System

Abstract

A system is provided.SOLUTION: The system includes a sensor means for detecting the presence or absence of excrement, a data transmission means for transmitting the detected data to the cloud, an analysis means for analyzing the data on the cloud and predicting the excretion timing, a notification means for notifying the user of the analysis result, and a database means for accumulating excretion data and tracking the progress of training.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional toilet training is often difficult to carry out effectively because it is difficult for parents or guardians to properly monitor their children's toileting timings. Furthermore, due to insufficient monitoring functions for elderly people, appropriate care may not be provided. There is a need to provide a system that solves these problems, makes toilet training effective and enjoyable, and also strengthens monitoring functions for elderly people. [Means for solving the problem]

[0005] The present invention provides a system that includes a sensor means for detecting the presence or absence of excrement, a data transmission means for transmitting the detected data to the cloud, an analysis means for analyzing the data on the cloud and predicting the timing of excrement, a notification means for notifying the user of the analysis results, and a database means for accumulating excrement data and tracking the training progress.The system also includes a display means for visualizing the toilet training progress and a health management means for estimating the user's health condition based on the database means and providing health advice to the user, thereby improving the efficiency of toilet training and strengthening the elderly monitoring function.

[0006] "Sensor means" refers to a device or system for detecting the presence or absence of fecal matter.

[0007] "Data transmission means" refers to a device or system for transmitting detected data to the cloud.

[0008] "Analysis means" refers to a device or system for predicting the timing of excretion based on data received on the cloud.

[0009] "Notification means" refers to a device or system for notifying the user of the analyzed results.

[0010] "Database Means" refers to a device or system for storing toileting data and tracking training progress.

[0011] "Display means" refers to a device or system for visualizing toilet training progress.

[0012] "Health management means" refers to a device or system for predicting health status based on database means and providing health advice to a user. [Brief explanation of the drawings]

[0013] [Figure 1]1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0014] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0017] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0018] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0019] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0021] [First embodiment]

[0022] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0023] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0024] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0026] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0029] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0031] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0032] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0033] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0034] The present invention is a system that includes a sensor means for detecting the presence or absence of excrement, a data transmission means for transmitting the detected data to the cloud, an analysis means for analyzing the data on the cloud and predicting the timing of excretion, a notification means for notifying the user of the analysis results, and a database means for storing excretion data and tracking the progress of training.

[0035] Sensor means

[0036] The "terminal" uses training pants with built-in temperature and moisture sensors. The temperature sensor detects temperature changes in excrement, while the moisture sensor detects bowel movements and urine. These sensors smoothly collect data for subsequent processing.

[0037] Data transmission method

[0038] The "terminal" transmits the acquired sensor data to the cloud. This data transmission has the function of converting the sense data into an appropriate format and transmitting it to a cloud platform (e.g., IoT platform). This allows the data acquired from the sensors to be securely aggregated in the cloud.

[0039] Analysis means

[0040] The "server" analyzes the data on a cloud platform. The analysis means executes an algorithm to predict when the pet will defecate based on the received data. For example, it can determine whether defecation has occurred based on the results of detecting certain temperature changes or humidity.

[0041] Notification means

[0042] The "server" has a means to notify the "user" of the analysis results. For example, it can send push notifications or alerts to the "user" (parent or guardian)'s smartphone. This allows parents to know in real time when their child has had an excretion event.

[0043] Database Means

[0044] The "server" accumulates each user's toileting data and stores it in a database. This database is used to track the progress of toilet training and evaluate the results. For example, the server can retrieve from the database the number of successful toileting attempts over the past week and report this to the "user."

[0045] Specific examples of operation

[0046] Toilet training for children

[0047] 1. The "device" begins collecting data from sensors once the child puts on the training pants.

[0048] 2. The temperature sensor detects changes in the child's body temperature, and the moisture detection sensor detects urine and feces.

[0049] 3. The "terminal" sends the detected data to the cloud.

[0050] 4. The "server" analyzes the data sent to the cloud and determines whether or not excretion has occurred.

[0051] 5. If it is determined that excretion has occurred, the "server" sends a notification to the "user."

[0052] 6. The "server" stores the waste data in a database and tracks the progress of potty training.

[0053] 7. Users can check their training progress and take appropriate action through a smartphone application.

[0054] Use to watch over the elderly

[0055] 1. Elderly people wear pants with built-in sensors.

[0056] 2. The "terminal" monitors the elderly person's daily bowel movements and sends the data to the cloud.

[0057] 3. The "server" analyzes the data and, if it detects any abnormalities based on excretion status, it promptly notifies the "user" (caregiver or family member).

[0058] 4. The analysis results are stored in a database and can be used for health management and monitoring as needed.

[0059] In this way, the system of the present invention improves the efficiency of toilet training for children, provides information for parents to respond in a timely manner, and strengthens the monitoring function for elderly people, allowing them to provide appropriate care.

[0060] The processing flow will be explained below.

[0061] Step 1:

[0062] The "terminal" initializes the temperature and moisture sensors, which prepares them for proper operation.

[0063] Step 2:

[0064] The device will begin collecting temperature and moisture data at the specified intervals, with data collection from the sensors occurring every minute.

[0065] Step 3:

[0066] The "terminal" converts the acquired temperature data and moisture detection data into an appropriate data format, for example, the temperature data is recorded as degrees Celsius and the moisture detection data is recorded as a Boolean value.

[0067] Step 4:

[0068] The terminal then sends the converted data to the cloud platform, where a cloud connection process is performed to ensure the data is sent securely.

[0069] Step 5:

[0070] The "server" analyzes the data received via a cloud platform, applying an algorithm to predict whether or not a bowel movement will occur based on temperature and moisture detection data.

[0071] Step 6:

[0072] The server determines whether excretion has occurred based on the analysis results, and if excretion is detected, the timing of excretion is predicted.

[0073] Step 7:

[0074] The "server" sends notifications to the "user" (parent or guardian) based on the analysis results. The notifications are sent as push notifications or messages via a smartphone application.

[0075] Step 8:

[0076] The "server" stores each user's toileting data in a database, which records the frequency and patterns of toileting and tracks training progress.

[0077] Step 9:

[0078] The "server" analyzes the progress of the training based on the accumulated data, and the analysis results are displayed on the display means so that the "user" can check them.

[0079] Step 10:

[0080] The "server" can estimate health conditions based on the accumulated data and provide hydration and medical advice, which can help "users" manage the health of children and the elderly.

[0081] By using the above processing steps, the present invention accurately grasps the timing of excretion, allowing parents and guardians to effectively support their children's toilet training. It also strengthens the monitoring function for elderly people, allowing them to provide more appropriate care.

[0082] Example 1

[0083] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0084] Conventional toilet management systems have difficulty detecting excrement in real time or predicting the most efficient timing for excretion. This makes it difficult for users to respond at the appropriate time, making toilet training and monitoring elderly people difficult. Furthermore, the management and analysis of collected excretion data are insufficient, making it difficult to accurately grasp the progress of training and health status.

[0085] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0086] In this invention, the server includes a sensor means having a built-in temperature sensor and moisture sensor for detecting the presence or absence of excrement, a data transmission means for converting the detected data into an appropriate format and transmitting it to a cloud platform, an analysis means for analyzing the received data on the cloud platform and executing an algorithm for predicting the timing of excrement, a notification means for transmitting the analysis results to the user as push notifications or alerts, and a database means for storing the excrement data in a database and tracking the progress of training. This enables real-time detection of excrement and prompt notification, making toilet training and elderly care more efficient. Furthermore, accurate management and analysis of collected data makes it possible to understand the progress of training and health status.

[0087] "Sensor means" is a device that incorporates temperature and moisture sensors to detect the presence or absence of excrement.

[0088] The "data transmission means" is a function that converts the detected sensor data into an appropriate format and transmits it to the cloud platform.

[0089] The "analysis means" is a function that analyzes the data received on the cloud platform and executes an algorithm to predict the timing of excretion.

[0090] "Notification means" is a function that sends the analysis results to the user as a push notification or alert.

[0091] "Database means" refers to a system for storing excretion data in a database and tracking training progress.

[0092] The "display means" is a screen display function for visualizing the progress of toilet training.

[0093] The "health management means" is a function that estimates the user's health condition based on accumulated excretion data and provides health advice to the user.

[0094] This invention is a system that detects the presence or absence of excrement, analyzes the data, and notifies the user. This system is primarily intended for use in toilet training children and monitoring elderly people, and by collecting and analyzing data in real time, it enables the prompt provision of information to users.

[0095] Sensor means

[0096] The "terminal" has a built-in temperature sensor and moisture detection sensor to detect the presence or absence of excrement. This terminal can be incorporated, for example, into training pants or underwear for the elderly. The temperature sensor detects changes in body temperature when excrement occurs, and the moisture detection sensor detects changes in humidity due to excrement. For example, when a child puts on training pants and urinates, the moisture detection sensor detects a sudden rise in humidity.

[0097] Data transmission method

[0098] The "terminal" converts the acquired sensor data into an appropriate format and sends it to the cloud platform. The data is converted into, for example, JSON format and sent using a secure protocol (e.g., HTTPS). It also has retry logic to confirm the success of the transmission. For example, it includes a step of confirming the success of the transmission within one second after sending the sensor data to the cloud.

[0099] Analysis means

[0100] The "server" analyzes the data received on the cloud platform. The analysis method, for example, is an analysis script written in Python, which processes the data using Apache Spark. This executes an algorithm to predict the timing of excretion. For example, when a child has an excretion, the timing of excretion can be immediately analyzed based on that data.

[0101] Notification means

[0102] The "server" has a means for notifying the user of the analysis results. Specifically, it can use a smartphone push notification service (e.g., Firebase Cloud Messaging) to send an alert to the user's smartphone. This allows the user to know in real time when their child has defecates. For example, if it is determined that a child has defecates, it can send a notification to the parent's smartphone saying, "Your child has defecates."

[0103] Database Means

[0104] The "server" stores the excretion data in a database and tracks the training progress. The data is stored in a database such as MySQL or MongoDB. Users can check past data through the application and evaluate their training progress and health status. For example, a week's worth of excretion data can be graphed to visualize frequency and timing.

[0105] Prompt Sentence Examples

[0106] "Please explain a system in which a device with a built-in temperature sensor and moisture detection sensor sends data to the cloud, where an algorithm analyzes the data and sends a notification."

[0107] This invention will improve the efficiency of toilet training for children, provide parents with real-time information to help them take appropriate measures, and strengthen the monitoring function for elderly people, allowing them to provide appropriate care.

[0108] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0109] Step 1:

[0110] Acquiring Sensor Data

[0111] The "terminal" uses a temperature sensor and a moisture detection sensor to detect the presence or absence of excrement. This sensor data captures changes in temperature and humidity in real time. For example, when a child is wearing training pants and urinates, the moisture detection sensor detects a sudden rise in humidity. At the same time, the temperature sensor also measures changes in body temperature. The temperature and humidity data are taken as input and provided to the next step.

[0112] Step 2:

[0113] Sending data to the cloud

[0114] The "terminal" converts the acquired sensor data into a predefined format (e.g., JSON format) and sends it to the cloud platform using a secure protocol (e.g., HTTPS). For example, it converts the sensor data into JSON format and sends a POST request to the cloud server. It receives sensor data as input and generates data sent to the cloud platform as output. If the transmission is not successful, it executes retry logic and retries until it is successful.

[0115] Step 3:

[0116] Data analysis on the cloud

[0117] The "server" analyzes the received data on a cloud platform. The analysis method includes algorithms that process the data using Python scripts and Apache Spark and predict when the pet will defecate. For example, it analyzes temperature changes and increases in humidity based on the received sensor data to determine whether defecation has occurred. It receives the sensor data sent to the cloud as input and generates an analysis result regarding whether defecation has occurred as output.

[0118] Step 4:

[0119] Notification of analysis results

[0120] The "server" sends push notifications and alerts to the user based on the analysis results. For example, it uses the Firebase Cloud Messaging service to send a notification to the user's smartphone saying, "Your child has defecate." It receives the analysis results as input and generates a push notification to the user as output. The user can check the defecation status in real time via their smartphone.

[0121] Step 5:

[0122] Accumulating data and tracking progress

[0123] The "server" accumulates the excretion data in a database and tracks the training progress. The database can be, for example, MySQL or MongoDB, and stores past excretion data. The user can review the past data through the application and evaluate the training progress and health status. The application receives excretion data as input and generates records stored in the database as output. As a concrete example, it displays a week's worth of excretion data in a graph and provides feedback to the user.

[0124] Through this series of processes, the system improves the efficiency of toilet training for children, provides real-time information for parents to take appropriate action, and strengthens monitoring functions for the elderly, enabling them to provide appropriate care.

[0125] (Application example 1)

[0126] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0127] With conventional excretion detection systems, it was difficult to grasp the timing of excretion in real time, resulting in a lack of information to provide appropriate care for elderly caregivers. Furthermore, there was no way to immediately notify users when an abnormality was detected, which often meant that a prompt response was not possible. Furthermore, health management did not effectively utilize past excretion data, making it difficult to provide effective care.

[0128] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0129] In this invention, the server includes a sensor means for detecting the presence or absence of excrement, a data transmission means for transmitting the detected data to the cloud, an analysis means for analyzing the data on the cloud and predicting the timing of excretion, a notification means for notifying the user of the analysis results, a database means for accumulating excretion data and tracking the progress of training, a monitoring means for monitoring the timing of excretion of the elderly person in real time, a notification execution means for sending a push notification when excretion is detected, an alert means for providing an emergency alert when an abnormality is detected, and a statistics display means for displaying statistics and trends of past excretion data. This makes it possible to grasp the excretion status of the elderly person in real time and to provide prompt notification when an abnormality occurs, thereby enabling appropriate care to be provided and health management to be performed by utilizing past data.

[0130] The "sensor means" is a detection device for detecting the presence or absence of excrement.

[0131] The "data transmission means" is a communication device for transmitting detected data to the cloud.

[0132] The "analysis means" is an analysis device that analyzes data on the cloud and predicts the timing of excretion.

[0133] The "notification means" is a communication device for notifying the user of the analysis results.

[0134] The "database means" is a storage device for storing excretion data and tracking the progress of training.

[0135] The "monitoring means" is a monitoring device for monitoring the timing of excretion of an elderly person in real time.

[0136] The "notification execution means" is a notification device for sending a push notification when excretion is detected.

[0137] The "alert means" is a warning device that provides an emergency alert when an abnormality is detected.

[0138] The "statistics display means" is a display device for displaying statistics and trends of past excretion data.

[0139] The present invention is a system for monitoring the excretion status of an elderly person in real time and for promptly notifying the elderly person if an abnormality is detected. Specific embodiments will be described below.

[0140] Overall system configuration

[0141] The server is composed of a sensor means for detecting the presence or absence of excrement, a data transmission means for transmitting the detected data to the cloud, an analysis means for analyzing the data on the cloud and predicting the timing of excretion, a notification means for notifying the user of the analysis results, a database means for accumulating excretion data and tracking the progress of training, a monitoring means for monitoring the excretion timing of the elderly person in real time, a notification execution means for sending a push notification when excretion is detected, an alert means for providing an emergency alert when an abnormality is detected, and a statistical display means for displaying statistics and trends of past excretion data.

[0142] Hardware and Software Use

[0143] The sensor means used is pants with built-in temperature and humidity sensors, which can accurately detect changes in temperature and humidity of excrement.

[0144] A cloud platform (e.g., Amazon Web Services (AWS) IoT or Google Cloud IoT) is used as a data transmission and database means, allowing data acquired from sensors to be securely transmitted to the cloud and stored there.

[0145] The analysis means and notification execution means are configured with a data analysis algorithm using a programming language such as Python, which makes it possible to predict the timing of excretion based on the received data and send a push notification of the analysis results to the user.

[0146] The monitoring and alerting methods use smartphone applications (e.g., Android or iOS apps) that allow caregivers and family members to check the elderly person's toileting status in real time and receive immediate notification if any abnormalities occur.

[0147] Adding specific examples

[0148] As a concrete example, consider a monitoring system that monitors the daily excretion of elderly people. The elderly person wears pants with built-in sensors, and when the sensors detect excretion, the data is sent to the cloud. The cloud server analyzes the data and sends a push notification to a smartphone application if excretion is detected. It also displays an emergency alert if an abnormality is detected.

[0149] An example of a prompt is as follows:

[0150] Create a Python program that uses data from temperature and humidity sensors to monitor an elderly person's toileting timing in real time, and sends a push notification to a smartphone if an abnormality is detected. Obtain data from the sensors, and send a notification if toileting is detected under certain conditions (e.g., temperature > 37°C, humidity > 75%). Include a section that obtains sensor data via HTTP request and performs cloud analysis.

[0151] In this way, the system of the present invention can grasp the excretory status of elderly people in real time and quickly notify them if any abnormalities occur, making it possible to provide appropriate care.In addition, health management can be performed by utilizing past data.

[0152] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0153] Step 1:

[0154] Once the elderly person puts on the sensor-embedded pants, the device starts collecting data from the temperature and humidity sensors. The input data includes the elderly person's body temperature and changes in humidity of excrement, and the output is real-time sensor data.

[0155] Step 2:

[0156] The terminal converts the acquired sensor data into an appropriate format and sends it to a cloud platform (e.g., AWS IoT or Google Cloud IoT). The input data includes real-time temperature and humidity data, and the output data is sent to the cloud.

[0157] Step 3:

[0158] The server receives the sensor data sent to the cloud and executes an analysis algorithm. The input data includes the sensor data, and the timing of excretion is predicted through data analysis. The output is the result of excretion detection.

[0159] Step 4:

[0160] Based on the analysis results, the server sends a push notification to the user (caregiver or family member) if excretion is detected. The input data includes the excretion detection judgment result, and the push notification is sent as output. Specifically, the notification message is sent to the smartphone.

[0161] Step 5:

[0162] The server stores the analysis results and sensor data in a database. The input data includes the analysis results and sensor data, and the output data is saved in the database. This allows for the accumulation of excretion data over a long period of time.

[0163] Step 6:

[0164] Users can view past elimination data and analysis results through a smartphone application. Input data includes elimination data from a database, and output includes statistics and health trends displayed on the screen. Specific operations include generating graphs and charts.

[0165] Step 7:

[0166] The server provides an emergency alert if an abnormality is detected. The input data includes anomaly detection information based on the analysis results, and the output is an alert notification. Specifically, the application notifies the user by audio alert or vibration.

[0167] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0168] The present invention is a system that combines a sensor means for detecting the presence or absence of excrement, a data transmission means for transmitting the detected data to the cloud, an analysis means for analyzing the data on the cloud and predicting the timing of excrement, a notification means for notifying the user of the analysis results, a database means for accumulating excrement data and tracking the progress of training, and an emotion engine that recognizes the user's emotions.

[0169] Sensor means

[0170] The "terminal" uses training pants with built-in temperature and moisture sensors. The temperature sensor detects temperature changes in excrement, while the moisture sensor detects bowel movements and urine. These sensors smoothly collect data for subsequent processing.

[0171] Data transmission method

[0172] The "terminal" transmits the acquired sensor data to the cloud. This data transmission has the function of converting the sensor data into an appropriate format and transmitting it to a cloud platform (e.g., an IoT platform). This allows the data acquired from the sensors to be securely aggregated in the cloud.

[0173] Analysis means

[0174] The "server" analyzes the data on a cloud platform. The analysis means executes an algorithm to predict when the pet will defecate based on the received data. For example, it can determine whether defecation has occurred based on the results of detecting certain temperature changes or humidity.

[0175] Notification means

[0176] The "server" has a means to notify the "user" of the analysis results. For example, it can send push notifications or alerts to the "user" (parent or guardian)'s smartphone. This allows parents to know in real time when their child has had an excretion event.

[0177] Database Means

[0178] The "server" stores each user's toileting data in a database. This database is used to track the progress of toilet training and evaluate results. For example, the server can retrieve from the database the number of successful toileting attempts over the past week and report this to the "user."

[0179] Emotion Engine

[0180] The "server" recognizes the "user's" emotions using an emotion engine. This emotion engine analyzes the user's facial expressions, voice, and behavioral patterns to assess their emotional state. Emotion recognition is used to adjust notification content and training progress interfaces.

[0181] Specific examples of operation

[0182] Toilet training for children

[0183] 1. The "device" begins collecting data from sensors once the child puts on the training pants.

[0184] 2. The temperature sensor detects changes in the child's body temperature, and the moisture detection sensor detects urine and feces.

[0185] 3. The "terminal" sends the detected data to the cloud.

[0186] 4. The "server" analyzes the data sent to the cloud and determines whether or not excretion has occurred.

[0187] 5. If it is determined that excretion has occurred, the "server" sends a notification to the "user."

[0188] 6. The emotion engine recognizes the user's emotions and adjusts the notification content.

[0189] 7. The "server" stores the excretion data in a database and tracks training progress.

[0190] 8. Progress display and interface will be adjusted based on the emotion engine.

[0191] 9. The "User" can check the progress of training and notifications through the application and take appropriate action.

[0192] Use to watch over the elderly

[0193] 1. Elderly people wear pants with built-in sensors.

[0194] 2. The "terminal" monitors the elderly person's daily bowel movements and sends the data to the cloud.

[0195] 3. The "server" analyzes the data and, if it detects any abnormalities based on excretion status, it promptly notifies the "user" (caregiver or family member).

[0196] 4. The emotion engine recognizes the user's emotions and adjusts the notification content.

[0197] 5. The analysis results are stored in a database and can be used for health management and monitoring as needed.

[0198] 6. The "server" estimates the user's health status and provides hydration and medical advice to the user.

[0199] In this way, the system of the present invention accurately tracks the timing of excretion, allowing parents and guardians to effectively support their children's toilet training. It also strengthens the monitoring function for elderly people, enabling more appropriate care. The addition of an emotion engine enables flexible responses based on the user's emotions, further improving the user experience.

[0200] The processing flow will be explained below.

[0201] Step 1:

[0202] The "terminal" starts the training pants, which have built-in temperature and moisture sensors, and completes initialization, which prepares the sensors for proper operation.

[0203] Step 2:

[0204] The device will begin collecting temperature and moisture data at the specified intervals, specifically every minute.

[0205] Step 3:

[0206] The "terminal" converts the acquired temperature and moisture detection data into the appropriate data format: temperature data is recorded in degrees Celsius, and moisture detection data is recorded as a Boolean value (e.g., True or False).

[0207] Step 4:

[0208] The "terminal" then sends the converted data to the cloud platform, a process that includes cloud connection processing and secure data transmission.

[0209] Step 5:

[0210] The server analyzes the data received via the cloud platform, applying an algorithm to predict whether or not a person will defecate based on temperature and moisture detection data.

[0211] Step 6:

[0212] The "server" uses an algorithm to determine whether an excretion has occurred, and if so, predicts the timing of the excretion.

[0213] Step 7:

[0214] The "server" sends notifications to the "user" (parent or guardian) based on the analysis results. The notifications are sent as push notifications or messages via a smartphone application.

[0215] Step 8:

[0216] The "server" recognizes the "user's" emotions using an emotion engine, which analyzes the user's facial expressions, voice, and behavioral patterns to evaluate their emotional state.

[0217] Step 9:

[0218] The "server" then adjusts the notification content based on the perceived emotion: for example, if the user is stressed, the notification will be changed to a gentle, encouraging message.

[0219] Step 10:

[0220] The "server" stores each user's toileting data in a database, which records the frequency and patterns of toileting and tracks training progress.

[0221] Step 11:

[0222] The "server" analyzes the training progress based on the accumulated data and displays it to the "user." The progress display and interface are adjusted based on the emotion engine.

[0223] Step 12:

[0224] The server uses the database data to predict the user's health and provides hydration and medical advice, which is also tailored based on the emotion engine.

[0225] Step 13:

[0226] Users can check the progress of training and notifications through a smartphone application and take appropriate action. For example, knowing when to guide a child to the toilet can help the child's training progress more effectively.

[0227] Through the above processing steps, the present invention accurately grasps the timing of excretion, enabling the "user" to effectively support their child's toilet training. It also strengthens the monitoring function for elderly people, enabling more appropriate care. The addition of an emotion engine enables flexible responses based on the user's emotions, further improving the user experience.

[0228] Example 2

[0229] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0230] Conventional toilet training and elderly care monitoring systems have difficulty predicting when a child will need to defecate, making it impossible to notify parents or guardians in real time. Furthermore, they lack the ability to respond flexibly based on the user's emotions, making it difficult to improve the efficiency of training and monitoring. Furthermore, they lack a means to rationally track the progress of training, making them difficult for users to use.

[0231] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes sensor means for detecting the presence or absence of excrement, data transmission means for transmitting the detected data to a cloud environment, analysis means for analyzing the data in the cloud environment and predicting the timing of excrement, notification means for notifying the user of the analysis results, database means for accumulating excrement data and tracking the training progress, and an emotion engine for recognizing the user's emotions and adjusting the notification content and interface. This enables accurate prediction of excrement timing and real-time notification, and further enables flexible response based on the user's emotions and efficient tracking of the training progress.

[0232] "Sensor means" refers to a device for detecting temperature and moisture, and is used to detect the presence or absence of excrement.

[0233] "Data transmission means" refers to devices and software for transmitting detected sensor data to a cloud environment.

[0234] "Analysis means" refers to algorithms and programs that analyze sensor data in a cloud environment and predict the timing of excretion.

[0235] "Notification means" refers to a device or system for notifying the user of the analysis results, and has the ability to send push notifications or alerts.

[0236] "Database means" refers to a system for accumulating excretion data and tracking the progress of training, and stores and manages the data.

[0237] An "emotion engine" is an algorithm or software that recognizes a user's emotions and adjusts notification content and interfaces accordingly.

[0238] A "cloud environment" refers to servers and infrastructure for storing and processing data over the Internet.

[0239] The present invention is a system that combines a sensor means for detecting the presence or absence of excrement, a data transmission means for transmitting the detected data to the cloud, an analysis means for analyzing the data in a cloud environment and predicting the timing of excretion, a notification means for notifying the user of the analysis results, a database means for accumulating excretion data and tracking the progress of training, and an emotion engine that recognizes the user's emotions.

[0240] Sensor means

[0241] The "terminal" uses training pants with built-in temperature and moisture sensors. These sensors are composed of common sensor technologies, such as ceramic temperature sensors and capacitive moisture sensors. The temperature sensor detects temperature changes in excrement, while the moisture sensor detects bowel movements and urine. These sensors smoothly collect data for subsequent processing.

[0242] Data transmission method

[0243] The "terminal" transmits the acquired sensor data to the cloud. This data transmission has the function of converting the sensor data into an appropriate format (e.g., JSON format) and transmitting it to an IoT platform (e.g., Amazon Web Services IoT Core or Google Cloud IoT Core). This allows the data acquired from the sensors to be securely aggregated in the cloud.

[0244] Analysis means

[0245] The "server" analyzes the data in a cloud environment using programming languages ​​such as Python and R, as well as machine learning libraries (such as TensorFlow and PyTorch). The "server" runs an algorithm that predicts the timing of excretion based on the received data, and determines whether excretion has occurred based on data on certain temperature changes and humidity.

[0246] Notification means

[0247] The "server" has a means to notify the "user" of the analysis results. Notifications are sent via push notifications or alerts via a smartphone app or similar. For example, a specific notification such as "Your child has urinated" is sent to the user.

[0248] Database Means

[0249] The "server" stores each user's toileting data in a database using a relational database system such as MySQL or PostgreSQL, which is used to track potty training progress and health data, and generates reports including total and successful toileting attempts over a specified period of time.

[0250] Emotion Engine

[0251] The "server" recognizes the "user's" emotions using an emotion engine. This emotion engine uses, for example, Microsoft Azure's emotion analysis API to analyze the user's facial expressions, voice, and behavioral patterns, and uses the results to adjust notification content and the interface.

[0252] Specific examples of operation

[0253] Examples of toilet training for children

[0254] The "device" begins collecting data from sensors once the child puts on the training pants. The temperature sensor detects changes in the child's body temperature, and the moisture detection sensor detects urination and feces. This data is sent to the cloud, where the "server" analyzes the data and determines whether or not the child has urinated. If it determines that an urinary tract excretion has occurred, the "server" sends a real-time notification to the "user," and an emotion engine recognizes the user's emotions and adjusts the content of the notification accordingly. As a result, the user can check the progress of their training and the content of the notification through the application.

[0255] Specific examples of watching over the elderly

[0256] When an elderly person wears pants equipped with a built-in sensor, the "terminal" monitors the elderly person's daily excretion and sends the data to the cloud. The "server" analyzes the data and, if it detects any abnormalities based on the excretion status, it promptly notifies the "user" (caregiver or family member). The emotion engine recognizes the "user's" emotions and adjusts the content of the notification. The analysis results are stored in a database and can be used as needed for health management and monitoring.

[0257] Prompt Sentence Examples

[0258] How can I help my child with toilet training?

[0259] "Please tell me how the elderly toilet monitoring system works."

[0260] The above is a specific embodiment of the system of the present invention.

[0261] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0262] Step 1:

[0263] The "terminal" acquires sensor data.

[0264] The "terminal" uses training pants with built-in temperature and moisture sensors. When the user puts on the training pants, the sensors are activated and detect changes in body temperature and moisture in real time. This input data (temperature and moisture data) is temporarily stored in the terminal's memory. Specifically, the temperature and moisture data is acquired every 30 seconds and saved as a log in a file.

[0265] Step 2:

[0266] The "terminal" sends sensor data to the cloud.

[0267] The terminal sends the acquired sensor data to the cloud at specific time intervals or when a detection event occurs. In this process, the data is converted into JSON format and sent to the cloud using the IoT platform's API. The input data is log information on temperature and moisture, and the output is the sensor data stored in the cloud. Specifically, if the sensor detects an abnormality, the data is sent immediately; if no abnormality is detected, the data is sent at a fixed time (for example, every hour).

[0268] Step 3:

[0269] The "server" analyzes the data.

[0270] The "server" receives the data sent to the cloud and begins analysis. A Python script is used for the analysis, and an algorithm using machine learning libraries (TensorFlow, PyTorch) is used to predict the timing of excretion. The input data is temperature and moisture data obtained from the cloud, and the output is information indicating whether or not an excretion has occurred and the timing. Specifically, if the temperature shows a certain change or the moisture sensor exceeds a certain threshold, it is determined that an excretion has occurred and the time is recorded.

[0271] Step 4:

[0272] The "server" notifies the "user" of the analysis results.

[0273] Based on the analysis results, the "server" sends a notification to the "user." The notification is provided in the form of a push notification or alert via a smartphone app. The input data is the analysis result information, and the output is the notification content that is displayed on the user's device. Specifically, a specific message such as "Your child has urinated" is sent.

[0274] Step 5:

[0275] The "server" stores the excretion data in a database.

[0276] The "server" stores the analysis results and sensor data in a database. The database used is MySQL or PostgreSQL, with the input data being the analysis results and the original sensor data, and the output being the past excretion history stored in the database. Specifically, it aggregates the excretion history on a daily or weekly basis and generates graphs and reports to track the training progress.

[0277] Step 6:

[0278] The "server" recognizes the "user's" emotions using an emotion engine.

[0279] The "server" recognizes the user's emotions using an emotion engine. The emotion engine uses, for example, Microsoft Azure's emotion analysis API to analyze the user's facial expressions, voice, and behavioral patterns. The input data is the user's image and voice data, and the output is the emotional state as a result of the analysis. Specifically, if the user is using a smartphone with a camera, the emotional state is analyzed from the user's facial expressions and the training app interface is adjusted accordingly.

[0280] As a result, the overall processing of this system is realized through the specific operations and data flow of each processing step.

[0281] (Application example 2)

[0282] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0283] When monitoring elderly people, it is necessary to monitor their excretion status in real time and quickly detect and notify abnormalities. It is also important to respond appropriately to abnormalities based on the emotional state of the caregiver or family member. Another challenge is how to effectively visualize toilet training progress and health management data.

[0284] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0285] In this invention, the server includes a sensor means for detecting the presence or absence of excrement, a data transmission means for transmitting the detected data to the cloud, an analysis means for analyzing the data on the cloud and predicting the timing of excrement, a notification means for notifying the user of the analysis results, a database means for accumulating excrement data and tracking the progress of training, an emotion recognition means for recognizing the user's emotion and adjusting the content of the notification, and a health management means for managing the health condition based on the database means. This makes it possible to monitor the excretion status of the elderly in real time, quickly detect and notify abnormalities, and further realize appropriate responses according to the emotional state and visualization of information.

[0286] The "sensor means" is a device for detecting the presence or absence of excrement. It often has a built-in temperature sensor and a moisture sensor.

[0287] "Data transmission means" refers to a device or program that transmits detected sensor data to the cloud. It includes a means for securely and efficiently sending data to a remote server.

[0288] "Analysis means" refers to the algorithm and its execution environment that analyzes data received on the cloud and predicts the timing of excretion.

[0289] The "notification means" is a device or program that notifies the user of the analysis results. Information is conveyed to the user in the form of push notifications, alerts, etc.

[0290] "Database means" refers to a system for accumulating and managing excretion data and training progress, including functions such as data storage, search, and management.

[0291] The "emotion recognition means" is a device or program that recognizes the user's emotional state and adjusts the notification content by analyzing facial expressions, voice, etc.

[0292] The "health management means" is a device or program for managing the health status based on the data stored in the database and providing advice to the user.

[0293] The "Elderly Care Monitoring Measure" is a system that monitors the excretory status of elderly people, detects abnormalities, and sends notifications in real time.

[0294] The "display means" is a device that visually displays the progress of toilet training and health management data.

[0295] This invention is a system that monitors the excretion status of elderly people and detects and notifies abnormalities in real time. The system is composed of the following main elements.

[0296] 1. Sensor means

[0297] The sensor means used is training pants with a built-in temperature sensor and moisture detection sensor. These training pants are worn by elderly people and are used to detect the presence or absence of excrement. The temperature sensor detects temperature changes of excrement, and the moisture detection sensor detects excrement.

[0298] 2. Data transmission method

[0299] The device transmits the detected sensor data to the cloud, which converts the sensor data into an appropriate format and transmits it to a cloud platform (e.g., AWS IoT Platform), where the data acquired from the sensors is securely aggregated in the cloud.

[0300] 3. Analysis method

[0301] The server analyzes the data on the cloud platform. The analysis means runs an algorithm to predict the timing of excretion and abnormalities based on the received data. For example, it determines whether excretion has occurred based on the results of detecting certain temperature changes or humidity, and immediately proceeds to the next step if an abnormality is detected.

[0302] 4. Means of notification

[0303] The server has a means to notify users (caregivers and family members) of the analysis results. Specifically, it sends push notifications to smartphones using Firebase Cloud Messaging (FCM). This allows caregivers and family members to understand the elderly person's toileting status in real time.

[0304] 5. Database Tools

[0305] The server uses a cloud database such as AWS DynamoDB to store excretion data and training progress. This database stores analysis results and serves as the foundation for health management. It is also used as a data source for visualizing training progress.

[0306] 6. Emotion recognition means

[0307] To recognize the user's emotions and adjust the content of notifications, emotion recognition is performed using the smartphone's camera and microphone. Specifically, OpenCV and Google Cloud Speech-to-Text are combined to analyze the user's facial expressions and voice to determine their emotional state. Based on this, the content and timing of notifications are adjusted.

[0308] 7. Health management measures

[0309] Based on the excretion data stored in the database, machine learning algorithms such as AWS SageMaker are used to manage health conditions. Health indicators and advice are generated and provided to users. This function allows for more accurate management of the health of elderly people.

[0310] Specific examples

[0311] For example, if an elderly person uses the toilet to defecate, but detects that their bowel movements are not normal, the system will immediately detect this and send a notification to the caregiver's smartphone:

[0312] "An abnormality has been detected in Elderly Person A's bowel movements. Please check as soon as possible."

[0313] Prompt Sentence Examples

[0314] "Write a Python program that sends temperature and humidity data obtained from a sensor at a specified PPM value (every 30 seconds) to the cloud and notifies you in real time when an abnormality is detected. The content of the notification will be adjusted based on the results of emotion recognition."

[0315] In this way, this invention can monitor the excretory status of elderly people in real time, quickly detect and notify abnormalities, and provide appropriate responses and visualize information based on the emotional state, strengthening support for caregivers and family members.

[0316] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0317] Step 1:

[0318] When the elderly person puts on the training pants, the device starts collecting data from the built-in temperature sensor and moisture detection sensor, measuring temperature changes and the presence or absence of moisture, and collecting data from each sensor.

[0319] Input: Data from temperature and moisture sensors

[0320] Output: Sensor data (temperature change and moisture content)

[0321] Step 2:

[0322] The device sends the acquired sensor data to the cloud. MQTT is used as the communication protocol to send the data to a cloud platform (e.g., AWS IoT). The sensor data is converted into an appropriate format and securely aggregated in the cloud.

[0323] Input: Sensor data

[0324] Output: Data stored on the cloud

[0325] Step 3:

[0326] The server analyzes the received data in the cloud and uses machine learning algorithms such as AWS SageMaker to monitor the timing of excretion and abnormalities. For example, it analyzes specific temperature and humidity changes to determine whether there are any abnormalities.

[0327] Input: Sensor data on the cloud

[0328] Output: Analysis result (normal / abnormal)

[0329] Step 4:

[0330] The server notifies the user of the analysis results via their smartphone. Push notifications and alerts are sent using Firebase Cloud Messaging (FCM). If an abnormality is detected, a notification is sent immediately to inform the user of the situation.

[0331] Input: Analysis results

[0332] Output: Push notification

[0333] Step 5:

[0334] The server analyzes the user's emotional state using the smartphone's camera and microphone for emotion recognition. It uses OpenCV and Google Cloud Speech-to-Text to analyze facial expressions and voice. Based on the user's emotion, the notification content is adjusted.

[0335] Input: User facial expression images, voice data

[0336] Output: Sentiment analysis results, adjusted notification content

[0337] Step 6:

[0338] The server stores the analyzed excretion data in a database, using a cloud database such as AWS DynamoDB to store the data and manage it for later analysis.

[0339] Input: Analysis results

[0340] Output: Excretion data stored in a database

[0341] Step 7:

[0342] The server processes the data in the database to manage the user's health, and uses machine learning algorithms to generate health advice for the user and provide it via a smartphone application.

[0343] Input: Excretion data in the database

[0344] Output: Health care advice

[0345] Step 8:

[0346] The server visualizes toilet training progress and health management data, displaying it in graphs and dashboard format on a smartphone application, allowing users to easily check individual data.

[0347] Input: Excretion data in the database

[0348] Output: visualized data (graphs, dashboards)

[0349] The above are the specific processing steps in the system of the present invention. This process enables real-time monitoring of the excretion status of elderly people and enables prompt and appropriate responses.

[0350] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0351] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0352] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0353] [Second embodiment]

[0354] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0355] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0356] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0357] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0358] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0359] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0360] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0361] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0362] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0363] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0364] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0365] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0366] The present invention is a system that includes a sensor means for detecting the presence or absence of excrement, a data transmission means for transmitting the detected data to the cloud, an analysis means for analyzing the data on the cloud and predicting the timing of excretion, a notification means for notifying the user of the analysis results, and a database means for storing excretion data and tracking the progress of training.

[0367] Sensor means

[0368] The "terminal" uses training pants with built-in temperature and moisture sensors. The temperature sensor detects temperature changes in excrement, while the moisture sensor detects bowel movements and urine. These sensors smoothly collect data for subsequent processing.

[0369] Data transmission method

[0370] The "terminal" transmits the acquired sensor data to the cloud. This data transmission has the function of converting the sense data into an appropriate format and transmitting it to a cloud platform (e.g., IoT platform). This allows the data acquired from the sensors to be securely aggregated in the cloud.

[0371] Analysis means

[0372] The "server" analyzes the data on a cloud platform. The analysis means executes an algorithm to predict when the pet will defecate based on the received data. For example, it can determine whether defecation has occurred based on the results of detecting certain temperature changes or humidity.

[0373] Notification means

[0374] The "server" has a means to notify the "user" of the analysis results. For example, it can send push notifications or alerts to the "user" (parent or guardian)'s smartphone. This allows parents to know in real time when their child has had an excretion event.

[0375] Database Means

[0376] The "server" accumulates each user's toileting data and stores it in a database. This database is used to track the progress of toilet training and evaluate the results. For example, the server can retrieve from the database the number of successful toileting attempts over the past week and report this to the "user."

[0377] Specific examples of operation

[0378] Toilet training for children

[0379] 1. The "device" begins collecting data from sensors once the child puts on the training pants.

[0380] 2. The temperature sensor detects changes in the child's body temperature, and the moisture detection sensor detects urine and feces.

[0381] 3. The "terminal" sends the detected data to the cloud.

[0382] 4. The "server" analyzes the data sent to the cloud and determines whether or not excretion has occurred.

[0383] 5. If it is determined that excretion has occurred, the "server" sends a notification to the "user."

[0384] 6. The "server" stores the waste data in a database and tracks the progress of potty training.

[0385] 7. Users can check their training progress and take appropriate action through a smartphone application.

[0386] Use to watch over the elderly

[0387] 1. Elderly people wear pants with built-in sensors.

[0388] 2. The "terminal" monitors the elderly person's daily bowel movements and sends the data to the cloud.

[0389] 3. The "server" analyzes the data and, if it detects any abnormalities based on excretion status, it promptly notifies the "user" (caregiver or family member).

[0390] 4. The analysis results are stored in a database and can be used for health management and monitoring as needed.

[0391] In this way, the system of the present invention improves the efficiency of toilet training for children, provides information for parents to respond in a timely manner, and strengthens the monitoring function for elderly people, allowing them to provide appropriate care.

[0392] The processing flow will be explained below.

[0393] Step 1:

[0394] The "terminal" initializes the temperature and moisture sensors, which prepares them for proper operation.

[0395] Step 2:

[0396] The device will begin collecting temperature and moisture data at the specified intervals, with data collection from the sensors occurring every minute.

[0397] Step 3:

[0398] The "terminal" converts the acquired temperature data and moisture detection data into an appropriate data format, for example, the temperature data is recorded as degrees Celsius and the moisture detection data is recorded as a Boolean value.

[0399] Step 4:

[0400] The terminal then sends the converted data to the cloud platform, where a cloud connection process is performed to ensure the data is sent securely.

[0401] Step 5:

[0402] The "server" analyzes the data received via a cloud platform, applying an algorithm to predict whether or not a bowel movement will occur based on temperature and moisture detection data.

[0403] Step 6:

[0404] The server determines whether excretion has occurred based on the analysis results, and if excretion is detected, the timing of excretion is predicted.

[0405] Step 7:

[0406] The "server" sends notifications to the "user" (parent or guardian) based on the analysis results. The notifications are sent as push notifications or messages via a smartphone application.

[0407] Step 8:

[0408] The "server" stores each user's toileting data in a database, which records the frequency and patterns of toileting and tracks training progress.

[0409] Step 9:

[0410] The "server" analyzes the progress of the training based on the accumulated data, and the analysis results are displayed on the display means so that the "user" can check them.

[0411] Step 10:

[0412] The "server" can estimate health conditions based on the accumulated data and provide hydration and medical advice, which can help "users" manage the health of children and the elderly.

[0413] By using the above processing steps, the present invention accurately grasps the timing of excretion, allowing parents and guardians to effectively support their children's toilet training. It also strengthens the monitoring function for elderly people, allowing them to provide more appropriate care.

[0414] Example 1

[0415] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0416] Conventional toilet management systems have difficulty detecting excrement in real time or predicting the most efficient timing for excretion. This makes it difficult for users to respond at the appropriate time, making toilet training and monitoring elderly people difficult. Furthermore, the management and analysis of collected excretion data are insufficient, making it difficult to accurately grasp the progress of training and health status.

[0417] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0418] In this invention, the server includes a sensor means having a built-in temperature sensor and moisture sensor for detecting the presence or absence of excrement, a data transmission means for converting the detected data into an appropriate format and transmitting it to a cloud platform, an analysis means for analyzing the received data on the cloud platform and executing an algorithm for predicting the timing of excrement, a notification means for transmitting the analysis results to the user as push notifications or alerts, and a database means for storing the excrement data in a database and tracking the progress of training. This enables real-time detection of excrement and prompt notification, making toilet training and elderly care more efficient. Furthermore, accurate management and analysis of collected data makes it possible to understand the progress of training and health status.

[0419] "Sensor means" is a device that incorporates temperature and moisture sensors to detect the presence or absence of excrement.

[0420] The "data transmission means" is a function that converts the detected sensor data into an appropriate format and transmits it to the cloud platform.

[0421] The "analysis means" is a function that analyzes the data received on the cloud platform and executes an algorithm to predict the timing of excretion.

[0422] "Notification means" is a function that sends the analysis results to the user as a push notification or alert.

[0423] "Database means" refers to a system for storing excretion data in a database and tracking training progress.

[0424] The "display means" is a screen display function for visualizing the progress of toilet training.

[0425] The "health management means" is a function that estimates the user's health condition based on accumulated excretion data and provides health advice to the user.

[0426] This invention is a system that detects the presence or absence of excrement, analyzes the data, and notifies the user. This system is primarily intended for use in toilet training children and monitoring elderly people, and by collecting and analyzing data in real time, it enables the prompt provision of information to users.

[0427] Sensor means

[0428] The "terminal" has a built-in temperature sensor and moisture detection sensor to detect the presence or absence of excrement. This terminal can be incorporated, for example, into training pants or underwear for the elderly. The temperature sensor detects changes in body temperature when excrement occurs, and the moisture detection sensor detects changes in humidity due to excrement. For example, when a child puts on training pants and urinates, the moisture detection sensor detects a sudden rise in humidity.

[0429] Data transmission method

[0430] The "terminal" converts the acquired sensor data into an appropriate format and sends it to the cloud platform. The data is converted into, for example, JSON format and sent using a secure protocol (e.g., HTTPS). It also has retry logic to confirm the success of the transmission. For example, it includes a step of confirming the success of the transmission within one second after sending the sensor data to the cloud.

[0431] Analysis means

[0432] The "server" analyzes the data received on the cloud platform. The analysis method, for example, is an analysis script written in Python, which processes the data using Apache Spark. This executes an algorithm to predict the timing of excretion. For example, when a child has an excretion, the timing of excretion can be immediately analyzed based on that data.

[0433] Notification means

[0434] The "server" has a means for notifying the user of the analysis results. Specifically, it can use a smartphone push notification service (e.g., Firebase Cloud Messaging) to send an alert to the user's smartphone. This allows the user to know in real time when their child has defecates. For example, if it is determined that a child has defecates, it can send a notification to the parent's smartphone saying, "Your child has defecates."

[0435] Database Means

[0436] The "server" stores the excretion data in a database and tracks the training progress. The data is stored in a database such as MySQL or MongoDB. Users can check past data through the application and evaluate their training progress and health status. For example, a week's worth of excretion data can be graphed to visualize frequency and timing.

[0437] Prompt Sentence Examples

[0438] "Please explain a system in which a device with a built-in temperature sensor and moisture detection sensor sends data to the cloud, where an algorithm analyzes the data and sends a notification."

[0439] This invention will improve the efficiency of toilet training for children, provide parents with real-time information to help them take appropriate measures, and strengthen the monitoring function for elderly people, allowing them to provide appropriate care.

[0440] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0441] Step 1:

[0442] Acquiring Sensor Data

[0443] The "terminal" uses a temperature sensor and a moisture detection sensor to detect the presence or absence of excrement. This sensor data captures changes in temperature and humidity in real time. For example, when a child is wearing training pants and urinates, the moisture detection sensor detects a sudden rise in humidity. At the same time, the temperature sensor also measures changes in body temperature. The temperature and humidity data are taken as input and provided to the next step.

[0444] Step 2:

[0445] Sending data to the cloud

[0446] The "terminal" converts the acquired sensor data into a predefined format (e.g., JSON format) and sends it to the cloud platform using a secure protocol (e.g., HTTPS). For example, it converts the sensor data into JSON format and sends a POST request to the cloud server. It receives sensor data as input and generates data sent to the cloud platform as output. If the transmission is not successful, it executes retry logic and retries until it is successful.

[0447] Step 3:

[0448] Data analysis on the cloud

[0449] The "server" analyzes the received data on a cloud platform. The analysis method includes algorithms that process the data using Python scripts and Apache Spark and predict when the pet will defecate. For example, it analyzes temperature changes and increases in humidity based on the received sensor data to determine whether defecation has occurred. It receives the sensor data sent to the cloud as input and generates an analysis result regarding whether defecation has occurred as output.

[0450] Step 4:

[0451] Notification of analysis results

[0452] The "server" sends push notifications and alerts to the user based on the analysis results. For example, it uses the Firebase Cloud Messaging service to send a notification to the user's smartphone saying, "Your child has defecate." It receives the analysis results as input and generates a push notification to the user as output. The user can check the defecation status in real time via their smartphone.

[0453] Step 5:

[0454] Accumulating data and tracking progress

[0455] The "server" accumulates the excretion data in a database and tracks the training progress. The database can be, for example, MySQL or MongoDB, and stores past excretion data. The user can review the past data through the application and evaluate the training progress and health status. The application receives excretion data as input and generates records stored in the database as output. As a concrete example, it displays a week's worth of excretion data in a graph and provides feedback to the user.

[0456] Through this series of processes, the system improves the efficiency of toilet training for children, provides real-time information for parents to take appropriate action, and strengthens monitoring functions for the elderly, enabling them to provide appropriate care.

[0457] (Application example 1)

[0458] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0459] With conventional excretion detection systems, it was difficult to grasp the timing of excretion in real time, resulting in a lack of information to provide appropriate care for elderly caregivers. Furthermore, there was no way to immediately notify users when an abnormality was detected, which often meant that a prompt response was not possible. Furthermore, health management did not effectively utilize past excretion data, making it difficult to provide effective care.

[0460] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0461] In this invention, the server includes a sensor means for detecting the presence or absence of excrement, a data transmission means for transmitting the detected data to the cloud, an analysis means for analyzing the data on the cloud and predicting the timing of excretion, a notification means for notifying the user of the analysis results, a database means for accumulating excretion data and tracking the progress of training, a monitoring means for monitoring the timing of excretion of the elderly person in real time, a notification execution means for sending a push notification when excretion is detected, an alert means for providing an emergency alert when an abnormality is detected, and a statistics display means for displaying statistics and trends of past excretion data. This makes it possible to grasp the excretion status of the elderly person in real time and to provide prompt notification when an abnormality occurs, thereby enabling appropriate care to be provided and health management to be performed by utilizing past data.

[0462] The "sensor means" is a detection device for detecting the presence or absence of excrement.

[0463] The "data transmission means" is a communication device for transmitting detected data to the cloud.

[0464] The "analysis means" is an analysis device that analyzes data on the cloud and predicts the timing of excretion.

[0465] The "notification means" is a communication device for notifying the user of the analysis results.

[0466] The "database means" is a storage device for storing excretion data and tracking the progress of training.

[0467] The "monitoring means" is a monitoring device for monitoring the timing of excretion of an elderly person in real time.

[0468] The "notification execution means" is a notification device for sending a push notification when excretion is detected.

[0469] The "alert means" is a warning device that provides an emergency alert when an abnormality is detected.

[0470] The "statistics display means" is a display device for displaying statistics and trends of past excretion data.

[0471] The present invention is a system for monitoring the excretion status of an elderly person in real time and for promptly notifying the elderly person if an abnormality is detected. Specific embodiments will be described below.

[0472] Overall system configuration

[0473] The server is composed of a sensor means for detecting the presence or absence of excrement, a data transmission means for transmitting the detected data to the cloud, an analysis means for analyzing the data on the cloud and predicting the timing of excretion, a notification means for notifying the user of the analysis results, a database means for accumulating excretion data and tracking the progress of training, a monitoring means for monitoring the excretion timing of the elderly person in real time, a notification execution means for sending a push notification when excretion is detected, an alert means for providing an emergency alert when an abnormality is detected, and a statistical display means for displaying statistics and trends of past excretion data.

[0474] Hardware and Software Use

[0475] The sensor means used is pants with built-in temperature and humidity sensors, which can accurately detect changes in temperature and humidity of excrement.

[0476] A cloud platform (e.g., Amazon Web Services (AWS) IoT or Google Cloud IoT) is used as a data transmission and database means, allowing data acquired from sensors to be securely transmitted to the cloud and stored there.

[0477] The analysis means and notification execution means are configured with a data analysis algorithm using a programming language such as Python, which makes it possible to predict the timing of excretion based on the received data and send a push notification of the analysis results to the user.

[0478] The monitoring and alerting methods use smartphone applications (e.g., Android or iOS apps) that allow caregivers and family members to check the elderly person's toileting status in real time and receive immediate notification if any abnormalities occur.

[0479] Adding specific examples

[0480] As a concrete example, consider a monitoring system that monitors the daily excretion of elderly people. The elderly person wears pants with built-in sensors, and when the sensors detect excretion, the data is sent to the cloud. The cloud server analyzes the data and sends a push notification to a smartphone application if excretion is detected. It also displays an emergency alert if an abnormality is detected.

[0481] An example of a prompt is as follows:

[0482] Create a Python program that uses data from temperature and humidity sensors to monitor an elderly person's toileting timing in real time, and sends a push notification to a smartphone if an abnormality is detected. Obtain data from the sensors, and send a notification if toileting is detected under certain conditions (e.g., temperature > 37°C, humidity > 75%). Include a section that obtains sensor data via HTTP request and performs cloud analysis.

[0483] In this way, the system of the present invention can grasp the excretory status of elderly people in real time and quickly notify them if any abnormalities occur, making it possible to provide appropriate care.In addition, health management can be performed by utilizing past data.

[0484] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0485] Step 1:

[0486] Once the elderly person puts on the sensor-embedded pants, the device starts collecting data from the temperature and humidity sensors. The input data includes the elderly person's body temperature and changes in humidity of excrement, and the output is real-time sensor data.

[0487] Step 2:

[0488] The terminal converts the acquired sensor data into an appropriate format and sends it to a cloud platform (e.g., AWS IoT or Google Cloud IoT). The input data includes real-time temperature and humidity data, and the output data is sent to the cloud.

[0489] Step 3:

[0490] The server receives the sensor data sent to the cloud and executes an analysis algorithm. The input data includes the sensor data, and the timing of excretion is predicted through data analysis. The output is the result of excretion detection.

[0491] Step 4:

[0492] Based on the analysis results, the server sends a push notification to the user (caregiver or family member) if excretion is detected. The input data includes the excretion detection judgment result, and the push notification is sent as output. Specifically, the notification message is sent to the smartphone.

[0493] Step 5:

[0494] The server stores the analysis results and sensor data in a database. The input data includes the analysis results and sensor data, and the output data is saved in the database. This allows for the accumulation of excretion data over a long period of time.

[0495] Step 6:

[0496] Users can view past elimination data and analysis results through a smartphone application. Input data includes elimination data from a database, and output includes statistics and health trends displayed on the screen. Specific operations include generating graphs and charts.

[0497] Step 7:

[0498] The server provides an emergency alert if an abnormality is detected. The input data includes anomaly detection information based on the analysis results, and the output is an alert notification. Specifically, the application notifies the user by audio alert or vibration.

[0499] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0500] The present invention is a system that combines a sensor means for detecting the presence or absence of excrement, a data transmission means for transmitting the detected data to the cloud, an analysis means for analyzing the data on the cloud and predicting the timing of excrement, a notification means for notifying the user of the analysis results, a database means for accumulating excrement data and tracking the progress of training, and an emotion engine that recognizes the user's emotions.

[0501] Sensor means

[0502] The "terminal" uses training pants with built-in temperature and moisture sensors. The temperature sensor detects temperature changes in excrement, while the moisture sensor detects bowel movements and urine. These sensors smoothly collect data for subsequent processing.

[0503] Data transmission method

[0504] The "terminal" transmits the acquired sensor data to the cloud. This data transmission has the function of converting the sensor data into an appropriate format and transmitting it to a cloud platform (e.g., an IoT platform). This allows the data acquired from the sensors to be securely aggregated in the cloud.

[0505] Analysis means

[0506] The "server" analyzes the data on a cloud platform. The analysis means executes an algorithm to predict when the pet will defecate based on the received data. For example, it can determine whether defecation has occurred based on the results of detecting certain temperature changes or humidity.

[0507] Notification means

[0508] The "server" has a means to notify the "user" of the analysis results. For example, it can send push notifications or alerts to the "user" (parent or guardian)'s smartphone. This allows parents to know in real time when their child has had an excretion event.

[0509] Database Means

[0510] The "server" stores each user's toileting data in a database. This database is used to track the progress of toilet training and evaluate results. For example, the server can retrieve from the database the number of successful toileting attempts over the past week and report this to the "user."

[0511] Emotion Engine

[0512] The "server" recognizes the "user's" emotions using an emotion engine. This emotion engine analyzes the user's facial expressions, voice, and behavioral patterns to assess their emotional state. Emotion recognition is used to adjust notification content and training progress interfaces.

[0513] Specific examples of operation

[0514] Toilet training for children

[0515] 1. The "device" begins collecting data from sensors once the child puts on the training pants.

[0516] 2. The temperature sensor detects changes in the child's body temperature, and the moisture detection sensor detects urine and feces.

[0517] 3. The "terminal" sends the detected data to the cloud.

[0518] 4. The "server" analyzes the data sent to the cloud and determines whether or not excretion has occurred.

[0519] 5. If it is determined that excretion has occurred, the "server" sends a notification to the "user."

[0520] 6. The emotion engine recognizes the user's emotions and adjusts the notification content.

[0521] 7. The "server" stores the excretion data in a database and tracks training progress.

[0522] 8. Progress display and interface will be adjusted based on the emotion engine.

[0523] 9. The "User" can check the progress of training and notifications through the application and take appropriate action.

[0524] Use to watch over the elderly

[0525] 1. Elderly people wear pants with built-in sensors.

[0526] 2. The "terminal" monitors the elderly person's daily bowel movements and sends the data to the cloud.

[0527] 3. The "server" analyzes the data and, if it detects any abnormalities based on excretion status, it promptly notifies the "user" (caregiver or family member).

[0528] 4. The emotion engine recognizes the user's emotions and adjusts the notification content.

[0529] 5. The analysis results are stored in a database and can be used for health management and monitoring as needed.

[0530] 6. The "server" estimates the user's health status and provides hydration and medical advice to the user.

[0531] In this way, the system of the present invention accurately tracks the timing of excretion, allowing parents and guardians to effectively support their children's toilet training. It also strengthens the monitoring function for elderly people, enabling more appropriate care. The addition of an emotion engine enables flexible responses based on the user's emotions, further improving the user experience.

[0532] The processing flow will be explained below.

[0533] Step 1:

[0534] The "terminal" starts the training pants, which have built-in temperature and moisture sensors, and completes initialization, which prepares the sensors for proper operation.

[0535] Step 2:

[0536] The device will begin collecting temperature and moisture data at the specified intervals, specifically every minute.

[0537] Step 3:

[0538] The "terminal" converts the acquired temperature and moisture detection data into the appropriate data format: temperature data is recorded in degrees Celsius, and moisture detection data is recorded as a Boolean value (e.g., True or False).

[0539] Step 4:

[0540] The "terminal" then sends the converted data to the cloud platform, a process that includes cloud connection processing and secure data transmission.

[0541] Step 5:

[0542] The server analyzes the data received via the cloud platform, applying an algorithm to predict whether or not a person will defecate based on temperature and moisture detection data.

[0543] Step 6:

[0544] The "server" uses an algorithm to determine whether an excretion has occurred, and if so, predicts the timing of the excretion.

[0545] Step 7:

[0546] The "server" sends notifications to the "user" (parent or guardian) based on the analysis results. The notifications are sent as push notifications or messages via a smartphone application.

[0547] Step 8:

[0548] The "server" recognizes the "user's" emotions using an emotion engine, which analyzes the user's facial expressions, voice, and behavioral patterns to evaluate their emotional state.

[0549] Step 9:

[0550] The "server" then adjusts the notification content based on the perceived emotion: for example, if the user is stressed, the notification will be changed to a gentle, encouraging message.

[0551] Step 10:

[0552] The "server" stores each user's toileting data in a database, which records the frequency and patterns of toileting and tracks training progress.

[0553] Step 11:

[0554] The "server" analyzes the training progress based on the accumulated data and displays it to the "user." The progress display and interface are adjusted based on the emotion engine.

[0555] Step 12:

[0556] The server uses the database data to predict the user's health and provides hydration and medical advice, which is also tailored based on the emotion engine.

[0557] Step 13:

[0558] Users can check the progress of training and notifications through a smartphone application and take appropriate action. For example, knowing when to guide a child to the toilet can help the child's training progress more effectively.

[0559] Through the above processing steps, the present invention accurately grasps the timing of excretion, enabling the "user" to effectively support their child's toilet training. It also strengthens the monitoring function for elderly people, enabling more appropriate care. The addition of an emotion engine enables flexible responses based on the user's emotions, further improving the user experience.

[0560] Example 2

[0561] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0562] Conventional toilet training and elderly care monitoring systems have difficulty predicting when a child will need to defecate, making it impossible to notify parents or guardians in real time. Furthermore, they lack the ability to respond flexibly based on the user's emotions, making it difficult to improve the efficiency of training and monitoring. Furthermore, they lack a means to rationally track the progress of training, making them difficult for users to use.

[0563] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes sensor means for detecting the presence or absence of excrement, data transmission means for transmitting the detected data to a cloud environment, analysis means for analyzing the data in the cloud environment and predicting the timing of excrement, notification means for notifying the user of the analysis results, database means for accumulating excrement data and tracking the training progress, and an emotion engine for recognizing the user's emotions and adjusting the notification content and interface. This enables accurate prediction of excrement timing and real-time notification, and further enables flexible response based on the user's emotions and efficient tracking of the training progress.

[0564] "Sensor means" refers to a device for detecting temperature and moisture, and is used to detect the presence or absence of excrement.

[0565] "Data transmission means" refers to devices and software for transmitting detected sensor data to a cloud environment.

[0566] "Analysis means" refers to algorithms and programs that analyze sensor data in a cloud environment and predict the timing of excretion.

[0567] "Notification means" refers to a device or system for notifying the user of the analysis results, and has the ability to send push notifications or alerts.

[0568] "Database means" refers to a system for accumulating excretion data and tracking the progress of training, and stores and manages the data.

[0569] An "emotion engine" is an algorithm or software that recognizes a user's emotions and adjusts notification content and interfaces accordingly.

[0570] A "cloud environment" refers to servers and infrastructure for storing and processing data over the Internet.

[0571] The present invention is a system that combines a sensor means for detecting the presence or absence of excrement, a data transmission means for transmitting the detected data to the cloud, an analysis means for analyzing the data in a cloud environment and predicting the timing of excretion, a notification means for notifying the user of the analysis results, a database means for accumulating excretion data and tracking the progress of training, and an emotion engine that recognizes the user's emotions.

[0572] Sensor means

[0573] The "terminal" uses training pants with built-in temperature and moisture sensors. These sensors are composed of common sensor technologies, such as ceramic temperature sensors and capacitive moisture sensors. The temperature sensor detects temperature changes in excrement, while the moisture sensor detects bowel movements and urine. These sensors smoothly collect data for subsequent processing.

[0574] Data transmission method

[0575] The "terminal" transmits the acquired sensor data to the cloud. This data transmission has the function of converting the sensor data into an appropriate format (e.g., JSON format) and transmitting it to an IoT platform (e.g., Amazon Web Services IoT Core or Google Cloud IoT Core). This allows the data acquired from the sensors to be securely aggregated in the cloud.

[0576] Analysis means

[0577] The "server" analyzes the data in a cloud environment using programming languages ​​such as Python and R, as well as machine learning libraries (such as TensorFlow and PyTorch). The "server" runs an algorithm that predicts the timing of excretion based on the received data, and determines whether excretion has occurred based on data on certain temperature changes and humidity.

[0578] Notification means

[0579] The "server" has a means to notify the "user" of the analysis results. Notifications are sent via push notifications or alerts via a smartphone app or similar. For example, a specific notification such as "Your child has urinated" is sent to the user.

[0580] Database Means

[0581] The "server" stores each user's toileting data in a database using a relational database system such as MySQL or PostgreSQL, which is used to track potty training progress and health data, and generates reports including total and successful toileting attempts over a specified period of time.

[0582] Emotion Engine

[0583] The "server" recognizes the "user's" emotions using an emotion engine. This emotion engine uses, for example, Microsoft Azure's emotion analysis API to analyze the user's facial expressions, voice, and behavioral patterns, and uses the results to adjust notification content and the interface.

[0584] Specific examples of operation

[0585] Examples of toilet training for children

[0586] The "device" begins collecting data from sensors once the child puts on the training pants. The temperature sensor detects changes in the child's body temperature, and the moisture detection sensor detects urination and feces. This data is sent to the cloud, where the "server" analyzes the data and determines whether or not the child has urinated. If it determines that an urinary tract excretion has occurred, the "server" sends a real-time notification to the "user," and an emotion engine recognizes the user's emotions and adjusts the content of the notification accordingly. As a result, the user can check the progress of their training and the content of the notification through the application.

[0587] Specific examples of watching over the elderly

[0588] When an elderly person wears pants equipped with a built-in sensor, the "terminal" monitors the elderly person's daily excretion and sends the data to the cloud. The "server" analyzes the data and, if it detects any abnormalities based on the excretion status, it promptly notifies the "user" (caregiver or family member). The emotion engine recognizes the "user's" emotions and adjusts the content of the notification. The analysis results are stored in a database and can be used as needed for health management and monitoring.

[0589] Prompt Sentence Examples

[0590] How can I help my child with toilet training?

[0591] "Please tell me how the elderly toilet monitoring system works."

[0592] The above is a specific embodiment of the system of the present invention.

[0593] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0594] Step 1:

[0595] The "terminal" acquires sensor data.

[0596] The "terminal" uses training pants with built-in temperature and moisture sensors. When the user puts on the training pants, the sensors are activated and detect changes in body temperature and moisture in real time. This input data (temperature and moisture data) is temporarily stored in the terminal's memory. Specifically, the temperature and moisture data is acquired every 30 seconds and saved as a log in a file.

[0597] Step 2:

[0598] The "terminal" sends sensor data to the cloud.

[0599] The terminal sends the acquired sensor data to the cloud at specific time intervals or when a detection event occurs. In this process, the data is converted into JSON format and sent to the cloud using the IoT platform's API. The input data is log information on temperature and moisture, and the output is the sensor data stored in the cloud. Specifically, if the sensor detects an abnormality, the data is sent immediately; if no abnormality is detected, the data is sent at a fixed time (for example, every hour).

[0600] Step 3:

[0601] The "server" analyzes the data.

[0602] The "server" receives the data sent to the cloud and begins analysis. A Python script is used for the analysis, and an algorithm using machine learning libraries (TensorFlow, PyTorch) is used to predict the timing of excretion. The input data is temperature and moisture data obtained from the cloud, and the output is information indicating whether or not an excretion has occurred and the timing. Specifically, if the temperature shows a certain change or the moisture sensor exceeds a certain threshold, it is determined that an excretion has occurred and the time is recorded.

[0603] Step 4:

[0604] The "server" notifies the "user" of the analysis results.

[0605] Based on the analysis results, the "server" sends a notification to the "user." The notification is provided in the form of a push notification or alert via a smartphone app. The input data is the analysis result information, and the output is the notification content that is displayed on the user's device. Specifically, a specific message such as "Your child has urinated" is sent.

[0606] Step 5:

[0607] The "server" stores the excretion data in a database.

[0608] The "server" stores the analysis results and sensor data in a database. The database used is MySQL or PostgreSQL, with the input data being the analysis results and the original sensor data, and the output being the past excretion history stored in the database. Specifically, it aggregates the excretion history on a daily or weekly basis and generates graphs and reports to track the training progress.

[0609] Step 6:

[0610] The "server" recognizes the "user's" emotions using an emotion engine.

[0611] The "server" recognizes the user's emotions using an emotion engine. The emotion engine uses, for example, Microsoft Azure's emotion analysis API to analyze the user's facial expressions, voice, and behavioral patterns. The input data is the user's image and voice data, and the output is the emotional state as a result of the analysis. Specifically, if the user is using a smartphone with a camera, the emotional state is analyzed from the user's facial expressions and the training app interface is adjusted accordingly.

[0612] As a result, the overall processing of this system is realized through the specific operations and data flow of each processing step.

[0613] (Application example 2)

[0614] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0615] When monitoring elderly people, it is necessary to monitor their excretion status in real time and quickly detect and notify abnormalities. It is also important to respond appropriately to abnormalities based on the emotional state of the caregiver or family member. Another challenge is how to effectively visualize toilet training progress and health management data.

[0616] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0617] In this invention, the server includes a sensor means for detecting the presence or absence of excrement, a data transmission means for transmitting the detected data to the cloud, an analysis means for analyzing the data on the cloud and predicting the timing of excrement, a notification means for notifying the user of the analysis results, a database means for accumulating excrement data and tracking the progress of training, an emotion recognition means for recognizing the user's emotion and adjusting the content of the notification, and a health management means for managing the health condition based on the database means. This makes it possible to monitor the excretion status of the elderly in real time, quickly detect and notify abnormalities, and further realize appropriate responses according to the emotional state and visualization of information.

[0618] The "sensor means" is a device for detecting the presence or absence of excrement. It often has a built-in temperature sensor and a moisture sensor.

[0619] "Data transmission means" refers to a device or program that transmits detected sensor data to the cloud. It includes a means for securely and efficiently sending data to a remote server.

[0620] "Analysis means" refers to the algorithm and its execution environment that analyzes data received on the cloud and predicts the timing of excretion.

[0621] The "notification means" is a device or program that notifies the user of the analysis results. Information is conveyed to the user in the form of push notifications, alerts, etc.

[0622] "Database means" refers to a system for accumulating and managing excretion data and training progress, including functions such as data storage, search, and management.

[0623] The "emotion recognition means" is a device or program that recognizes the user's emotional state and adjusts the notification content by analyzing facial expressions, voice, etc.

[0624] The "health management means" is a device or program for managing the health status based on the data stored in the database and providing advice to the user.

[0625] The "Elderly Care Monitoring Measure" is a system that monitors the excretory status of elderly people, detects abnormalities, and sends notifications in real time.

[0626] The "display means" is a device that visually displays the progress of toilet training and health management data.

[0627] This invention is a system that monitors the excretion status of elderly people and detects and notifies abnormalities in real time. The system is composed of the following main elements.

[0628] 1. Sensor means

[0629] The sensor means used is training pants with a built-in temperature sensor and moisture detection sensor. These training pants are worn by elderly people and are used to detect the presence or absence of excrement. The temperature sensor detects temperature changes of excrement, and the moisture detection sensor detects excrement.

[0630] 2. Data transmission method

[0631] The device transmits the detected sensor data to the cloud, which converts the sensor data into an appropriate format and transmits it to a cloud platform (e.g., AWS IoT Platform), where the data acquired from the sensors is securely aggregated in the cloud.

[0632] 3. Analysis method

[0633] The server analyzes the data on the cloud platform. The analysis means runs an algorithm to predict the timing of excretion and abnormalities based on the received data. For example, it determines whether excretion has occurred based on the results of detecting certain temperature changes or humidity, and immediately proceeds to the next step if an abnormality is detected.

[0634] 4. Means of notification

[0635] The server has a means to notify users (caregivers and family members) of the analysis results. Specifically, it sends push notifications to smartphones using Firebase Cloud Messaging (FCM). This allows caregivers and family members to understand the elderly person's toileting status in real time.

[0636] 5. Database Tools

[0637] The server uses a cloud database such as AWS DynamoDB to store excretion data and training progress. This database stores analysis results and serves as the foundation for health management. It is also used as a data source for visualizing training progress.

[0638] 6. Emotion recognition means

[0639] To recognize the user's emotions and adjust the content of notifications, emotion recognition is performed using the smartphone's camera and microphone. Specifically, OpenCV and Google Cloud Speech-to-Text are combined to analyze the user's facial expressions and voice to determine their emotional state. Based on this, the content and timing of notifications are adjusted.

[0640] 7. Health management measures

[0641] Based on the excretion data stored in the database, machine learning algorithms such as AWS SageMaker are used to manage health conditions. Health indicators and advice are generated and provided to users. This function allows for more accurate management of the health of elderly people.

[0642] Specific examples

[0643] For example, if an elderly person uses the toilet to defecate, but detects that their bowel movements are not normal, the system will immediately detect this and send a notification to the caregiver's smartphone:

[0644] "An abnormality has been detected in Elderly Person A's bowel movements. Please check as soon as possible."

[0645] Prompt Sentence Examples

[0646] "Write a Python program that sends temperature and humidity data obtained from a sensor at a specified PPM value (every 30 seconds) to the cloud and notifies you in real time when an abnormality is detected. The content of the notification will be adjusted based on the results of emotion recognition."

[0647] In this way, this invention can monitor the excretory status of elderly people in real time, quickly detect and notify abnormalities, and provide appropriate responses and visualize information based on the emotional state, strengthening support for caregivers and family members.

[0648] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0649] Step 1:

[0650] When the elderly person puts on the training pants, the device starts collecting data from the built-in temperature sensor and moisture detection sensor, measuring temperature changes and the presence or absence of moisture, and collecting data from each sensor.

[0651] Input: Data from temperature and moisture sensors

[0652] Output: Sensor data (temperature change and moisture content)

[0653] Step 2:

[0654] The device sends the acquired sensor data to the cloud. MQTT is used as the communication protocol to send the data to a cloud platform (e.g., AWS IoT). The sensor data is converted into an appropriate format and securely aggregated in the cloud.

[0655] Input: Sensor data

[0656] Output: Data stored on the cloud

[0657] Step 3:

[0658] The server analyzes the received data in the cloud and uses machine learning algorithms such as AWS SageMaker to monitor the timing of excretion and abnormalities. For example, it analyzes specific temperature and humidity changes to determine whether there are any abnormalities.

[0659] Input: Sensor data on the cloud

[0660] Output: Analysis result (normal / abnormal)

[0661] Step 4:

[0662] The server notifies the user of the analysis results via their smartphone. Push notifications and alerts are sent using Firebase Cloud Messaging (FCM). If an abnormality is detected, a notification is sent immediately to inform the user of the situation.

[0663] Input: Analysis results

[0664] Output: Push notification

[0665] Step 5:

[0666] The server analyzes the user's emotional state using the smartphone's camera and microphone for emotion recognition. It uses OpenCV and Google Cloud Speech-to-Text to analyze facial expressions and voice. Based on the user's emotion, the notification content is adjusted.

[0667] Input: User facial expression images, voice data

[0668] Output: Sentiment analysis results, adjusted notification content

[0669] Step 6:

[0670] The server stores the analyzed excretion data in a database, using a cloud database such as AWS DynamoDB to store the data and manage it for later analysis.

[0671] Input: Analysis results

[0672] Output: Excretion data stored in a database

[0673] Step 7:

[0674] The server processes the data in the database to manage the user's health, and uses machine learning algorithms to generate health advice for the user and provide it via a smartphone application.

[0675] Input: Excretion data in the database

[0676] Output: Health care advice

[0677] Step 8:

[0678] The server visualizes toilet training progress and health management data, displaying it in graphs and dashboard format on a smartphone application, allowing users to easily check individual data.

[0679] Input: Excretion data in the database

[0680] Output: visualized data (graphs, dashboards)

[0681] The above are the specific processing steps in the system of the present invention. This process enables real-time monitoring of the excretion status of elderly people and enables prompt and appropriate responses.

[0682] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0683] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0684] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0685] [Third embodiment]

[0686] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0687] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0688] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0689] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0690] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0691] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0692] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0693] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0694] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0695] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0696] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0697] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0698] The present invention is a system that includes a sensor means for detecting the presence or absence of excrement, a data transmission means for transmitting the detected data to the cloud, an analysis means for analyzing the data on the cloud and predicting the timing of excretion, a notification means for notifying the user of the analysis results, and a database means for storing excretion data and tracking the progress of training.

[0699] Sensor means

[0700] The "terminal" uses training pants with built-in temperature and moisture sensors. The temperature sensor detects temperature changes in excrement, while the moisture sensor detects bowel movements and urine. These sensors smoothly collect data for subsequent processing.

[0701] Data transmission method

[0702] The "terminal" transmits the acquired sensor data to the cloud. This data transmission has the function of converting the sense data into an appropriate format and transmitting it to a cloud platform (e.g., IoT platform). This allows the data acquired from the sensors to be securely aggregated in the cloud.

[0703] Analysis means

[0704] The "server" analyzes the data on a cloud platform. The analysis means executes an algorithm to predict when the pet will defecate based on the received data. For example, it can determine whether defecation has occurred based on the results of detecting certain temperature changes or humidity.

[0705] Notification means

[0706] The "server" has a means to notify the "user" of the analysis results. For example, it can send push notifications or alerts to the "user" (parent or guardian)'s smartphone. This allows parents to know in real time when their child has had an excretion event.

[0707] Database Means

[0708] The "server" accumulates each user's toileting data and stores it in a database. This database is used to track the progress of toilet training and evaluate the results. For example, the server can retrieve from the database the number of successful toileting attempts over the past week and report this to the "user."

[0709] Specific examples of operation

[0710] Toilet training for children

[0711] 1. The "device" begins collecting data from sensors once the child puts on the training pants.

[0712] 2. The temperature sensor detects changes in the child's body temperature, and the moisture detection sensor detects urine and feces.

[0713] 3. The "terminal" sends the detected data to the cloud.

[0714] 4. The "server" analyzes the data sent to the cloud and determines whether or not excretion has occurred.

[0715] 5. If it is determined that excretion has occurred, the "server" sends a notification to the "user."

[0716] 6. The "server" stores the waste data in a database and tracks the progress of potty training.

[0717] 7. Users can check their training progress and take appropriate action through a smartphone application.

[0718] Use to watch over the elderly

[0719] 1. Elderly people wear pants with built-in sensors.

[0720] 2. The "terminal" monitors the elderly person's daily bowel movements and sends the data to the cloud.

[0721] 3. The "server" analyzes the data and, if it detects any abnormalities based on excretion status, it promptly notifies the "user" (caregiver or family member).

[0722] 4. The analysis results are stored in a database and can be used for health management and monitoring as needed.

[0723] In this way, the system of the present invention improves the efficiency of toilet training for children, provides information for parents to respond in a timely manner, and strengthens the monitoring function for elderly people, allowing them to provide appropriate care.

[0724] The processing flow will be explained below.

[0725] Step 1:

[0726] The "terminal" initializes the temperature and moisture sensors, which prepares them for proper operation.

[0727] Step 2:

[0728] The device will begin collecting temperature and moisture data at the specified intervals, with data collection from the sensors occurring every minute.

[0729] Step 3:

[0730] The "terminal" converts the acquired temperature data and moisture detection data into an appropriate data format, for example, the temperature data is recorded as degrees Celsius and the moisture detection data is recorded as a Boolean value.

[0731] Step 4:

[0732] The terminal then sends the converted data to the cloud platform, where a cloud connection process is performed to ensure the data is sent securely.

[0733] Step 5:

[0734] The "server" analyzes the data received via a cloud platform, applying an algorithm to predict whether or not a bowel movement will occur based on temperature and moisture detection data.

[0735] Step 6:

[0736] The server determines whether excretion has occurred based on the analysis results, and if excretion is detected, the timing of excretion is predicted.

[0737] Step 7:

[0738] The "server" sends notifications to the "user" (parent or guardian) based on the analysis results. The notifications are sent as push notifications or messages via a smartphone application.

[0739] Step 8:

[0740] The "server" stores each user's toileting data in a database, which records the frequency and patterns of toileting and tracks training progress.

[0741] Step 9:

[0742] The "server" analyzes the progress of the training based on the accumulated data, and the analysis results are displayed on the display means so that the "user" can check them.

[0743] Step 10:

[0744] The "server" can estimate health conditions based on the accumulated data and provide hydration and medical advice, which can help "users" manage the health of children and the elderly.

[0745] By using the above processing steps, the present invention accurately grasps the timing of excretion, allowing parents and guardians to effectively support their children's toilet training. It also strengthens the monitoring function for elderly people, allowing them to provide more appropriate care.

[0746] Example 1

[0747] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0748] Conventional toilet management systems have difficulty detecting excrement in real time or predicting the most efficient timing for excretion. This makes it difficult for users to respond at the appropriate time, making toilet training and monitoring elderly people difficult. Furthermore, the management and analysis of collected excretion data are insufficient, making it difficult to accurately grasp the progress of training and health status.

[0749] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0750] In this invention, the server includes a sensor means having a built-in temperature sensor and moisture sensor for detecting the presence or absence of excrement, a data transmission means for converting the detected data into an appropriate format and transmitting it to a cloud platform, an analysis means for analyzing the received data on the cloud platform and executing an algorithm for predicting the timing of excrement, a notification means for transmitting the analysis results to the user as push notifications or alerts, and a database means for storing the excrement data in a database and tracking the progress of training. This enables real-time detection of excrement and prompt notification, making toilet training and elderly care more efficient. Furthermore, accurate management and analysis of collected data makes it possible to understand the progress of training and health status.

[0751] "Sensor means" is a device that incorporates temperature and moisture sensors to detect the presence or absence of excrement.

[0752] The "data transmission means" is a function that converts the detected sensor data into an appropriate format and transmits it to the cloud platform.

[0753] The "analysis means" is a function that analyzes the data received on the cloud platform and executes an algorithm to predict the timing of excretion.

[0754] "Notification means" is a function that sends the analysis results to the user as a push notification or alert.

[0755] "Database means" refers to a system for storing excretion data in a database and tracking training progress.

[0756] The "display means" is a screen display function for visualizing the progress of toilet training.

[0757] The "health management means" is a function that estimates the user's health condition based on accumulated excretion data and provides health advice to the user.

[0758] This invention is a system that detects the presence or absence of excrement, analyzes the data, and notifies the user. This system is primarily intended for use in toilet training children and monitoring elderly people, and by collecting and analyzing data in real time, it enables the prompt provision of information to users.

[0759] Sensor means

[0760] The "terminal" has a built-in temperature sensor and moisture detection sensor to detect the presence or absence of excrement. This terminal can be incorporated, for example, into training pants or underwear for the elderly. The temperature sensor detects changes in body temperature when excrement occurs, and the moisture detection sensor detects changes in humidity due to excrement. For example, when a child puts on training pants and urinates, the moisture detection sensor detects a sudden rise in humidity.

[0761] Data transmission method

[0762] The "terminal" converts the acquired sensor data into an appropriate format and sends it to the cloud platform. The data is converted into, for example, JSON format and sent using a secure protocol (e.g., HTTPS). It also has retry logic to confirm the success of the transmission. For example, it includes a step of confirming the success of the transmission within one second after sending the sensor data to the cloud.

[0763] Analysis means

[0764] The "server" analyzes the data received on the cloud platform. The analysis method, for example, is an analysis script written in Python, which processes the data using Apache Spark. This executes an algorithm to predict the timing of excretion. For example, when a child has an excretion, the timing of excretion can be immediately analyzed based on that data.

[0765] Notification means

[0766] The "server" has a means for notifying the user of the analysis results. Specifically, it can use a smartphone push notification service (e.g., Firebase Cloud Messaging) to send an alert to the user's smartphone. This allows the user to know in real time when their child has defecates. For example, if it is determined that a child has defecates, it can send a notification to the parent's smartphone saying, "Your child has defecates."

[0767] Database Means

[0768] The "server" stores the excretion data in a database and tracks the training progress. The data is stored in a database such as MySQL or MongoDB. Users can check past data through the application and evaluate their training progress and health status. For example, a week's worth of excretion data can be graphed to visualize frequency and timing.

[0769] Prompt Sentence Examples

[0770] "Please explain a system in which a device with a built-in temperature sensor and moisture detection sensor sends data to the cloud, where an algorithm analyzes the data and sends a notification."

[0771] This invention will improve the efficiency of toilet training for children, provide parents with real-time information to help them take appropriate measures, and strengthen the monitoring function for elderly people, allowing them to provide appropriate care.

[0772] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0773] Step 1:

[0774] Acquiring Sensor Data

[0775] The "terminal" uses a temperature sensor and a moisture detection sensor to detect the presence or absence of excrement. This sensor data captures changes in temperature and humidity in real time. For example, when a child is wearing training pants and urinates, the moisture detection sensor detects a sudden rise in humidity. At the same time, the temperature sensor also measures changes in body temperature. The temperature and humidity data are taken as input and provided to the next step.

[0776] Step 2:

[0777] Sending data to the cloud

[0778] The "terminal" converts the acquired sensor data into a predefined format (e.g., JSON format) and sends it to the cloud platform using a secure protocol (e.g., HTTPS). For example, it converts the sensor data into JSON format and sends a POST request to the cloud server. It receives sensor data as input and generates data sent to the cloud platform as output. If the transmission is not successful, it executes retry logic and retries until it is successful.

[0779] Step 3:

[0780] Data analysis on the cloud

[0781] The "server" analyzes the received data on a cloud platform. The analysis method includes algorithms that process the data using Python scripts and Apache Spark and predict when the pet will defecate. For example, it analyzes temperature changes and increases in humidity based on the received sensor data to determine whether defecation has occurred. It receives the sensor data sent to the cloud as input and generates an analysis result regarding whether defecation has occurred as output.

[0782] Step 4:

[0783] Notification of analysis results

[0784] The "server" sends push notifications and alerts to the user based on the analysis results. For example, it uses the Firebase Cloud Messaging service to send a notification to the user's smartphone saying, "Your child has defecate." It receives the analysis results as input and generates a push notification to the user as output. The user can check the defecation status in real time via their smartphone.

[0785] Step 5:

[0786] Accumulating data and tracking progress

[0787] The "server" accumulates the excretion data in a database and tracks the training progress. The database can be, for example, MySQL or MongoDB, and stores past excretion data. The user can review the past data through the application and evaluate the training progress and health status. The application receives excretion data as input and generates records stored in the database as output. As a concrete example, it displays a week's worth of excretion data in a graph and provides feedback to the user.

[0788] Through this series of processes, the system improves the efficiency of toilet training for children, provides real-time information for parents to take appropriate action, and strengthens monitoring functions for the elderly, enabling them to provide appropriate care.

[0789] (Application example 1)

[0790] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0791] With conventional excretion detection systems, it was difficult to grasp the timing of excretion in real time, resulting in a lack of information to provide appropriate care for elderly caregivers. Furthermore, there was no way to immediately notify users when an abnormality was detected, which often meant that a prompt response was not possible. Furthermore, health management did not effectively utilize past excretion data, making it difficult to provide effective care.

[0792] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0793] In this invention, the server includes a sensor means for detecting the presence or absence of excrement, a data transmission means for transmitting the detected data to the cloud, an analysis means for analyzing the data on the cloud and predicting the timing of excretion, a notification means for notifying the user of the analysis results, a database means for accumulating excretion data and tracking the progress of training, a monitoring means for monitoring the timing of excretion of the elderly person in real time, a notification execution means for sending a push notification when excretion is detected, an alert means for providing an emergency alert when an abnormality is detected, and a statistics display means for displaying statistics and trends of past excretion data. This makes it possible to grasp the excretion status of the elderly person in real time and to provide prompt notification when an abnormality occurs, thereby enabling appropriate care to be provided and health management to be performed by utilizing past data.

[0794] The "sensor means" is a detection device for detecting the presence or absence of excrement.

[0795] The "data transmission means" is a communication device for transmitting detected data to the cloud.

[0796] The "analysis means" is an analysis device that analyzes data on the cloud and predicts the timing of excretion.

[0797] The "notification means" is a communication device for notifying the user of the analysis results.

[0798] The "database means" is a storage device for storing excretion data and tracking the progress of training.

[0799] The "monitoring means" is a monitoring device for monitoring the timing of excretion of an elderly person in real time.

[0800] The "notification execution means" is a notification device for sending a push notification when excretion is detected.

[0801] The "alert means" is a warning device that provides an emergency alert when an abnormality is detected.

[0802] The "statistics display means" is a display device for displaying statistics and trends of past excretion data.

[0803] The present invention is a system for monitoring the excretion status of an elderly person in real time and for promptly notifying the elderly person if an abnormality is detected. Specific embodiments will be described below.

[0804] Overall system configuration

[0805] The server is composed of a sensor means for detecting the presence or absence of excrement, a data transmission means for transmitting the detected data to the cloud, an analysis means for analyzing the data on the cloud and predicting the timing of excretion, a notification means for notifying the user of the analysis results, a database means for accumulating excretion data and tracking the progress of training, a monitoring means for monitoring the excretion timing of the elderly person in real time, a notification execution means for sending a push notification when excretion is detected, an alert means for providing an emergency alert when an abnormality is detected, and a statistical display means for displaying statistics and trends of past excretion data.

[0806] Hardware and Software Use

[0807] The sensor means used is pants with built-in temperature and humidity sensors, which can accurately detect changes in temperature and humidity of excrement.

[0808] A cloud platform (e.g., Amazon Web Services (AWS) IoT or Google Cloud IoT) is used as a data transmission and database means, allowing data acquired from sensors to be securely transmitted to the cloud and stored there.

[0809] The analysis means and notification execution means are configured with a data analysis algorithm using a programming language such as Python, which makes it possible to predict the timing of excretion based on the received data and send a push notification of the analysis results to the user.

[0810] The monitoring and alerting methods use smartphone applications (e.g., Android or iOS apps) that allow caregivers and family members to check the elderly person's toileting status in real time and receive immediate notification if any abnormalities occur.

[0811] Adding specific examples

[0812] As a concrete example, consider a monitoring system that monitors the daily excretion of elderly people. The elderly person wears pants with built-in sensors, and when the sensors detect excretion, the data is sent to the cloud. The cloud server analyzes the data and sends a push notification to a smartphone application if excretion is detected. It also displays an emergency alert if an abnormality is detected.

[0813] An example of a prompt is as follows:

[0814] Create a Python program that uses data from temperature and humidity sensors to monitor an elderly person's toileting timing in real time, and sends a push notification to a smartphone if an abnormality is detected. Obtain data from the sensors, and send a notification if toileting is detected under certain conditions (e.g., temperature > 37°C, humidity > 75%). Include a section that obtains sensor data via HTTP request and performs cloud analysis.

[0815] In this way, the system of the present invention can grasp the excretory status of elderly people in real time and quickly notify them if any abnormalities occur, making it possible to provide appropriate care.In addition, health management can be performed by utilizing past data.

[0816] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0817] Step 1:

[0818] Once the elderly person puts on the sensor-embedded pants, the device starts collecting data from the temperature and humidity sensors. The input data includes the elderly person's body temperature and changes in humidity of excrement, and the output is real-time sensor data.

[0819] Step 2:

[0820] The terminal converts the acquired sensor data into an appropriate format and sends it to a cloud platform (e.g., AWS IoT or Google Cloud IoT). The input data includes real-time temperature and humidity data, and the output data is sent to the cloud.

[0821] Step 3:

[0822] The server receives the sensor data sent to the cloud and executes an analysis algorithm. The input data includes the sensor data, and the timing of excretion is predicted through data analysis. The output is the result of excretion detection.

[0823] Step 4:

[0824] Based on the analysis results, the server sends a push notification to the user (caregiver or family member) if excretion is detected. The input data includes the excretion detection judgment result, and the push notification is sent as output. Specifically, the notification message is sent to the smartphone.

[0825] Step 5:

[0826] The server stores the analysis results and sensor data in a database. The input data includes the analysis results and sensor data, and the output data is saved in the database. This allows for the accumulation of excretion data over a long period of time.

[0827] Step 6:

[0828] Users can view past elimination data and analysis results through a smartphone application. Input data includes elimination data from a database, and output includes statistics and health trends displayed on the screen. Specific operations include generating graphs and charts.

[0829] Step 7:

[0830] The server provides an emergency alert if an abnormality is detected. The input data includes anomaly detection information based on the analysis results, and the output is an alert notification. Specifically, the application notifies the user by audio alert or vibration.

[0831] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0832] The present invention is a system that combines a sensor means for detecting the presence or absence of excrement, a data transmission means for transmitting the detected data to the cloud, an analysis means for analyzing the data on the cloud and predicting the timing of excrement, a notification means for notifying the user of the analysis results, a database means for accumulating excrement data and tracking the progress of training, and an emotion engine that recognizes the user's emotions.

[0833] Sensor means

[0834] The "terminal" uses training pants with built-in temperature and moisture sensors. The temperature sensor detects temperature changes in excrement, while the moisture sensor detects bowel movements and urine. These sensors smoothly collect data for subsequent processing.

[0835] Data transmission method

[0836] The "terminal" transmits the acquired sensor data to the cloud. This data transmission has the function of converting the sensor data into an appropriate format and transmitting it to a cloud platform (e.g., an IoT platform). This allows the data acquired from the sensors to be securely aggregated in the cloud.

[0837] Analysis means

[0838] The "server" analyzes the data on a cloud platform. The analysis means executes an algorithm to predict when the pet will defecate based on the received data. For example, it can determine whether defecation has occurred based on the results of detecting certain temperature changes or humidity.

[0839] Notification means

[0840] The "server" has a means to notify the "user" of the analysis results. For example, it can send push notifications or alerts to the "user" (parent or guardian)'s smartphone. This allows parents to know in real time when their child has had an excretion event.

[0841] Database Means

[0842] The "server" stores each user's toileting data in a database. This database is used to track the progress of toilet training and evaluate results. For example, the server can retrieve from the database the number of successful toileting attempts over the past week and report this to the "user."

[0843] Emotion Engine

[0844] The "server" recognizes the "user's" emotions using an emotion engine. This emotion engine analyzes the user's facial expressions, voice, and behavioral patterns to assess their emotional state. Emotion recognition is used to adjust notification content and training progress interfaces.

[0845] Specific examples of operation

[0846] Toilet training for children

[0847] 1. The "device" begins collecting data from sensors once the child puts on the training pants.

[0848] 2. The temperature sensor detects changes in the child's body temperature, and the moisture detection sensor detects urine and feces.

[0849] 3. The "terminal" sends the detected data to the cloud.

[0850] 4. The "server" analyzes the data sent to the cloud and determines whether or not excretion has occurred.

[0851] 5. If it is determined that excretion has occurred, the "server" sends a notification to the "user."

[0852] 6. The emotion engine recognizes the user's emotions and adjusts the notification content.

[0853] 7. The "server" stores the excretion data in a database and tracks training progress.

[0854] 8. Progress display and interface will be adjusted based on the emotion engine.

[0855] 9. The "User" can check the progress of training and notifications through the application and take appropriate action.

[0856] Use to watch over the elderly

[0857] 1. Elderly people wear pants with built-in sensors.

[0858] 2. The "terminal" monitors the elderly person's daily bowel movements and sends the data to the cloud.

[0859] 3. The "server" analyzes the data and, if it detects any abnormalities based on excretion status, it promptly notifies the "user" (caregiver or family member).

[0860] 4. The emotion engine recognizes the user's emotions and adjusts the notification content.

[0861] 5. The analysis results are stored in a database and can be used for health management and monitoring as needed.

[0862] 6. The "server" estimates the user's health status and provides hydration and medical advice to the user.

[0863] In this way, the system of the present invention accurately tracks the timing of excretion, allowing parents and guardians to effectively support their children's toilet training. It also strengthens the monitoring function for elderly people, enabling more appropriate care. The addition of an emotion engine enables flexible responses based on the user's emotions, further improving the user experience.

[0864] The processing flow will be explained below.

[0865] Step 1:

[0866] The "terminal" starts the training pants, which have built-in temperature and moisture sensors, and completes initialization, which prepares the sensors for proper operation.

[0867] Step 2:

[0868] The device will begin collecting temperature and moisture data at the specified intervals, specifically every minute.

[0869] Step 3:

[0870] The "terminal" converts the acquired temperature and moisture detection data into the appropriate data format: temperature data is recorded in degrees Celsius, and moisture detection data is recorded as a Boolean value (e.g., True or False).

[0871] Step 4:

[0872] The "terminal" then sends the converted data to the cloud platform, a process that includes cloud connection processing and secure data transmission.

[0873] Step 5:

[0874] The server analyzes the data received via the cloud platform, applying an algorithm to predict whether or not a person will defecate based on temperature and moisture detection data.

[0875] Step 6:

[0876] The "server" uses an algorithm to determine whether an excretion has occurred, and if so, predicts the timing of the excretion.

[0877] Step 7:

[0878] The "server" sends notifications to the "user" (parent or guardian) based on the analysis results. The notifications are sent as push notifications or messages via a smartphone application.

[0879] Step 8:

[0880] The "server" recognizes the "user's" emotions using an emotion engine, which analyzes the user's facial expressions, voice, and behavioral patterns to evaluate their emotional state.

[0881] Step 9:

[0882] The "server" then adjusts the notification content based on the perceived emotion: for example, if the user is stressed, the notification will be changed to a gentle, encouraging message.

[0883] Step 10:

[0884] The "server" stores each user's toileting data in a database, which records the frequency and patterns of toileting and tracks training progress.

[0885] Step 11:

[0886] The "server" analyzes the training progress based on the accumulated data and displays it to the "user." The progress display and interface are adjusted based on the emotion engine.

[0887] Step 12:

[0888] The server uses the database data to predict the user's health and provides hydration and medical advice, which is also tailored based on the emotion engine.

[0889] Step 13:

[0890] Users can check the progress of training and notifications through a smartphone application and take appropriate action. For example, knowing when to guide a child to the toilet can help the child's training progress more effectively.

[0891] Through the above processing steps, the present invention accurately grasps the timing of excretion, enabling the "user" to effectively support their child's toilet training. It also strengthens the monitoring function for elderly people, enabling more appropriate care. The addition of an emotion engine enables flexible responses based on the user's emotions, further improving the user experience.

[0892] Example 2

[0893] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0894] Conventional toilet training and elderly care monitoring systems have difficulty predicting when a child will need to defecate, making it impossible to notify parents or guardians in real time. Furthermore, they lack the ability to respond flexibly based on the user's emotions, making it difficult to improve the efficiency of training and monitoring. Furthermore, they lack a means to rationally track the progress of training, making them difficult for users to use.

[0895] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes sensor means for detecting the presence or absence of excrement, data transmission means for transmitting the detected data to a cloud environment, analysis means for analyzing the data in the cloud environment and predicting the timing of excrement, notification means for notifying the user of the analysis results, database means for accumulating excrement data and tracking the training progress, and an emotion engine for recognizing the user's emotions and adjusting the notification content and interface. This enables accurate prediction of excrement timing and real-time notification, and further enables flexible response based on the user's emotions and efficient tracking of the training progress.

[0896] "Sensor means" refers to a device for detecting temperature and moisture, and is used to detect the presence or absence of excrement.

[0897] "Data transmission means" refers to devices and software for transmitting detected sensor data to a cloud environment.

[0898] "Analysis means" refers to algorithms and programs that analyze sensor data in a cloud environment and predict the timing of excretion.

[0899] "Notification means" refers to a device or system for notifying the user of the analysis results, and has the ability to send push notifications or alerts.

[0900] "Database means" refers to a system for accumulating excretion data and tracking the progress of training, and stores and manages the data.

[0901] An "emotion engine" is an algorithm or software that recognizes a user's emotions and adjusts notification content and interfaces accordingly.

[0902] A "cloud environment" refers to servers and infrastructure for storing and processing data over the Internet.

[0903] The present invention is a system that combines a sensor means for detecting the presence or absence of excrement, a data transmission means for transmitting the detected data to the cloud, an analysis means for analyzing the data in a cloud environment and predicting the timing of excretion, a notification means for notifying the user of the analysis results, a database means for accumulating excretion data and tracking the progress of training, and an emotion engine that recognizes the user's emotions.

[0904] Sensor means

[0905] The "terminal" uses training pants with built-in temperature and moisture sensors. These sensors are composed of common sensor technologies, such as ceramic temperature sensors and capacitive moisture sensors. The temperature sensor detects temperature changes in excrement, while the moisture sensor detects bowel movements and urine. These sensors smoothly collect data for subsequent processing.

[0906] Data transmission method

[0907] The "terminal" transmits the acquired sensor data to the cloud. This data transmission has the function of converting the sensor data into an appropriate format (e.g., JSON format) and transmitting it to an IoT platform (e.g., Amazon Web Services IoT Core or Google Cloud IoT Core). This allows the data acquired from the sensors to be securely aggregated in the cloud.

[0908] Analysis means

[0909] The "server" analyzes the data in a cloud environment using programming languages ​​such as Python and R, as well as machine learning libraries (such as TensorFlow and PyTorch). The "server" runs an algorithm that predicts the timing of excretion based on the received data, and determines whether excretion has occurred based on data on certain temperature changes and humidity.

[0910] Notification means

[0911] The "server" has a means to notify the "user" of the analysis results. Notifications are sent via push notifications or alerts via a smartphone app or similar. For example, a specific notification such as "Your child has urinated" is sent to the user.

[0912] Database Means

[0913] The "server" stores each user's toileting data in a database using a relational database system such as MySQL or PostgreSQL, which is used to track potty training progress and health data, and generates reports including total and successful toileting attempts over a specified period of time.

[0914] Emotion Engine

[0915] The "server" recognizes the "user's" emotions using an emotion engine. This emotion engine uses, for example, Microsoft Azure's emotion analysis API to analyze the user's facial expressions, voice, and behavioral patterns, and uses the results to adjust notification content and the interface.

[0916] Specific examples of operation

[0917] Examples of toilet training for children

[0918] The "device" begins collecting data from sensors once the child puts on the training pants. The temperature sensor detects changes in the child's body temperature, and the moisture detection sensor detects urination and feces. This data is sent to the cloud, where the "server" analyzes the data and determines whether or not the child has urinated. If it determines that an urinary tract excretion has occurred, the "server" sends a real-time notification to the "user," and an emotion engine recognizes the user's emotions and adjusts the content of the notification accordingly. As a result, the user can check the progress of their training and the content of the notification through the application.

[0919] Specific examples of watching over the elderly

[0920] When an elderly person wears pants equipped with a built-in sensor, the "terminal" monitors the elderly person's daily excretion and sends the data to the cloud. The "server" analyzes the data and, if it detects any abnormalities based on the excretion status, it promptly notifies the "user" (caregiver or family member). The emotion engine recognizes the "user's" emotions and adjusts the content of the notification. The analysis results are stored in a database and can be used as needed for health management and monitoring.

[0921] Prompt Sentence Examples

[0922] How can I help my child with toilet training?

[0923] "Please tell me how the elderly toilet monitoring system works."

[0924] The above is a specific embodiment of the system of the present invention.

[0925] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0926] Step 1:

[0927] The "terminal" acquires sensor data.

[0928] The "terminal" uses training pants with built-in temperature and moisture sensors. When the user puts on the training pants, the sensors are activated and detect changes in body temperature and moisture in real time. This input data (temperature and moisture data) is temporarily stored in the terminal's memory. Specifically, the temperature and moisture data is acquired every 30 seconds and saved as a log in a file.

[0929] Step 2:

[0930] The "terminal" sends sensor data to the cloud.

[0931] The terminal sends the acquired sensor data to the cloud at specific time intervals or when a detection event occurs. In this process, the data is converted into JSON format and sent to the cloud using the IoT platform's API. The input data is log information on temperature and moisture, and the output is the sensor data stored in the cloud. Specifically, if the sensor detects an abnormality, the data is sent immediately; if no abnormality is detected, the data is sent at a fixed time (for example, every hour).

[0932] Step 3:

[0933] The "server" analyzes the data.

[0934] The "server" receives the data sent to the cloud and begins analysis. A Python script is used for the analysis, and an algorithm using machine learning libraries (TensorFlow, PyTorch) is used to predict the timing of excretion. The input data is temperature and moisture data obtained from the cloud, and the output is information indicating whether or not an excretion has occurred and the timing. Specifically, if the temperature shows a certain change or the moisture sensor exceeds a certain threshold, it is determined that an excretion has occurred and the time is recorded.

[0935] Step 4:

[0936] The "server" notifies the "user" of the analysis results.

[0937] Based on the analysis results, the "server" sends a notification to the "user." The notification is provided in the form of a push notification or alert via a smartphone app. The input data is the analysis result information, and the output is the notification content that is displayed on the user's device. Specifically, a specific message such as "Your child has urinated" is sent.

[0938] Step 5:

[0939] The "server" stores the excretion data in a database.

[0940] The "server" stores the analysis results and sensor data in a database. The database used is MySQL or PostgreSQL, with the input data being the analysis results and the original sensor data, and the output being the past excretion history stored in the database. Specifically, it aggregates the excretion history on a daily or weekly basis and generates graphs and reports to track the training progress.

[0941] Step 6:

[0942] The "server" recognizes the "user's" emotions using an emotion engine.

[0943] The "server" recognizes the user's emotions using an emotion engine. The emotion engine uses, for example, Microsoft Azure's emotion analysis API to analyze the user's facial expressions, voice, and behavioral patterns. The input data is the user's image and voice data, and the output is the emotional state as a result of the analysis. Specifically, if the user is using a smartphone with a camera, the emotional state is analyzed from the user's facial expressions and the training app interface is adjusted accordingly.

[0944] As a result, the overall processing of this system is realized through the specific operations and data flow of each processing step.

[0945] (Application example 2)

[0946] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0947] When monitoring elderly people, it is necessary to monitor their excretion status in real time and quickly detect and notify abnormalities. It is also important to respond appropriately to abnormalities based on the emotional state of the caregiver or family member. Another challenge is how to effectively visualize toilet training progress and health management data.

[0948] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0949] In this invention, the server includes a sensor means for detecting the presence or absence of excrement, a data transmission means for transmitting the detected data to the cloud, an analysis means for analyzing the data on the cloud and predicting the timing of excrement, a notification means for notifying the user of the analysis results, a database means for accumulating excrement data and tracking the progress of training, an emotion recognition means for recognizing the user's emotion and adjusting the content of the notification, and a health management means for managing the health condition based on the database means. This makes it possible to monitor the excretion status of the elderly in real time, quickly detect and notify abnormalities, and further realize appropriate responses according to the emotional state and visualization of information.

[0950] The "sensor means" is a device for detecting the presence or absence of excrement. It often has a built-in temperature sensor and a moisture sensor.

[0951] "Data transmission means" refers to a device or program that transmits detected sensor data to the cloud. It includes a means for securely and efficiently sending data to a remote server.

[0952] "Analysis means" refers to the algorithm and its execution environment that analyzes data received on the cloud and predicts the timing of excretion.

[0953] The "notification means" is a device or program that notifies the user of the analysis results. Information is conveyed to the user in the form of push notifications, alerts, etc.

[0954] "Database means" refers to a system for accumulating and managing excretion data and training progress, including functions such as data storage, search, and management.

[0955] The "emotion recognition means" is a device or program that recognizes the user's emotional state and adjusts the notification content by analyzing facial expressions, voice, etc.

[0956] The "health management means" is a device or program for managing the health status based on the data stored in the database and providing advice to the user.

[0957] The "Elderly Care Monitoring Measure" is a system that monitors the excretory status of elderly people, detects abnormalities, and sends notifications in real time.

[0958] The "display means" is a device that visually displays the progress of toilet training and health management data.

[0959] This invention is a system that monitors the excretion status of elderly people and detects and notifies abnormalities in real time. The system is composed of the following main elements.

[0960] 1. Sensor means

[0961] The sensor means used is training pants with a built-in temperature sensor and moisture detection sensor. These training pants are worn by elderly people and are used to detect the presence or absence of excrement. The temperature sensor detects temperature changes of excrement, and the moisture detection sensor detects excrement.

[0962] 2. Data transmission method

[0963] The device transmits the detected sensor data to the cloud, which converts the sensor data into an appropriate format and transmits it to a cloud platform (e.g., AWS IoT Platform), where the data acquired from the sensors is securely aggregated in the cloud.

[0964] 3. Analysis method

[0965] The server analyzes the data on the cloud platform. The analysis means runs an algorithm to predict the timing of excretion and abnormalities based on the received data. For example, it determines whether excretion has occurred based on the results of detecting certain temperature changes or humidity, and immediately proceeds to the next step if an abnormality is detected.

[0966] 4. Means of notification

[0967] The server has a means to notify users (caregivers and family members) of the analysis results. Specifically, it sends push notifications to smartphones using Firebase Cloud Messaging (FCM). This allows caregivers and family members to understand the elderly person's toileting status in real time.

[0968] 5. Database Tools

[0969] The server uses a cloud database such as AWS DynamoDB to store excretion data and training progress. This database stores analysis results and serves as the foundation for health management. It is also used as a data source for visualizing training progress.

[0970] 6. Emotion recognition means

[0971] To recognize the user's emotions and adjust the content of notifications, emotion recognition is performed using the smartphone's camera and microphone. Specifically, OpenCV and Google Cloud Speech-to-Text are combined to analyze the user's facial expressions and voice to determine their emotional state. Based on this, the content and timing of notifications are adjusted.

[0972] 7. Health management measures

[0973] Based on the excretion data stored in the database, machine learning algorithms such as AWS SageMaker are used to manage health conditions. Health indicators and advice are generated and provided to users. This function allows for more accurate management of the health of elderly people.

[0974] Specific examples

[0975] For example, if an elderly person uses the toilet to defecate, but detects that their bowel movements are not normal, the system will immediately detect this and send a notification to the caregiver's smartphone:

[0976] "An abnormality has been detected in Elderly Person A's bowel movements. Please check as soon as possible."

[0977] Prompt Sentence Examples

[0978] "Write a Python program that sends temperature and humidity data obtained from a sensor at a specified PPM value (every 30 seconds) to the cloud and notifies you in real time when an abnormality is detected. The content of the notification will be adjusted based on the results of emotion recognition."

[0979] In this way, this invention can monitor the excretory status of elderly people in real time, quickly detect and notify abnormalities, and provide appropriate responses and visualize information based on the emotional state, strengthening support for caregivers and family members.

[0980] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0981] Step 1:

[0982] When the elderly person puts on the training pants, the device starts collecting data from the built-in temperature sensor and moisture detection sensor, measuring temperature changes and the presence or absence of moisture, and collecting data from each sensor.

[0983] Input: Data from temperature and moisture sensors

[0984] Output: Sensor data (temperature change and moisture content)

[0985] Step 2:

[0986] The device sends the acquired sensor data to the cloud. MQTT is used as the communication protocol to send the data to a cloud platform (e.g., AWS IoT). The sensor data is converted into an appropriate format and securely aggregated in the cloud.

[0987] Input: Sensor data

[0988] Output: Data stored on the cloud

[0989] Step 3:

[0990] The server analyzes the received data in the cloud and uses machine learning algorithms such as AWS SageMaker to monitor the timing of excretion and abnormalities. For example, it analyzes specific temperature and humidity changes to determine whether there are any abnormalities.

[0991] Input: Sensor data on the cloud

[0992] Output: Analysis result (normal / abnormal)

[0993] Step 4:

[0994] The server notifies the user of the analysis results via their smartphone. Push notifications and alerts are sent using Firebase Cloud Messaging (FCM). If an abnormality is detected, a notification is sent immediately to inform the user of the situation.

[0995] Input: Analysis results

[0996] Output: Push notification

[0997] Step 5:

[0998] The server analyzes the user's emotional state using the smartphone's camera and microphone for emotion recognition. It uses OpenCV and Google Cloud Speech-to-Text to analyze facial expressions and voice. Based on the user's emotion, the notification content is adjusted.

[0999] Input: User facial expression images, voice data

[1000] Output: Sentiment analysis results, adjusted notification content

[1001] Step 6:

[1002] The server stores the analyzed excretion data in a database, using a cloud database such as AWS DynamoDB to store the data and manage it for later analysis.

[1003] Input: Analysis results

[1004] Output: Excretion data stored in a database

[1005] Step 7:

[1006] The server processes the data in the database to manage the user's health, and uses machine learning algorithms to generate health advice for the user and provide it via a smartphone application.

[1007] Input: Excretion data in the database

[1008] Output: Health care advice

[1009] Step 8:

[1010] The server visualizes toilet training progress and health management data, displaying it in graphs and dashboard format on a smartphone application, allowing users to easily check individual data.

[1011] Input: Excretion data in the database

[1012] Output: visualized data (graphs, dashboards)

[1013] The above are the specific processing steps in the system of the present invention. This process enables real-time monitoring of the excretion status of elderly people and enables prompt and appropriate responses.

[1014] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1015] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1016] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1017] [Fourth embodiment]

[1018] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1019] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1020] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1021] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1022] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1023] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1024] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1025] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1026] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1027] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1028] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1029] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1030] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1031] The present invention is a system that includes a sensor means for detecting the presence or absence of excrement, a data transmission means for transmitting the detected data to the cloud, an analysis means for analyzing the data on the cloud and predicting the timing of excretion, a notification means for notifying the user of the analysis results, and a database means for storing excretion data and tracking the progress of training.

[1032] Sensor means

[1033] The "terminal" uses training pants with built-in temperature and moisture sensors. The temperature sensor detects temperature changes in excrement, while the moisture sensor detects bowel movements and urine. These sensors smoothly collect data for subsequent processing.

[1034] Data transmission method

[1035] The "terminal" transmits the acquired sensor data to the cloud. This data transmission has the function of converting the sense data into an appropriate format and transmitting it to a cloud platform (e.g., IoT platform). This allows the data acquired from the sensors to be securely aggregated in the cloud.

[1036] Analysis means

[1037] The "server" analyzes the data on a cloud platform. The analysis means executes an algorithm to predict when the pet will defecate based on the received data. For example, it can determine whether defecation has occurred based on the results of detecting certain temperature changes or humidity.

[1038] Notification means

[1039] The "server" has a means to notify the "user" of the analysis results. For example, it can send push notifications or alerts to the "user" (parent or guardian)'s smartphone. This allows parents to know in real time when their child has had an excretion event.

[1040] Database Means

[1041] The "server" accumulates each user's toileting data and stores it in a database. This database is used to track the progress of toilet training and evaluate the results. For example, the server can retrieve from the database the number of successful toileting attempts over the past week and report this to the "user."

[1042] Specific examples of operation

[1043] Toilet training for children

[1044] 1. The "device" begins collecting data from sensors once the child puts on the training pants.

[1045] 2. The temperature sensor detects changes in the child's body temperature, and the moisture detection sensor detects urine and feces.

[1046] 3. The "terminal" sends the detected data to the cloud.

[1047] 4. The "server" analyzes the data sent to the cloud and determines whether or not excretion has occurred.

[1048] 5. If it is determined that excretion has occurred, the "server" sends a notification to the "user."

[1049] 6. The "server" stores the waste data in a database and tracks the progress of potty training.

[1050] 7. Users can check their training progress and take appropriate action through a smartphone application.

[1051] Use to watch over the elderly

[1052] 1. Elderly people wear pants with built-in sensors.

[1053] 2. The "terminal" monitors the elderly person's daily bowel movements and sends the data to the cloud.

[1054] 3. The "server" analyzes the data and, if it detects any abnormalities based on excretion status, it promptly notifies the "user" (caregiver or family member).

[1055] 4. The analysis results are stored in a database and can be used for health management and monitoring as needed.

[1056] In this way, the system of the present invention improves the efficiency of toilet training for children, provides information for parents to respond in a timely manner, and strengthens the monitoring function for elderly people, allowing them to provide appropriate care.

[1057] The processing flow will be explained below.

[1058] Step 1:

[1059] The "terminal" initializes the temperature and moisture sensors, which prepares them for proper operation.

[1060] Step 2:

[1061] The device will begin collecting temperature and moisture data at the specified intervals, with data collection from the sensors occurring every minute.

[1062] Step 3:

[1063] The "terminal" converts the acquired temperature data and moisture detection data into an appropriate data format, for example, the temperature data is recorded as degrees Celsius and the moisture detection data is recorded as a Boolean value.

[1064] Step 4:

[1065] The terminal then sends the converted data to the cloud platform, where a cloud connection process is performed to ensure the data is sent securely.

[1066] Step 5:

[1067] The "server" analyzes the data received via a cloud platform, applying an algorithm to predict whether or not a bowel movement will occur based on temperature and moisture detection data.

[1068] Step 6:

[1069] The server determines whether excretion has occurred based on the analysis results, and if excretion is detected, the timing of excretion is predicted.

[1070] Step 7:

[1071] The "server" sends notifications to the "user" (parent or guardian) based on the analysis results. The notifications are sent as push notifications or messages via a smartphone application.

[1072] Step 8:

[1073] The "server" stores each user's toileting data in a database, which records the frequency and patterns of toileting and tracks training progress.

[1074] Step 9:

[1075] The "server" analyzes the progress of the training based on the accumulated data, and the analysis results are displayed on the display means so that the "user" can check them.

[1076] Step 10:

[1077] The "server" can estimate health conditions based on the accumulated data and provide hydration and medical advice, which can help "users" manage the health of children and the elderly.

[1078] By using the above processing steps, the present invention accurately grasps the timing of excretion, allowing parents and guardians to effectively support their children's toilet training. It also strengthens the monitoring function for elderly people, allowing them to provide more appropriate care.

[1079] Example 1

[1080] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1081] Conventional toilet management systems have difficulty detecting excrement in real time or predicting the most efficient timing for excretion. This makes it difficult for users to respond at the appropriate time, making toilet training and monitoring elderly people difficult. Furthermore, the management and analysis of collected excretion data are insufficient, making it difficult to accurately grasp the progress of training and health status.

[1082] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1083] In this invention, the server includes a sensor means having a built-in temperature sensor and moisture sensor for detecting the presence or absence of excrement, a data transmission means for converting the detected data into an appropriate format and transmitting it to a cloud platform, an analysis means for analyzing the received data on the cloud platform and executing an algorithm for predicting the timing of excrement, a notification means for transmitting the analysis results to the user as push notifications or alerts, and a database means for storing the excrement data in a database and tracking the progress of training. This enables real-time detection of excrement and prompt notification, making toilet training and elderly care more efficient. Furthermore, accurate management and analysis of collected data makes it possible to understand the progress of training and health status.

[1084] "Sensor means" is a device that incorporates temperature and moisture sensors to detect the presence or absence of excrement.

[1085] The "data transmission means" is a function that converts the detected sensor data into an appropriate format and transmits it to the cloud platform.

[1086] The "analysis means" is a function that analyzes the data received on the cloud platform and executes an algorithm to predict the timing of excretion.

[1087] "Notification means" is a function that sends the analysis results to the user as a push notification or alert.

[1088] "Database means" refers to a system for storing excretion data in a database and tracking training progress.

[1089] The "display means" is a screen display function for visualizing the progress of toilet training.

[1090] The "health management means" is a function that estimates the user's health condition based on accumulated excretion data and provides health advice to the user.

[1091] This invention is a system that detects the presence or absence of excrement, analyzes the data, and notifies the user. This system is primarily intended for use in toilet training children and monitoring elderly people, and by collecting and analyzing data in real time, it enables the prompt provision of information to users.

[1092] Sensor means

[1093] The "terminal" has a built-in temperature sensor and moisture detection sensor to detect the presence or absence of excrement. This terminal can be incorporated, for example, into training pants or underwear for the elderly. The temperature sensor detects changes in body temperature when excrement occurs, and the moisture detection sensor detects changes in humidity due to excrement. For example, when a child puts on training pants and urinates, the moisture detection sensor detects a sudden rise in humidity.

[1094] Data transmission method

[1095] The "terminal" converts the acquired sensor data into an appropriate format and sends it to the cloud platform. The data is converted into, for example, JSON format and sent using a secure protocol (e.g., HTTPS). It also has retry logic to confirm the success of the transmission. For example, it includes a step of confirming the success of the transmission within one second after sending the sensor data to the cloud.

[1096] Analysis means

[1097] The "server" analyzes the data received on the cloud platform. The analysis method, for example, is an analysis script written in Python, which processes the data using Apache Spark. This executes an algorithm to predict the timing of excretion. For example, when a child has an excretion, the timing of excretion can be immediately analyzed based on that data.

[1098] Notification means

[1099] The "server" has a means for notifying the user of the analysis results. Specifically, it can use a smartphone push notification service (e.g., Firebase Cloud Messaging) to send an alert to the user's smartphone. This allows the user to know in real time when their child has defecates. For example, if it is determined that a child has defecates, it can send a notification to the parent's smartphone saying, "Your child has defecates."

[1100] Database Means

[1101] The "server" stores the excretion data in a database and tracks the training progress. The data is stored in a database such as MySQL or MongoDB. Users can check past data through the application and evaluate their training progress and health status. For example, a week's worth of excretion data can be graphed to visualize frequency and timing.

[1102] Prompt Sentence Examples

[1103] "Please explain a system in which a device with a built-in temperature sensor and moisture detection sensor sends data to the cloud, where an algorithm analyzes the data and sends a notification."

[1104] This invention will improve the efficiency of toilet training for children, provide parents with real-time information to help them take appropriate measures, and strengthen the monitoring function for elderly people, allowing them to provide appropriate care.

[1105] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1106] Step 1:

[1107] Acquiring Sensor Data

[1108] The "terminal" uses a temperature sensor and a moisture detection sensor to detect the presence or absence of excrement. This sensor data captures changes in temperature and humidity in real time. For example, when a child is wearing training pants and urinates, the moisture detection sensor detects a sudden rise in humidity. At the same time, the temperature sensor also measures changes in body temperature. The temperature and humidity data are taken as input and provided to the next step.

[1109] Step 2:

[1110] Sending data to the cloud

[1111] The "terminal" converts the acquired sensor data into a predefined format (e.g., JSON format) and sends it to the cloud platform using a secure protocol (e.g., HTTPS). For example, it converts the sensor data into JSON format and sends a POST request to the cloud server. It receives sensor data as input and generates data sent to the cloud platform as output. If the transmission is not successful, it executes retry logic and retries until it is successful.

[1112] Step 3:

[1113] Data analysis on the cloud

[1114] The "server" analyzes the received data on a cloud platform. The analysis method includes algorithms that process the data using Python scripts and Apache Spark and predict when the pet will defecate. For example, it analyzes temperature changes and increases in humidity based on the received sensor data to determine whether defecation has occurred. It receives the sensor data sent to the cloud as input and generates an analysis result regarding whether defecation has occurred as output.

[1115] Step 4:

[1116] Notification of analysis results

[1117] The "server" sends push notifications and alerts to the user based on the analysis results. For example, it uses the Firebase Cloud Messaging service to send a notification to the user's smartphone saying, "Your child has defecate." It receives the analysis results as input and generates a push notification to the user as output. The user can check the defecation status in real time via their smartphone.

[1118] Step 5:

[1119] Accumulating data and tracking progress

[1120] The "server" accumulates the excretion data in a database and tracks the training progress. The database can be, for example, MySQL or MongoDB, and stores past excretion data. The user can review the past data through the application and evaluate the training progress and health status. The application receives excretion data as input and generates records stored in the database as output. As a concrete example, it displays a week's worth of excretion data in a graph and provides feedback to the user.

[1121] Through this series of processes, the system improves the efficiency of toilet training for children, provides real-time information for parents to take appropriate action, and strengthens monitoring functions for the elderly, enabling them to provide appropriate care.

[1122] (Application example 1)

[1123] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1124] With conventional excretion detection systems, it was difficult to grasp the timing of excretion in real time, resulting in a lack of information to provide appropriate care for elderly caregivers. Furthermore, there was no way to immediately notify users when an abnormality was detected, which often meant that a prompt response was not possible. Furthermore, health management did not effectively utilize past excretion data, making it difficult to provide effective care.

[1125] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1126] In this invention, the server includes a sensor means for detecting the presence or absence of excrement, a data transmission means for transmitting the detected data to the cloud, an analysis means for analyzing the data on the cloud and predicting the timing of excretion, a notification means for notifying the user of the analysis results, a database means for accumulating excretion data and tracking the progress of training, a monitoring means for monitoring the timing of excretion of the elderly person in real time, a notification execution means for sending a push notification when excretion is detected, an alert means for providing an emergency alert when an abnormality is detected, and a statistics display means for displaying statistics and trends of past excretion data. This makes it possible to grasp the excretion status of the elderly person in real time and to provide prompt notification when an abnormality occurs, thereby enabling appropriate care to be provided and health management to be performed by utilizing past data.

[1127] The "sensor means" is a detection device for detecting the presence or absence of excrement.

[1128] The "data transmission means" is a communication device for transmitting detected data to the cloud.

[1129] The "analysis means" is an analysis device that analyzes data on the cloud and predicts the timing of excretion.

[1130] The "notification means" is a communication device for notifying the user of the analysis results.

[1131] The "database means" is a storage device for storing excretion data and tracking the progress of training.

[1132] The "monitoring means" is a monitoring device for monitoring the timing of excretion of an elderly person in real time.

[1133] The "notification execution means" is a notification device for sending a push notification when excretion is detected.

[1134] The "alert means" is a warning device that provides an emergency alert when an abnormality is detected.

[1135] The "statistics display means" is a display device for displaying statistics and trends of past excretion data.

[1136] The present invention is a system for monitoring the excretion status of an elderly person in real time and for promptly notifying the elderly person if an abnormality is detected. Specific embodiments will be described below.

[1137] Overall system configuration

[1138] The server is composed of a sensor means for detecting the presence or absence of excrement, a data transmission means for transmitting the detected data to the cloud, an analysis means for analyzing the data on the cloud and predicting the timing of excretion, a notification means for notifying the user of the analysis results, a database means for accumulating excretion data and tracking the progress of training, a monitoring means for monitoring the excretion timing of the elderly person in real time, a notification execution means for sending a push notification when excretion is detected, an alert means for providing an emergency alert when an abnormality is detected, and a statistical display means for displaying statistics and trends of past excretion data.

[1139] Hardware and Software Use

[1140] The sensor means used is pants with built-in temperature and humidity sensors, which can accurately detect changes in temperature and humidity of excrement.

[1141] A cloud platform (e.g., Amazon Web Services (AWS) IoT or Google Cloud IoT) is used as a data transmission and database means, allowing data acquired from sensors to be securely transmitted to the cloud and stored there.

[1142] The analysis means and notification execution means are configured with a data analysis algorithm using a programming language such as Python, which makes it possible to predict the timing of excretion based on the received data and send a push notification of the analysis results to the user.

[1143] The monitoring and alerting methods use smartphone applications (e.g., Android or iOS apps) that allow caregivers and family members to check the elderly person's toileting status in real time and receive immediate notification if any abnormalities occur.

[1144] Adding specific examples

[1145] As a concrete example, consider a monitoring system that monitors the daily excretion of elderly people. The elderly person wears pants with built-in sensors, and when the sensors detect excretion, the data is sent to the cloud. The cloud server analyzes the data and sends a push notification to a smartphone application if excretion is detected. It also displays an emergency alert if an abnormality is detected.

[1146] An example of a prompt is as follows:

[1147] Create a Python program that uses data from temperature and humidity sensors to monitor an elderly person's toileting timing in real time, and sends a push notification to a smartphone if an abnormality is detected. Obtain data from the sensors, and send a notification if toileting is detected under certain conditions (e.g., temperature > 37°C, humidity > 75%). Include a section that obtains sensor data via HTTP request and performs cloud analysis.

[1148] In this way, the system of the present invention can grasp the excretory status of elderly people in real time and quickly notify them if any abnormalities occur, making it possible to provide appropriate care.In addition, health management can be performed by utilizing past data.

[1149] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1150] Step 1:

[1151] Once the elderly person puts on the sensor-embedded pants, the device starts collecting data from the temperature and humidity sensors. The input data includes the elderly person's body temperature and changes in humidity of excrement, and the output is real-time sensor data.

[1152] Step 2:

[1153] The terminal converts the acquired sensor data into an appropriate format and sends it to a cloud platform (e.g., AWS IoT or Google Cloud IoT). The input data includes real-time temperature and humidity data, and the output data is sent to the cloud.

[1154] Step 3:

[1155] The server receives the sensor data sent to the cloud and executes an analysis algorithm. The input data includes the sensor data, and the timing of excretion is predicted through data analysis. The output is the result of excretion detection.

[1156] Step 4:

[1157] Based on the analysis results, the server sends a push notification to the user (caregiver or family member) if excretion is detected. The input data includes the excretion detection judgment result, and the push notification is sent as output. Specifically, the notification message is sent to the smartphone.

[1158] Step 5:

[1159] The server stores the analysis results and sensor data in a database. The input data includes the analysis results and sensor data, and the output data is saved in the database. This allows for the accumulation of excretion data over a long period of time.

[1160] Step 6:

[1161] Users can view past elimination data and analysis results through a smartphone application. Input data includes elimination data from a database, and output includes statistics and health trends displayed on the screen. Specific operations include generating graphs and charts.

[1162] Step 7:

[1163] The server provides an emergency alert if an abnormality is detected. The input data includes anomaly detection information based on the analysis results, and the output is an alert notification. Specifically, the application notifies the user by audio alert or vibration.

[1164] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1165] The present invention is a system that combines a sensor means for detecting the presence or absence of excrement, a data transmission means for transmitting the detected data to the cloud, an analysis means for analyzing the data on the cloud and predicting the timing of excrement, a notification means for notifying the user of the analysis results, a database means for accumulating excrement data and tracking the progress of training, and an emotion engine that recognizes the user's emotions.

[1166] Sensor means

[1167] The "terminal" uses training pants with built-in temperature and moisture sensors. The temperature sensor detects temperature changes in excrement, while the moisture sensor detects bowel movements and urine. These sensors smoothly collect data for subsequent processing.

[1168] Data transmission method

[1169] The "terminal" transmits the acquired sensor data to the cloud. This data transmission has the function of converting the sensor data into an appropriate format and transmitting it to a cloud platform (e.g., an IoT platform). This allows the data acquired from the sensors to be securely aggregated in the cloud.

[1170] Analysis means

[1171] The "server" analyzes the data on a cloud platform. The analysis means executes an algorithm to predict when the pet will defecate based on the received data. For example, it can determine whether defecation has occurred based on the results of detecting certain temperature changes or humidity.

[1172] Notification means

[1173] The "server" has a means to notify the "user" of the analysis results. For example, it can send push notifications or alerts to the "user" (parent or guardian)'s smartphone. This allows parents to know in real time when their child has had an excretion event.

[1174] Database Means

[1175] The "server" stores each user's toileting data in a database. This database is used to track the progress of toilet training and evaluate results. For example, the server can retrieve from the database the number of successful toileting attempts over the past week and report this to the "user."

[1176] Emotion Engine

[1177] The "server" recognizes the "user's" emotions using an emotion engine. This emotion engine analyzes the user's facial expressions, voice, and behavioral patterns to assess their emotional state. Emotion recognition is used to adjust notification content and training progress interfaces.

[1178] Specific examples of operation

[1179] Toilet training for children

[1180] 1. The "device" begins collecting data from sensors once the child puts on the training pants.

[1181] 2. The temperature sensor detects changes in the child's body temperature, and the moisture detection sensor detects urine and feces.

[1182] 3. The "terminal" sends the detected data to the cloud.

[1183] 4. The "server" analyzes the data sent to the cloud and determines whether or not excretion has occurred.

[1184] 5. If it is determined that excretion has occurred, the "server" sends a notification to the "user."

[1185] 6. The emotion engine recognizes the user's emotions and adjusts the notification content.

[1186] 7. The "server" stores the excretion data in a database and tracks training progress.

[1187] 8. Progress display and interface will be adjusted based on the emotion engine.

[1188] 9. The "User" can check the progress of training and notifications through the application and take appropriate action.

[1189] Use to watch over the elderly

[1190] 1. Elderly people wear pants with built-in sensors.

[1191] 2. The "terminal" monitors the elderly person's daily bowel movements and sends the data to the cloud.

[1192] 3. The "server" analyzes the data and, if it detects any abnormalities based on excretion status, it promptly notifies the "user" (caregiver or family member).

[1193] 4. The emotion engine recognizes the user's emotions and adjusts the notification content.

[1194] 5. The analysis results are stored in a database and can be used for health management and monitoring as needed.

[1195] 6. The "server" estimates the user's health status and provides hydration and medical advice to the user.

[1196] In this way, the system of the present invention accurately tracks the timing of excretion, allowing parents and guardians to effectively support their children's toilet training. It also strengthens the monitoring function for elderly people, enabling more appropriate care. The addition of an emotion engine enables flexible responses based on the user's emotions, further improving the user experience.

[1197] The processing flow will be explained below.

[1198] Step 1:

[1199] The "terminal" starts the training pants, which have built-in temperature and moisture sensors, and completes initialization, which prepares the sensors for proper operation.

[1200] Step 2:

[1201] The device will begin collecting temperature and moisture data at the specified intervals, specifically every minute.

[1202] Step 3:

[1203] The "terminal" converts the acquired temperature and moisture detection data into the appropriate data format: temperature data is recorded in degrees Celsius, and moisture detection data is recorded as a Boolean value (e.g., True or False).

[1204] Step 4:

[1205] The "terminal" then sends the converted data to the cloud platform, a process that includes cloud connection processing and secure data transmission.

[1206] Step 5:

[1207] The server analyzes the data received via the cloud platform, applying an algorithm to predict whether or not a person will defecate based on temperature and moisture detection data.

[1208] Step 6:

[1209] The "server" uses an algorithm to determine whether an excretion has occurred, and if so, predicts the timing of the excretion.

[1210] Step 7:

[1211] The "server" sends notifications to the "user" (parent or guardian) based on the analysis results. The notifications are sent as push notifications or messages via a smartphone application.

[1212] Step 8:

[1213] The "server" recognizes the "user's" emotions using an emotion engine, which analyzes the user's facial expressions, voice, and behavioral patterns to evaluate their emotional state.

[1214] Step 9:

[1215] The "server" then adjusts the notification content based on the perceived emotion: for example, if the user is stressed, the notification will be changed to a gentle, encouraging message.

[1216] Step 10:

[1217] The "server" stores each user's toileting data in a database, which records the frequency and patterns of toileting and tracks training progress.

[1218] Step 11:

[1219] The "server" analyzes the training progress based on the accumulated data and displays it to the "user." The progress display and interface are adjusted based on the emotion engine.

[1220] Step 12:

[1221] The server uses the database data to predict the user's health and provides hydration and medical advice, which is also tailored based on the emotion engine.

[1222] Step 13:

[1223] Users can check the progress of training and notifications through a smartphone application and take appropriate action. For example, knowing when to guide a child to the toilet can help the child's training progress more effectively.

[1224] Through the above processing steps, the present invention accurately grasps the timing of excretion, enabling the "user" to effectively support their child's toilet training. It also strengthens the monitoring function for elderly people, enabling more appropriate care. The addition of an emotion engine enables flexible responses based on the user's emotions, further improving the user experience.

[1225] Example 2

[1226] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1227] Conventional toilet training and elderly care monitoring systems have difficulty predicting when a child will need to defecate, making it impossible to notify parents or guardians in real time. Furthermore, they lack the ability to respond flexibly based on the user's emotions, making it difficult to improve the efficiency of training and monitoring. Furthermore, they lack a means to rationally track the progress of training, making them difficult for users to use.

[1228] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes sensor means for detecting the presence or absence of excrement, data transmission means for transmitting the detected data to a cloud environment, analysis means for analyzing the data in the cloud environment and predicting the timing of excrement, notification means for notifying the user of the analysis results, database means for accumulating excrement data and tracking the training progress, and an emotion engine for recognizing the user's emotions and adjusting the notification content and interface. This enables accurate prediction of excrement timing and real-time notification, and further enables flexible response based on the user's emotions and efficient tracking of the training progress.

[1229] "Sensor means" refers to a device for detecting temperature and moisture, and is used to detect the presence or absence of excrement.

[1230] "Data transmission means" refers to devices and software for transmitting detected sensor data to a cloud environment.

[1231] "Analysis means" refers to algorithms and programs that analyze sensor data in a cloud environment and predict the timing of excretion.

[1232] "Notification means" refers to a device or system for notifying the user of the analysis results, and has the ability to send push notifications or alerts.

[1233] "Database means" refers to a system for accumulating excretion data and tracking the progress of training, and stores and manages the data.

[1234] An "emotion engine" is an algorithm or software that recognizes a user's emotions and adjusts notification content and interfaces accordingly.

[1235] A "cloud environment" refers to servers and infrastructure for storing and processing data over the Internet.

[1236] The present invention is a system that combines a sensor means for detecting the presence or absence of excrement, a data transmission means for transmitting the detected data to the cloud, an analysis means for analyzing the data in a cloud environment and predicting the timing of excretion, a notification means for notifying the user of the analysis results, a database means for accumulating excretion data and tracking the progress of training, and an emotion engine that recognizes the user's emotions.

[1237] Sensor means

[1238] The "terminal" uses training pants with built-in temperature and moisture sensors. These sensors are composed of common sensor technologies, such as ceramic temperature sensors and capacitive moisture sensors. The temperature sensor detects temperature changes in excrement, while the moisture sensor detects bowel movements and urine. These sensors smoothly collect data for subsequent processing.

[1239] Data transmission method

[1240] The "terminal" transmits the acquired sensor data to the cloud. This data transmission has the function of converting the sensor data into an appropriate format (e.g., JSON format) and transmitting it to an IoT platform (e.g., Amazon Web Services IoT Core or Google Cloud IoT Core). This allows the data acquired from the sensors to be securely aggregated in the cloud.

[1241] Analysis means

[1242] The "server" analyzes the data in a cloud environment using programming languages ​​such as Python and R, as well as machine learning libraries (such as TensorFlow and PyTorch). The "server" runs an algorithm that predicts the timing of excretion based on the received data, and determines whether excretion has occurred based on data on certain temperature changes and humidity.

[1243] Notification means

[1244] The "server" has a means to notify the "user" of the analysis results. Notifications are sent via push notifications or alerts via a smartphone app or similar. For example, a specific notification such as "Your child has urinated" is sent to the user.

[1245] Database Means

[1246] The "server" stores each user's toileting data in a database using a relational database system such as MySQL or PostgreSQL, which is used to track potty training progress and health data, and generates reports including total and successful toileting attempts over a specified period of time.

[1247] Emotion Engine

[1248] The "server" recognizes the "user's" emotions using an emotion engine. This emotion engine uses, for example, Microsoft Azure's emotion analysis API to analyze the user's facial expressions, voice, and behavioral patterns, and uses the results to adjust notification content and the interface.

[1249] Specific examples of operation

[1250] Examples of toilet training for children

[1251] The "device" begins collecting data from sensors once the child puts on the training pants. The temperature sensor detects changes in the child's body temperature, and the moisture detection sensor detects urination and feces. This data is sent to the cloud, where the "server" analyzes the data and determines whether or not the child has urinated. If it determines that an urinary tract excretion has occurred, the "server" sends a real-time notification to the "user," and an emotion engine recognizes the user's emotions and adjusts the content of the notification accordingly. As a result, the user can check the progress of their training and the content of the notification through the application.

[1252] Specific examples of watching over the elderly

[1253] When an elderly person wears pants equipped with a built-in sensor, the "terminal" monitors the elderly person's daily excretion and sends the data to the cloud. The "server" analyzes the data and, if it detects any abnormalities based on the excretion status, it promptly notifies the "user" (caregiver or family member). The emotion engine recognizes the "user's" emotions and adjusts the content of the notification. The analysis results are stored in a database and can be used as needed for health management and monitoring.

[1254] Prompt Sentence Examples

[1255] How can I help my child with toilet training?

[1256] "Please tell me how the elderly toilet monitoring system works."

[1257] The above is a specific embodiment of the system of the present invention.

[1258] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1259] Step 1:

[1260] The "terminal" acquires sensor data.

[1261] The "terminal" uses training pants with built-in temperature and moisture sensors. When the user puts on the training pants, the sensors are activated and detect changes in body temperature and moisture in real time. This input data (temperature and moisture data) is temporarily stored in the terminal's memory. Specifically, the temperature and moisture data is acquired every 30 seconds and saved as a log in a file.

[1262] Step 2:

[1263] The "terminal" sends sensor data to the cloud.

[1264] The terminal sends the acquired sensor data to the cloud at specific time intervals or when a detection event occurs. In this process, the data is converted into JSON format and sent to the cloud using the IoT platform's API. The input data is log information on temperature and moisture, and the output is the sensor data stored in the cloud. Specifically, if the sensor detects an abnormality, the data is sent immediately; if no abnormality is detected, the data is sent at a fixed time (for example, every hour).

[1265] Step 3:

[1266] The "server" analyzes the data.

[1267] The "server" receives the data sent to the cloud and begins analysis. A Python script is used for the analysis, and an algorithm using machine learning libraries (TensorFlow, PyTorch) is used to predict the timing of excretion. The input data is temperature and moisture data obtained from the cloud, and the output is information indicating whether or not an excretion has occurred and the timing. Specifically, if the temperature shows a certain change or the moisture sensor exceeds a certain threshold, it is determined that an excretion has occurred and the time is recorded.

[1268] Step 4:

[1269] The "server" notifies the "user" of the analysis results.

[1270] Based on the analysis results, the "server" sends a notification to the "user." The notification is provided in the form of a push notification or alert via a smartphone app. The input data is the analysis result information, and the output is the notification content that is displayed on the user's device. Specifically, a specific message such as "Your child has urinated" is sent.

[1271] Step 5:

[1272] The "server" stores the excretion data in a database.

[1273] The "server" stores the analysis results and sensor data in a database. The database used is MySQL or PostgreSQL, with the input data being the analysis results and the original sensor data, and the output being the past excretion history stored in the database. Specifically, it aggregates the excretion history on a daily or weekly basis and generates graphs and reports to track the training progress.

[1274] Step 6:

[1275] The "server" recognizes the "user's" emotions using an emotion engine.

[1276] The "server" recognizes the user's emotions using an emotion engine. The emotion engine uses, for example, Microsoft Azure's emotion analysis API to analyze the user's facial expressions, voice, and behavioral patterns. The input data is the user's image and voice data, and the output is the emotional state as a result of the analysis. Specifically, if the user is using a smartphone with a camera, the emotional state is analyzed from the user's facial expressions and the training app interface is adjusted accordingly.

[1277] As a result, the overall processing of this system is realized through the specific operations and data flow of each processing step.

[1278] (Application example 2)

[1279] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1280] When monitoring elderly people, it is necessary to monitor their excretion status in real time and quickly detect and notify abnormalities. It is also important to respond appropriately to abnormalities based on the emotional state of the caregiver or family member. Another challenge is how to effectively visualize toilet training progress and health management data.

[1281] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1282] In this invention, the server includes a sensor means for detecting the presence or absence of excrement, a data transmission means for transmitting the detected data to the cloud, an analysis means for analyzing the data on the cloud and predicting the timing of excrement, a notification means for notifying the user of the analysis results, a database means for accumulating excrement data and tracking the progress of training, an emotion recognition means for recognizing the user's emotion and adjusting the content of the notification, and a health management means for managing the health condition based on the database means. This makes it possible to monitor the excretion status of the elderly in real time, quickly detect and notify abnormalities, and further realize appropriate responses according to the emotional state and visualization of information.

[1283] The "sensor means" is a device for detecting the presence or absence of excrement. It often has a built-in temperature sensor and a moisture sensor.

[1284] "Data transmission means" refers to a device or program that transmits detected sensor data to the cloud. It includes a means for securely and efficiently sending data to a remote server.

[1285] "Analysis means" refers to the algorithm and its execution environment that analyzes data received on the cloud and predicts the timing of excretion.

[1286] The "notification means" is a device or program that notifies the user of the analysis results. Information is conveyed to the user in the form of push notifications, alerts, etc.

[1287] "Database means" refers to a system for accumulating and managing excretion data and training progress, including functions such as data storage, search, and management.

[1288] The "emotion recognition means" is a device or program that recognizes the user's emotional state and adjusts the notification content by analyzing facial expressions, voice, etc.

[1289] The "health management means" is a device or program for managing the health status based on the data stored in the database and providing advice to the user.

[1290] The "Elderly Care Monitoring Measure" is a system that monitors the excretory status of elderly people, detects abnormalities, and sends notifications in real time.

[1291] The "display means" is a device that visually displays the progress of toilet training and health management data.

[1292] This invention is a system that monitors the excretion status of elderly people and detects and notifies abnormalities in real time. The system is composed of the following main elements.

[1293] 1. Sensor means

[1294] The sensor means used is training pants with a built-in temperature sensor and moisture detection sensor. These training pants are worn by elderly people and are used to detect the presence or absence of excrement. The temperature sensor detects temperature changes of excrement, and the moisture detection sensor detects excrement.

[1295] 2. Data transmission method

[1296] The device transmits the detected sensor data to the cloud, which converts the sensor data into an appropriate format and transmits it to a cloud platform (e.g., AWS IoT Platform), where the data acquired from the sensors is securely aggregated in the cloud.

[1297] 3. Analysis method

[1298] The server analyzes the data on the cloud platform. The analysis means runs an algorithm to predict the timing of excretion and abnormalities based on the received data. For example, it determines whether excretion has occurred based on the results of detecting certain temperature changes or humidity, and immediately proceeds to the next step if an abnormality is detected.

[1299] 4. Means of notification

[1300] The server has a means to notify users (caregivers and family members) of the analysis results. Specifically, it sends push notifications to smartphones using Firebase Cloud Messaging (FCM). This allows caregivers and family members to understand the elderly person's toileting status in real time.

[1301] 5. Database Tools

[1302] The server uses a cloud database such as AWS DynamoDB to store excretion data and training progress. This database stores analysis results and serves as the foundation for health management. It is also used as a data source for visualizing training progress.

[1303] 6. Emotion recognition means

[1304] To recognize the user's emotions and adjust the content of notifications, emotion recognition is performed using the smartphone's camera and microphone. Specifically, OpenCV and Google Cloud Speech-to-Text are combined to analyze the user's facial expressions and voice to determine their emotional state. Based on this, the content and timing of notifications are adjusted.

[1305] 7. Health management measures

[1306] Based on the excretion data stored in the database, machine learning algorithms such as AWS SageMaker are used to manage health conditions. Health indicators and advice are generated and provided to users. This function allows for more accurate management of the health of elderly people.

[1307] Specific examples

[1308] For example, if an elderly person uses the toilet to defecate, but detects that their bowel movements are not normal, the system will immediately detect this and send a notification to the caregiver's smartphone:

[1309] "An abnormality has been detected in Elderly Person A's bowel movements. Please check as soon as possible."

[1310] Prompt Sentence Examples

[1311] "Write a Python program that sends temperature and humidity data obtained from a sensor at a specified PPM value (every 30 seconds) to the cloud and notifies you in real time when an abnormality is detected. The content of the notification will be adjusted based on the results of emotion recognition."

[1312] In this way, this invention can monitor the excretory status of elderly people in real time, quickly detect and notify abnormalities, and provide appropriate responses and visualize information based on the emotional state, strengthening support for caregivers and family members.

[1313] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1314] Step 1:

[1315] When the elderly person puts on the training pants, the device starts collecting data from the built-in temperature sensor and moisture detection sensor, measuring temperature changes and the presence or absence of moisture, and collecting data from each sensor.

[1316] Input: Data from temperature and moisture sensors

[1317] Output: Sensor data (temperature change and moisture content)

[1318] Step 2:

[1319] The device sends the acquired sensor data to the cloud. MQTT is used as the communication protocol to send the data to a cloud platform (e.g., AWS IoT). The sensor data is converted into an appropriate format and securely aggregated in the cloud.

[1320] Input: Sensor data

[1321] Output: Data stored on the cloud

[1322] Step 3:

[1323] The server analyzes the received data in the cloud and uses machine learning algorithms such as AWS SageMaker to monitor the timing of excretion and abnormalities. For example, it analyzes specific temperature and humidity changes to determine whether there are any abnormalities.

[1324] Input: Sensor data on the cloud

[1325] Output: Analysis result (normal / abnormal)

[1326] Step 4:

[1327] The server notifies the user of the analysis results via their smartphone. Push notifications and alerts are sent using Firebase Cloud Messaging (FCM). If an abnormality is detected, a notification is sent immediately to inform the user of the situation.

[1328] Input: Analysis results

[1329] Output: Push notification

[1330] Step 5:

[1331] The server analyzes the user's emotional state using the smartphone's camera and microphone for emotion recognition. It uses OpenCV and Google Cloud Speech-to-Text to analyze facial expressions and voice. Based on the user's emotion, the notification content is adjusted.

[1332] Input: User facial expression images, voice data

[1333] Output: Sentiment analysis results, adjusted notification content

[1334] Step 6:

[1335] The server stores the analyzed excretion data in a database, using a cloud database such as AWS DynamoDB to store the data and manage it for later analysis.

[1336] Input: Analysis results

[1337] Output: Excretion data stored in a database

[1338] Step 7:

[1339] The server processes the data in the database to manage the user's health, and uses machine learning algorithms to generate health advice for the user and provide it via a smartphone application.

[1340] Input: Excretion data in the database

[1341] Output: Health care advice

[1342] Step 8:

[1343] The server visualizes toilet training progress and health management data, displaying it in graphs and dashboard format on a smartphone application, allowing users to easily check individual data.

[1344] Input: Excretion data in the database

[1345] Output: visualized data (graphs, dashboards)

[1346] The above are the specific processing steps in the system of the present invention. This process enables real-time monitoring of the excretion status of elderly people and enables prompt and appropriate responses.

[1347] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1348] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1349] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1350] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1351] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1352] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1353] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1354] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1355] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1356] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1357] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1358] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1359] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1360] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1361] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1362] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1363] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1364] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1365] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1366] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1367] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1368] The following is further disclosed regarding the above embodiment.

[1369] (Claim 1)

[1370] sensor means for detecting the presence or absence of excrement;

[1371] data transmission means for transmitting the detected data to a cloud;

[1372] An analytical method for analyzing data on the cloud and predicting the timing of excretion;

[1373] a notification means for notifying a user of the analysis result;

[1374] and database means for accumulating toileting data and tracking training progress.

[1375] (Claim 2)

[1376] 10. The system of claim 1, further comprising a display means for visualizing toilet training progress.

[1377] (Claim 3)

[1378] 10. The system of claim 1, further comprising a health management means for inferring a health condition based on said database means and providing health advice to the user.

[1379] "Example 1"

[1380] (Claim 1)

[1381] a sensor means having a built-in temperature sensor and a moisture sensor for detecting the presence or absence of excrement;

[1382] a data transmission means for converting the detected data into a suitable format and transmitting the data to a cloud platform;

[1383] an analysis means for analyzing the received data on the cloud platform and executing an algorithm for predicting the timing of excretion;

[1384] A notification means for sending the analysis results to the user as a push notification or an alert;

[1385] and database means for storing the toileting data in a database and for tracking training progress.

[1386] (Claim 2)

[1387] 10. The system of claim 1, further comprising a display means for visualizing toilet training progress.

[1388] (Claim 3)

[1389] 10. The system of claim 1, further comprising a health management means for inferring a health condition based on said database means and providing health advice to the user.

[1390] "Application Example 1"

[1391] (Claim 1)

[1392] sensor means for detecting the presence or absence of excrement;

[1393] data transmission means for transmitting the detected data to a cloud;

[1394] An analytical method for analyzing data on the cloud and predicting the timing of excretion;

[1395] a notification means for notifying a user of the analysis result;

[1396] database means for accumulating toileting data and tracking training progress;

[1397] a monitoring means for monitoring the timing of excretion of an elderly person in real time;

[1398] a notification execution means for sending a push notification when excretion is detected;

[1399] an alerting means for providing an emergency alert upon detection of an anomaly;

[1400] a statistical display means for displaying statistics and trends of past excretion data;

[1401] A system including:

[1402] (Claim 2)

[1403] 10. The system of claim 1, further comprising a display means for visualizing toilet training progress.

[1404] (Claim 3)

[1405] 10. The system of claim 1, further comprising a health management means for inferring a health condition based on said database means and providing health advice to the user.

[1406] "Example 2: Combining Emotion Engines"

[1407] (Claim 1)

[1408] sensor means for detecting the presence or absence of excrement;

[1409] data transmission means for transmitting the detected data to a cloud environment;

[1410] An analytical means for analyzing data in a cloud environment and predicting the timing of excretion;

[1411] a notification means for notifying a user of the analysis result;

[1412] database means for accumulating toileting data and tracking training progress;

[1413] An emotion engine that recognizes user emotions and adjusts notification content and interfaces;

[1414] A system including:

[1415] (Claim 2)

[1416] 10. The system of claim 1, wherein the display means is used to visualize toilet training progress.

[1417] (Claim 3)

[1418] 10. The system of claim 1, further comprising health management means for using said database means to infer a health condition and provide health advice to a user.

[1419] "Application example 2 when combining emotion engines"

[1420] (Claim 1)

[1421] sensor means for detecting the presence or absence of excrement;

[1422] data transmission means for transmitting the detected data to a cloud;

[1423] An analytical method for analyzing data on the cloud and predicting the timing of excretion;

[1424] a notification means for notifying a user of the analysis result;

[1425] database means for accumulating toileting data and tracking training progress;

[1426] emotion recognition means for recognizing the emotion of a user and adjusting the notification content;

[1427] and health management means for managing health conditions based on said database means.

[1428] (Claim 2)

[1429] 10. The system of claim 1, further comprising an elderly care unit for performing toilet monitoring and abnormality detection and sending notifications in real time.

[1430] (Claim 3)

[1431] 10. The system of claim 1, further comprising a display means for visualizing toilet training progress and health management data. [Explanation of symbols]

[1432] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. sensor means for detecting the presence or absence of excrement; data transmission means for transmitting the detected data to a cloud; An analytical method for analyzing data on the cloud and predicting the timing of excretion; a notification means for notifying a user of the analysis result; and database means for accumulating toileting data and tracking training progress.

2. 10. The system of claim 1, further comprising display means for visualizing toilet training progress.

3. The system according to claim 1 , further comprising a health management means for predicting a health condition based on said database means and providing health advice to the user.

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