System

Abstract

An object of a system according to an embodiment is to evaluate the state of the stomach and intestines on the basis of the use status of a toilet and provide appropriate advice.SOLUTION: A system according to an embodiment includes a toilet usage status monitoring unit, a waste data analysis unit, a gastrointestinal condition evaluation unit, and an advice providing unit. The toilet usage status monitoring unit monitors the usage status of the toilet. The waste data analyzer analyzes the data collected by the toilet usage monitoring unit. The gastrointestinal condition evaluation unit evaluates a gastrointestinal condition of the user based on the data analyzed by the excrement data analysis unit. The advice providing unit provides appropriate advice on the basis of the result evaluated by the gastrointestinal condition evaluation unit.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 technologies do not adequately evaluate gastrointestinal conditions or provide appropriate advice based on toilet usage, and there is room for improvement.

[0005] The system according to the embodiment aims to evaluate the state of the gastrointestinal tract based on toilet usage and provide appropriate advice. [Means for solving the problem]

[0006] The system according to the embodiment includes a toilet usage status monitoring unit, an excrement data analysis unit, a gastrointestinal state evaluation unit, and an advice providing unit. The toilet usage status monitoring unit monitors toilet usage status. The excrement data analysis unit analyzes data collected by the toilet usage status monitoring unit. The gastrointestinal state evaluation unit evaluates the user's gastrointestinal state based on the data analyzed by the excrement data analysis unit. The advice providing unit provides appropriate advice based on the results of the evaluation by the gastrointestinal state evaluation unit. [Effects of the Invention]

[0007] The system according to the embodiment can evaluate the state of the gastrointestinal tract based on the usage of the toilet and provide appropriate advice. [Brief explanation of the drawings]

[0008] [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. DETAILED DESCRIPTION OF THE INVENTION

[0009] 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.

[0010] First, the terms used in the following description will be explained.

[0011] 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, the 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), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] 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.

[0013] 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.

[0014] 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), and Bluetooth (registered trademark).

[0015] 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."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 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.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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).

[0019] 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.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the pointer. 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 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. 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.

[0022] 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.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 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.

[0025] 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. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The gastrointestinal care system according to an embodiment of the present invention is a toilet equipped with a generative AI that cares for the user's gastrointestinal health. This system analyzes toilet usage and excrement data to understand the user's gastrointestinal condition and provide appropriate advice. This allows the gastrointestinal care system to effectively care for the user's gastrointestinal health.

[0029] The gastrointestinal care system according to the embodiment includes a toilet usage monitoring unit, a waste data analysis unit, a gastrointestinal condition evaluation unit, and an advice providing unit. The toilet usage monitoring unit monitors toilet usage. For example, it uses sensors to collect information such as the frequency and duration of toilet use, and the amount and shape of waste. The toilet usage monitoring unit can also simultaneously measure the user's weight fluctuations and analyze the correlation between weight and excretion patterns. The waste data analysis unit analyzes the data collected by the toilet usage monitoring unit. For example, it detects the color, shape, and odor of waste using sensors and evaluates the gastrointestinal condition based on the data. The waste data analysis unit can also analyze the microbial composition of waste and evaluate the state of the intestinal flora. The gastrointestinal condition evaluation unit evaluates the user's gastrointestinal condition based on the data analyzed by the waste data analysis unit. For example, it comprehensively assesses the gastrointestinal health condition based on the color, shape, and frequency of use of waste. The gastrointestinal condition evaluation unit can also take the user's genetic information into account to evaluate individual health risks. The advice providing unit provides appropriate advice based on the results of the evaluation by the gastrointestinal state evaluation unit. For example, the advice may be in the form of, "The color of your recent stool has been abnormal. We recommend that you review your diet." The advice providing unit may also provide feedback on the user's emotional state in real time and offer advice to encourage relaxation. This allows the gastrointestinal care system according to the embodiment to effectively care for the user's gastrointestinal health. For example, abnormalities in stool can be detected early and appropriate measures can be taken. Furthermore, by providing advice customized for each user, individual health management can be supported.

[0030] The toilet usage status monitoring unit can simultaneously measure weight fluctuations and analyze the correlation between weight fluctuations and excretion patterns.The toilet usage status monitoring unit, for example, has a built-in scale that measures the user's weight each time they use the toilet.This allows the relationship between weight fluctuations and excretion patterns to be analyzed and the health status to be evaluated.This allows the relationship between weight fluctuations and excretion patterns to be analyzed and the health status to be evaluated.

[0031] The toilet usage monitoring unit can monitor the amount of water intake in addition to the frequency and duration of use and analyze the correlation with excretion patterns. For example, when monitoring toilet usage, the toilet usage monitoring unit records the user's water intake. For example, it measures the intake amount using a smart water bottle and analyzes the correlation with excretion patterns. This allows the correlation between water intake and excretion patterns to be analyzed and the health condition to be evaluated.

[0032] The toilet usage monitoring unit can link the monitoring data with other health devices to perform comprehensive health management. For example, the toilet usage monitoring unit can link the toilet usage monitoring data with a smartwatch and integrate it with data such as heart rate and exercise volume to perform comprehensive health management. For example, it can analyze the relationship between toilet usage frequency and exercise volume. This allows the relationship between toilet usage and other health indicators to be evaluated and comprehensive health management to be performed.

[0033] When monitoring usage, the toilet usage monitoring unit can record dietary details and analyze the relationship between diet and excretion patterns. For example, when monitoring toilet usage, the toilet usage monitoring unit uses an app that records the user's dietary details and analyzes the data in combination with excretion patterns. For example, it evaluates the impact of a specific meal on excretion patterns. This allows the relationship between dietary details and excretion patterns to be analyzed and health status to be evaluated.

[0034] The excrement data analysis unit can analyze the microbial composition of excrement and evaluate the state of the intestinal flora. For example, in analyzing excrement data, the excrement data analysis unit uses a sensor for analyzing the microbial composition and evaluates the state of the intestinal flora. For example, it monitors the increase or decrease of specific microorganisms. This allows the microbial composition to be analyzed and the state of the intestinal flora to be evaluated.

[0035] The excrement data analysis unit measures the temperature and pH value of the excrement, and is able to evaluate the gastrointestinal health condition in more detail. For example, in analyzing excrement data, the excrement data analysis unit uses a temperature sensor and a pH sensor to measure the temperature and pH value of the excrement and evaluate the gastrointestinal health condition. For example, abnormal temperature and pH values ​​are detected. This allows the temperature and pH values ​​to be measured and the gastrointestinal health condition to be evaluated in more detail.

[0036] The excrement data analysis unit can integrate the results of the excrement data analysis with other health data to perform a comprehensive health evaluation. The excrement data analysis unit, for example, integrates the results of the excrement data analysis with blood test results to build a system for performing a comprehensive health evaluation. For example, the health condition can be evaluated by combining nutrient levels in the blood with excrement data. This allows the excrement data to be integrated with other health data to perform a comprehensive health evaluation.

[0037] The excrement data analysis unit can analyze the excrement data analysis results in association with lifestyle habits and provide health management advice. For example, the excrement data analysis unit can analyze the excrement data analysis results in association with the user's exercise amount data and provide health management advice. For example, the excretion pattern on days when the amount of exercise is low can be evaluated. This allows the relationship between lifestyle habits and excretion patterns to be analyzed and health management advice to be provided.

[0038] The gastrointestinal state evaluation unit can evaluate individual health risks by taking genetic information into consideration. For example, in evaluating a gastrointestinal state, the gastrointestinal state evaluation unit analyzes the user's genetic information and evaluates the impact of specific gene mutations on gastrointestinal health. For example, the gastrointestinal state evaluation unit identifies individual health risks based on the genetic information. This makes it possible to evaluate individual health risks by taking genetic information into consideration.

[0039] The gastrointestinal condition evaluation unit can evaluate long-term health trends by referring to past medical history. For example, in gastrointestinal condition evaluation, the gastrointestinal condition evaluation unit builds a system that evaluates long-term health trends by referring to the user's past medical history. For example, the health condition is evaluated based on past medical history and treatment history. This makes it possible to evaluate long-term health trends by referring to past medical history.

[0040] The gastrointestinal condition evaluation unit can integrate the gastrointestinal condition evaluation results with other health data to perform a comprehensive health evaluation. The gastrointestinal condition evaluation unit, for example, integrates the gastrointestinal condition evaluation results with heart rate data to build a system for performing a comprehensive health evaluation. For example, it evaluates the correlation between heart rate fluctuations and gastrointestinal health status. This allows the gastrointestinal condition evaluation results to be integrated with other health data to perform a comprehensive health evaluation.

[0041] The gastrointestinal state evaluation unit can analyze the gastrointestinal state evaluation result in association with lifestyle habits and provide health management advice. For example, the gastrointestinal state evaluation unit can analyze the gastrointestinal state evaluation result in association with the user's dietary content data and provide health management advice. For example, the gastrointestinal state evaluation unit can evaluate the impact of a specific meal on gastrointestinal health. This makes it possible to analyze the association between lifestyle habits and gastrointestinal health state and provide health management advice.

[0042] The health management data recording unit records genetic information and can manage individual health risks over the long term. For example, in recording data for health management, the health management data recording unit records a user's genetic information and builds a system for managing individual health risks over the long term. For example, the system evaluates risk when a specific gene mutation is present. This allows genetic information to be recorded and individual health risks to be managed over the long term.

[0043] The health management data recording unit can record past medical history and track long-term health trends. For example, in data recording for health management, the health management data recording unit records a user's past medical history and builds a system that tracks long-term health trends. For example, the health condition is evaluated based on past medical history and treatment history. This makes it possible to record past medical history and track long-term health trends.

[0044] The health management data recording unit can link the health management data records with other health devices to perform comprehensive health management. For example, the health management data recording unit can link the health management data records with a smart watch and integrate them with data such as heart rate and exercise amount to perform comprehensive health management. For example, the unit can analyze the correlation between frequency of toilet use and exercise amount. This allows the health management data records to be linked with other health devices to perform comprehensive health management.

[0045] The health management data recording unit records health management data records in association with lifestyle habits, and can provide health management advice. For example, the health management data recording unit records health management data records in association with dietary content data of the user, and can provide health management advice. For example, the health management data recording unit evaluates the impact of a specific meal on gastrointestinal health. This allows the lifestyle habits and health management data records to be recorded in association with each other, and health management advice to be provided.

[0046] The individual health advice unit can provide advice based on individual health risks, taking into account genetic information. For example, in providing individual health advice, the individual health advice unit analyzes the user's genetic information and evaluates the impact of specific genetic mutations on health risks. For example, the individual health risks are identified based on the genetic information. This allows advice based on individual health risks, taking into account genetic information, to be provided.

[0047] The individual health advice unit can refer to the user's past medical history and provide advice based on long-term health trends. The individual health advice unit, for example, builds a system that refers to the user's past medical history and provides advice based on long-term health trends when providing individual health advice. For example, the system evaluates the user's health condition based on their past medical history and treatment history. This makes it possible to refer to the user's past medical history and provide advice based on long-term health trends.

[0048] The individual health advice unit can link the individual health advice with other health devices to provide comprehensive health management. For example, the individual health advice unit can link the individual health advice with a smart watch and integrate it with data such as heart rate and exercise amount to provide comprehensive health management. For example, the unit can analyze the correlation between toilet use frequency and exercise amount. This allows the individual health advice to be linked with other health devices to provide comprehensive health management.

[0049] The individual health advice unit can provide individual health advice in association with lifestyle habits and provide health management advice. For example, the individual health advice unit can provide individual health advice in association with dietary content data of the user and provide health management advice. For example, the individual health advice unit can evaluate the impact of a specific meal on gastrointestinal health. This allows the individual health advice to be provided in association with lifestyle habits and provide health management advice.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The gastrointestinal care system can further include a voice recognition unit. The voice recognition unit analyzes the voices emitted by the user when using the toilet to evaluate the user's health condition. For example, the voice recognition unit can monitor the frequency of coughing and sneezing to evaluate the health condition of the respiratory system. The voice recognition unit can also analyze the tone and tempo of the user's voice to evaluate the level of stress and fatigue. This enables health evaluation based on voice data and realizes more comprehensive health management.

[0052] The gastrointestinal care system may further include a sleep monitoring unit. The sleep monitoring unit monitors the user's sleep patterns and evaluates the quality of their sleep. For example, sensors may be used to detect movements and heart rate during sleep and analyze the depth of sleep and frequency of interruptions. The sleep monitoring unit may also record the user's sleep environment (temperature, humidity, sound, etc.) and identify factors that affect the quality of sleep. This allows for health evaluation based on sleep data and more comprehensive health management.

[0053] The gastrointestinal care system may further include an exercise monitoring unit. The exercise monitoring unit monitors the user's daily exercise volume and evaluates their health condition. For example, it may use a pedometer or an acceleration sensor to record the user's walking distance and exercise intensity. The exercise monitoring unit may also analyze the user's exercise patterns and evaluate the risk of insufficient or excessive exercise. This allows for health evaluation based on exercise data, enabling more comprehensive health management.

[0054] The gastrointestinal care system can further include a nutritional intake monitoring unit. The nutritional intake monitoring unit records the user's diet and evaluates nutritional balance. For example, it can take photos of the meal and use image analysis technology to identify ingredients and nutrients. The nutritional intake monitoring unit can also evaluate the risk of nutritional deficiencies or overintake based on the user's dietary history. This enables health assessment based on nutritional data and more comprehensive health management.

[0055] The gastrointestinal care system can further include an environmental monitoring unit. The environmental monitoring unit monitors the user's living environment (temperature, humidity, air quality, etc.) and evaluates its impact on health. For example, it can use sensors to detect indoor temperature and humidity and provide advice on maintaining a comfortable environment. The environmental monitoring unit can also detect harmful substances in the air (PM2.5, VOCs, etc.) and evaluate health risks. This enables health assessment based on environmental data, enabling more comprehensive health management.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The toilet usage monitoring unit monitors toilet usage. For example, sensors are used to collect information such as the frequency and duration of toilet use, and the amount and shape of excrement. The toilet usage monitoring unit can also simultaneously measure the user's weight fluctuations and analyze the correlation between weight and excretion patterns. Step 2: The excrement data analysis unit analyzes the data collected by the toilet usage monitoring unit. For example, sensors are used to detect the color, shape, and odor of excrement, and the state of the gastrointestinal tract is evaluated based on this data. The excrement data analysis unit can also analyze the microbial composition of excrement to evaluate the state of the intestinal flora. Step 3: The gastrointestinal condition evaluation unit evaluates the user's gastrointestinal condition based on the data analyzed by the excrement data analysis unit. For example, it comprehensively assesses the gastrointestinal health status based on the color and shape of the excrement, frequency of use, etc. The gastrointestinal condition evaluation unit can also evaluate individual health risks by taking into account the user's genetic information. Step 4: The advice providing unit provides appropriate advice based on the results of the evaluation by the gastrointestinal condition evaluation unit. For example, the advice may be something like, "The color of your recent stool has been abnormal. We recommend that you review your diet." The advice providing unit can also provide feedback on the user's emotional state in real time and offer advice to encourage relaxation.

[0058] (Example 2) The gastrointestinal care system according to an embodiment of the present invention is a toilet equipped with a generative AI that cares for the user's gastrointestinal health. This system analyzes toilet usage and excrement data to understand the user's gastrointestinal condition and provide appropriate advice. This allows the gastrointestinal care system to effectively care for the user's gastrointestinal health.

[0059] The gastrointestinal care system according to the embodiment includes a toilet usage monitoring unit, a waste data analysis unit, a gastrointestinal condition evaluation unit, and an advice providing unit. The toilet usage monitoring unit monitors toilet usage. For example, it uses sensors to collect information such as the frequency and duration of toilet use, and the amount and shape of waste. The toilet usage monitoring unit can also simultaneously measure the user's weight fluctuations and analyze the correlation between weight and excretion patterns. The waste data analysis unit analyzes the data collected by the toilet usage monitoring unit. For example, it detects the color, shape, and odor of waste using sensors and evaluates the gastrointestinal condition based on the data. The waste data analysis unit can also analyze the microbial composition of waste and evaluate the state of the intestinal flora. The gastrointestinal condition evaluation unit evaluates the user's gastrointestinal condition based on the data analyzed by the waste data analysis unit. For example, it comprehensively assesses the gastrointestinal health condition based on the color, shape, and frequency of use of waste. The gastrointestinal condition evaluation unit can also take the user's genetic information into account to evaluate individual health risks. The advice providing unit provides appropriate advice based on the results of the evaluation by the gastrointestinal state evaluation unit. For example, the advice may be in the form of, "The color of your recent stool has been abnormal. We recommend that you review your diet." The advice providing unit may also provide feedback on the user's emotional state in real time and offer advice to encourage relaxation. This allows the gastrointestinal care system according to the embodiment to effectively care for the user's gastrointestinal health. For example, abnormalities in stool can be detected early and appropriate measures can be taken. Furthermore, by providing advice customized for each user, individual health management can be supported.

[0060] The toilet usage status monitoring unit can simultaneously measure weight fluctuations and analyze the correlation between weight fluctuations and excretion patterns.The toilet usage status monitoring unit, for example, has a built-in scale that measures the user's weight each time they use the toilet.This allows the relationship between weight fluctuations and excretion patterns to be analyzed and the health status to be evaluated.This allows the relationship between weight fluctuations and excretion patterns to be analyzed and the health status to be evaluated.

[0061] The toilet usage monitoring unit can monitor the amount of water intake in addition to the frequency and duration of use and analyze the correlation with excretion patterns. For example, when monitoring toilet usage, the toilet usage monitoring unit records the user's water intake. For example, it measures the intake amount using a smart water bottle and analyzes the correlation with excretion patterns. This allows the correlation between water intake and excretion patterns to be analyzed and the health condition to be evaluated.

[0062] The toilet usage monitoring unit uses the emotion estimation function to monitor the user's emotional state while using the toilet and analyze the impact of stress and tension on the user's excretion pattern. For example, the toilet usage monitoring unit uses a camera or microphone to analyze the user's facial expressions and voice while using the toilet and estimates the user's emotional state. This evaluates the impact of stress and tension on the user's excretion pattern. This allows the relationship between the user's emotional state and the excretion pattern to be analyzed and the user's health condition to be evaluated.

[0063] The toilet usage monitoring unit can link the monitoring data with other health devices to perform comprehensive health management. For example, the toilet usage monitoring unit can link the toilet usage monitoring data with a smartwatch and integrate it with data such as heart rate and exercise volume to perform comprehensive health management. For example, it can analyze the relationship between toilet usage frequency and exercise volume. This allows the relationship between toilet usage and other health indicators to be evaluated and comprehensive health management to be performed.

[0064] When monitoring usage, the toilet usage monitoring unit can record dietary details and analyze the relationship between diet and excretion patterns. For example, when monitoring toilet usage, the toilet usage monitoring unit uses an app that records the user's dietary details and analyzes the data in combination with excretion patterns. For example, it evaluates the impact of a specific meal on excretion patterns. This allows the relationship between dietary details and excretion patterns to be analyzed and health status to be evaluated.

[0065] The toilet usage status monitoring unit can use the emotion estimation function to provide real-time feedback on the user's emotional state while using the toilet and provide advice to encourage relaxation. For example, the toilet usage status monitoring unit can use the emotion estimation function to analyze the user's emotional state while using the toilet in real time and provide advice to encourage relaxation. For example, it can suggest breathing techniques to help users relax. This allows for real-time feedback on the user's emotional state and the provision of advice to encourage relaxation.

[0066] The excrement data analysis unit can analyze the microbial composition of excrement and evaluate the state of the intestinal flora. For example, in analyzing excrement data, the excrement data analysis unit uses a sensor for analyzing the microbial composition and evaluates the state of the intestinal flora. For example, it monitors the increase or decrease of specific microorganisms. This allows the microbial composition to be analyzed and the state of the intestinal flora to be evaluated.

[0067] The excrement data analysis unit measures the temperature and pH value of the excrement, and is able to evaluate the gastrointestinal health condition in more detail. For example, in analyzing excrement data, the excrement data analysis unit uses a temperature sensor and a pH sensor to measure the temperature and pH value of the excrement and evaluate the gastrointestinal health condition. For example, abnormal temperature and pH values ​​are detected. This allows the temperature and pH values ​​to be measured and the gastrointestinal health condition to be evaluated in more detail.

[0068] The excrement data analysis unit can use the emotion estimation function to estimate the emotional state based on the results of the excrement data analysis and evaluate the impact of stress on the gastrointestinal tract. The excrement data analysis unit, for example, uses the emotion estimation function based on the results of the excrement data analysis to estimate the user's emotional state. For example, it evaluates whether high stress levels lead to a deterioration in gastrointestinal health. This makes it possible to evaluate the relationship between the emotional state and gastrointestinal health.

[0069] The excrement data analysis unit can integrate the results of the excrement data analysis with other health data to perform a comprehensive health evaluation. The excrement data analysis unit, for example, integrates the results of the excrement data analysis with blood test results to build a system for performing a comprehensive health evaluation. For example, the health condition can be evaluated by combining nutrient levels in the blood with excrement data. This allows the excrement data to be integrated with other health data to perform a comprehensive health evaluation.

[0070] The excrement data analysis unit can analyze the excrement data analysis results in association with lifestyle habits and provide health management advice. For example, the excrement data analysis unit can analyze the excrement data analysis results in association with the user's exercise amount data and provide health management advice. For example, the excretion pattern on days when the amount of exercise is low can be evaluated. This allows the relationship between lifestyle habits and excretion patterns to be analyzed and health management advice to be provided.

[0071] The excrement data analysis unit can use the emotion estimation function to provide real-time feedback on the emotional state based on the results of the excrement data analysis, and provide advice to elicit positive emotions. The excrement data analysis unit can, for example, use the emotion estimation function based on the results of the excrement data analysis to provide real-time feedback on the user's emotional state. For example, it can provide advice to elicit positive emotions. This allows real-time feedback on the emotional state to provide advice to elicit positive emotions.

[0072] The gastrointestinal state evaluation unit can evaluate individual health risks by taking genetic information into consideration. For example, in evaluating a gastrointestinal state, the gastrointestinal state evaluation unit analyzes the user's genetic information and evaluates the impact of specific gene mutations on gastrointestinal health. For example, the gastrointestinal state evaluation unit identifies individual health risks based on the genetic information. This makes it possible to evaluate individual health risks by taking genetic information into consideration.

[0073] The gastrointestinal condition evaluation unit can evaluate long-term health trends by referring to past medical history. For example, in gastrointestinal condition evaluation, the gastrointestinal condition evaluation unit builds a system that evaluates long-term health trends by referring to the user's past medical history. For example, the health condition is evaluated based on past medical history and treatment history. This makes it possible to evaluate long-term health trends by referring to past medical history.

[0074] The gastrointestinal state evaluation unit can use the emotion estimation function to estimate the emotional state based on the gastrointestinal state evaluation result and provide advice based on the emotion. The gastrointestinal state evaluation unit, for example, uses the emotion estimation function based on the gastrointestinal state evaluation result to estimate the emotional state of the user. For example, advice on how to relax when stress is high can be provided. This makes it possible to provide advice based on the emotional state.

[0075] The gastrointestinal condition evaluation unit can integrate the gastrointestinal condition evaluation results with other health data to perform a comprehensive health evaluation. The gastrointestinal condition evaluation unit, for example, integrates the gastrointestinal condition evaluation results with heart rate data to build a system for performing a comprehensive health evaluation. For example, it evaluates the correlation between heart rate fluctuations and gastrointestinal health status. This allows the gastrointestinal condition evaluation results to be integrated with other health data to perform a comprehensive health evaluation.

[0076] The gastrointestinal state evaluation unit can analyze the gastrointestinal state evaluation result in association with lifestyle habits and provide health management advice. For example, the gastrointestinal state evaluation unit can analyze the gastrointestinal state evaluation result in association with the user's dietary content data and provide health management advice. For example, the gastrointestinal state evaluation unit can evaluate the impact of a specific meal on gastrointestinal health. This makes it possible to analyze the association between lifestyle habits and gastrointestinal health state and provide health management advice.

[0077] The gastrointestinal state evaluation unit can use the emotion estimation function to provide real-time feedback on the emotional state based on the gastrointestinal state evaluation result and provide advice to elicit positive emotions. The gastrointestinal state evaluation unit can, for example, use the emotion estimation function based on the gastrointestinal state evaluation result to provide real-time feedback on the user's emotional state. For example, advice to elicit positive emotions is provided. This makes it possible to provide real-time feedback on the emotional state and advice to elicit positive emotions.

[0078] The health management data recording unit records genetic information and can manage individual health risks over the long term. For example, in recording data for health management, the health management data recording unit records a user's genetic information and builds a system for managing individual health risks over the long term. For example, the system evaluates risk when a specific gene mutation is present. This allows genetic information to be recorded and individual health risks to be managed over the long term.

[0079] The health management data recording unit can record past medical history and track long-term health trends. For example, in data recording for health management, the health management data recording unit records a user's past medical history and builds a system that tracks long-term health trends. For example, the health condition is evaluated based on past medical history and treatment history. This makes it possible to record past medical history and track long-term health trends.

[0080] The health management data recording unit can use the emotion estimation function to estimate an emotional state based on the health management data record and provide health management advice based on the emotion. The health management data recording unit, for example, uses the emotion estimation function based on the health management data record to estimate the user's emotional state. For example, advice on how to relax when stress is high can be provided. This makes it possible to provide health management advice based on the emotional state.

[0081] The health management data recording unit can link the health management data records with other health devices to perform comprehensive health management. For example, the health management data recording unit can link the health management data records with a smart watch and integrate them with data such as heart rate and exercise amount to perform comprehensive health management. For example, the unit can analyze the correlation between frequency of toilet use and exercise amount. This allows the health management data records to be linked with other health devices to perform comprehensive health management.

[0082] The health management data recording unit records health management data records in association with lifestyle habits, and can provide health management advice. For example, the health management data recording unit records health management data records in association with dietary content data of the user, and can provide health management advice. For example, the health management data recording unit evaluates the impact of a specific meal on gastrointestinal health. This allows the lifestyle habits and health management data records to be recorded in association with each other, and health management advice to be provided.

[0083] The health management data recording unit can use the emotion estimation function to provide real-time feedback on the emotional state based on the health management data records and provide advice to elicit positive emotions. The health management data recording unit can, for example, use the emotion estimation function based on the health management data records to provide real-time feedback on the user's emotional state. For example, advice to elicit positive emotions can be provided. This allows real-time feedback on the emotional state to provide advice to elicit positive emotions.

[0084] The individual health advice unit can provide advice based on individual health risks, taking into account genetic information. For example, in providing individual health advice, the individual health advice unit analyzes the user's genetic information and evaluates the impact of specific genetic mutations on health risks. For example, the individual health risks are identified based on the genetic information. This allows advice based on individual health risks, taking into account genetic information, to be provided.

[0085] The individual health advice unit can refer to the user's past medical history and provide advice based on long-term health trends. The individual health advice unit, for example, builds a system that refers to the user's past medical history and provides advice based on long-term health trends when providing individual health advice. For example, the system evaluates the user's health condition based on their past medical history and treatment history. This makes it possible to refer to the user's past medical history and provide advice based on long-term health trends.

[0086] The individual health advice unit can use the emotion estimation function to estimate an emotional state based on the individual health advice and provide advice based on the emotion. For example, the individual health advice unit uses the emotion estimation function based on the individual health advice to estimate the user's emotional state. For example, advice on how to relax when stress is high can be provided. This makes it possible to provide advice based on the emotional state.

[0087] The individual health advice unit can link the individual health advice with other health devices to provide comprehensive health management. For example, the individual health advice unit can link the individual health advice with a smart watch and integrate it with data such as heart rate and exercise amount to provide comprehensive health management. For example, the unit can analyze the correlation between toilet use frequency and exercise amount. This allows the individual health advice to be linked with other health devices to provide comprehensive health management.

[0088] The individual health advice unit can provide individual health advice in association with lifestyle habits and provide health management advice. For example, the individual health advice unit can provide individual health advice in association with dietary content data of the user and provide health management advice. For example, the individual health advice unit can evaluate the impact of a specific meal on gastrointestinal health. This allows the individual health advice to be provided in association with lifestyle habits and provide health management advice.

[0089] The individual health advice unit can use the emotion estimation function to provide real-time feedback on the emotional state based on the individual health advice, thereby providing advice that elicits positive emotions. The individual health advice unit can, for example, use the emotion estimation function based on the individual health advice to provide real-time feedback on the user's emotional state. For example, it can provide advice that elicits positive emotions. This allows for real-time feedback on the emotional state and the provision of advice that elicits positive emotions.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The gastrointestinal care system can further include a voice recognition unit. The voice recognition unit analyzes the voices emitted by the user when using the toilet to evaluate the user's health condition. For example, the voice recognition unit can monitor the frequency of coughing and sneezing to evaluate the health condition of the respiratory system. The voice recognition unit can also analyze the tone and tempo of the user's voice to evaluate the level of stress and fatigue. This enables health evaluation based on voice data and realizes more comprehensive health management.

[0092] The gastrointestinal care system may further include a sleep monitoring unit. The sleep monitoring unit monitors the user's sleep patterns and evaluates the quality of their sleep. For example, sensors may be used to detect movements and heart rate during sleep and analyze the depth of sleep and frequency of interruptions. The sleep monitoring unit may also record the user's sleep environment (temperature, humidity, sound, etc.) and identify factors that affect the quality of sleep. This allows for health evaluation based on sleep data and more comprehensive health management.

[0093] The gastrointestinal care system may further include an exercise monitoring unit. The exercise monitoring unit monitors the user's daily exercise volume and evaluates their health condition. For example, it may use a pedometer or an acceleration sensor to record the user's walking distance and exercise intensity. The exercise monitoring unit may also analyze the user's exercise patterns and evaluate the risk of insufficient or excessive exercise. This allows for health evaluation based on exercise data, enabling more comprehensive health management.

[0094] The gastrointestinal care system can further include a nutritional intake monitoring unit. The nutritional intake monitoring unit records the user's diet and evaluates nutritional balance. For example, it can take photos of the meal and use image analysis technology to identify ingredients and nutrients. The nutritional intake monitoring unit can also evaluate the risk of nutritional deficiencies or overintake based on the user's dietary history. This enables health assessment based on nutritional data and more comprehensive health management.

[0095] The gastrointestinal care system can further include an environmental monitoring unit. The environmental monitoring unit monitors the user's living environment (temperature, humidity, air quality, etc.) and evaluates its impact on health. For example, it can use sensors to detect indoor temperature and humidity and provide advice on maintaining a comfortable environment. The environmental monitoring unit can also detect harmful substances in the air (PM2.5, VOCs, etc.) and evaluate health risks. This enables health assessment based on environmental data, enabling more comprehensive health management.

[0096] The gastrointestinal care system can also use emotion estimation to monitor the user's emotional state and provide stress management advice. For example, a camera and microphone can be used to analyze the user's facial expressions and voice while using the restroom to estimate their emotional state. This can then be used to evaluate the impact of stress and tension on bowel patterns and provide advice to promote relaxation. For example, it could suggest deep breathing or meditation techniques.

[0097] The gastrointestinal care system can also use the emotion estimation function to provide real-time feedback on the user's emotional state and offer advice to elicit positive emotions. For example, the emotion estimation function can be used to analyze the user's emotional state in real time when using the restroom, and advice to elicit positive emotions can be offered. For example, this could involve playing relaxing music or displaying a positive message.

[0098] The gastrointestinal care system can further use an emotion estimation function to monitor the user's emotional state and provide dietary advice based on the emotion. For example, the emotion estimation function can be used to analyze the user's emotional state, and if stress levels are high, foods with a relaxing effect can be suggested. Examples of such foods include chamomile tea and dark chocolate. This allows for dietary advice based on the user's emotional state.

[0099] The gastrointestinal care system can further use an emotion estimation function to monitor the user's emotional state and provide exercise advice based on the emotion. For example, the emotion estimation function can be used to analyze the user's emotional state, and if stress levels are high, the system can suggest exercises that have a relaxing effect, such as yoga or stretching. This allows the system to provide exercise advice based on the user's emotional state.

[0100] The gastrointestinal care system can further use an emotion estimation function to monitor the user's emotional state and provide sleep advice based on the emotion. For example, the emotion estimation function can be used to analyze the user's emotional state, and if stress levels are high, a relaxing sleep environment can be suggested. For example, using an aroma diffuser or playing relaxing music can be suggested. This allows for sleep advice based on the user's emotional state.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The toilet usage monitoring unit monitors toilet usage. For example, sensors are used to collect information such as the frequency and duration of toilet use, and the amount and shape of excrement. The toilet usage monitoring unit can also simultaneously measure the user's weight fluctuations and analyze the correlation between weight and excretion patterns. Step 2: The excrement data analysis unit analyzes the data collected by the toilet usage monitoring unit. For example, sensors are used to detect the color, shape, and odor of excrement, and the state of the gastrointestinal tract is evaluated based on this data. The excrement data analysis unit can also analyze the microbial composition of excrement to evaluate the state of the intestinal flora. Step 3: The gastrointestinal condition evaluation unit evaluates the user's gastrointestinal condition based on the data analyzed by the excrement data analysis unit. For example, it comprehensively assesses the gastrointestinal health status based on the color and shape of the excrement, frequency of use, etc. The gastrointestinal condition evaluation unit can also evaluate individual health risks by taking into account the user's genetic information. Step 4: The advice providing unit provides appropriate advice based on the results of the evaluation by the gastrointestinal condition evaluation unit. For example, the advice may be something like, "The color of your recent stool has been abnormal. We recommend that you review your diet." The advice providing unit can also provide feedback on the user's emotional state in real time and offer advice to encourage relaxation.

[0103] 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.

[0104] 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> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). 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 speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. 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. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0108] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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 and / or a LAN.

[0109] 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.

[0110] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0111] 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 user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0116] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0117] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0118] 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.

[0119] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 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.

[0123] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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 and / or a LAN.

[0124] 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.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0126] 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 user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0131] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0132] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0133] 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.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 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.

[0138] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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 and / or a LAN.

[0139] 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.

[0140] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0141] 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 image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0142] 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.

[0143] The control object 443 includes a display device, LEDs in the eyes, and motors that drive 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.

[0144] 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.

[0145] 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.

[0146] 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. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0147] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0148] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] 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.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0152] 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.

[0153] FIG. 9 illustrates 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 behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions 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.

[0154] 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.

[0155] 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).

[0156] 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 expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, 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 expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, 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.

[0157] 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."

[0158] 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.

[0159] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0164] The hardware resource that executes the specific process 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 process may be a single processor.

[0165] 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.

[0166] 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.

[0167] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0168] 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.

[0169] 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. [Explanation of symbols]

[0170] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a toilet usage status monitoring unit that monitors the usage status of the toilet; an excrement data analysis unit that analyzes the data collected by the toilet usage status monitoring unit; a gastrointestinal state evaluation unit that evaluates the gastrointestinal state of the user based on the data analyzed by the excrement data analysis unit; an advice providing unit that provides appropriate advice based on the results of the evaluation by the gastrointestinal state evaluating unit. A system characterized by:

2. The toilet usage status monitoring unit When monitoring the toilet usage status, the weight fluctuation of the user is measured at the same time, and the correlation between the weight fluctuation and the excretion pattern is analyzed.

2. The system of claim 1.

3. The toilet usage status monitoring unit Link monitoring data with other health devices for comprehensive health management 2. The system of claim 1.

4. The excrement data analysis unit Analyzing the microbial composition of excrement and assessing the state of intestinal flora 2. The system of claim 1.

5. The gastrointestinal condition evaluation unit Evaluating individual health risks based on the user's genetic information 2. The system of claim 1.

6. The health management data recording section Using an emotion estimation function to estimate the user's emotional state based on health care data records and provide emotion-based health care advice.

2. The system of claim 1.

7. Individual Health Advice Department Using an emotion estimation function to estimate the user's emotional state based on personalized health advice and provide the emotion-based advice.

2. The system of claim 1.

8. The toilet usage status monitoring unit Using emotion estimation functionality, the user's emotional state is monitored while using the toilet, and the impact of stress and tension on excretion patterns is analyzed.

2. The system of claim 1.

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