JP7847938B2 - Information processing device, information processing method, and information processing program - Google Patents

Description

This invention relates to an information processing device, an information processing method, and an information processing program.

Conventionally, technologies for providing users with various types of information regarding absorbent materials are known. One example of such technology is a technique that suggests how to apply the absorbent material based on the posture of the user when urine leakage occurs, using measurement results from a sensor attached to the absorbent material. Another technology suggests appropriate absorbent materials and replacement timing based on the urine absorption capacity of the absorbent material.

Japanese Patent Publication No. 2018-202154 Japanese Patent Publication No. 2018-206381

However, the conventional technologies described above cannot always accurately predict the timing of excretion.

For example, the conventional technology described above only uses the measurement results of sensors attached to absorbent materials, and therefore cannot always accurately predict the timing of excretion.

This application was made in view of the above, and aims to accurately predict the timing of excretion.

The information processing device according to this application is characterized by comprising: an acquisition unit that acquires internal information, which is information relating to excretion within the body, and external information, which is information different from the internal information and relating to information outside the body; and a prediction unit that predicts the timing of future excretion by a wearer of an absorbent article based on the internal information and the external information.

According to one embodiment, the timing of excretion can be predicted with high accuracy.

Figure 1 shows an overall picture of the information processing according to this embodiment. Figure 2 shows an example of noise detection according to the embodiment. Figure 3 shows an example of the configuration of an information processing device according to the embodiment. Figure 4 shows an example of a wearer information storage unit according to the embodiment. Figure 5 shows an example of an internal/external information storage unit according to the embodiment. Figure 6 shows an example of a schedule information storage unit according to an embodiment. Figure 7A is an explanatory diagram illustrating the decision-making process for determining the timing of toilet guidance. Figure 7B is an explanatory diagram illustrating the decision-making process for determining the timing of toilet guidance. Figure 7C is an explanatory diagram illustrating the decision-making process for determining the timing of toilet guidance. Figure 8 is a flowchart showing the determination process procedure for deciding whether or not to use a pad. Figure 9 is a flowchart showing the learning process procedure according to the embodiment. Figure 10 is a flowchart showing the prediction processing procedure according to the embodiment. Figure 11 shows an example of a hardware configuration.

This specification and the accompanying drawings make it clear at least the following:

An information processing device characterized by comprising: an acquisition unit that acquires internal information, which is information relating to excretion within the body, and external information, which is information different from the internal information and relating to the outside of the body; and a prediction unit that predicts the timing of future excretion by a wearer of an absorbent article based on the internal and external information.

Such an information processing device makes it possible to accurately predict the timing of excretion. Furthermore, improved prediction accuracy allows, for example, the time spent waiting for excretion by the person receiving care to be effectively reduced. Therefore, such an information processing device enables more efficient excretion care. In other words, such an information processing device enables more accurate and advanced excretion care.

Furthermore, the information processing device acquires information regarding the amount of waste products accumulated in the body as internal body information.

This type of information processing device acquires information about the amount of waste accumulated in the body, making it possible to identify the amount of waste that tends to accumulate in the body before it is excreted.

Furthermore, the information processing device acquires information regarding excrement expelled from the body as external information.

According to this type of information processing device, information regarding excretion from the body is acquired as external information. By combining information regarding internal excretion with information regarding excretion from the body, it becomes possible to predict the timing of excretion with greater accuracy.

Furthermore, the information processing device acquires excretion information as external information, indicating that excrement has been expelled from the body to the outside.

According to this information processing device, by acquiring excretion information as external information, indicating that waste has been excreted from the body to the outside, it becomes possible to identify the amount of waste that tends to accumulate in the body before excretion occurs.

Furthermore, the information processing device predicts the timing of excretion, based on the information acquired prior to the current time, as the timing of excretion when the wearer will excrete after the current time.

According to this type of information processing device, based on information acquired prior to the current time, it can predict the timing of the wearer's excretion, making it possible to predict an excretion timing tailored to the individual wearer.

Furthermore, the information processing device predicts the timing of excretion based on information obtained from information acquired prior to the present time, specifically information regarding the amount of excretion accumulated within the body at the time of excretion.

According to this information processing device, by using information obtained from data acquired prior to the present time, specifically information regarding the amount of waste accumulated in the body at the time of excretion, the timing of excretion can be predicted. This prediction is achieved by utilizing trends related to the excretion threshold—the amount of waste that tends to accumulate in the body before excretion—to accurately predict the timing of excretion.

Furthermore, the information processing device predicts the timing of the excretion based on trend information indicating the trend regarding the amount of stored material.

According to this type of information processing device, by predicting the timing of excretion based on trend information indicating the trend in the amount of accumulated material, it becomes possible to predict the timing of excretion that is tailored to each individual wearer with high accuracy.

Furthermore, the information processing device predicts the timing of excretion based on the information regarding the accumulated amount and the amount of excrement currently accumulated in the wearer's body.

This type of information processing device predicts the timing of excretion based on information about the amount of accumulated waste and the amount of waste currently accumulated in the wearer's body. Therefore, it becomes possible to predict the timing of excretion with high accuracy, tailored to the individual wearer.

Furthermore, the information processing device predicts the timing of excretion based on the relationship between the food and drink information consumed by the wearer and the wearer's excretion status, as well as the internal and external information.

According to this information processing device, the relationship between the wearer's food and drink intake information and their excretion status, along with internal and external information, allows for the prediction of excretion timing. This prediction process incorporates the ability to adjust the timing of excretion according to the wearer's eating and drinking status, resulting in a more accurate prediction of when excretion will occur.

Furthermore, the information processing device predicts the timing of excretion based on the relationship between drug information regarding the drug administered to the wearer and the wearer's excretion status, as well as the internal and external information.

This type of information processing device allows for the prediction of excretion timing based on the relationship between drug information administered to the wearer and the wearer's excretion status, as well as internal and external information. The prediction process can incorporate the ability to adjust the timing of excretion according to laxative use, resulting in a more accurate prediction of the timing of excretion.

Furthermore, the information processing device includes an information control unit that performs predetermined controls regarding care for the wearer based on the relationship between food and drink information and excretion status, and the relationship between medication information and excretion status.

This type of information processing device allows for the provision of optimal care to the wearer by performing predetermined controls on care based on the relationship between dietary information and excretion status, and the relationship between medication information and excretion status.

Furthermore, the information processing device controls the food and beverages provided to the wearer, or the laxatives administered to the wearer, based on the aforementioned relationship.

Such an information processing device allows for the control of food and beverages given to the wearer, or laxatives administered, based on relationships, thereby effectively improving the wearer's quality of life (QOL).

Furthermore, the information processing device acquires the internal body information detected by the first sensor attached to the wearer's body, and acquires the external body information detected by the second sensor attached to the absorbent article.

According to this type of information processing device, internal body information detected by a first sensor attached to the wearer's body is acquired, and external body information detected by a second sensor attached to an absorbent article is acquired. Therefore, internal and external body information can be acquired continuously under the same environment.

Furthermore, the information processing device includes a suggestion unit that makes predetermined suggestions to the person providing care for the wearer, based on the excretion timing predicted by the prediction unit.

This type of information processing device makes specific suggestions to those providing care to the wearer based on predicted excretion timings. Because it can suggest care at various precisely calculated timings, it can streamline the caregiver's work. Furthermore, it can improve the quality of care the wearer receives.

Furthermore, the information processing device includes a determination unit that determines a predetermined timing for care related to the wearer based on the excretion timing predicted by the prediction unit.

According to this information processing device, the timing of care for the wearer can be determined accurately based on the predicted timing of excretion.

Furthermore, the information processing device determines the timing for replacing the absorbent item worn by the wearer based on the predicted timing of excretion, and proposes that the absorbent item be replaced at the specified timing.

This type of information processing device can effectively reduce the risk of urinary incontinence by determining the timing for changing the absorbent material worn by the wearer based on the predicted timing of excretion, and by suggesting that the absorbent material be changed at this time.

Furthermore, if the information processing device determines that the amount of excretion predicted at the excretion timing exceeds the remaining absorbable amount (the amount of excretion the absorbent article can absorb), it determines a predetermined timing prior to the excretion timing as the replacement timing.

According to this information processing device, if the predicted amount of excretion at the time of excretion is determined to exceed the remaining absorbent capacity of the absorbent material, a predetermined time before the time of excretion is determined as the replacement time. Therefore, if it is determined that there is a high risk of urinary leakage if the absorbent material is not replaced and urination occurs again, it can suggest replacing the material before the predicted time of the next urination.

Furthermore, the information processing device determines the timing for guiding the wearer to the toilet based on the predicted timing of defecation, and proposes guiding the wearer to the toilet at that timing.

According to this information processing device, based on the predicted timing of urination, the timing for guiding the wearer to the toilet is determined, and a suggestion is made to guide the wearer to the toilet at this timing. This allows for the presentation of the most realistic timing that can improve the efficiency of toilet guidance.

Furthermore, the information processing device determines the timing for guiding the wearer to the toilet based on the predicted timing of defecation and the schedule of the subject or the wearer.

According to this information processing device, the timing for guiding the wearer to the toilet is determined based on the predicted timing of elimination and the wearer's schedule, thereby presenting the most realistic timing for efficient toilet guidance.

Furthermore, the information processing device determines the timing for guiding the wearer to the toilet based on the need for guidance to the toilet, which is determined for each wearer based on the timing of their excretion, and the schedule.

This type of information processing device determines the need for toilet guidance for each user based on their elimination timing, and then, based on the schedule, determines the optimal timing for guiding the user to the toilet. This increases the success rate of toilet training and makes toilet guidance more efficient, presenting the most realistic timing. As a result, users (e.g., toddlers) become more aware of the importance of using the toilet for elimination, and those involved (e.g., childcare workers) can utilize their time more effectively within the schedule.

Furthermore, such information processing devices can support the efficient work of those involved in caregiving (e.g., caregivers) in stressful caregiving settings, thereby creating a more comfortable working environment for them. As a result, the wearer (e.g., care recipient) will receive appropriate care, leading to an improvement in their quality of life (QOL).

Furthermore, the information processing device includes a determination unit that determines whether or not to use a replacement absorbent pad in conjunction with the absorbent item worn by the wearer, based on the excretion timing predicted by the prediction unit. The suggestion unit makes a suggestion according to the determination result of the aforementioned determination unit.

This type of information processing device can determine whether or not to use a replacement absorbent pad in conjunction with the absorbent item worn by the wearer, based on the predicted timing of excretion. By making suggestions based on this determination, it becomes possible to provide diaper care tailored specifically to bowel movements.

Furthermore, the information processing device determines, based on the type of excrement that may be excreted at the aforementioned excretion timing, whether or not to use a replacement absorbent pad in conjunction with the absorbent item worn by the wearer.

According to this information processing device, based on the type of excrement that may be excreted at the time of excretion, it can determine whether or not to use a replacement absorbent pad in conjunction with the absorbent item worn by the wearer, thereby enabling the suggestion of diaper care tailored to both urination and defecation.

Furthermore, the information processing device determines, based on the state of the excrement that may be excreted at the aforementioned excretion timing, whether or not to use a replacement absorbent pad in conjunction with the absorbent item worn by the wearer.

This type of information processing device can determine whether or not to use a replacement absorbent pad in conjunction with the absorbent item worn by the wearer, based on the state of the excrement that may be excreted at the time of excretion. This allows for the suggestion of diaper care that effectively reduces the risk of fecal leakage.

Furthermore, the information processing device determines whether or not to use a replacement absorbent pad in conjunction with the absorbent item worn by the wearer, based on the wearer's body movements predicted at the time of excretion.

This type of information processing device can determine whether or not to use a replacement absorbent pad in conjunction with the absorbent item worn by the wearer, based on the wearer's predicted body movements at the time of excretion. This allows for the suggestion of diaper care that effectively reduces the risk of fecal leakage.

Below, an example of an embodiment for implementing an information processing device, an information processing method, and an information processing program (hereinafter referred to as "embodiment") will be described in detail with reference to the drawings. Note that this embodiment does not limit the information processing device, information processing method, and information processing program. Furthermore, the same parts will be denoted by the same reference numerals in the following embodiments, and redundant descriptions will be omitted.

[1. Overview of Information Processing According to the Embodiment]
First, we will explain the overview of the information processing according to the embodiment, based on the premise. Conventionally, there are known technologies that predict excretion using information obtained from sensors that detect the state inside the body, or using information obtained from sensors that detect the state outside the body (for example, whether or not excretion has occurred) by being attached to absorbent items such as diapers. However, conventionally, since information indicating the state inside the body and information indicating the state outside the body were used individually to predict excretion, it could not be said that the timing of excretion could be predicted with high accuracy.

Therefore, in this embodiment, we conceived the idea of predicting future excretion timing by using information obtained by combining internal information, which is information related to excretion within the body, and external information, which is information related to excreted waste products from the body to the outside. In other words, in this embodiment, the following information processing is performed as part of the information processing according to the embodiment.

Specifically, in this embodiment, internal information, which is information regarding excretion within the body, and external information, which is information regarding excreted material from the body to the outside, are acquired. Based on the acquired internal and external information, the timing of future excretion by the wearer of the absorbent article is predicted. For example, in this embodiment, internal information detected by a first sensor attached to the wearer's body is acquired, and external information detected by a second sensor attached to the absorbent article is acquired.

Furthermore, in this embodiment, the timing of excretion is predicted based on information obtained from acquired internal and external information, specifically information obtained before the present time, regarding the amount of waste accumulated in the body when the waste was excreted.

[2. Information processing system according to the embodiment]
Next, an information processing system according to the embodiment will be described using Figure 1. Figure 1 is a diagram showing the overall picture of the information processing according to the embodiment. As shown in Figure 1, 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 to each other via a network N (not shown) so as to be able to communicate by wire or wireless. Note that the information processing system 1 shown in Figure 1 may include multiple first sensors SN1, multiple second sensors SN2, multiple target devices 30, and multiple information processing devices 100. Note that a detection device having the functions of both the first sensor SN1 and the second sensor SN2 may be mounted on the same device. Specifically, the first sensor SN1 and the second sensor SN2 may be configured not as separate devices as shown in Figure 1, but as a single detection device having the detection functions of both.

[3. Regarding each device]
Next, each device 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 and is used by being attached to the body of a wearer wearing an absorbent article. 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 and measures the amount of urine accumulated in the bladder at that time. Also, for example, the first sensor SN1 uses ultrasound to measure changes in rectal distension and measures the amount of stool accumulated in the rectum at that time. In addition to ultrasound, other detection means such as impedance detection, image analysis, optical sensors, and invisible light detection may be used as detection means for the first sensor SN1.

Furthermore, for example, the first sensor SN1 performs the detection (measurement) process described above at predetermined intervals (e.g., every minute), and transmits the detection results to the information processing device 100 at predetermined intervals (e.g., every minute).

Furthermore, the first sensor SN1 can also detect state information indicating the condition of the bladder, such as the degree of bladder expansion and bladder size. Additionally, the first sensor SN1 can detect state information indicating the condition of the intestines, such as intestinal movement (peristalsis).

The second sensor SN2 is an example of a second sensor and is used by attaching it to an absorbent item worn by the wearer (disposable "child diapers" if the wearer is an infant, or disposable "adult diapers" if the wearer is an adult). The second sensor SN2 detects external information, which is information about excretion from the body to the outside. For example, the second sensor SN2 detects that excretion has occurred from the body to the outside as external information. In other words, the second sensor SN2 performs excretion detection.

For example, the second sensor SN2 detects the presence or absence of excretion based on changes in impedance within the absorbent article. Specifically, the second sensor SN2 detects the magnitude of the impedance between conductive members attached to the absorbent article and detects the presence or absence of excretion based on the pattern of change in the detected impedance over time. Then, the second sensor SN2 determines whether the excretion is feces or urine based on the rate of change in impedance over time after a predetermined period has elapsed since the excretion was detected. Note that the detection means of the second sensor SN2 may also include other detection means besides impedance, such as conductive sensors, temperature, humidity, color, odor, and chemical sensors (for detecting specific chemical substances).

The user device 30 is an information processing terminal used by the person providing care to the wearer. The user device 30 may be, for example, a smartphone, tablet, notebook PC (Personal Computer), desktop PC, mobile phone, or PDA (Personal Digital Assistant). Furthermore, assuming the user is a staff member of a designated facility (e.g., a nursing home), the user device 30 may be an information processing terminal with a nurse call function.

The information processing device 100 is an information processing device that performs the information processing according to the above embodiment, and is implemented by a server device, a cloud system, etc. In this embodiment, the information processing device 100 is assumed to be a server device.

[4. An example of information processing according to the embodiment]
From here, an example of information processing according to the embodiment will be explained using Figure 1. In Figure 1, an example is shown in which the timing of future excretion by wearer U11 (care recipient), who is residing in a designated care facility and wearing an adult diaper DP1 (hereinafter abbreviated as "diaper DP1"), is predicted based on internal and external information obtained from the wearer U11, and the prediction result is notified. In this example, the person providing care to wearer U11 is a care worker or the like.

On the other hand, the information processing according to this embodiment can process not only adults wearing adult diapers, but also infants wearing children's diapers. Furthermore, in such cases, the target individuals might be, for example, childcare workers at a nursery school.

In the example shown in Figure 1, the wearer U11 has the first sensor SN1 attached to their waist (for example, near the lower abdomen), and is also wearing a diaper DP1 to which the second sensor SN2 is attached.

In this state, the first sensor SN1 detects (measures) the amount of waste (urine, feces) accumulated in the body (bladder, intestines) at predetermined intervals (for example, every minute) and transmits the measurement results to the information processing device 100 at these predetermined intervals (for example, every minute). Therefore, the information processing device 100 continuously acquires the amount of waste (urine, feces) accumulated in the body (bladder, intestines) at predetermined intervals (an example of internal information) (step S11). More specifically, the information processing device 100 continuously acquires the combination of the amount of waste (urine, feces) accumulated in the body (bladder, intestines) and the date and time when this amount was measured.

Here, as in step S11, the information processing device 100 acquires combinations of the amount of fluid stored in the body and the date and time as needed. Therefore, the amount of fluid stored in the body corresponding to each date and time is stored as a history of the amount of fluid stored in the body/external information storage unit 122, which will be described later. As a result, a curve showing the change in the amount of fluid stored in the body over time is obtained from this history. Figure 1 illustrates such curves for the wearer U11: curve CV11 corresponding to urine and curve CV12 corresponding to feces. Curve CV11 is obtained by plotting the horizontal axis as the date and time (minutes) and the vertical axis as the amount of urine (ml) stored in the wearer U11's bladder at the corresponding date and time (minutes). Curve CV12 is obtained by plotting the horizontal axis as the date and time (minutes) and the vertical axis as the amount of feces (g) stored in the wearer U11's rectum at the corresponding date and time (minutes).

Furthermore, as the history of internal accumulation is accumulated in this manner, the second sensor SN2 detects the presence or absence of excretion based on the impedance change within the diaper DP1, as described above. If excretion is detected, it transmits excretion detection information (an example of external information) to the information processing device 100. Therefore, as the history of internal accumulation is accumulated, 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 excretion.

As described above, the second sensor SN2 is a device capable of determining the type of excreted material, such as whether it is feces or urine. However, in this embodiment, the second sensor SN2 only detects excretion and does not perform the process of determining the type of excreted material. Specifically, the second sensor SN2 does not perform the process of determining whether the excreted material is urine or feces; the information processing device 100 determines the type of excreted material based on internal body information. On the other hand, the second sensor SN2 may also perform the type of excreted material determination, in which case the information processing device 100 may use the determination result from the second sensor SN2. Furthermore, the information processing device 100 may determine whether the excreted material is feces or urine with higher accuracy by combining the determination result based on internal body information and the determination result from the second sensor SN2.

Furthermore, as the history of the amount of fluid accumulated in the body is accumulated, if the information processing device 100 acquires excretion detection information indicating that excretion has occurred (when excretion is detected), it determines the type of excreted material based on the internal body information obtained from the first sensor SN1 at the time of excretion (step S13). Specifically, at the time of excretion, the information processing device 100 determines whether the excreted material by the wearer U11 is urine or feces, based on the internal body information obtained from the first sensor SN1. Since the first sensor SN1 also detects state information indicating the state of the bladder and intestines, the information processing device 100 determines whether the excreted material is urine or feces based on the state information at the time of excretion. For example, if the information processing device 100 determines, based on the state information, that the bladder was moving at the time of excretion, it determines that the excreted material is urine. On the other hand, if the information processing device 100 determines, based on the state information, that the rectum was moving at the time of excretion, it determines that the excreted material is feces.

Furthermore, when the information processing device 100 acquires excretion detection information indicating that excretion has occurred (excretion is detected), it identifies how much of the excretory material indicated by the determination result in step S13 had accumulated in the body (bladder or intestines) before it was excreted (step S14). Specifically, the information processing device 100 identifies how much of the excretory material indicated by the determination result had accumulated in the body (bladder or intestines) before it was excreted, based on the history of accumulated amounts in the body corresponding to the excretory material indicated by the determination result, and the date and time indicating the time of excretion.

In other words, the information processing device 100 identifies an accumulation threshold, which is the amount of excrement accumulated in the body at the time the excrement indicated by the determination result was excreted, based on the history accumulated up to the time of excretion, the history of the amount of excrement accumulated in the body corresponding to the excrement indicated by the determination result, and the date and time indicating the time of excretion.

For example, suppose the information processing device 100 determines in step S13 that urine has been excreted. In this case, the information processing device 100 identifies the accumulation threshold, which is the amount of urine accumulated in the bladder (inside the body) at the time the urine was excreted, based on the curve CV11 (history of the amount of urine accumulated in the body) obtained up to the point of urine excretion and the date and time of urine excretion. In the example in Figure 1, the curve CV11 shows four peaks circled in the diagram, and the amount of urine accumulated in the body corresponding to these peaks is the accumulation threshold.

According to the example in Figure 1, the amount of urine accumulated in the body corresponding to peak PK11 is "270 ml". This example shows that urination was detected at the time on the horizontal axis corresponding to peak PK11 (for convenience, referred to as "time D11"), and that the accumulation threshold, which is the amount of urine accumulated in the bladder at time D11 when urination occurred, was "270 ml". Furthermore, in this example, it can be rephrased as urination by the wearer U11 occurring at time D11, when "270 ml" of urine had accumulated in the bladder.

Furthermore, according to the example in Figure 1, the amount of urine accumulated in the body corresponding to the peak PK12 is "260 ml". This example shows that urination was detected at the time on the horizontal axis corresponding to the peak PK12 (for convenience, referred to as "time D12"), and that the accumulation threshold, which is the amount of urine accumulated in the bladder at the time of urination (time D12), was "260 ml". In this example, it can be rephrased as urination by the wearer U11 occurring at time D12, when "260 ml" of urine had accumulated in the bladder.

The same explanation can be applied to peaks PK13 and PK14, so a detailed explanation is omitted.

We have explained an example where the result of the determination in step S13 is urine, but we will now also explain the case of feces. For example, suppose the information processing device 100 determines in step S13 that feces have been excreted. In this case, the information processing device 100 identifies the accumulation threshold, which is the amount of feces that was accumulated in the rectum (inside the body) when the feces were excreted, based on the curve CV12 (history of accumulated amount in the body) obtained up to the point in time when the feces were excreted and the date and time when the feces were excreted. Here, in the example in Figure 1, the curve CV12 shows three peaks circled in the diagram, and the amount of accumulated amount in the body corresponding to these peaks is the accumulation threshold.

According to the example in Figure 1, the amount of internal accumulation corresponding to peak PK21 is "85g". This example shows that defecation was detected at the time on the horizontal axis corresponding to peak PK21 (for convenience, referred to as "time D21"), and that the accumulation threshold, which is the amount of stool accumulated in the rectum at time D21 when defecation occurred, was "85g". Furthermore, in this example, it can be rephrased as the wearer U11 defecating at time D21, when "85g" of stool had accumulated in the rectum.

The same explanation can be applied to peaks PK22 and PK23, so a detailed explanation is omitted.

Now, by repeating steps S11 to S14 in this way, learning data for obtaining trends regarding the accumulation threshold is accumulated. Therefore, the information processing device 100 learns a model based on the history of the amount of accumulated waste in the body, where the accumulation threshold is identified at each stage (step S15). For example, the information processing device 100 generates a model that learns the relationship based on the trend between the amount of accumulated waste in the body (bladder, intestines) and the time from the point in time when this amount of accumulated waste is reached until the accumulation threshold is reached. For example, the information processing device 100 takes the current amount of accumulated waste in the body as input and generates a model that outputs the time from the current amount of accumulated waste in the body until the accumulation threshold is reached (until it is excreted).

Furthermore, for example, the information processing device 100 repeatedly updates the model based on the most recent predetermined period of history of the amount of waste accumulated in the body, where the accumulation threshold is identified as it progresses, thereby generating a more up-to-date model. Such a model corresponds to trend information that shows the trend regarding the amount of waste accumulated in the body (accumulation threshold) when waste is excreted from the body.

In the example shown in Figure 1, the information processing device 100 generates a predictive model MD11 for the wearer U11, based on the history of urine accumulation in the body (curve CV11). It takes the current amount of urine accumulation in the body as input and outputs the time it takes to reach the accumulation threshold (until urine is excreted). Similarly, in the example shown in Figure 1, the information processing device 100 generates a predictive model MD12 for the wearer U11, based on the history of fecal accumulation in the body (curve CV12). It takes the current amount of fecal accumulation in the body as input and outputs the time it takes to reach the accumulation threshold (until fecal excretion). Furthermore, the information processing device 100 updates predictive models MD11 and MD12 based on the history for a more recent predetermined period.

Furthermore, in this state, the information processing device 100 determines whether it is time to perform predictive processing to predict the timing of future excretion by the wearer U11 (step S16). For example, the information processing device 100 determines whether it is time to perform predictive processing based on whether or not excretion has been detected by the second sensor SN2. For example, if excretion has been detected by the second sensor SN2, the information processing device 100 can determine that it is time to perform predictive processing, meaning that it will predict the timing of excretion after the current time when the excretion occurred. Alternatively, for example, if the information processing device 100 receives a request from a user (for example, a person providing care for the wearer), it may determine that it is time to perform predictive processing, meaning that it will predict the timing of excretion after the current time when the request was made.

Then, while the information processing device 100 determines that it is not yet time to perform prediction processing (Step S16; No), it waits until it determines that it is time to perform prediction processing. On the other hand, when the information processing device 100 determines that it is now time to perform prediction processing (Step S16; Yes), it uses the latest prediction model generated so far to predict the timing of excretion by the wearer U11 that will occur after the current time when the prediction processing is performed (Step S17). In other words, the information processing device 100 predicts the timing of excretion by the wearer U11 that will occur after the current time, based on trend information that shows a trend related to the accumulation threshold, which is obtained from the history of the amount of accumulated substances in the body acquired at a stage prior to the current time when the prediction processing is performed.

In the example shown in Figure 1, the information processing device 100 predicts the timing of excretion (urination) by the wearer U11, based on the prediction model MD11 and the amount of urine accumulated in the wearer U11's body at the present time when the prediction processing is performed. According to the example curve CV11 shown in Figure 1, the amount of urine accumulated in the wearer U11's body at the present time is "20 ml". Therefore, the information processing device 100 takes the urine volume "20 ml" as input and applies the time output by the prediction model MD11 to the current time to predict the time period when the accumulation threshold will be reached (the time period when urination will occur).

Furthermore, in the example shown in Figure 1, the information processing device 100 predicts the timing of defecation (defecation) by the wearer U11, based on the prediction model MD12 and the amount of stool accumulated in the wearer U11's body at the present time when the prediction processing is performed. According to the example of curve CV12 shown in Figure 1, the amount of stool accumulated in the wearer U11's body at the present time is "30g". Therefore, the information processing device 100 takes the stool amount "30g" as input and applies the time output by the prediction model MD12 to the current time to predict the time period when the accumulation threshold will be reached (the time period when defecation will occur).

Furthermore, the information processing device 100 notifies the person responsible for providing care to the wearer U11 of the prediction results (step S18). In the example in Figure 1, the person responsible for providing care to the wearer U11 is the person T11. Therefore, the information processing device 100 notifies the person T11 of the prediction results by transmitting them to the person T11's device 30. For example, if the information processing device 100 predicts that urination will occur between 10:30 and 11:00, and defecation will occur between 11:00 and 11:30, it transmits these prediction results to the person T11's device 30.

As explained using Figure 1, the information processing device 100 according to this embodiment continuously acquires the amount of waste accumulated in the body (information from the first sensor), and when excretion occurs, it acquires excretion detection information (information from the second sensor) indicating excretion detection. The information processing device 100 then combines this acquired information to determine the amount of waste accumulated in the body (bladder, intestines) at which excretion occurred, and identifies the amount of waste accumulated at that time (i.e., the accumulation threshold).

The information processing device 100 then uses trend information (predictive model) that shows the trend related to this accumulation threshold, calculated based on the history of the amount of accumulated substances in the body obtained before the current time when the prediction processing is performed, and the accumulation threshold identified within this history, to predict the timing of excretion for urine and feces after the current time.

According to this information processing device 100, by considering both information indicating the internal and external state of the body to predict the timing of excretion, it becomes possible to predict the timing of excretion with greater accuracy compared to conventional technologies that only consider the use of these information individually. Furthermore, the improved prediction accuracy allows, for example, the person receiving care to effectively reduce the waiting time for excretion, thereby enabling more efficient excretion care. In other words, the information processing device 100 according to this embodiment can realize more accurate and advanced excretion care.

[5. Other Embodiments]
The information processing device 100 may predict the timing of excretion by a process different from the information processing described in Figure 1. Below, a process different from the information processing described in Figure 1 will be described as another embodiment.

[5-1. Prediction using trend information other than the model]
Figure 1 shows an example in which the information processing device 100 predicts the timing of excretion using a predictive model generated based on the history of the amount of accumulated material in the body and the accumulation threshold identified within this history, as trend information indicating the trend regarding the accumulation threshold. However, the information processing device 100 does not necessarily need to use such a model as trend information indicating the trend regarding the accumulation threshold; it may also predict the timing of excretion by using various statistical information as trend information, as described below. This point will be explained using the example in Figure 1.

For example, when the information processing device 100 identifies the accumulation threshold in step S14, it skips the model generation process in step S15 and proceeds to step S16 to determine whether it is time to perform predictive processing to predict the timing of future excretion by the wearer U11.

Then, when the information processing device 100 determines that it is time to perform predictive processing (step S16; Yes), it calculates the trend of the accumulation threshold using the accumulation threshold included in the history of accumulated amounts in the body acquired during a predetermined period prior to the present time (for example, the history for the more recent predetermined period). Specifically, the information processing device 100 calculates the average of the accumulation threshold for this period based on the accumulation threshold included in the history of accumulated amounts in the body acquired during a predetermined period prior to the present time. This average of the accumulation threshold corresponds to trend information indicating the trend of the amount of accumulated waste (accumulation threshold) that was stored in the body when the waste was excreted from the body to the outside.

In the example shown in Figure 1, the information processing device 100 calculates the average accumulation threshold (urine) by averaging the accumulation thresholds included in curve CV11, which corresponds to a predetermined period of history of urine accumulation in the body prior to the present time. Similarly, in the example shown in Figure 1, the information processing device 100 calculates the average accumulation threshold (feces) by averaging the accumulation thresholds included in curve CV12, which corresponds to a predetermined period of history of fecal accumulation in the body prior to the present time.

The information processing device 100 then predicts the timing of excretion by the wearer U11, based on the average of the accumulation threshold and the amount of excrement accumulated in the wearer U11's body at the present time (the amount accumulated in the body).

In the example shown in Figure 1, the information processing device 100 predicts the timing of excretion (urination) by the wearer U11, based on the average of the accumulation threshold (urine) and the amount of urine accumulated in the wearer U11's body at the present time when the prediction processing is performed. According to the example of curve CV11 shown in Figure 1, the amount of urine accumulated in the wearer U11's body at the present time is "20 ml". Therefore, the information processing device 100 predicts the time it takes for the urine volume of "20 ml" to reach the average of the accumulation threshold (urine).

For example, suppose wearer U11 is "an 80-year-old woman weighing in the 50kg range," and statistical values for urine accumulation rate (how quickly urine accumulates) are available for "80-year-old women weighing in the 50kg range." In such a case, the information processing device 100 predicts the time it takes for the urine volume of "20ml" to reach the average accumulation threshold (urine) based on the urine volume of "20ml" and the statistical values for urine accumulation rate.

The information processing device 100 then applies the predicted time to the current time to predict the time period when the average accumulation threshold (urine) will be reached (the time period when urination occurs).

Furthermore, for example, suppose statistical data on urination intervals (the time between urinations) is available for a woman in her 80s weighing around 50 kg. In such a case, the information processing device 100 may predict the time it takes to reach the average accumulation threshold (urine) from a urine volume of 20 ml, based on the urine volume of 20 ml and the statistical data on urination intervals.

The following also explains stool. In the example shown in Figure 1, the information processing device 100 predicts the timing of defecation (defecation) by the wearer U11, based on the average of the accumulation threshold (stool) and the amount of stool accumulated in the wearer U11's body at the present time when the prediction processing is performed. According to the example of curve CV12 shown in Figure 1, the amount of stool accumulated in the wearer U11's body at the present time is "30g". Therefore, the information processing device 100 predicts the time it takes for the amount of stool "30g" to reach the average of the accumulation threshold (stool).

For example, let's assume that for an "80-year-old woman weighing around 50 kg," a statistical value for the rate at which stool accumulates is obtained based on intestinal peristalsis. In such a case, the information processing device 100 predicts the time it takes for the stool volume of "30 g" to reach the average accumulation threshold (stool) based on the stool volume of "30 g" and the statistical value for the stool accumulation rate.

The information processing device 100 then applies the predicted time to the current time to predict the time period (time period when defecation occurs) when the average of the accumulated amount threshold (feces) is reached.

Furthermore, for example, suppose statistical data on the interval between bowel movements (the time between one bowel movement and the next) is available for a "woman in her 80s, weighing in the 50 kg range." In such a case, the information processing device 100 may predict the time it takes to reach the average accumulation threshold (feces) from the stool volume of "30 g" based on the stool volume and the statistical data on the bowel movement interval.

[5-2. Prediction using information from other wearers]
In the example shown in Figure 1, the information processing device 100 predicts the timing of future excretion by the wearer being processed (wearer U11 in Figure 1) based on internal and external information obtained from the wearer being processed. However, the information processing device 100 may also predict the timing of future excretion by the wearer being processed based on internal and external information obtained from other wearers different from the wearer being processed. For example, the information processing device 100 may predict the timing of future excretion by the wearer being processed solely from internal and external information obtained from other wearers different from the wearer being processed, or it may predict the timing of future excretion by the wearer being processed by combining internal and external information obtained from the wearer being processed with internal and external information obtained from other wearers. This point will be explained using the example in Figure 1.

For example, when generating a predictive model for wearer U11, in the early stages when sufficient training data has not been accumulated (for example, the stage when predicting the timing of excretion for wearer U11 for the first time), the information processing device 100 may not be able to generate a highly accurate predictive model. Therefore, in such cases, the information processing device 100 predicts the timing of wearer U11's excretion based on trend information obtained for other users similar to wearer U11.

For example, the information processing device 100 predicts the timing of wearer U11's excretion based on a predictive model obtained for other users with similar attributes (age, gender, physical information (weight, etc.)) to wearer U11, and the amount of excrement currently accumulated in wearer U11's body. Alternatively, the information processing device 100 may predict the timing of wearer U11's excretion based on the trend of the accumulation threshold (average of the accumulation threshold) obtained for other users with similar attributes to wearer U11, and the amount of excrement currently accumulated in wearer U11's body.

This information processing device 100 makes it possible to accurately predict the timing of excretion even when the amount of information necessary for prediction is insufficient.

[5-3. Noise detection combining internal and external information]
In the example shown in Figure 1, the first sensor SN1 detects (measures) the amount of waste (urine, feces) accumulated in the body (bladder, intestines) at predetermined intervals (for example, every minute) and transmits the measurement results to the information processing device 100 at these predetermined intervals (for example, every minute). As a result, the information processing device 100 can obtain curves CV11 and CV12 that show the change over time in the amount of waste (urine, feces) accumulated in the body (bladder, intestines) (history of the amount of waste accumulated in the body).

Furthermore, as explained above, the second sensor SN2 detects the presence or absence of excretion based on impedance changes within the absorbent material. For example, it can detect the magnitude of the impedance between conductive members attached to the diaper DP1. Therefore, the information processing device 100 can periodically acquire impedance values from the second sensor SN2, in addition to periodically acquiring values of the amount of accumulated material in the body from the first sensor SN1.

As a result, the information processing device 100 can obtain not only curves (curves CV11 and CV12) showing the time-dependent changes in the amount of substance accumulated in the body (history of the amount of substance accumulated in the body), but also curves showing the time-dependent changes in impedance values (history of impedance values). Therefore, based on these two types of curves, which are related to each other, the information processing device 100 may detect noise contained in the curves. Specifically, the information processing device 100 detects noise (noise peaks) contained in both curves by comparing the curve showing the time-dependent changes in the amount of substance accumulated in the body with the curve showing the time-dependent changes in impedance values. This point will be explained using Figure 2.

Figure 2 shows an example of noise detection according to the embodiment. Figure 2(a) illustrates curve C111, based on the impedance value originating from the second sensor SN2, as an example of a curve showing the change in impedance value over time. Figure 2(b) illustrates curve C121, also based on the impedance value originating from the second sensor SN2, as an example of a curve showing the change in impedance value over time. For the sake of explanation, curves C111 and C121 are assumed to have different shapes. The vertical axis of curves C111 and C121 represents the impedance value.

First, let's explain Figure 2(a). In the example in Figure 2(a), the information processing device 100 compares curve CV11 (from Figure 1) and curve C111 to determine whether or not noise peaks exist in at least one of the curves. For example, the information processing device 100 detects noise in the peaks included in curve CV11 that exceed a predetermined threshold. Similarly, the information processing device 100 detects noise in the peaks included in curve CV111 that exceed a predetermined threshold.

In this state, the information processing device 100 detects noise peaks by comparing curve CV11 and curve C111, for example, based on whether or not there are corresponding peaks in both curves. Here, "correspondence" refers to the degree of agreement between the peak position and the peak shape.

In the example shown in Figure 2(a), the information processing device 100 determines that the peak PK11 of curve CV11 corresponds to the peak PK111 of curve C111. Furthermore, the information processing device 100 determines that the peak PK12 of curve CV11 corresponds to the peak PK121 of curve C111. Additionally, the information processing device 100 determines that the peak PK14 of curve CV11 corresponds to the peak PK141 of curve C111.

On the other hand, in the example shown in Figure 2(a), the information processing device 100 determines that there is no peak on the curve C111 side that corresponds to peak PK13 on curve CV11. In this case, the information processing device 100 determines that peak PK13 on curve CV11 is a noise peak, and as a result, detects peak PK13 as a noise peak.

Furthermore, if the information processing device 100 detects a noise peak on the curve CV11 side, as in this example, it can exclude peak PK13 when determining the accumulation threshold (step S14 in Figure 1). This type of information processing device 100 prevents situations where a peak that is actually noise is mistakenly identified as a legitimate peak when determining the accumulation threshold. This allows for the learning of a more accurate model, ultimately improving the accuracy of excretion timing prediction.

Next, Figure 2(b) will be explained. In the example in Figure 2(b), the information processing device 100 determines whether or not noise peaks exist in at least one of the curves by comparing curve CV12 (from Figure 1) and curve C121. For example, the information processing device 100 detects noise in the peaks included in curve CV12 that exceed a predetermined threshold. Similarly, the information processing device 100 detects noise in the peaks included in curve CV121 that exceed a predetermined threshold.

Then, similar to the example in Figure 2(a), the information processing device 100 detects noise peaks by comparing curve CV12 and curve C121, for example, based on whether or not there are corresponding peaks in both curves.

In the example shown in Figure 2(b), the information processing device 100 determines that the peak PK21 of curve CV12 corresponds to the peak PK211 of curve C121. Furthermore, the information processing device 100 determines that the peak PK22 of curve CV12 corresponds to the peak PK221 of curve C121. Additionally, the information processing device 100 determines that the peak PK23 of curve CV12 corresponds to the peak PK231 of curve C131.

On the other hand, in the example shown in Figure 2(b), the information processing device 100 determines that there is no peak on the curve C12 side 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, detects peak PK241 as a noise peak.

Furthermore, when the information processing device 100 detects a noise peak on the curve CV121 side, as in this example, it can determine the accuracy of the detection result indicated by the excretion detection information when it acquires excretion detection information from the first sensor SN1 (step S13 in Figure 1). This improves the accuracy of the process for identifying the accumulation threshold based on the excretion detection information (step S14 in Figure 1). Such an information processing device 100 allows for the learning of a more accurate model, thereby improving the accuracy of excretion timing prediction.

Furthermore, the example in Figure 2 shows an example where the information processing device 100 compares the amount of substance accumulated in the body over time with an impedance value. However, the comparison target for the amount of substance accumulated in the body does not necessarily have to be an impedance value; any index value that serves as an indicator for excretion detection may be used. For example, the comparison target for the amount of substance accumulated in the body may be the concentration of odor components. In such a case, the first sensor SN1 corresponds to an odor sensor that detects excretion from changes in odor within the diaper DP1.

Furthermore, the noise detection process described in Figure 2 is performed, for example, by the threshold determination unit 133 described below. The information processing device 100 may also have a dedicated processing unit for noise detection.

[6. Configuration of Information Processing Equipment]
Next, an information processing device 100 according to an embodiment will be described using Figure 3. Figure 3 is a diagram showing an example configuration of the information processing device 100 according to an embodiment. As shown in Figure 3, the information processing device 100 has a communication unit 110, a storage unit 120, and a control unit 130.

(Regarding Communications Unit 110)
The communication unit 110 is implemented, for example, by a NIC (Network Interface Card). The communication unit 110 is connected to the network N by wire or wireless connection and transmits and receives information between, for example, the first sensor SN1, the second sensor SN2, and the target person device 30.

(Regarding memory unit 120)
The memory unit 120 is implemented by, for example, semiconductor memory elements such as RAM (Random Access Memory) and flash memory, or by a storage device such as a hard disk or optical disc. The memory unit 120 includes a wearer information storage unit 121, an internal/external information storage unit 122, a schedule information storage unit 123, and an induction timing storage unit 124.

(Regarding the wearer information storage unit 121)
The wearer information storage unit 121 stores various information about the wearer. Here, Figure 4 shows an example of the wearer information storage unit 121 according to this embodiment. In the example in Figure 4, the wearer information storage unit 121 has items such as "facility ID (Identifier)", "wearer ID (Identifier)", "target person ID (Identifier)", "absorbent item", "meal history", "medication history", "excretion history", "body movement history", "excretion timing", and "replacement timing".

The "Facility ID" is identification information that identifies the facility where the wearer identified by the "Wearer ID" resides (e.g., a nursing home or daycare center), and the facility where the target person identified by the "Target Person ID" works (e.g., a nursing home or daycare center). The "Wearer ID" indicates identification information that identifies the wearer wearing the absorbent item. Such wearer may be, for example, an elderly person receiving care in a nursing home, or a young child attending a daycare center. The "Target Person ID" is identification information that identifies a user who provides care to the wearer identified by the "Wearer ID" and who wishes to receive various notifications and suggestions using the information processing system 1 according to this embodiment.

"Absorbent Article" refers to information about absorbent articles used by a wearer identified by the "Wearer ID." "Absorbent Article" includes information such as the type of absorbent article, its category, product name, product number, and absorbency (capacity). In the example in Figure 4, the absorbent article "Absorbent Article #11" is associated with wearer ID "U11." This example shows that the wearer identified by wearer ID "U11" (wearer U11) is wearing the absorbent article indicated by "Absorbent Article #11."

"Meal history" refers to information indicating the wearer's eating and drinking history, such as "when, what, and how much they ate (or drank)," as identified by the "wearer ID." The "meal history" may be registered by the wearer, or it may be an image of the food or drink given to the wearer. Furthermore, if a sensor (such as a camera) is attached to the container holding the food or drink, the information processing device 100 may acquire the information detected by the sensor (e.g., an image) as the meal history and store it in the wearer information storage unit 121. In the example in Figure 4, the wearer ID "U11" is associated with the meal history entry "Meal History #11." This example shows that the wearer identified by the wearer ID "U11" (wearer U11) has consumed the food and drink indicated in "Meal History #11."

The "medication history" is information indicating the wearer's laxative administration history, such as "when, what kind of laxative, and in what quantity" the wearer, identified by the "wearer ID," was administered. The "medication history" may be registered by the wearer, or, if the administration of laxatives to the wearer is detected by a predetermined sensor (such as a camera), the information processing device 100 may acquire the information detected by the sensor (e.g., captured images) as the medication history and store it in the wearer information storage unit 121. In the example in Figure 4, the wearer ID "U11" is associated with the medication history "medication history #11." This example shows that the wearer identified by the wearer ID "U11" (wearer U11) has been administered laxatives as indicated in "medication history #11."

The "Excretion History" is information indicating the wearer's defecation history, such as "when, what kind of stool (e.g., color, consistency, etc.), and how much stool was excreted" by the wearer identified by the "Wearer ID." The "Excretion History" may be registered by the wearer, or, if the condition of the absorbent material at the time of defecation is detected by a predetermined sensor (e.g., camera), the information processing device 100 may acquire the information detected by the sensor (e.g., captured image) as the defecation history and store it in the wearer information storage unit 121. In the example in Figure 4, the wearer ID "U11" is associated with the defecation history "Defecation History #11." This example shows that the wearer identified by the wearer ID "U11" (wearer U11) has been defecating as indicated by "Defecation History #11."

"Body movement history" is information indicating the history of body movements (postures) of the wearer, identified by the "wearer ID," such as "when and what kind of body movements and postures the wearer was in (for example, on a bed)." The "body movement history" may be registered by the wearer, or, if the wearer's movements are detected by a predetermined sensor (such as a camera), the information processing device 100 may acquire the information detected by the sensor (for example, captured images) as body movement history and store it in the wearer information storage unit 121. In the example in Figure 4, the body movement history "Body Movement History #11" is associated with the wearer ID "U11." This example shows that the wearer identified by the wearer ID "U11" (wearer U11) has been taking the body movements and postures indicated by "Body Movement History #11."

"Excretion timing" refers to the predicted excretion timing for the wearer identified by the "wearer ID," and is information indicating the excretion timing predicted by the information processing explained in Figure 1. In the example in Figure 4, the wearer ID "U11" is associated with the excretion timing "Excretion timing #11." This example shows that the wearer identified by the wearer ID "U11" (wearer U11) is predicted to excrete at the timing (e.g., time period) indicated by "Excretion timing #11."

The "replacement timing" is the replacement timing determined for the wearer identified by the "wearer ID," and is information indicating the replacement timing determined by the first determination unit 137, which will be described later. In the example in Figure 4, the replacement timing "replacement timing #11" is associated with the wearer ID "U11." This example shows a case where it is determined that the absorbent article should be replaced for the wearer identified by the wearer ID "U11" (wearer U11) at the timing (e.g., time period) indicated by "replacement timing #11."

Note that in the example in Figure 4, conceptual symbols such as absorbent item #11, meal history #11, medication history #11, excretion history #11, body movement history #11, excretion timing #11, and replacement timing #11 are used. However, in reality, appropriate numerical values, text, images (videos), etc., representing these will be registered.

(Regarding the internal/external information storage unit 122)
The internal and external information storage unit 122 stores internal information, which is information related to excretion within the body, and external information, which is information related to excrement that has been excreted from the body to the outside. Here, Figure 5 shows an example of the internal and external information storage unit 122 according to this embodiment. In the example in Figure 5, the internal and external information storage unit 122 has items such as "wearer ID (Identifier)", "type of excrement", "date and time information", "amount accumulated in the body", "status information", "detection presence or absence", and "amount of excretion".

The "Wearer ID" indicates the identification information of the wearer of the absorbent material. The "Excrement Type" indicates whether the excrement is urine or feces.

The "date and time information" indicates the date and time when the "amount of waste accumulated in the body (bladder, intestines)" was detected (measured) by the first sensor SN1. As explained in Figure 1, the first sensor SN1 detects (measures) the amount of waste (urine, feces) accumulated in the body (bladder, intestines) at a predetermined interval (for example, every minute) and transmits the measurement results to the information processing device 100 at this predetermined interval (for example, every minute). Therefore, the "date and time information" in Figure 5 corresponds to this example.

"Internal accumulation amount" refers to the amount of waste (urine, feces) accumulated in the wearer's body (bladder, intestines) at the date and time indicated by the "date and time information."

"Status information" refers to information indicating the state of the bladder (bladder condition), such as the degree of bladder expansion and bladder size. Similarly, "status information" refers to information indicating the state of the intestines (intestinal condition), such as intestinal movement (peristalsis). Detection of "status information" is performed by the first sensor, SN1.

"Detection Status" indicates whether or not urine or feces have been excreted. As explained in Figure 1, the second sensor SN2 detects the presence or absence of excretion, and if excretion is detected, it transmits excretion detection information to the information processing device 100. Therefore, when excretion detection information indicating that excretion has occurred is acquired (excretion is detected), "○" is entered in the field corresponding to the "Date and Time Information" and "Detection Status" for this date and time. Furthermore, the "Amount of Accumulated Substance in the Body" to which the "○" for Detection Status is associated is the accumulation threshold.

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

For example, in the example in Figure 5, the wearer ID "U11" is associated with the following: type of excrement "urine", date and time information "February 15, 2020, 16:59", amount accumulated in the body "250 ml", status information "bladder status #112", detection status "○", and amount excreted "200 ml". This example shows that the wearer identified by wearer ID "U11" (wearer U11) urinated at the time "February 15, 2020, 16:59", when "250 ml" of urine had accumulated in the bladder. This example also shows that the amount of urine actually excreted by wearer U11 at this time was "200 ml". Furthermore, this example shows that at the time of urination "February 15, 2020, 16:59", wearer U11's bladder was in a state like "bladder status #112".

Furthermore, in the example shown in Figure 5, the wearer ID "U11" is associated with the following: excrement type "feces," date and time information "February 15, 2020, 16:22," amount accumulated in the body "100g," detection status "○," and amount excreted "90g." This example shows that the wearer identified by wearer ID "U11" (wearer U11) defecated at the time "February 15, 2020, 16:22," when "100g" of feces had accumulated in the bladder. This example also shows that the actual amount of feces excreted by wearer U11 at that time was "90g."

Note that in the example in Figure 5, conceptual symbols such as "bladder state #111" and "intestinal state #111" are used; however, in reality, appropriate numerical values, text, etc., representing these states will be registered.

(Regarding the schedule information storage unit 123)
The schedule information stores the user's schedule. Here, Figure 6 shows an example of the schedule information storage unit 123 according to the embodiment. In the example in Figure 6, the schedule information storage unit 123 has items such as "Facility ID (Identifier)", "User Type", "ID", and "Schedule".

The "Facility ID" corresponds to the Facility ID in Figure 4. The "User Type" indicates whether the corresponding "Schedule" belongs to the target person or the wearer. The "ID" indicates the identification information that identifies the user corresponding to the "User Type." For example, the "ID" corresponding to the user type "Target Person" is the "Target Person ID" (Figure 4), and the "ID" corresponding to the user type "Wearer" is the "Wearer ID" (Figure 4). The "Schedule" shows the schedule of the user identified by the "ID" at the facility indicated by the "Facility ID."

In the example in Figure 6, the facility ID "FA1", the user type "Target Person", the ID "T11", and the schedule "Schedule #SK11" are associated. This example shows that the schedule for target person "T11" at "Facility FA1" is indicated by "Schedule #SK11".

Note that while the example in Figure 6 uses conceptual symbols such as schedule #SK11, in reality, appropriate numerical values, text, timetables, etc., representing these symbols will be registered.

(Induction timing memory unit 124)
The guidance timing memory unit 124 stores information regarding the timing of guiding the wearer to the toilet. The guidance timing is determined by the second determination unit 138, which will be described later. The guidance timing stored in the guidance timing memory unit 124 will be explained in detail with reference to Figure 7.

(Regarding the control unit 130)
Returning to Figure 3, the control unit 130 is realized by the execution of various programs stored in the memory device inside the information processing device 100 using RAM as the working area, by a CPU (Central Processing Unit) or MPU (Micro Processing Unit), etc. Furthermore, the control unit 130 is realized by an integrated circuit such as an ASIC (Application Specific Integrated Circuit) or FPGA (Field Programmable Gate Array).

As shown in Figure 3, the control unit 130 comprises 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 proposal unit 139, and a second determination unit 140, and realizes or executes the information processing functions and operations described below. Note that the internal configuration of the control unit 130 is not limited to the configuration shown in Figure 3; other configurations are also acceptable as long as they perform the information processing described later. Furthermore, the connection relationships of the various processing units within the control unit 130 are not limited to those shown in Figure 3; other connection relationships are also acceptable.

(Regarding acquisition unit 131)
The acquisition unit 131 acquires internal information, which is information related to excretion within the body, and external information, which is information different from internal information and related to the outside of the body. For example, as external information, the acquisition unit 131 acquires information about excrement that has been excreted from the body to the outside. For example, as internal information, the acquisition unit 131 acquires information about the amount of excrement accumulated in the body. Also, for example, as external information, the acquisition unit 131 acquires excretion information, which indicates that excrement has been excreted from the body to the outside. For example, the acquisition unit 131 acquires internal information detected by a first sensor attached to the wearer's body and acquires external information detected by a second sensor attached to an absorbent article.

In the example shown in Figure 1, the first sensor SN1 detects (measures) the amount of waste (urine, feces) accumulated in the body (bladder, intestines) at predetermined intervals (e.g., every minute) and transmits the measurement results to the information processing device 100 at these predetermined intervals (e.g., every minute). Therefore, in the example shown in Figure 1, the acquisition unit 131 continuously acquires the amount of waste (urine, feces) accumulated in the body (bladder, intestines) at that time from the first sensor SN1 at predetermined intervals (an example of internal information). For example, the acquisition unit 131 continuously acquires a combination of the "amount of waste (urine, feces) accumulated in the body (bladder, intestines)" and "date and time information" indicating the date and time when this amount was measured. Furthermore, the acquisition unit 131 stores the amount of waste (urine, feces) corresponding to each date and time in the internal/external information storage unit 122 as a history of the amount of waste (urine, feces).

Furthermore, in the example shown in Figure 1, the second sensor SN2 detects the presence or absence of excretion based on impedance changes within the diaper DP1. If excretion is detected, it transmits excretion detection information (an example of external information) to the information processing device 100. Therefore, in the example shown in Figure 1, the acquisition unit 131 acquires excretion detection information (an example of external information) from the second sensor SN2. When the acquisition unit 131 acquires excretion detection information, it inputs "○" to the "Detection Presence/Absence" field in the internal/external information storage unit 122 based on the determination result (whether urine or feces was excreted) and the date and time the excretion detection information was acquired.

Furthermore, the first sensor can also detect state information indicating the condition of the bladder (bladder state), such as the degree of bladder expansion and the size of the bladder. The first sensor can also detect state information indicating the condition of the intestines (intestinal state), such as intestinal movement (peristalsis). Therefore, the acquisition unit 131 also acquires this state information from the first sensor.

Furthermore, the acquisition unit 131 can also acquire various information about the wearer, such as information about the absorbent items worn by the wearer, dietary history (information about food and beverages consumed by the wearer), medication history (information about medications administered to the wearer), excretion history, and body movement history. The acquisition unit 131 then stores this acquired information in the wearer information storage unit 121.

Furthermore, the acquisition unit 131 may also acquire urination prediction information indicating the predicted timing of urination by the person being cared for (for example, a wearer of absorbent material) and defecation prediction information indicating the predicted timing of defecation by the person being cared for. Therefore, the acquisition unit 131 also functions as a processing unit corresponding to the excretion information acquisition unit.

Here, the urination prediction information acquired by the acquisition unit 131, which acts as an 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 is information indicating the excretion timing (urination timing or defecation timing) predicted by the prediction unit 135 in the information processing according to the embodiment described in Figure 1.

On the other hand, the urination prediction information acquired by the acquisition unit 131, which acts as an excretion information acquisition unit, is not limited to information indicating the timing of excretion predicted by the prediction unit 135 in the information processing according to the embodiment. For example, the acquisition unit 131 may acquire urination prediction information indicating the timing at which urination is predicted to occur using any method. Similarly, the acquisition unit 131 may acquire defecation prediction information indicating the timing at which defecation is predicted to occur using any method.

For example, Figure 1 shows an example where the timing of excretion is predicted based on internal and external information. However, the timing of excretion may also be predicted based solely on internal information. In this case, the acquisition unit 131 acquires information indicating the timing of excretion predicted solely on internal information. Similarly, the timing of excretion may also be predicted based solely on external information. In this case, the acquisition unit 131 acquires information indicating the timing of excretion predicted solely on external information.

(Regarding the first determination unit 132)
When the acquisition unit 131 acquires excretion detection information (when excretion is detected), the first determination unit 132 determines the type of excreted material based on the internal body information obtained from the first sensor at the time of excretion. Specifically, the first determination unit 132 determines whether the excreted material from the wearer being processed is urine or feces, based on the internal body information obtained from the first sensor at the time of excretion.

(Regarding the threshold determination unit 133)
When the acquisition unit 131 acquires excretion detection information indicating that excretion has occurred (excretion is detected), the threshold determination unit 133 determines how much of the excrete indicated by the determination result of the first determination unit 132 had accumulated in the body before it was excreted. Specifically, the threshold determination unit 133 determines how much of the excrete indicated by the determination result had accumulated in the body before it was excreted, based on the history of accumulated amounts in the body corresponding to the excrete indicated by the determination result, and the date and time indicating the time of excretion. In other words, the threshold determination unit 133 determines the accumulation threshold, which is the amount of excrete that was accumulated in the body when the excrete indicated by the determination result was excreted, based on the history of accumulated amounts in the body corresponding to the excrete indicated by the determination result, and the date and time indicating the time of excretion.

(Regarding the generation unit 134)
The generation unit 134 learns a model based on a history of the amount of waste accumulated in the body, where the accumulation threshold is identified at any given time. For example, the generation unit 134 generates a model that learns the relationship between the amount of waste accumulated in the body (bladder, intestines) and the time it takes from the time the amount of waste accumulated reaches that level until the accumulation threshold is reached. For example, the generation unit 134 takes the current amount of waste accumulated in the body as input and generates a model that outputs the time it takes from the current amount of waste accumulated in the body until the accumulation threshold is reached (until it is excreted).

Furthermore, for example, the generation unit 134 repeatedly updates the model based on the history of the amount of accumulation in the body, specifically the history of the most recent predetermined period, where the accumulation threshold is identified as it progresses, thereby generating a more up-to-date model.

(Regarding the prediction unit 135)
The prediction unit 135 predicts the timing of future excretion by the wearer of the absorbent material, based on the internal and external information acquired by the acquisition unit 131. For example, the prediction unit 135 predicts the timing of excretion by the wearer later than the current time, based on the information acquired by the acquisition unit 131 that was acquired before the current time.

More specifically, the prediction unit 135 predicts the timing of excretion based on information obtained from information acquired by the acquisition unit 131, specifically information regarding the amount of excrement accumulated in the body at the time of excretion. For example, the prediction unit 135 predicts the timing of excretion based on trend information indicating a trend in the amount of accumulation. Alternatively, the prediction unit 135 predicts the timing of excretion based on information regarding the amount of accumulation and the amount of excrement currently accumulated in the wearer's body.

For example, when the prediction unit 135 determines that it is time to perform prediction processing, it predicts the timing of excretion by the wearer to be processed, based on trend information indicating a trend related to the accumulation threshold, which is obtained from the history of accumulated amounts in the body acquired at a stage prior to the time of prediction processing, and the amount of excrement accumulated in the wearer's body at the present time. Specifically, the prediction unit 135 predicts the timing of excretion based on the model (prediction model) generated by the generation unit 134 as such trend information, and the amount of excrement accumulated in the wearer's body at the present time.

For example, the prediction unit 135 takes the amount of urine currently accumulated in the wearer's body as input and applies the time output by the urine-corresponding model to the current time to predict the time when the accumulation threshold will be reached (the time when urination will occur). Similarly, the prediction unit 135 takes the amount of feces currently accumulated in the wearer's body as input and applies the time output by the feces-corresponding model to the current time to predict the time when the accumulation threshold will be reached (the time when defecation will occur).

Furthermore, the prediction unit 135 may also notify the prediction results. Specifically, the prediction unit 135 notifies the person responsible for providing care to the wearer of the item being processed of the prediction results. For example, the prediction unit 135 notifies the person responsible by transmitting the prediction results to the person's device 30.

Furthermore, the prediction unit 135 may predict the timing of excretion using trend information other than the model. For example, the prediction unit 135 calculates the average of the accumulation thresholds during a predetermined period, based on the history of accumulation amounts in the body acquired during that period prior to the present. Then, the prediction unit 135 predicts the timing of excretion based on the calculated average of the accumulation thresholds and various statistical values.

(Variations of processing by the prediction unit 135 (1))
The prediction unit 135 may predict the timing of excretion based on the relationship between the food and drink information (food and drink status) consumed by the wearer being processed and the wearer's excretion status, as well as internal and external information. For example, the prediction unit 135 corrects the predicted timing of excretion based on internal and external information, based on the relationship between the food and drink information (food and drink status) consumed by the wearer being processed and the wearer's excretion status. This point will be explained using the wearer U11 (the wearer being processed) shown in Figure 1 as an example.

For example, the learning generation unit 134 accesses the wearer information storage unit 121 and obtains the "meal history" and "excretion history" corresponding to wearer U11. In the example in Figure 4, the generation unit 134 accesses the wearer information storage unit 121 and obtains "meal history #11" and "excretion history #11". The generation unit 134 then learns the relationship between eating and drinking habits and excretion habits, such as "what, how much, is eaten (or drunk), how much urine tends to be excreted, and at what interval after ingestion." In other words, the generation unit 134 learns the tendency of excretion times according to eating and drinking habits.

In this state, when the prediction unit 135 determines that it is time to perform prediction processing, it takes the amount of urine currently accumulated in the wearer's body as input and applies the time output by the prediction model MD11 corresponding to the urine to the current time to predict the time when the accumulation threshold will be reached (the time when urination will occur). Then, the prediction unit 135 corrects the predicted time by applying the excretion time trend (a trend learned by the generation unit 134) according to the eating and drinking situation to the predicted time.

Furthermore, the generation unit 134 may learn a model that incorporates, for example, the trend of excretion time according to the above-mentioned eating and drinking situation, in addition to the trend of the amount of fluid accumulated in the body and the time from the point in time when such an amount of fluid is accumulated until the accumulation threshold is reached. For example, the generation unit 134 may generate a model that takes the current amount of fluid accumulated in the body, the most recent time when eating or drinking occurred, and the eating and drinking situation (what and how much was eaten) as input, and outputs the time from the current amount of fluid accumulated in the body until the accumulation threshold is reached (until excretion). In this case, the prediction unit 135 predicts the time of urination by applying the time output from the model to the current time.

According to this information processing device 100, in addition to internal and external information, dietary information can also be combined. Therefore, the fact that the timing of excretion can be varied according to dietary information can be incorporated into the prediction process, resulting in a more accurate prediction of when excretion will occur.

Furthermore, while this example uses urine as the excrement, the information processing device 100 can learn the trend in a similar manner even when the excrement is feces.

(Variations of processing by the prediction unit 135 (2))
Furthermore, the prediction unit 135 may predict the timing of excretion based on the relationship between drug information regarding the drug administered to the wearer being treated and the wearer's excretion status, as well as internal and external information. For example, the prediction unit 135 corrects the predicted timing of excretion based on internal and external information, based on the relationship between drug information (drug status) regarding the drug administered to the wearer being treated and the wearer's excretion status. This point will be explained using the wearer U11 (the wearer being treated) shown in Figure 1 as an example.

For example, the learning generation unit 134 accesses the wearer information storage unit 121 and obtains the "meal history," "medication history," and "excretion history" corresponding to wearer U11. In the example in Figure 4, the generation unit 134 accesses the wearer information storage unit 121 and obtains "meal history #11," "medication history #11," and "excretion history #11." The generation unit 134 then learns the relationship between laxative use and excretion time, such as "when food and drink are consumed, what timing, what type of laxative, and in what quantity tends to result in the excretion of a certain amount of stool, with what consistency, and at what interval after administration." In other words, the generation unit 134 learns the tendency of excretion time according to laxative use, taking into account the food and drink situation.

In this state, when the prediction unit 135 determines that it is time to perform prediction processing, it takes the amount of stool currently accumulated in the wearer's body as input and applies the time output by the prediction model MD12 corresponding to the stool to the current time to predict the time when the accumulation threshold will be reached (the time when defecation will occur). Then, the prediction unit 135 corrects the predicted time by applying a trend in defecation time that takes into account the eating and drinking situation, based on the laxative status (a trend learned by the generation unit 134), to the predicted time.

Furthermore, the generation unit 134 may learn a model that further incorporates, for example, the trend of excretion time depending on laxative use, in addition to the trend of the amount of waste accumulated in the body and the time from the point in time when such an amount of waste is accumulated until the accumulation threshold is reached. For example, the generation unit 134 may take the current amount of waste accumulated in the body, food and drink status, and laxative status as input and generate a model that outputs the time from the current amount of waste accumulated in the body until the accumulation threshold is reached (until excretion). In this case, the prediction unit 135 predicts the time of defecation by applying the time output from the model to the current time.

According to this information processing device 100, laxative status can be combined with internal and external internal information. Therefore, the fact that the timing of excretion can be varied according to the laxative status can be incorporated into the prediction process. As a result, the timing of excretion can be predicted with greater accuracy.

(Regarding the information control unit 136)
The information control unit 136 controls the food and beverages provided to the wearer based on the relationship between the food and beverage information regarding the food and beverages consumed by the wearer and the wearer's excretion status. In addition, the information control unit 136 controls the food and beverages provided to the wearer based on the relationship between the drug information regarding the drugs administered to the wearer and the wearer's excretion status.

For example, through learning by the generation unit 134 as described in the variations above, it may be possible to obtain a relationship between dietary conditions and ease of defecation, such as what kind of dietary conditions tend to make bowel movements easier without relying on laxatives. Therefore, the information control unit 136 determines, based on the relationship between dietary conditions and ease of defecation, what kind of diet would make it easier for the wearer U11 to have a bowel movement without relying on laxatives. The information control unit 136 then proposes this determination result to the target person T11 as the optimal dietary content for the wearer U11.

This information processing device 100 can suggest meals aimed at promoting natural bowel movements, thereby supporting the wearer in achieving independent excretion. Furthermore, this means the information processing device 100 can effectively improve the wearer's Quality of Life (QOL).

Furthermore, for example, through learning by the generation unit 134 as described in the above variations, it may be possible to obtain relationships between the three factors: diet, laxative use, and bowel movement status, such as how bowel movement status (e.g., stool consistency, volume, and timing of bowel movement) changes depending on diet and laxative use. Therefore, the information control unit 136 determines the type of laxative, the strength of the laxative, the amount of laxative, and the timing of laxative administration for the wearer U11 based on the relationships established between diet, laxative use, and bowel movement status. The information control unit 136 then proposes the optimal laxative administration method for the wearer U11 to the target person T11 based on these determination results.

This information processing device 100 allows for the administration of laxatives in a way that reduces the burden on the wearer and promotes natural bowel movements, thereby effectively improving the wearer's quality of life (QOL).

(Regarding Decision Section 137)
The first decision unit 137 determines a predetermined timing for care to the wearer of the item to be treated, based on the excretion timing predicted by the prediction unit 135. Specifically, the first decision unit 137 determines the timing for changing the absorbent item worn by the wearer of the item to be treated, based on the excretion timing. For example, if the first decision unit 137 determines that the amount of excretion predicted to actually be excreted at the time of excretion exceeds the remaining absorbable amount, which is the amount of excretion that the absorbent item can absorb, it determines a predetermined timing before this excretion timing as the change timing. Furthermore, the change timing determined by the first decision unit 137 is proposed to the person concerned by the proposal unit 139, which will be described later. This point will be explained using the example in Figure 1.

For example, the prediction unit 135 accesses the internal/external information storage unit 122 and calculates a trend in urine discharge (an example of discharge volume) based on the history of "excretion volume" (amount of excretion excreted into the absorbent material) corresponding to the wearer U11, specifically the history of "urine" as the type of excretion. Then, the prediction unit 135 predicts the urine discharge volume at the predicted excretion timing based on the calculated trend in urine discharge volume and the predicted excretion timing for the wearer U11. In this example, the prediction unit 135 predicts a urine discharge volume of "200 ml" at the excretion timing.

Furthermore, in this state, the first determination unit 137 accesses the wearer information storage unit 121 and identifies the absorbency (capacity) of the diaper DP1 being used by wearer U11. Then, based on the identified absorbency (capacity) and the history of "excretion volume" corresponding to wearer U11, the first determination unit 137 calculates the remaining absorbable amount, which is the amount of urine that diaper DP1 can still absorb. Here, it is assumed that the first determination unit 137 calculated the remaining absorbable amount, which is the amount of urine that diaper DP1 can still absorb, to be "150 ml".

Furthermore, according to the example above, at the time of excretion, the predicted amount of urine to be actually excreted (200 ml) exceeds the remaining absorbable capacity (50 ml) of diaper DP1 by "50 ml".

Here, let's assume that diaper DP1 is not changed while its absorbent capacity remains at "150 ml," and for example, urination occurs at the excretion timing predicted by the prediction unit 135. In this case, the amount of urine exceeding the absorbent capacity (50 ml) will leak out of diaper DP1. Therefore, in this example, where the predicted amount of excretion at the excretion timing exceeds the absorbent capacity, the first determination unit 137 determines a predetermined timing before the expected excretion timing to change diaper DP1.

For example, the first determination unit 137 determines the timing for changing diaper DP1 to be a timing before the predicted excretion timing, such that the information regarding the amount of urine accumulated in the bladder (internal accumulation) and the information regarding the accumulation threshold have a predetermined relationship. As an example, the first determination unit 137 predicts the timing when the amount of urine accumulated in the bladder (internal accumulation) will reach a predetermined amount before reaching the accumulation threshold, and determines the predicted timing as the timing for changing diaper DP1.

Furthermore, the proposal unit 139 proposes to the target user T11 that the replacement be performed at the replacement timing determined by the first decision unit 137. Specifically, the proposal unit 139 transmits the replacement timing determined by the first decision unit 137 as proposal information to the target user device 30 of the target user T11.

Generally, absorbent items are replaced each time stool is excreted, while those with water-absorbing capacity are often replaced after multiple urinations. However, there is a limit to this absorbency (capacity), so if the absorbent item is not replaced after multiple urinations, the unabsorbed urine will eventually leak out. Therefore, this information processing device 100 can suggest replacing the absorbent item before the predicted timing of the next urination if it is determined that there is a high risk of urine leakage if the absorbent item is not replaced before the next urination. As a result, the information processing device 100 can suggest an appropriate replacement timing for each user, effectively reducing the risk of urine leakage.

(Regarding Section 2, Item 138)
The second decision unit 138 determines the timing for guiding the wearer to the toilet based on the excretion timing predicted by the prediction unit 135. For example, the second decision unit 138 determines the timing for guiding the wearer to the toilet based on the excretion timing predicted by the prediction unit 135 and the schedule of the person being treated or the wearer. For example, the second decision unit 138 determines the timing for guiding the wearer to the toilet based on the necessity of guiding the wearer to the toilet determined for each wearer based on the excretion timing and the above schedule. The suggestion unit 139 also suggests to the person being treated that they be guided to the toilet at the timing determined by the second decision unit 138.

This point will be explained using Figures 7A to 7C. Figures 7A to 7C are explanatory diagrams illustrating the decision-making process for determining the timing of toilet guidance. Hereafter, unless it is necessary to distinguish between Figures 7A to 7C, they will simply be referred to as "Figure 7." Note that the schedule information showing the subject's schedule and the schedule information showing the wearer's schedule are examples of information related to the person receiving care (e.g., the wearer). For example, the schedule information showing the subject's schedule and the schedule information showing the wearer's schedule are examples of information related to the care of the person receiving care (e.g., the wearer).

In the example in Figure 7, the wearer to be processed is a child enrolled in Nursery School FA2 (a facility identified by facility ID "FA2"). Therefore, in Figure 7, the person providing care to the wearer is a childcare worker belonging to Nursery School FA2. Specifically, according to the example in Figure 6, one class at Nursery School FA2 has 10 children, wearers U21 to U30, and 4 childcare workers, T21 to T24, provide care for these 10 children. Furthermore, in the example in Figure 7, it is assumed that the timing (time of day) of excretion (either urine or feces) for wearers U21 to U30 is predicted.

From here, an example of the decision process by which the second decision unit 138 determines the timing of toilet guidance will be explained step-by-step using Figures 7A to 7C. First, as shown in Figure 7A, the second decision unit 138 calculates backward from the predicted urination or defecation timing (time slot) and determines the degree of need for toilet guidance (level of urge to urinate/defecate) at each time slot from low to high (step S61). For example, based on the perspective that the degree of need for toilet guidance (level of urge to urinate/defecate) is highest during the time slot corresponding to the urination/defecation timing, the second decision unit 138 determines that the degree of need for toilet guidance decreases as the time slot is earlier than the urination/defecation timing.

This point will be explained using the example of wearer U21, a young child. According to the example in Figure 7A, wearer U21 is predicted to need to go to the toilet between 11:00 and 11:30. Therefore, the second decision unit 138 determines that for wearer U21, the need for toilet guidance (level of urge to urinate or defecate) will be highest during the time period between 11:00 and 11:30. Furthermore, based on this determination, the second decision unit 138 determines that the need for toilet guidance (level of urge to urinate or defecate) is moderate for the time period one step before the 11:00-11:30 time period, between 10:00 and 11:00. Furthermore, the second decision unit 138 determines that the need for toilet assistance (level of urge to urinate or defecate) is low for the time period one step earlier than the time period "10:00 to 11:00," namely "8:00 to 10:00."

Furthermore, in the example shown in Figure 7A, the same method is used to determine the need for toilet assistance for wearers U22 to U30, classifying them as either "low," "medium," or "high."

Next, the second decision unit 138, based on the judgment that wearers with a "medium" or higher need for toilet assistance have a reasonable urge to urinate or defecate, performs the following processing based on the judgment result in step S61, assuming that toilet assistance will be provided to such wearers. For example, the second decision unit 138 determines the time period during which more than half of wearers U21 to U30 will need to go to the toilet (they will likely feel a certain degree of urge to urinate or defecate) (step S62).

According to the example in Figure 7A, during the time period of "10:00 to 10:30," six wearers—wearers U21, U23, U25, U26, U29, and U30—were determined to have a "medium" or higher need for toilet assistance. Therefore, in the example in Figure 7A, the second decision unit 138 determines that the time period "10:00 to 10:30" is a time period during which more than half of the wearers would need to use the toilet.

Furthermore, according to the example in Figure 6A, during the time period of "10:30 to 11:00," 10 wearers, from wearers U21 to U30, were determined to have a "medium" or higher need for toilet assistance. Therefore, in the example in Figure 7A, the second determination unit 138 also determines that the time period of "10:30 to 11:00" is a time period during which more than half of the wearers would need to go to the toilet.

Furthermore, according to the example in Figure 7A, during the time period of "11:00 to 11:30," 10 wearers, from wearers U21 to U30, were determined to have a "medium" or higher need for toilet assistance. Therefore, in the example in Figure 7A, the second determination unit 138 also determines that the time period of "11:00 to 11:30" is a time period during which more than half of the wearers will need to go to the toilet.

For example, if a toddler can be guided to the toilet when they feel the urge to urinate, toilet training is likely to be successful. Conversely, if a toddler is taken to the toilet when they don't feel the urge, they may develop a dislike for going to the toilet, resulting in toilet training failure. Furthermore, if a toddler is taken to the toilet but does not urinate, this time is wasted for the caregiver. Therefore, guiding more toddlers who feel the urge to urinate or defecate to the toilet at once increases the success rate of toilet training and improves the efficiency of toilet guidance, benefiting both the toddlers and the caregivers. For these reasons, step S62 aims to determine the time of day when the success rate of toilet training can be increased and when the largest possible number of children can be efficiently guided to the toilet.

Ideally, toilet training should be conducted during times when the success rate is higher and when as many children as possible can be efficiently guided to the toilet. However, since childcare workers and children have their own schedules, it is not always possible to guide children to the toilet or perform toileting activities during these times. Therefore, the following process takes into account the individual schedules of childcare workers and children to determine the most realistic time slot for efficient toilet guidance and successful toileting.

Next, Figure 7B will be explained. The second determination unit 138 determines the time periods when each of the wearers U21 to U30 (who are infants) can go to the toilet, based on their daily schedules (step S63). Figure 7B shows an example of schedule #SK2, which is a daily schedule common to all wearers U21 to U30. The second determination unit 138 can obtain schedule #SK2 from the schedule information storage unit 123.

Then, for example, the second decision unit 138 determines the time periods within this schedule #SK2 during which each wearer U21 to U30 is permitted to go to the toilet. For example, as shown in Figure 7B, the second decision unit 138 determines that the following time periods—"8:00 AM to 8:30 AM" for arrival at the nursery, and "9:00 AM to 9:30 AM," "11:00 AM to 11:30 AM," and "12:30 PM to 1:30 PM" for free time—are permitted for each wearer U21 to U30 to go to the toilet.

Furthermore, the second decision unit 138 determines the time periods during which each of the target individuals T21 to T24 (who are childcare workers) can guide the child to the toilet (step S64). Figure 7B shows examples of the daily schedules for each of the target individuals T21 to T24. The second decision unit 138 can obtain these schedules from the schedule information storage unit 123.

Furthermore, as shown in the example in Figure 7B, the daily schedules for each of the subjects T21 to T24 assign tasks such as "arrival greetings," "visual check-ups," "cleaning," "supervision," "morning meeting," "outdoor activities," "lunch preparation," "lunch," "toothbrushing," "communication log," and "napping duty" to different time slots. However, these schedules also include periods of time when no tasks are assigned.

Therefore, for example, the second decision unit 138 determines that a time period in which more than half of the subjects T21 to T24 are able to guide others to the toilet is a time period in which the subjects are able to guide others to the toilet. For example, the second decision unit 138 determines that a time period in which more than half of the subjects are able to guide others to the toilet is a time period in which the subjects are able to guide others to the toilet, within a non-work-related time period.

According to the example in Figure 7B, during the time period from 9:00 to 9:30, three of the subjects T21 to T24—T21, T22, and T24—have no work duties, meaning that during this time, subjects T21, T22, and T24 can provide restroom assistance. In other words, during the time period from 9:00 to 9:30, more than half of the subjects can provide restroom assistance. Therefore, in the example in Figure 7B, the second decision unit 138 determines that the time period from 9:00 to 9:30 is a time period during which subjects can provide restroom assistance.

Furthermore, according to the example in Figure 7B, during the time period from 11:00 to 11:30, two of the subjects T21 to T24, T23 and T24, have no work duties during this time. Therefore, during this time, subjects T23 and T24 can provide toilet assistance. In other words, during the time period from 11:00 to 11:30, more than half of the subjects can provide toilet assistance. Therefore, in the example in Figure 7B, the second decision unit 138 also determines that the time period from 11:00 to 11:30 is a time period during which subjects can provide toilet assistance.

Furthermore, according to the example in Figure 7B, during the time period from 12:30 to 13:00, all subjects T21 to T24 have no work duties and are therefore free from work. During this time, subjects T21 to T24 can provide toilet assistance. In other words, during the time period from 12:30 to 13:00, more than half of the subjects can provide toilet assistance. Therefore, in the example in Figure 7B, the second decision unit 138 also determines that the time period from 12:30 to 13:00 is a time period during which subjects can provide toilet assistance.

Furthermore, the time slots "1:30 PM to 2:00 PM" and "2:00 PM to 2:30 PM" can also be similarly determined to be time slots during which it is possible to guide the person to the restroom.

Next, Figure 7C will be explained. The second determination unit 138, based on the time periods corresponding to the determination results in S62-S64, determines the time period that satisfies all time period conditions as the guidance timing for guiding the wearer to the restroom (step S65).

First, in step S62, the second determination unit 138 determines the time period that can increase the success rate of toilet training and allows for efficient toilet guidance of as many people as possible (the time period during which efficient toilet guidance, taking toilet training into consideration, can be achieved). In the example in Figure 7A, the second determination unit 138 determines that such time periods are "10:00 to 10:30", "10:30 to 11:00", and "11:00 to 11:30", and these determination results are shown by a "double circle" in Figure 7C.

Furthermore, in step S63, the second determination unit 138 determines the time periods during which each wearer U21 to U30 can go to the toilet. In the example in Figure 7B, the second determination unit 138 determines that these time periods are "8:00 to 8:30", "9:00 to 9:30", "11:00 to 11:30", and "12:30 to 13:00", and these determination results are shown by circles in Figure 7C.

Furthermore, in step S64, the second decision unit 138 determines the time periods during which the subject can be guided to the toilet. In the example in Figure 7B, the second decision unit 138 determines that these time periods are "9:30 AM to 10:30 AM," "11:00 AM to 11:30 AM," "12:30 PM to 1:00 PM," "1:30 PM to 2:00 PM," and "2:00 PM to 2:30 PM." These determination results are shown by circles in Figure 7C.

In this state, the second determination unit 138 defines the time period to which the "double circle" and "circle mark" correspond, that is, the time period corresponding to the judgment results in S62 to S64, as the time period condition. The second determination unit 138 then determines the time period that satisfies all of these time period conditions as the timing for guiding the wearer to the restroom. According to the example in Figure 7C, the time period that satisfies all time period conditions is "11:00 to 11:30".

Furthermore, according to the example in Figure 7A, during the time period "11:00 to 11:30," the need for toilet guidance is determined to be "medium" or higher for all wearers U21 to U30, suggesting that toilet training will be successful if wearers U21 to U30 are guided to the toilet during this time period. Also, according to the example in Figure 7B, during the time period "11:00 to 11:30," subjects T23 and T24 are capable of guiding the wearers to the toilet. Therefore, the second decision unit 138 determines "11:00 to 11:30," which satisfies all time period conditions, as the timing for guiding the wearers to the toilet. The second decision unit 138 also determines "subjects T23 and T24" as the subjects responsible for toilet guidance during this time period, and "U21 to U30" as the wearers who should actually be guided to the toilet.

Furthermore, the suggestion unit 139 proposes to the target person that they be guided to the toilet at the guidance timing determined by the second decision unit 138. Specifically, the suggestion unit 139 transmits suggestion information, indicating the guidance timing "11:00 to 11:30" determined by the second decision unit 138, the target persons responsible for toilet guidance ("Target Persons T23 and T24"), and the target persons to be guided to the toilet ("U21 to U30"), to a designated information processing terminal, for example, on the FA2 nursery school. The suggestion unit 139 may also transmit suggestion information related to the target person devices 30 of "Target Persons T23 and T24," who are responsible for toilet guidance. Additionally, for example, the second decision unit 138 associates this suggestion information with the facility ID "FA2" and stores it in the guidance timing storage unit 124.

As explained in Figure 7, the information processing device 100 determines the timing for guiding the wearer to the toilet based on the predicted timing of elimination, the need for toilet guidance for each wearer, the target person's schedule, and the wearer's schedule. This information processing device 100 can present the most realistic timing, increasing the success rate of toilet training and improving the efficiency of toilet guidance. Furthermore, this makes it easier for the wearer (infant) to become aware of the importance of using the toilet for elimination, and allows the target person (caregiver) to effectively utilize their time within their schedule.

Note that while Figure 7 illustrates the guidance being given to an infant, the target of this guidance is not limited to infants. The target of this guidance can be any wearer who can achieve the desired outcome through such guidance; for example, a person receiving care in a nursing home or similar facility.

For example, when the person being guided to the toilet is a person receiving care, the information processing device 100's suggestion of guidance timing allows the caregiver to work more efficiently in the stressful caregiving environment, thus supporting a more comfortable working environment for the caregiver. Furthermore, because a more comfortable working environment for the caregiver is created, the wearer (person receiving care) can receive appropriate care, ultimately leading to an improvement in their quality of life (QOL). For example, the wearer (person receiving care) can receive appropriate care toward independent toileting. Therefore, the suggestion of guidance timing by the information processing device 100 creates benefits for both the caregiver and the wearer (person receiving care).

In the above example, the second decision unit 138 determined the timing for guiding the wearer to the toilet based on the predicted excretion timing derived from internal and external bodily information. However, the second decision unit 138 may also determine the timing for guiding the wearer to the toilet based on the urination prediction information and defecation prediction information acquired by the acquisition unit 131 (excretion information acquisition unit).

(Regarding Proposal 139)
The suggestion unit 139 makes a predetermined suggestion to the person responsible for caring for the wearer of the garment, based on the excretion timing predicted by the prediction unit 135. For example, the suggestion unit 139 makes a suggestion regarding the timing determined by the first decision unit 137 and the second decision unit 138.

For example, if the suggestion unit 139 determines the timing for replacing the absorbent material worn by the wearer of the item being processed, based on the excretion timing predicted by the prediction unit 135, it will suggest replacing the absorbent material at this timing.

Furthermore, for example, if the suggestion unit 139 determines the timing for guiding the wearer to the toilet based on the excretion timing predicted by the prediction unit 135, it will suggest guiding the wearer to the toilet at that timing.

Furthermore, the suggestion unit 139 may output suggestion information regarding excretion care for the care recipient based on the urination prediction information and defecation prediction information acquired by the acquisition unit 131 (excretion information acquisition unit) and information regarding the care of the care recipient (wearer). Therefore, the suggestion unit 139 also functions as a processing unit corresponding to the output unit. In this regard, for example, if the acquisition unit 131 acquires urination prediction information and defecation prediction information, and the induction timing is determined based on this information, the suggestion unit 139 outputs information indicating this induction timing (suggestion information regarding excretion care for the care recipient) determined by the second determination unit 138 to the target device 30.

Furthermore, for example, if the prediction unit determines whether or not to use a replacement absorbent pad in conjunction with the absorbent item worn by the wearer, based on the predicted timing of excretion, the suggestion unit 139 can also make suggestions based on this determination. The determination of whether or not to use a replacement absorbent pad in conjunction with the absorbent item worn by the wearer is performed by the second determination unit 140. The second determination unit 140 will be described below.

(Regarding the second determination unit 140)
Before explaining the second determination unit 140, let's explain the challenges of diaper pads (urine absorption pads). A diaper pad is an auxiliary pad that is placed inside the outer diaper (outer garment) and used in conjunction with it. Hereafter, diaper pads may be simply referred to as "pads."

For example, in nursing care settings, pads are often used in conjunction with diapers. By using pads, only the pad needs to be changed after urination, which reduces costs and the burden of changing diapers.

On the other hand, when pads are used in conjunction with diapers, the space within the diaper for stool is reduced. Therefore, when pads are used, the risk of stool leakage from the diaper is higher compared to when pads are not used (when diapers are used alone). For this reason, from the perspective of reducing the risk of stool leakage, it is preferable not to use pads.

Here, if the timing of bowel movements were known in advance, it would be possible to switch from using diapers alone to using pads in combination with diapers to coincide with that timing. However, in the past, where the timing of bowel movements could not be accurately determined, diaper care proposals mainly focused on urination. Specifically, in the past, where the timing of bowel movements could not be accurately determined, the care proposals mainly focused on the constant use of pads because the timing of bowel movements was unknown.

To address the above-mentioned challenges and enable the proposal of diaper care specifically tailored to bowel movements, this embodiment provides a system where the wearer determines whether or not to use a replacement absorbent pad in conjunction with the absorbent item (adult diaper as an outer layer) worn by the wearer, based on the predicted timing of defecation (here, the timing of bowel movements) predicted by the prediction unit 135. As explained above, the prediction processing by the prediction unit 135 enables highly accurate prediction of the timing of bowel movements. Therefore, by utilizing this timing, it becomes possible to effectively determine whether diaper use alone is sufficient or whether a pad should be used in combination, thus enabling the proposal of diaper care specifically tailored to bowel movements.

Specifically, the second determination unit 140 determines whether or not to use a replacement absorbent pad in conjunction with the absorbent item worn by the wearer, based on the excretion timing predicted by the prediction unit 135. The suggestion unit 139 then makes a suggestion based on the determination result of the second determination unit 140. For example, the second determination unit 140 determines whether or not to use a replacement absorbent pad in conjunction with the absorbent item worn by the wearer, based on the type of excrement that may be excreted at the time of excretion. Alternatively, the second determination unit 140 may determine whether or not to use a replacement absorbent pad in conjunction with the absorbent item worn by the wearer, based on the state of the excrement that may be excreted at the time of excretion. Furthermore, the second determination unit 140 may determine whether or not to use a replacement absorbent pad in conjunction with the absorbent item worn by the wearer, based on the wearer's body movements predicted at the time of excretion.

The determination process performed by the second determination unit 140 will be explained using Figure 8. Figure 8 is a flowchart showing the determination process procedure for determining whether or not to use a pad. In Figure 8, the wearer undergoing the process is referred to as wearer U11.

As explained above, the prediction unit 135 predicts the timing of excretion for each type of waste (urine, feces). Therefore, the second determination unit 140 determines, based on the predicted timing of excretion, whether the next type of waste to be excreted is feces or not (step S701). In other words, the second determination unit 140 determines, based on the predicted timing of excretion, whether the next excretion will be defecation or urination.

The second determination unit 140 determines that the next type of excretion to be excreted is not feces (i.e., it determines that urine will be excreted) (Step S701; No.), and therefore determines that a pad should be used in conjunction with diaper DP1. In this case, the suggestion unit 139 suggests to the subject T11 that a pad be used in conjunction with diaper DP1. Thus, based on the determination that urine will be excreted next, a suggestion for diaper care aimed at urination can be made.

On the other hand, if the second determination unit 140 determines that the type of excrement that may be excreted next is feces (i.e., it determines that defecation will occur) (step S701; Yes), it determines whether the remaining time until the predicted excretion timing (in this case, the defecation timing) is less than one hour (step S702).

The second determination unit 140 determines that if the remaining time until the predicted defecation time is not less than one hour (step S702; No), it will decide to use a pad in conjunction with diaper DP1. In this case, the suggestion unit 139 will suggest to the subject T11 that they use a pad in conjunction with diaper DP1. Thus, even if the next excretion is expected to be feces, if there is sufficient time before the defecation time, the determination result of using a pad allows for the suggestion of diaper care for urination, in preparation for the possibility of urination before the defecation time.

On the other hand, if the second determination unit 140 determines that the remaining time until the predicted defecation timing is less than one hour (step S702; Yes), it determines whether the stool consistency (fecal type) to be excreted at the predicted defecation timing will be soft or watery (step S703). For example, the acquisition unit 131 also acquires state information indicating the intestinal state detected by the first sensor SN1. Therefore, the second determination unit 140 can determine whether the stool consistency (fecal type) to be excreted at the defecation timing will be soft or watery based on the intestinal state indicated by this state information.

The second determination unit 140 then determines that the stool consistency (form) expected to be excreted at the predicted defecation time is soft or watery (step S703; Yes), and determines that a pad should not be used with diaper DP1. In other words, the second determination unit 140 determines that diaper DP1 should be used alone without a pad. Furthermore, in this case, the suggestion unit 139 suggests to the subject T11 that a pad should not be used with diaper DP1. For example, if a pad is currently being used with diaper DP1, the suggestion unit 139 suggests to the subject T11 that the pad be removed. Thus, when there is a possibility of defecation occurring relatively soon, and when soft or watery stools, which tend to have a higher risk of leakage compared to solid stools, are likely to be excreted, the determination result of using the diaper alone allows for the suggestion of diaper care specifically tailored to defecation.

Furthermore, since loose or watery stools pose a very high risk of leakage, the proposed unit 139 may be controlled to output an alert to the subject device 30 of subject T11 if it is determined that the stool consistency (fecal type) at the time of defecation is loose or watery (step S703; Yes).

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

For example, if, based on the movement history, it is determined that the wearer U11 tends to move easily during the time period corresponding to the timing of defecation, the second determination unit 140 can determine that the frequency of movement at the predicted time of defecation is not low (it is high). On the other hand, if, based on the movement history, it is determined that the wearer U11 tends to move less easily during the time period corresponding to the timing of defecation, the second determination unit 140 can determine that the frequency of movement at the predicted time of defecation is low.

The second determination unit 140 then determines that the wearer U11's body movements are not infrequent (step S704; No), and therefore determines that a pad should be used in conjunction with the diaper DP1. In this case, the suggestion unit 139 suggests to the wearer T11 that a pad be used in conjunction with the diaper DP1. For example, if body movements are vigorous, space is more likely to be created within the diaper to accommodate stool, and solid stool is likely to fit well into this space. In other words, if there is a possibility of solid stool being excreted while body movements are vigorous, the risk of stool leakage decreases, and the determination that using a pad is acceptable allows for the suggestion of diaper care specifically tailored to bowel movements.

On the other hand, if the second determination unit 140 determines that the wearer U11's body movements are infrequent (step S704; Yes), it determines that a pad should not be used with diaper DP1. In other words, the second determination unit 140 determines that diaper DP1 should be used alone without a pad. Furthermore, in this case, the suggestion unit 139 suggests to the person T11 that a pad should not be used with diaper DP1. For example, if a pad is currently being used with diaper DP1, the suggestion unit 139 suggests to the person T11 that the pad be removed. For instance, if body movements are minimal, it becomes difficult to create enough space within the diaper to contain stool, increasing the risk of compressed stool leakage. Therefore, if there is a possibility of solid stool being excreted with minimal body movements, the risk of stool leakage increases. Based on this determination, it is possible to suggest diaper care specifically tailored to bowel movements, such as not using a pad.

[7. Processing Procedure]
Next, the information processing procedure according to the embodiment will be described using Figures 9 and 10. Figure 9 describes the procedure for the learning process for training a model, which is part of the information processing according to the embodiment. Figure 10 describes the procedure for the excretion timing prediction process using the trained model, which is part of the information processing according to the embodiment.

[7-1. Processing Procedure (1)]
First, the procedure of the learning process according to the embodiment will be explained using Figure 9. Figure 9 is a flowchart of the learning process procedure according to the embodiment.

First, the acquisition unit 131 acquires the amount of waste (urine, feces) accumulated in the body (bladder, intestines) at predetermined intervals (step S801). Specifically, the acquisition unit 131 acquires the amount of waste detected by the first sensor attached to the waist of the user being processed. This allows the information processing device 100 to obtain a history of the amount of waste accumulated in the body for both urine and feces.

Furthermore, the first determination unit 132 determines whether or not the waste was excreted within the absorbent material worn by the user being processed (step S802). For example, if the acquisition unit 131 detects excretion by the second sensor attached to the absorbent material worn by the user being processed, it acquires excretion detection information indicating the detection of excretion. Therefore, the first determination unit 132 determines whether or not the waste was excreted within the absorbent material based on whether or not the acquisition unit 131 has acquired the excretion detection information.

The first determination unit 132 waits until it can determine that excretion has occurred, as long as it has determined that no excretion has occurred (step S802; No.) because the acquisition unit 131 has not acquired excretion detection information.

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

Furthermore, the threshold determination unit 133 identifies the amount of excrement that accumulated in the body before it was excreted, based on the determination result in step S803 (step S804). Specifically, the threshold determination unit 133 identifies the amount of excrement that accumulated in the body before it was excreted, based on the history of accumulation up to the time of excretion, which corresponds to the amount of excrement that accumulated in the body, and the date and time indicating the time of excretion.

In other words, the threshold determination unit 133 identifies the accumulation threshold, which is the amount of excreted material accumulated in the body at the time the excreted material was excreted, based on the history of accumulated material up to the time of excretion, the history of the amount of excreted material in the body corresponding to the excreted material indicated by the determination result, and the date and time indicating the time of excretion.

Furthermore, the generation unit 134 learns a model based on a history of the amount of accumulation in the body, where the accumulation threshold is identified at each stage (step S805). For example, the generation unit 134 generates a model (predictive model) that learns the relationship between the amount of accumulation in the body and the time from the point in time when such an amount of accumulation is reached until the accumulation threshold is reached. For example, the generation unit 134 takes the current amount of accumulation in the body as input and generates a model that outputs the time from the current amount of accumulation in the body until the accumulation threshold is reached (until it is excreted).

[7-2. Processing Procedure (2)]
First, the procedure for the prediction process according to the embodiment will be explained using Figure 10. Figure 10 is a flowchart showing the prediction process procedure according to the embodiment.

First, the prediction unit 135 determines whether it is time to perform prediction processing to predict the timing of excretion (step S901). For example, the prediction unit 135 may determine whether it is time to perform prediction processing based on whether or not excretion has been detected by the second sensor SN2. Alternatively, the prediction unit 135 may determine whether it is time to perform prediction processing based on whether or not it has received a request from the person providing care to the wearer being processed. While the prediction unit 135 determines that it is not time to perform prediction processing (step S901; No), it waits until it can determine that it is time to perform prediction processing.

On the other hand, if the prediction unit 135 determines that it is time to perform prediction processing (step S901; Yes), it uses the latest prediction model to predict the timing of excretion by the wearer of the garment to be processed, which will occur after the current time when the prediction processing is performed (step S902). Specifically, the prediction unit 135 predicts the timing of excretion by the wearer, which will occur after the current time, based on the prediction model and the amount of excrement accumulated in the wearer's body at the current time when the prediction processing is performed.

For example, the prediction unit 135 predicts the timing of urination, which will occur later than the current time, based on the prediction model and the amount of urine currently accumulated in the wearer's body. Similarly, the prediction unit 135 predicts the timing of defecation, which will occur later than the current time, based on the prediction model and the amount of feces currently accumulated in the wearer's body.

Furthermore, the prediction unit 135 notifies the person responsible for providing care to the wearer of the treatment target of the prediction result (step S903). For example, the prediction unit 135 notifies the person responsible by transmitting the prediction result to the person's device 30.

[8. Other Embodiments]
The information processing device 100 according to the above embodiment may be implemented in various other forms besides the above embodiment. Therefore, other embodiments of the information processing device 100 will be described below.

[8-1. Variation (1)]
In the above embodiment, the prediction unit 135 predicts the timing of excretion based on internal and external information, and the first decision unit 137 determines a predetermined timing for care to the wearer of the item to be treated based on the excretion timing predicted by the prediction unit 135. The proposal unit 139 also proposes that the absorbent item be replaced at this replacement timing. Furthermore, the external information used by the prediction unit 135 is shown to be information about excrement that has been excreted from the body to the outside, i.e., excretion detection information from the second sensor SN2.

However, the first decision unit 137 may determine what type or what absorbent capacity (capacity) of absorbent material should be replaced by combining it with other external information besides the excretion detection information. For example, when the replacement timing determined this time is proposed by the proposal unit 139, the first decision unit 137 determines whether the prediction unit 135 predicts that urination will also occur between the replacement timing determined this time and the next predicted defecation timing.

Then, if the first determination unit 137 determines that urination will also occur between the determined replacement timing and the predicted next defecation timing, it determines what type or absorbent capacity (capacity) of absorbent material should be used at the determined replacement timing, based on the number of urinations during this period, the amount of urine at each urination, and the amount of stool at the defecation timing. The number of urinations, the amount of urine at each urination, and the amount of stool are also predicted by the prediction unit 135.

Here, we assume that, between the determined exchange timing and the predicted timing of the next bowel movement, two urinations are predicted, and the amount of urine each time is predicted to be normal (average), as is the amount of stool at the predicted time of bowel movement.

In such cases, the first determination unit 137 determines the predicted defecation timing as the next replacement timing relative to the currently determined replacement timing. The first determination unit 137 also determines that an absorbent item of a type (or absorbency) capable of absorbing the volume of urine from the two urinations and the volume of stool at the defecation timing should be replaced at the currently determined replacement timing. Furthermore, the suggestion unit 139 proposes an absorbent item of the type (or absorbency) determined by the first determination unit 137 to the person concerned. The suggestion unit 139 can also propose the next replacement timing determined by the first determination unit 137 to the person concerned.

According to this information processing device 100, even if several excretions occur between changes, an absorbent item with sufficient capacity to cover them can be fitted at the current change timing. This reduces the need for sudden changes before the next change timing, and as a result, it becomes possible to support the person being cared for (e.g., a caregiver) in working more efficiently.

Furthermore, if the time between the determined replacement timing and the predicted timing of the next bowel movement exceeds a predetermined threshold, the first determination unit 137 may, instead of determining this predicted bowel movement timing as the next replacement timing relative to the determined replacement timing, determine, for example, the timing at which a second urination is predicted to occur as the next replacement timing. In this example, the first determination unit 137 may also determine and propose an absorbent item of a type (or absorbency) capable of absorbing the amount of urine from two urinations as the absorbent item to be replaced at the determined replacement timing.

This information processing device 100 can prevent skin irritation caused by prolonged wear of absorbent items after urination, until defecation occurs.

[8-2. Variation (2)]
Furthermore, when the prediction unit 135 predicts a gradual excretion timing (for example, a gradual excretion timing such as the first excretion timing and the second excretion timing), as in the example above, it can also estimate the excretion probability of what and how much will be excreted at each stage (each time). In the above modified example (1), the first decision unit 137 determined the absorbent item to be used at the current replacement timing based on the urination and defecation status up to the next excretion timing, without taking such excretion probabilities into account. However, the first decision unit 137 may also determine the absorbent item to be used at the current replacement timing by further considering such excretion probabilities.

For example, when the newly determined exchange timing is proposed by the proposal unit 139, the first determination unit 137 determines whether there is an excretion timing at which excretion is predicted to occur with a high probability, exceeding a predetermined threshold, among the excretion probabilities predicted for each of the stepwise excretion timings after the newly determined exchange timing.

The first determination unit 137 then determines, among the various excretion timings, that there is an excretion timing at which excretion is predicted to occur with a high probability, exceeding a predetermined threshold. If it determines that there is such a timing with a high probability of excretion, it determines this timing with a high probability of excretion as the next replacement timing after the currently determined replacement timing. Furthermore, based on the excretion status up to the high-probability excretion timing (for example, the type of excretion (urine or feces), the number of excretions, and the amount excreted each time), the first determination unit 137 determines what type, or what absorbent capacity (capacity), of absorbent material should be used for replacement at the currently determined replacement timing.

According to this information processing device 100, for example, if it is predicted that excretion (e.g., urination) will occur with a low probability on the first occasion and with a high probability on the second occasion, an absorbent item with sufficient capacity to cover both the first and second excretion without leakage can be fitted at the current replacement timing. This reduces the need for a sudden replacement before the next replacement timing, and as a result, it becomes possible to support the person being treated (e.g., a caregiver) in working more efficiently.

Furthermore, the first determination unit 137 may not only determine the absorbent item to be used at the determined replacement timing, but may also determine the timing for guiding the wearer to the toilet. According to the above example, if the first determination unit 137 determines that there is a timing at which excretion is predicted to occur with a high probability, exceeding a predetermined threshold, it will determine this timing with a high probability of excretion as the guidance timing.

This information processing device 100 helps to prevent situations where the burden and time spent guiding a person to the toilet are wasted because they did not actually defecate. Therefore, it supports the person being guided (for example, a caregiver) in working more efficiently.

[8-3. Variation (3)]
Furthermore, the information control unit 136 may propose lifestyle guidance to the wearer based on the wearer's excretion status (e.g., "Excretion History" in Figure 4) and food and drink information (e.g., "Meal History" in Figure 4). For example, the information control unit 136 controls the content of lifestyle guidance based on the predicted urine volume (amount of urine in the bladder, amount of urine excreted, etc.) and the predicted number of urinations per day. To give one example, if the predicted urine volume (amount of urine in the bladder, amount of urine excreted, etc.) and the predicted number of urinations per day are less than a predetermined threshold, the information control unit 136 will determine that the wearer has insufficient body water and will control and propose lifestyle guidance that increases fluid intake.

Furthermore, if the timing of urination is later than normal, the information control unit 136 can similarly determine that the body's water content is low and control and propose lifestyle guidance that increases fluid intake. While this example uses urination, the information control unit 136 can similarly optimize lifestyle guidance based on defecation status as well, tailoring it to the wearer.

Furthermore, the information control unit 136 may be configured to detect urinary storage disorders early by detecting the amount of urine stored in the bladder after urination. For example, if the information control unit 136 detects urination and determines that the amount of urine stored in the bladder after urination (this may be the amount of urine after a single urination, or the average of the amounts of urine after several urinations) is large, it may notify the user that there is a possibility of sinusitis.

[9. Other]
Of the processes described above as being performed automatically, all or part of them may be performed manually. Furthermore, all or part of the processes described as being performed manually may be performed automatically using known methods. In addition, the processing procedures, specific names, and various data and parameters shown in the above documents and drawings may be changed at will unless otherwise specified. For example, the various information shown in each drawing is not limited to the information illustrated.

Furthermore, the components of each illustrated device are functionally conceptual and do not necessarily have to be physically configured as shown. In other words, the specific forms of distribution and integration of each device are not limited to those shown. Also, each component may be configured by functionally or physically distributing and integrating all or part of it in any unit, depending on various loads and usage conditions. Moreover, the processes described above may be combined and executed as appropriate, within a non-contradictory range.

[10. Hardware Configuration]
Furthermore, the information processing device 100 according to the above-described embodiment is realized by a computer 1000 having a configuration such as that shown in Figure 11. Figure 11 is a diagram showing an example of a hardware configuration. The computer 1000 is connected to an output device 1010 and an input device 1020, and an arithmetic unit 1030, a cache 1040, a memory 1050, an output interface 1060, an input interface 1070, and a network interface 1080 are connected by a bus 1090.

The arithmetic unit 1030 operates based on programs stored in the cache 1040 and memory 1050, as well as programs read from the input device 1020, and executes various processes. The cache 1040 is a cache that temporarily stores data used by the arithmetic unit 1030 for various calculations, such as RAM. The memory 1050 is a storage device where data used by the arithmetic unit 1030 for various calculations and various databases are registered. This memory is implemented using ROM (Read Only Memory), HDD (Hard Disk Drive), flash memory, etc.

The output IF 1060 is an interface for transmitting information to be output to output devices 1010, such as monitors and printers. It may be implemented using connectors conforming to standards such as USB (Universal Serial Bus), DVI (Digital Visual Interface), or HDMI (High Definition Multimedia Interface). On the other hand, the input IF 1070 is an interface for receiving information from various input devices 1020, such as mice, keyboards, and scanners. It may be implemented using, for example, USB.

For example, the input device 1020 may be implemented as a device that reads information from optical recording media such as CDs (Compact Discs), DVDs (Digital Versatile Discs), and PDs (Phase-change rewritable Disks), magneto-optical recording media such as MOs (Magneto-Optical disks), tape media, magnetic recording media, or semiconductor memory. Alternatively, the input device 1020 may be implemented as an external storage medium such as a USB memory stick.

The network interface 1080 has the function of receiving data from other devices via the network N and sending it to the computing device 1030, and also transmitting data generated by the computing device 1030 to other devices via the network N.

Here, the arithmetic unit 1030 controls the output device 1010 and input device 1020 via the output IF 1060 and input IF 1070. For example, the arithmetic unit 1030 loads a program from the input device 1020 and memory 1050 into the cache 1040 and executes the loaded program. For example, if computer 1000 functions as information processing device 100, the arithmetic unit 1030 of computer 1000 realizes the functions of the control unit 130 by executing the program loaded into the cache 1040.

1 Information Processing System 30 Target Device 100 Information Processing Device 120 Storage Unit 121 Wearer Information Storage Unit 122 Internal/External 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 Identification 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

Claims (19)

An acquisition unit that acquires internal information indicating the accumulation of excrement in the body of a wearer wearing an absorbent item, and external information indicating the date and time of the wearer's excretion,
A model that has learned the relationship between the history of accumulation status indicated by the internal body information and the history of excretion dates and times indicated by the external body information, and a prediction unit that predicts the timing of future excretion by the wearer based on the internal body information acquired at the present time , based on a model that has been learned to output the remaining time until the next excretion occurs when the internal body information is input.
An information processing device characterized by the following : The acquisition unit further acquires information regarding the wearer's eating and drinking habits,
The information processing device according to claim 1, characterized in that the prediction unit predicts the timing of excretion based on the relationship between the history of the state of excrement indicated by the internal information, the history of excretion indicated by the external information, and the history of eating and drinking indicated by the eating and drinking information, using the information acquired at the present time and the eating and drinking information. The acquisition unit further acquires drug information regarding the wearer's medication status,
The information processing apparatus according to claim 1 or 2, characterized in that the prediction unit predicts the timing of excretion based on the relationship between the history of the status of excretion indicated by the internal information, the history of excretion indicated by the external information, and the history of medication status indicated by the drug information, using the information acquired at the present time and the drug information. The information processing apparatus according to claim 2 or 3 , further comprising an information control unit that makes suggestions for controlling the wearer's excretion based on the trends indicated by the aforementioned relationship. The information processing apparatus according to claim 4, characterized in that the information control unit makes a suggestion for food and drink to control the wearer's excretion based on the trend shown by the relationship with the eating and drinking status, or makes a suggestion for medication to control the wearer's excretion based on the trend shown by the relationship with the medication status . The information processing device according to any one of claims 1 to 5, characterized in that the acquisition unit acquires the internal body information detected by a first sensor attached to the wearer's body and acquires the external body information detected by a second sensor attached to the absorbent article. The information processing device according to any one of claims 1 to 6 , further comprising a suggestion unit that makes a predetermined suggestion to the person responsible for providing care to the wearer based on the excretion timing predicted by the prediction unit. The system further includes a determination unit that determines a predetermined timing for care of the wearer based on the excretion timing predicted by the prediction unit,
The information processing apparatus according to claim 7 , characterized in that the proposal unit makes a proposal regarding the timing determined by the decision unit. The determination unit determines the timing for changing the absorbent article worn by the wearer, based on the excretion timing predicted by the prediction unit.
The information processing apparatus according to claim 8 , characterized in that the suggestion unit suggests replacing the absorbent article at the replacement timing. The prediction unit predicts the amount of excretion at the excretion timing based on the history of the amount of excretion discharged into the absorbent article.
The information processing apparatus according to claim 9, characterized in that, if the determination unit determines that the amount of excretion predicted at the excretion timing exceeds the remaining absorbable amount, which is the amount of excretion that the absorbable article can absorb, it determines a predetermined timing prior to the excretion timing as the replacement timing. The determination unit determines the timing for guiding the wearer to the toilet based on the timing of defecation predicted by the prediction unit.
The information processing device according to any one of claims 8 to 10 , characterized in that the suggestion unit suggests guiding the wearer to the toilet at the guidance timing. The device further has a storage unit that stores schedule information indicating the schedule of the subject and the wearer,
The information processing apparatus according to claim 11, characterized in that the determination unit determines the timing for guiding the wearer to the toilet based on the timing of defecation predicted by the prediction unit, the schedule of the subject indicated by the schedule information obtained from the storage unit , and the schedule of the wearer. The information processing device according to claim 12, characterized in that the determination unit determines the timing for guiding the wearer to the toilet based on the necessity of guiding the wearer to the toilet determined for each wearer based on the timing of excretion and the schedule. The system further includes a determination unit that determines whether or not to use a replacement absorbent pad in combination with the absorbent item worn by the wearer, based on the excretion timing predicted by the prediction unit.
The information processing apparatus according to any one of claims 7 to 13 , characterized in that the proposal unit makes a proposal in accordance with the determination result by the determination unit. The prediction unit further predicts the type of excrement that may be excreted at the excretion timing, based on the relationship between the history of the accumulation status of the type of excrement indicated by the internal information and the history of the excretion date and time indicated by the external information.
The information processing apparatus according to claim 14, characterized in that the determination unit determines whether or not to use a replacement absorbent pad in combination with the absorbent article worn by the wearer, based on the type of excrement that can be excreted at the excretion timing . The acquisition unit further acquires state information indicating the state of excretions within the wearer's body,
The information processing apparatus according to claim 15, characterized in that the determination unit determines the state of the excrement that can be excreted at the excretion timing based on the history of the state of the excrement indicated by the state information, and determines whether or not to use a replacement absorbent pad in combination with the absorbent article worn by the wearer based on the determined state . The acquisition unit further acquires sensor information in which the wearer's condition is detected.
The information processing device according to any one of claims 14 to 16, characterized in that the determination unit determines the wearer's body movements predicted at the time of excretion based on the wearer's body movement history indicated by the sensor information, and determines whether or not to use a replacement absorbent pad in combination with the absorbent article worn by the wearer based on the determined body movements. An information processing method performed by an information processing device,
An acquisition step to acquire internal information indicating the accumulation of excretions in the body of a wearer wearing an absorbent item, and external information indicating the date and time of the wearer's excretion,
An information processing method characterized by including a prediction step of predicting the timing of future excretion by the wearer based on the internal body information acquired at the present time , using a model that has learned the relationship between the history of accumulation status indicated by the internal body information and the history of excretion dates and times indicated by the external body information, and which has been learned to output the remaining time until the next excretion occurs when the internal body information is input. A procedure for acquiring internal information indicating the accumulation of excretions in the body of a wearer wearing an absorbent item, and external information indicating the date and time of the wearer's excretion.
A model that has learned the relationship between the history of accumulation status indicated by the internal body information and the history of excretion dates and times indicated by the external body information, and which has been learned to output the remaining time until the next excretion when the internal body information is input , a prediction procedure that predicts the timing of future excretion by the wearer from the internal body information acquired at the present time , and an information processing program to be executed by a computer.