object of processing will be referred to as wearer U11 for explanation.

[0300] As explained above, the prediction unit 135 predicts the excretion timing based on the type of excrement (urine, feces), and therefore the second determination unit 140 determines whether the type of excrement that may be excreted next time is feces based on the predicted excretion timing (step S701). In other words, the second determination unit 140 determines whether to defecate or urinate next time based on the predicted excretion timing.

[0301] If the second determination unit 140 determines that the type of excrement that may be excreted next time is not feces (determined to be urination) (step S701; "No"), it determines that the pad should be used together with the diaper DP1. In this case, the suggestion unit 139 suggests to the recipient T11 that the pad be used together with the diaper DP1. In this way, if the next excretion is urine, a suggestion for diaper care related to urination can be made based on the determination that the pad should be used together.

[0302] On the other hand, when the second determination unit 140 determines that the type of excrement that may be excreted next time is feces (determined to be the case of defecation) (step S701; "Yes"), it determines whether the remaining time until the predicted excretion time (here, the defecation time) is less than one hour (step S702).

[0303] If the second determination unit 140 determines that the remaining time until the predicted defecation time is not less than 1 hour (step S702; "No"), it determines that the pad and diaper DP1 should be used together. In this case, the suggestion unit 139 suggests to the recipient T11 that the pad and diaper DP1 be used together. In this way, even if the next excretion is a bowel movement, if it is determined that there is sufficient time until the defecation time, a suggestion for diaper care oriented towards urination can be made based on the determination of using the pad together, in case urination occurs before the defecation time.

[0304] On the other hand, if the second determination unit 140 determines that the remaining time until the predicted defecation time is less than 1 hour (step S702; "Yes"), it determines whether the stool excreted at the predicted defecation time is soft or watery (step S703). For example, the acquisition unit 131 also acquires state information representing the intestinal state detected by the first sensor SN1. Therefore, the second determination unit 140 can determine whether the stool excreted at the defecation time is soft or watery based on the intestinal state represented by the state information.

[0305] Furthermore, if the second determination unit 140 determines that the stool to be expelled at the predicted defecation time will be soft or watery (step S703; "Yes"), it determines that the pad should not be used together with the diaper DP1. In other words, the second determination unit 140 determines that the pad should not be used together and that the diaper DP1 should be used alone. In addition, in this case, the suggestion unit 139 suggests to the recipient T11 that the pad should not be used together with the diaper DP1. For example, if the pad is currently being used together with the diaper DP1, the suggestion unit 139 suggests to the recipient T11 that the pad be removed. In this way, when there is a possibility of defecation in the near future and the stool is likely to be soft or watery, which has a higher risk of leakage than solid stool, a suggestion for diaper care in response to defecation can be made based on the determination result of using the diaper alone.

[0306] Furthermore, since the risk of soft or watery stool leakage is very high, when it is determined that the state (stool nature) of the stool excreted at the defecation time is soft or watery (step S703; "Yes"), the suggestion unit 139 can control the output of an alarm to the object device 30 of the object T11.

[0307] On the other hand, if the second determination unit 140 determines that the stool excreted at the predicted defecation time is not soft or watery (in the case of solid stool) (step S703; "No"), it determines whether the frequency of the wearer U11's body movements is low (step S704). For example, the second determination unit 240 can access the wearer information storage unit 121 and determine (predict) whether the frequency of body movements at the predicted defecation time is low based on the wearer U11's "body movement history".

[0308] For example, if the wearer U11 tends to perform actions during the time period corresponding to defecation time based on the body movement history record, the second determination unit 140 can determine that the frequency of body movements during the predicted defecation time is not low (but high). On the other hand, if the wearer U11 tends not to perform actions during the time period corresponding to defecation time based on the body movement history record, the second determination unit 140 can determine that the frequency of body movements during the predicted defecation time is low.

[0309] Furthermore, if the second determination unit 140 determines that the frequency of the wearer U11's body movements is not low (step S704; "No"), it determines that the pad should be used together with the diaper DP1. In this case, the suggestion unit 139 suggests to the recipient T11 that the pad should be used together with the diaper DP1. For example, when the body movements are vigorous, space is easily formed inside the diaper to contain feces, and the possibility of solid feces being successfully contained in such space is high. In other words, when there is a possibility of excreting solid feces while the body movements are vigorous, the risk of leakage is reduced, and therefore, based on the determination that the pad can be used together, a suggestion for diaper care oriented towards defecation can be made.

[0310] On the other hand, if the second determination unit 140 determines that the frequency of the wearer U11's body movements is low (step S704; "Yes"), it determines that the pad should not be used together with the diaper DP1. In other words, the second determination unit 140 determines that the pad should not be used together and that the diaper DP1 should be used alone. In addition, in this case, the suggestion unit 139 suggests to the recipient T11 that the pad should not be used together with the diaper DP1. For example, if the pad is currently being used together with the diaper DP1, the suggestion unit 139 suggests to the recipient T11 that the pad be removed. For example, when body movements are restricted, it is not easy to create space inside the diaper to contain feces, and the risk of leakage of compressed feces increases. That is to say, when there is a possibility of excreting solid feces while body movements are restricted, the risk of leakage is high. Therefore, based on the determination that it is best not to use the pad together, a suggestion for diaper care oriented towards defecation can be made.

[0311] [7. Processing Procedure]

[0312] Next, use Figure 9 and Figure 10 This will explain the information processing process involved in the implementation method. Figure 9 The document describes the learning process used to learn the model within the information processing involved in the implementation method. Figure 10 The process of predicting the timing of excretion using a learned model in the information processing involved in the implementation method is described.

[0313] [7-1. Processing Procedure (1)]

[0314] First, use Figure 9 This will explain the learning process involved in the implementation method. Figure 9 This is a flowchart illustrating the learning process involved in the implementation method.

[0315] First, the acquisition unit 131 acquires the amount of excrement (urine, feces) accumulated in the body (bladder, intestines) at predetermined intervals, i.e., the body accumulation amount (step S801). Specifically, the acquisition unit 131 acquires the body accumulation amount detected by a first sensor worn around the waist of the user who is being processed. Thus, the information processing device 100 is able to obtain historical records of the body accumulation amounts of urine and feces, respectively.

[0316] Furthermore, the first determination unit 132 determines whether excretion has occurred into the absorbent material worn by the user, which is the object of treatment (step S802). For example, if a second sensor installed on the absorbent material worn by the user, which is the object of treatment, detects excretion, the acquisition unit 131 acquires excretion detection information indicating the excretion detection. Therefore, the first determination unit 132 determines whether excretion has occurred into the absorbent material based on whether the acquisition unit 131 has acquired the excretion detection information.

[0317] During the period when the acquisition unit 131 determines that no excretion has occurred based on the fact that the acquisition unit 131 has not acquired excretion detection information (step S802; "No"), the first determination unit 132 remains in standby mode until it can determine that excretion has occurred.

[0318] On the other hand, when the first determination unit 132 determines that excretion has occurred based on the excretion detection information obtained by the acquisition unit 131 (step S802; "Yes"), it determines the type of excrement excreted based on the body information obtained from the first sensor at this time (step S803). Specifically, the first determination unit 132 determines whether the excrement of the wearer being processed is urine or feces based on the body information obtained from the first sensor at this time. For example, the first determination unit 132 can determine the type of excrement excreted based on the bladder state and intestinal state indicated by the state information obtained from the first sensor.

[0319] Furthermore, the threshold determination unit 133 determines the amount of excrement accumulated in the body as indicated by the determination result in step S803 before excretion occurs (step S804). Specifically, the threshold determination unit 133 determines the amount of excrement accumulated in the body as indicated by the determination result before excretion occurs based on historical records of the amount of excrement accumulated in the body corresponding to the excrement indicated by the determination result stored before the time point of excretion, and the date and time indicating the time point of excretion.

[0320] That is, the threshold determination unit 133 determines the amount of excrement accumulated in the body when the excrement represented by the judgment result is excreted from the body to the outside, i.e., the accumulation amount threshold, based on the historical record of the amount of excrement in the body corresponding to the excrement represented by the judgment result stored before the time point of excretion, and the date and time representing the time point of excretion.

[0321] Furthermore, the generation unit 134 learns the model based on the historical record of the amount of accumulation in the body, that is, the historical record of the state in which the accumulation threshold is determined at any time (step S805). For example, the generation unit 134 generates a model (predictive model) that learns the correlation between the amount of accumulation in the body and the time from the point in time when such an amount of accumulation in the body is reached until the accumulation threshold is reached. For example, the generation unit 134 generates the following model: the amount of accumulation in the body at the current time is input into the model, and the model outputs the time from the amount of accumulation in the body at the current time until the accumulation threshold is reached (until excretion).

[0322] [7-2. Processing Procedure (2)]

[0323] First, use Figure 10 This will explain the predictive processing involved in the implementation method. Figure 10 This is a flowchart illustrating the prediction processing involved in the implementation method.

[0324] First, the prediction unit 135 determines whether it is time to perform prediction processing for predicting excretion timing (step S901). For example, the prediction unit 135 may determine whether it is time to perform prediction processing based on whether the second sensor SN2 detects excretion. Alternatively, the prediction unit 135 may determine whether it is time to perform prediction processing based on whether a request is received from the person receiving care as the subject of the processing. During the period when it is determined that it is not time to perform prediction processing (step S901; "No"), the prediction unit 135 remains in standby mode until it can be determined that it is time to perform prediction processing.

[0325] On the other hand, when it is determined that the time for predictive processing has been reached (step S901; "Yes"), the prediction unit 135 uses the latest prediction model to predict the excretion time of the wearer who is the subject of processing after the current time point when the time for predictive processing has been reached (step S902). Specifically, the prediction unit 135 predicts the excretion time of the wearer after the current time point based on the prediction model and the amount of excrement accumulated in the body of the wearer who is the subject of processing at the current time point when the time for predictive processing has been reached.

[0326] For example, the prediction unit 135 predicts the urination timing after the current time point based on a prediction model and the amount of urine accumulated in the wearer's body at the current time point. Additionally, the prediction unit 135 predicts the defecation timing after the current time point based on a prediction model and the amount of feces accumulated in the wearer's body at the current time point.

[0327] Additionally, the prediction unit 135 notifies the recipient of the prediction result to the wearer who is being treated (step S903). For example, the prediction unit 135 notifies the recipient of the prediction result by sending the prediction result to the recipient's device 30.

[0328] [8. Other implementation methods]

[0329] The information processing apparatus 100 described above can be implemented in various ways other than those described above. Therefore, other embodiments of the information processing apparatus 100 will be described below.

[0330] [8-1. Variation Example (1)]

[0331] The above embodiments show an example where the prediction unit 135 predicts the excretion timing based on in vivo and external information, and the first decision unit 137 determines a prescribed timing related to the care of the wearer as the treatment subject based on the excretion timing predicted by the prediction unit 135. Additionally, an example is shown where the suggestion unit 139 suggests replacing the absorbent article at that replacement timing. Furthermore, an example is shown where the external information used by the prediction unit 135 is excretion detection information obtained from the second sensor SN2, which is information related to excrement excreted from the body to the outside.

[0332] However, the first decision unit 137 can combine other external information besides excretion detection information to determine what type or what absorbent capacity (volume) of absorbent material should be replaced. For example, when the suggestion unit 139 suggests the replacement time determined this time, the first decision unit 137 also determines whether the prediction unit 135 predicts that urination will occur again from the replacement time determined this time until the predicted time of the next defecation.

[0333] Furthermore, if it can be determined that urination will occur between the determined replacement time and the predicted next defecation time, the first decision unit 137 determines the type or absorbency (capacity) of absorbent material to be replaced at the determined replacement time based on the number of urinations, the amount of urine per urination, and the amount of stool per defecation during that period. In addition, the prediction unit 135 also predicts the number of urinations, the amount of urine per urination, and the amount of stool per defecation.

[0334] Here, it is assumed that a second urination will occur between the time the change of time is determined and the time the next urination is predicted to occur. In addition, the predicted amount of urine for each urination is the normal amount (average amount), and the amount of stool at the urination time is the normal amount (average amount).

[0335] In this case, the first decision unit 137 determines the predicted defecation time as the next replacement time after the currently determined replacement time. Furthermore, the first decision unit 137 determines the type (or absorbency) of absorbent material capable of absorbing the amount of urine twice and the amount of stool at the aforementioned defecation time as the absorbent material that should be replaced at the currently determined replacement time. Additionally, the suggestion unit 139 suggests to the recipient the type (or absorbency) of absorbent material determined by the first decision unit 137. Furthermore, the suggestion unit 139 can also suggest to the recipient the next replacement time determined by the first decision unit 137.

[0336] According to such an information processing device 100, an absorbent material with the ability to contain even multiple excretions during the period before the next replacement time can be worn at the current replacement time. Therefore, it is possible to suppress the situation where an emergency replacement is required before the next replacement time, and as a result, it is possible to assist the recipient (e.g., a caregiver) to work efficiently.

[0337] Furthermore, if the time between the determined replacement time and the predicted next defecation time exceeds a predetermined threshold, the first decision unit 137 may choose not to set the predicted defecation time as the next replacement time after the determined replacement time. For example, it may set the predicted time of the second urination as the replacement time. In this example, the first decision unit 137 determines the absorbent material with the type (or absorbency) of urine that can absorb the amount of urine from two urinations as the absorbent material that should be replaced at the determined replacement time and makes a suggestion.

[0338] According to such an information processing device 100, it is possible to prevent skin inflammation caused by wearing absorbent items that have been urinated on for a long time before defecation.

[0339] [8-2. Variation Example (2)]

[0340] Furthermore, when predicting phased excretion times (e.g., the first excretion time, the second excretion time, etc.) as described in the example above, the prediction unit 135 can also estimate the excretion probability, such as the amount excreted in each phase (each time). In the modified example (1) above, an example is shown where, without considering such excretion probability, the first decision unit 137 determines the absorbent material to be used at the current replacement time based on the urination and defecation status before the next excretion time. However, the first decision unit 137 can also determine the absorbent material to be used at the current replacement time by also considering such excretion probability.

[0341] For example, when the proposal unit 139 proposes the replacement timing for this decision, the first decision unit 137 determines whether there is a discharge timing that is predicted to have a high probability of discharge due to exceeding a predetermined threshold among the predicted discharge probabilities of each stage of discharge timing after the replacement timing is decided.

[0342] Furthermore, if the first decision unit 137 determines that there is a time when the probability of excretion at each stage of excretion exceeds a predetermined threshold and is predicted to be high-probability, then it determines that time when the probability of excretion is high as the next replacement time after the current replacement time. Additionally, the first decision unit 137 determines the type or capacity of absorbent material to be replaced at the current replacement time based on the excretion history prior to the time when the probability of excretion is high (e.g., type of excrement (urine or feces), frequency of excretion, and volume of each excretion).

[0343] When citing examples where it is predicted that excretion (e.g., urination) will occur with a low probability the first time after the determined replacement time, and it is predicted that excretion (e.g., urination) will occur with a high probability the second time, the information processing device 100 can, at the current replacement time, wear an absorbent material that has the ability to prevent leakage even if excretion occurs the first time and to contain the second excretion. Therefore, it can suppress the situation where an emergency replacement is needed before the next replacement time, and as a result, it can assist the recipient (e.g., a caregiver) to work efficiently.

[0344] Furthermore, the first decision unit 137 not only determines the absorbent material to be used at the determined replacement time, but also determines the guidance time for guiding the wearer, who is the subject of treatment, to the toilet. Based on the above example, if the first decision unit 137 determines that among the excretion probabilities of each stage of excretion, there is an excretion time predicted to have a high probability of excretion due to exceeding a predetermined threshold, it determines that excretion time with a high probability of excretion as the guidance time.

[0345] According to such an information processing device 100, it is possible to suppress the increased burden and wasted time of guidance work caused by guiding people to the toilet but not excreting, thus enabling the recipient (e.g., caregiver) to work efficiently.

[0346] [8-3. Variation Example (3)]

[0347] Additionally, the information control unit 136 can base its actions on the excretion status of the wearer who has excreted into the absorbent material (e.g., the excretion status of the wearer into the absorbent material). Figure 4 "Excretion history" and dietary information (e.g., Figure 4The information control unit 136 uses the "meal history" data to suggest lifestyle guidance suitable for the wearer. For example, the information control unit 136 controls the content of the lifestyle guidance based on predicted urine output (urine volume in the bladder, urine output, etc.) and predicted daily urination frequency. As an example, if the predicted urine output (urine volume in the bladder, urine output, etc.) and predicted daily urination frequency are less than a predetermined threshold, the information control unit 136 controls and suggests lifestyle guidance such as increasing water intake based on the judgment that there is insufficient water in the body.

[0348] Furthermore, when urination occurs later than usual, the information control unit 136 can also adjust lifestyle guidance and make suggestions, such as increasing water intake, based on the assessment that the body's water content is low. While this example focuses on urination-related excretion, the information control unit 136 can also adjust excretion-related excretion to ensure that the lifestyle guidance is most suitable for the wearer.

[0349] In addition, the information control unit 136 can detect urinary storage disorders early by detecting the amount of urine stored after urination. For example, if it is determined that the amount of urine stored in the bladder after urination (which can be the amount of urine stored after one urination or the average amount of urine stored after multiple urinations) is high, the information control unit 136 will notify that there may be a urinary storage disorder.

[0350] [9. Others]

[0351] All or part of the processes described above as being performed automatically can also be performed manually. Furthermore, all or part of the processes described as being performed manually can also be performed automatically using known methods. Moreover, unless otherwise stated, the processing procedures, specific names, and information including various data and parameters shown in the above documents and figures can be arbitrarily changed. For example, the various information shown in the figures is not limited to the information shown in the figures.

[0352] Furthermore, the constituent elements of the devices illustrated are functional concepts and may not be physically configured as shown. That is, the specific manner in which the devices are distributed / combined is not limited to the illustrated arrangement. Additionally, each constituent element may be configured such that all or part of it is distributed / combined functionally or physically in arbitrary units according to various loads, usage conditions, etc. Furthermore, the aforementioned processes can be appropriately combined to perform operations within the bounds of non-contradiction.

[0353] [10. Hardware Structure]

[0354] Furthermore, the information processing device 100 involved in the above-described embodiments is, for example, made by... Figure 11 The structure shown is implemented in a computer 1000. Figure 11 This is a diagram illustrating an example of a hardware structure. Computer 1000 is connected to output device 1010 and input device 1020, and arithmetic unit 1030, flash memory 1040, memory 1050, output IF (Interface) 1060, input IF 1070, and network IF 1080 are connected via bus 1090.

[0355] The arithmetic unit 1030 performs various processes based on programs stored in the cache 1040 and memory 1050, as well as programs read from the input device 1020. The cache 1040 is a temporary storage device, such as RAM, for data used by the arithmetic unit 1030 in performing various operations. The memory 1050 is a storage device for registering data and various databases used by the arithmetic unit 1030 in performing various operations; it is a memory implemented using ROM (Read Only Memory), HDD (Hard Disk Drive), flash memory, or the like.

[0356] Output IF 1060 is an interface for sending information as an output target to output devices 1010 such as monitors and printers that output various information. It can be implemented using standard connectors such as USB (Universal Serial Bus), DVI (Digital Visual Interface), or HDMI (High Definition Multimedia Interface). On the other hand, input IF 1070 is an interface for receiving information from various input devices 1020 such as mice, keyboards, and scanners, and can be implemented using, for example, USB.

[0357] For example, the input device 1020 can be implemented as a device for reading information from optical recording media such as CD (Compact Disc), DVD (Digital Versatile Disc), PD (Phase Change Rewritable Disk), magneto-optical recording media such as MO (Magneto-Optical Disk), tape media, magnetic recording media, or semiconductor memory. Alternatively, the input device 1020 can be implemented as an external storage medium such as a USB memory.

[0358] The network IF 1080 has the following functions: receiving data from other devices via network N and sending the data to the computing device 1030; in addition, sending data generated by the computing device 1030 to other devices via network N.

[0359] Here, the arithmetic unit 1030 controls the output device 1010 and the input device 1020 via the output IF 1060 and the input IF 1070. For example, the arithmetic unit 1030 loads a program from the input device 1020 and the memory 1050 into the cache 1040 and executes the loaded program. For example, when the computer 1000 functions as an information processing device 100, the arithmetic unit 1030 of the computer 1000 executes the program loaded into the cache 1040, thereby realizing the function of the control unit 130.

Claims

1. An information processing method, executed by an information processing device, characterized in that it includes the following steps: The acquisition process involves acquiring internal and external information, wherein the internal information relates to the amount of waste accumulated in the body of the wearer of the absorbent material, and the external information relates to the date and time of the wearer's excretion; and The prediction process uses a model that has learned the correlation between the historical records of the accumulation status shown by the internal information and the historical records of the excretion date and time shown by the external information to predict the excretion time of the wearer in the future based on the internal information obtained at the current time.

2. The information processing method according to claim 1, characterized in that, In the acquisition process, dietary information related to the wearer's dietary status is also acquired. In the prediction process, based on the model, the timing of excretion is predicted according to the in vivo information and dietary information obtained at the current time point.

3. The information processing method according to claim 1, characterized in that, In the acquisition process, medication information related to the wearer's medication status is also acquired. In the prediction process, based on the model, the excretion timing is predicted according to the in vivo information and the drug information obtained at the current time point.

4. The information processing method according to claim 2 or 3, characterized in that, It also includes an information control process in which a proposal for controlling the wearer's excretion is made based on the tendency indicated by the correlation.

5. The information processing method according to claim 4, characterized in that, In the information control process, dietary suggestions for controlling the wearer's excretion are made based on the tendency indicated by the correlation between dietary status and excretion status, or medication suggestions for controlling the wearer's excretion are made based on the tendency indicated by the correlation between medication status and excretion status.

6. The information processing method according to claim 1, characterized in that, In the acquisition process, the internal information detected by a first sensor worn on the wearer's body is acquired, and the external information detected by a second sensor installed on the absorbent article is acquired.

7. The information processing method according to claim 1, characterized in that, It also includes a proposal process, in which a prescribed proposal is made to the caregiver of the wearer based on the excretion timing predicted by the prediction process.

8. The information processing method according to claim 7, characterized in that, It also includes a decision process, in which the prescribed timing for caring for the wearer is determined based on the excretion timing predicted by the prediction process. In the proposal process, a proposal is made related to the timing determined by the decision process.

9. The information processing method according to claim 8, characterized in that, In the decision-making process, the timing for replacing the absorbent material worn by the wearer is determined based on the excretion timing predicted by the prediction process. In the proposed process, it is proposed that the absorbent material be replaced at the replacement time.

10. The information processing method according to claim 9, characterized in that, In the prediction process, the amount of excretion at the scheduled time is predicted based on historical records of excretion amounts discharged into the absorbent material. In the decision-making process, if it is determined that the predicted amount of excretion at the excretion timing exceeds the amount of remaining excrement that the absorbent article can absorb, the timing specified before the excretion timing is determined as the replacement timing.

11. The information processing method according to any one of claims 8 to 10, characterized in that, In the decision-making process, the guidance timing for directing the wearer to the toilet is determined based on the excretion timing predicted through the prediction process. In the proposed procedure, it is proposed to guide the wearer to the restroom at the specified guidance time.

12. The information processing method according to claim 11, characterized in that, It also includes a storage process, in which timetable information showing the schedules of the object and the wearer is stored. In the decision-making process, the guidance timing for guiding the wearer to the toilet is determined based on the excretion timing predicted by the prediction process and the schedule information of the subject and the wearer stored in the storage process.

13. The information processing method according to claim 12, characterized in that, In the decision-making process, the timing for guiding the wearer to the toilet is determined based on the schedule and the necessity of guiding the wearer to the toilet based on the excretion timing for each wearer.

14. The information processing method according to claim 7, characterized in that, It also includes a determination step, in which, based on the excretion timing predicted by the prediction step, it is determined whether to use the replacement absorbent pad together with the absorbent item worn by the wearer. In the proposal process, a proposal is made that corresponds to the determination result of the determination process.

15. The information processing method according to claim 14, characterized in that, In the prediction process, the type of excrement that may be excreted at the specified excretion time is predicted based on the correlation between the historical records of excrement conditions shown in the in vivo information and the historical records of excretion shown in the external information. In the determination process, a decision is made based on the type of excrement that may be excreted at the excretion time to determine whether to use the replacement absorbent pad together with the absorbent item worn by the wearer.

16. The information processing method according to claim 15, characterized in that, In the acquisition process, status information indicating the state of excrement inside the wearer's body is also acquired. In the determination process, based on the historical records of the state of the excrement shown in the state information, the state of the excrement that may be excreted at the excretion time is determined, and based on the determined state, it is determined whether to use the replacement absorbent pad together with the absorbent item worn by the wearer.

17. The information processing method according to claim 14 or 15, characterized in that, In the acquisition process, sensor information that detects the appearance of the wearer is also acquired. In the determination process, based on the historical records of the wearer's body movements shown by the sensor information, the predicted body movements that the wearer will perform at the excretion time are determined, and based on the determined body movements, it is determined whether to use the replacement absorbent pad together with the absorbent item worn by the wearer.

18. An information processing device, characterized in that, have: The acquisition unit acquires internal and external information, wherein the internal information relates to the amount of excrement accumulated in the body of the wearer of the absorbent material, and the external information relates to the date and time of the wearer's excretion; and The prediction unit, based on a model that has learned the correlation between historical records of the accumulation status shown in the internal information and historical records of the excretion date and time shown in the external information, predicts the excretion timing of the wearer in the future based on the internal information obtained at the current time.

19. A recording medium storing an information processing program for causing a computer to perform the following processes: The acquisition process involves acquiring internal and external information, wherein the internal information relates to the amount of waste accumulated in the body of the wearer of the absorbent material, and the external information relates to the date and time of the wearer's excretion; and The prediction process uses a model that learns the correlation between the historical records of the accumulation status shown by the internal information and the historical records of the excretion date and time shown by the external information. Based on the internal information obtained at the current time, it predicts the excretion timing of the wearer in the future.

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