System

Abstract

An object of a system according to an embodiment is to analyze images of feces and urine and efficiently grasp the health condition of an individual.SOLUTION: A system includes a camera, a hip pressure measurement system, an analysis unit, and a notification unit. The camera is installed in the toilet bowl. A hip pressure measurement system measures weight and pressure (hip pressure) at a toilet seat and foot rest to identify an individual. The analysis unit analyzes an image of feces and urine acquired by the camera. The notification unit notifies a result obtained by the analysis 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 techniques have not adequately managed fecal and urinary health, making it difficult to efficiently grasp an individual's health condition.

[0005] The system according to the embodiment aims to analyze images of feces and urine to efficiently understand the health condition of an individual. [Means for solving the problem]

[0006] The system according to the embodiment includes a camera, a buttocks pressure measurement system, an analysis unit, and a notification unit. The camera is installed on the toilet bowl. The buttocks pressure measurement system measures weight and pressure (buttocks pressure) using the toilet seat and footrest to identify an individual. The analysis unit analyzes images of feces and urine captured by the camera. The notification unit notifies the user of the results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze images of feces and urine and efficiently grasp the health condition of an individual. [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 touch of 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) A health management system according to an embodiment of the present invention is a system that analyzes images of stool and urine to manage the health condition of an individual. This enables the health management system to analyze images of stool and urine and manage the health condition of an individual in detail.

[0029] A health management system according to an embodiment includes a camera, a buttock pressure measurement system, an analysis unit, and a notification unit. The camera is installed on the toilet bowl and captures images of stool and urine. For example, the camera is installed inside the toilet bowl and can capture high-resolution images. The camera is also waterproof and can withstand the environment inside the toilet bowl. The buttock pressure measurement system identifies an individual by measuring weight and pressure (buttock pressure) using the toilet seat and footrest. For example, the buttock pressure measurement system measures weight using a pressure sensor built into the toilet seat. It can also measure pressure using a pressure sensor installed in the footrest. The buttock pressure measurement system uses an algorithm to identify an individual based on the measurement data. The analysis unit analyzes the stool and urine images captured by the camera. For example, the analysis unit analyzes the shape and color of stool and the transparency of urine using an image analysis algorithm. The analysis unit can also evaluate a health condition based on the image data of stool and urine. The analysis unit records data on a daily / weekly / monthly basis so that the user can view it. The notification unit notifies the user of the results obtained by the analysis unit. For example, the notification unit notifies the user of the analysis results via a smartphone app. The notification unit can also issue an alert if an abnormality is detected. Furthermore, the notification unit can also send notifications via messaging apps such as LINE. This allows the health management system according to the embodiment to analyze images of stool and urine and manage an individual's health condition. For example, a user can record daily output and stool condition to understand changes in their health condition. Furthermore, if an abnormality is detected, a prompt response can be made.

[0030] The buttock pressure measurement system can measure temperature changes on the toilet seat and add body temperature data to improve the accuracy of personal identification. For example, the buttock pressure measurement system installs a temperature sensor on the toilet seat and measures body temperature data when sitting. Combining the body temperature data with the buttock pressure data improves the accuracy of personal identification. For example, minute variations in body temperature can be analyzed to identify individual users. The buttock pressure measurement system can also collect data from the temperature sensor in real time and reflect it in the personal identification algorithm. In this way, adding body temperature data improves the accuracy of personal identification.

[0031] A buttock pressure measurement system can add a vibration sensor and analyze minute movements and vibration patterns to identify individuals. For example, a buttock pressure measurement system installs a vibration sensor on a toilet seat to measure minute movements and vibration patterns when sitting. This data is combined with buttock pressure data and analyzed to improve the accuracy of individual identification. For example, the system learns the characteristics of movements when sitting and identifies individual users. Furthermore, the vibration sensor can detect minute movements using an acceleration sensor or gyro sensor. As a result, adding a vibration sensor improves the accuracy of individual identification.

[0032] The buttock pressure measurement system can be applied to office chairs or car seats, allowing personal authentication simply by sitting on it. For example, a system can be developed that incorporates the buttock pressure measurement system into an office chair and authenticates individuals simply by sitting on it. For example, it can be linked to an office security system, ensuring that only authenticated users can access PCs and confidential information. The buttock pressure measurement system can also be incorporated into car seats and authenticate individuals simply by sitting on them. For example, personal authentication can be performed when starting a car engine, ensuring that only authenticated users can drive. This will improve the convenience of personal authentication when applied to office chairs and car seats.

[0033] The buttock pressure measurement system can be incorporated into fitness equipment to monitor a user's weight fluctuations and pressure changes in real time. A buttock pressure measurement system can be developed that can be incorporated into fitness equipment to monitor a user's weight fluctuations and pressure changes in real time. For example, it can be incorporated into a treadmill or exercise bike to collect data during exercise. The buttock pressure measurement system can also evaluate a user's exercise performance based on the collected data and provide feedback. Thus, by incorporating it into fitness equipment, a user's weight fluctuations and pressure changes can be monitored in real time.

[0034] The analysis unit can analyze the audio data in the toilet bowl and evaluate the health condition from the discharge sound. The analysis unit, for example, installs a microphone in the toilet bowl and collects the discharge sound. The analysis unit analyzes the collected audio data and evaluates the health condition from the characteristics of the discharge sound. For example, it analyzes the intensity and rhythm of the discharge sound to detect abnormalities in the digestive system. The analysis unit can also perform frequency analysis of the audio data and identify patterns of the discharge sound. In this way, by analyzing the audio data, the health condition can be evaluated from the discharge sound.

[0035] The analysis unit can evaluate health status by combining an odor sensor in the toilet bowl and analyzing odor data. For example, the analysis unit installs an odor sensor in the toilet bowl and collects odor data from excrement. It analyzes the collected odor data to evaluate health status. For example, it analyzes the strength and components of odors to detect abnormalities in the digestive system. The analysis unit can also perform component analysis of odor data and measure the concentration of specific chemical substances. This allows health status to be evaluated by analyzing odor data.

[0036] The analysis unit applies feces and urine image analysis technology to pet health management, and can analyze pet excrement to evaluate the health condition. The analysis unit, for example, develops a system that applies feces and urine image analysis technology to pet health management. For example, a camera is installed in a pet toilet, and images of the excrement are analyzed to evaluate the health condition. The analysis unit can also analyze the shape and color of pet excrement to evaluate the health condition. This makes it possible to evaluate the health condition of a pet by analyzing pet excrement.

[0037] The analysis unit applies feces and urine image analysis technology to the agricultural field, and can analyze livestock excrement to evaluate their health condition. The analysis unit, for example, develops a system that applies feces and urine image analysis technology to the agricultural field. For example, it photographs livestock excrement and evaluates its health condition using image analysis technology. The analysis unit can also analyze the shape and color of livestock excrement to evaluate its health condition. This makes it possible to evaluate the health condition of livestock by analyzing their excrement.

[0038] The analysis unit measures the water level fluctuations in the toilet bowl, allowing for more accurate measurement of the discharge amount. For example, the analysis unit installs a water level sensor in the toilet bowl and measures water level fluctuations caused by waste. This allows for the development of a system that accurately measures the discharge amount. For example, the amount of water level fluctuation is analyzed and the amount of waste is calculated. The water level sensor can also measure the water level using an ultrasonic sensor or a pressure sensor. This allows for more accurate measurement of the discharge amount by measuring the water level fluctuations.

[0039] The analysis unit can evaluate the health condition by combining the temperature changes inside the toilet bowl and analyzing the temperature data of the waste. For example, the analysis unit installs a temperature sensor inside the toilet bowl and collects temperature data of the waste. The analysis unit analyzes the collected temperature data and evaluates the health condition. For example, the temperature changes of the waste are analyzed to detect abnormalities in the digestive system. The temperature sensor can also measure temperature using an infrared sensor or a thermistor. This allows the health condition to be evaluated by analyzing the temperature data.

[0040] The analysis unit can apply the technology for determining the amount of waste generated to a kitchen waste disposal system, automatically measuring and managing the amount of waste. The analysis unit, for example, develops a system that applies the technology for determining the amount of waste generated to a kitchen waste disposal system. For example, sensors can be installed in trash cans to automatically measure and manage the amount of waste. The analysis unit can also monitor the amount of waste in real time and optimize the timing of waste collection. This makes it possible to automatically measure and manage the amount of waste, thereby enabling efficient waste disposal.

[0041] The analysis unit applies the technology to determine the amount of waste generated to a factory's waste management system, enabling real-time monitoring of the amount of waste. The analysis unit, for example, develops a system that applies the technology to determine the amount of waste generated to a factory's waste management system. For example, sensors can be installed in waste containers to monitor the amount of waste in real time. The analysis unit can also optimize waste disposal schedules based on the amount of waste. This makes it possible to monitor the amount of waste in real time, enabling efficient waste disposal.

[0042] A voice assistant function can be added to the data linkage system, allowing users to check their health status and receive alerts via voice. For example, a voice assistant function can be added to the data linkage system, allowing users to check their health status and receive alerts via voice. For example, if users inquire about health data via voice, the voice assistant will respond. The data linkage system can also issue a voice alert if an abnormality is detected. This means that by adding the voice assistant function, users can check their health status and receive alerts via voice.

[0043] The data linkage system can link with smartwatches and fitness trackers to manage comprehensive health data. For example, a data linkage system can be developed that links with smartwatches and fitness trackers to manage comprehensive health data. For example, it can link heart rate and step count data to comprehensively evaluate health status. The data linkage system can also make health management suggestions based on data obtained from smartwatches and fitness trackers. This allows comprehensive health data to be managed by linking with smartwatches and fitness trackers.

[0044] The data linkage system can be expanded to the entire smart home, allowing for health management and life checks for the entire house. For example, a system can be developed that can be expanded to the entire smart home to manage the health of the entire house and check the health of the entire family. For example, sensors can be installed in each room to monitor the health of all family members. The data linkage system can also issue an alert if an abnormality is detected. This makes it possible to manage the health of the entire house and check the health of the entire family by expanding the system to the entire smart home.

[0045] The data linkage system can be introduced into nursing care facilities to centrally manage the health status of multiple residents. For example, a data linkage system can be introduced into nursing care facilities to develop a system that centrally manages the health status of multiple residents. For example, it can monitor each resident's health data in real time and issue an alert if an abnormality is detected. The data linkage system can also optimize nursing care plans based on the resident's health status. As a result, introducing the system into nursing care facilities will enable centralized management of the health status of multiple residents.

[0046] The medical collaboration system will add a remote medical consultation function, allowing doctors to check patients' health data in real time and make diagnoses. For example, the medical collaboration system will add a remote medical consultation function and develop a system that allows doctors to check patients' health data in real time. For example, a diagnosis will be made via video call and necessary prescriptions will be given. The medical collaboration system can also provide support for doctors to make diagnoses based on the patient's health data. By adding the remote medical consultation function, doctors will be able to check patients' health data in real time and make diagnoses.

[0047] The medical collaboration system can add an AI diagnostic assistance function and make diagnostic suggestions based on the analysis results of Gemini. For example, the medical collaboration system can add an AI diagnostic assistance function and develop a system that makes diagnostic suggestions based on the analysis results of Gemini. For example, AI can assist in diagnosis based on the analysis results and support the doctor's diagnosis. In addition, the AI ​​diagnostic assistance function can make diagnostic suggestions using machine learning algorithms. As a result, by adding the AI ​​diagnostic assistance function, it becomes possible to make diagnostic suggestions based on the analysis results of Gemini.

[0048] The medical collaboration system can be introduced into fitness gyms and sports clubs, allowing trainers to manage the health status of members. For example, a system can be developed for introducing the medical collaboration system into fitness gyms and sports clubs, allowing trainers to manage the health status of members. For example, the system can monitor training data in real time and provide appropriate training plans. The medical collaboration system can also allow trainers to create individual fitness plans based on members' health data. This allows trainers to manage the health status of members by introducing the system into fitness gyms and sports clubs.

[0049] The medical collaboration system can be applied to health management systems in schools and companies to centrally manage the health status of students and employees. For example, the medical collaboration system can be applied to health management systems in schools and companies to develop a system that centrally manages the health status of students and employees. For example, it can monitor health data in real time and issue alerts if there are any abnormalities. The medical collaboration system can also make health management suggestions based on the health data of students and employees. This means that by applying it to health management systems in schools and companies, the health status of students and employees can be centrally managed.

[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 health management system also includes a voice recognition unit, allowing users to check their health status and receive alerts by voice. For example, when a user asks, "How is my health today?", the voice recognition unit responds with the analysis results by voice. If an abnormality is detected, a voice alert can also be issued. Thus, by adding the voice recognition unit, users can check their health status without using their hands.

[0052] The health management system further includes a nutrition management section that can collect the user's dietary data and reflect it in the evaluation of the user's health condition. For example, when the user inputs the details of their diet, the nutrition management section analyzes the balance of nutrients and uses this information to evaluate the user's health condition. The nutrition management section can also suggest meals based on the user's health condition. Thus, adding the nutrition management section enables more comprehensive health management.

[0053] The health management system further includes an exercise management unit that can collect the user's exercise data and reflect it in the evaluation of the user's health condition. For example, when the user inputs the details of the exercise, the exercise management unit analyzes the amount of exercise and calories burned, and uses this information to evaluate the user's health condition. The exercise management unit can also propose an exercise plan based on the user's health condition. Thus, adding the exercise management unit enables more comprehensive health management.

[0054] The health management system further includes a sleep management unit that can collect the user's sleep data and reflect it in the evaluation of their health condition. For example, when the user inputs the amount of sleep and quality, the sleep management unit analyzes the data and uses it to evaluate their health condition. The sleep management unit can also make suggestions for improving sleep according to the user's health condition. Thus, adding a sleep management unit enables more comprehensive health management.

[0055] The health management system also includes a stress management unit that can monitor the user's stress level and reflect it in the evaluation of their health condition. For example, when the user inputs their stress level, the stress management unit analyzes the data and uses it to evaluate their health condition. The stress management unit can also suggest stress reduction measures based on the user's health condition. Thus, adding a stress management unit enables more comprehensive health management.

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

[0057] Step 1: The camera is placed inside the toilet bowl to capture images of feces and urine. For example, the camera is placed inside the toilet bowl and can capture high-resolution images. It is also waterproof and can withstand the environment inside the toilet bowl. Step 2: The buttock pressure measurement system identifies individuals by measuring their weight and pressure (buttock pressure) using the toilet seat and footrest. For example, it measures weight using a pressure sensor built into the toilet seat and measures pressure using a pressure sensor installed in the footrest. It then uses an algorithm to identify individuals based on the measurement data. Step 3: The analysis unit analyzes the images of the stool and urine captured by the camera. For example, it uses an image analysis algorithm to analyze the shape and color of the stool and the transparency of the urine, and evaluates the health status based on the image data of the stool and urine. It also records the data on a daily / weekly / monthly basis so that the user can review it. Step 4: The notification unit notifies the user of the results obtained by the analysis unit. For example, the analysis results can be notified to the user via a smartphone app, and an alert can be issued if an abnormality is detected. Notifications can also be sent via messaging apps such as LINE.

[0058] (Example 2) A health management system according to an embodiment of the present invention is a system that analyzes images of stool and urine to manage the health condition of an individual. This enables the health management system to analyze images of stool and urine and manage the health condition of an individual in detail.

[0059] A health management system according to an embodiment includes a camera, a buttock pressure measurement system, an analysis unit, and a notification unit. The camera is installed on the toilet bowl and captures images of stool and urine. For example, the camera is installed inside the toilet bowl and can capture high-resolution images. The camera is also waterproof and can withstand the environment inside the toilet bowl. The buttock pressure measurement system identifies an individual by measuring weight and pressure (buttock pressure) using the toilet seat and footrest. For example, the buttock pressure measurement system measures weight using a pressure sensor built into the toilet seat. It can also measure pressure using a pressure sensor installed in the footrest. The buttock pressure measurement system uses an algorithm to identify an individual based on the measurement data. The analysis unit analyzes the stool and urine images captured by the camera. For example, the analysis unit analyzes the shape and color of stool and the transparency of urine using an image analysis algorithm. The analysis unit can also evaluate a health condition based on the image data of stool and urine. The analysis unit records data on a daily / weekly / monthly basis so that the user can view it. The notification unit notifies the user of the results obtained by the analysis unit. For example, the notification unit notifies the user of the analysis results via a smartphone app. The notification unit can also issue an alert if an abnormality is detected. Furthermore, the notification unit can also send notifications via messaging apps such as LINE. This allows the health management system according to the embodiment to analyze images of stool and urine and manage an individual's health condition. For example, a user can record daily output and stool condition to understand changes in their health condition. Furthermore, if an abnormality is detected, a prompt response can be made.

[0060] The buttock pressure measurement system can measure temperature changes on the toilet seat and add body temperature data to improve the accuracy of personal identification. For example, the buttock pressure measurement system installs a temperature sensor on the toilet seat and measures body temperature data when sitting. Combining the body temperature data with the buttock pressure data improves the accuracy of personal identification. For example, minute variations in body temperature can be analyzed to identify individual users. The buttock pressure measurement system can also collect data from the temperature sensor in real time and reflect it in the personal identification algorithm. In this way, adding body temperature data improves the accuracy of personal identification.

[0061] A buttock pressure measurement system can add a vibration sensor and analyze minute movements and vibration patterns to identify individuals. For example, a buttock pressure measurement system installs a vibration sensor on a toilet seat to measure minute movements and vibration patterns when sitting. This data is combined with buttock pressure data and analyzed to improve the accuracy of individual identification. For example, the system learns the characteristics of movements when sitting and identifies individual users. Furthermore, the vibration sensor can detect minute movements using an acceleration sensor or gyro sensor. As a result, adding a vibration sensor improves the accuracy of individual identification.

[0062] The buttock pressure measurement system uses an emotion estimation function to estimate a user's emotional state from weight fluctuations and pressure changes when sitting on a toilet seat, and can be used for individual identification. For example, a buttock pressure measurement system may be equipped with an emotion estimation function on a toilet seat and analyze weight fluctuations and pressure changes when sitting. The user's emotional state is estimated based on this data, improving the accuracy of individual identification. For example, stress and relaxation states can be identified. The emotion estimation function can also estimate the emotional state using a machine learning algorithm. As a result, using the emotion estimation function improves the accuracy of individual identification.

[0063] The buttock pressure measurement system can be applied to office chairs or car seats, allowing personal authentication simply by sitting on it. For example, a system can be developed that incorporates the buttock pressure measurement system into an office chair and authenticates individuals simply by sitting on it. For example, it can be linked to an office security system, ensuring that only authenticated users can access PCs and confidential information. The buttock pressure measurement system can also be incorporated into car seats and authenticate individuals simply by sitting on them. For example, personal authentication can be performed when starting a car engine, ensuring that only authenticated users can drive. This will improve the convenience of personal authentication when applied to office chairs and car seats.

[0064] The buttock pressure measurement system can be incorporated into fitness equipment to monitor a user's weight fluctuations and pressure changes in real time. A buttock pressure measurement system can be developed that can be incorporated into fitness equipment to monitor a user's weight fluctuations and pressure changes in real time. For example, it can be incorporated into a treadmill or exercise bike to collect data during exercise. The buttock pressure measurement system can also evaluate a user's exercise performance based on the collected data and provide feedback. Thus, by incorporating it into fitness equipment, a user's weight fluctuations and pressure changes can be monitored in real time.

[0065] By adding an emotion estimation function to the buttock pressure measurement system, it is possible to grasp the user's emotional state simply by sitting on it and make suggestions for stress management and relaxation. For example, by adding an emotion estimation function to the buttock pressure measurement system, a system can be developed that grasps the user's emotional state simply by sitting on it. For example, it can be incorporated into an office chair, detecting stress levels and making relaxation suggestions. In addition, the emotion estimation function can estimate the emotional state using a machine learning algorithm. By adding the emotion estimation function, it is possible to grasp the user's emotional state and make suggestions for stress management and relaxation.

[0066] The analysis unit can analyze the audio data in the toilet bowl and evaluate the health condition from the discharge sound. The analysis unit, for example, installs a microphone in the toilet bowl and collects the discharge sound. The analysis unit analyzes the collected audio data and evaluates the health condition from the characteristics of the discharge sound. For example, it analyzes the intensity and rhythm of the discharge sound to detect abnormalities in the digestive system. The analysis unit can also perform frequency analysis of the audio data and identify patterns of the discharge sound. In this way, by analyzing the audio data, the health condition can be evaluated from the discharge sound.

[0067] The analysis unit can evaluate health status by combining an odor sensor in the toilet bowl and analyzing odor data. For example, the analysis unit installs an odor sensor in the toilet bowl and collects odor data from excrement. It analyzes the collected odor data to evaluate health status. For example, it analyzes the strength and components of odors to detect abnormalities in the digestive system. The analysis unit can also perform component analysis of odor data and measure the concentration of specific chemical substances. This allows health status to be evaluated by analyzing odor data.

[0068] The analysis unit can use the emotion estimation function to estimate the user's emotional state based on the results of image analysis of stool and urine, and reflect this in the evaluation of the health condition. The analysis unit develops a system that estimates the user's emotional state based on the results of image analysis of stool and urine, for example. For example, if the state of excrement is poor, stress or anxiety can be estimated and reflected in the evaluation of the health condition. The emotion estimation function can also estimate the emotional state using a machine learning algorithm. As a result, the emotion estimation function can be used to reflect the emotional state in the evaluation of the health condition.

[0069] The analysis unit applies feces and urine image analysis technology to pet health management, and can analyze pet excrement to evaluate the health condition. The analysis unit, for example, develops a system that applies feces and urine image analysis technology to pet health management. For example, a camera is installed in a pet toilet, and images of the excrement are analyzed to evaluate the health condition. The analysis unit can also analyze the shape and color of pet excrement to evaluate the health condition. This makes it possible to evaluate the health condition of a pet by analyzing pet excrement.

[0070] The analysis unit applies feces and urine image analysis technology to the agricultural field, and can analyze livestock excrement to evaluate their health condition. The analysis unit, for example, develops a system that applies feces and urine image analysis technology to the agricultural field. For example, it photographs livestock excrement and evaluates its health condition using image analysis technology. The analysis unit can also analyze the shape and color of livestock excrement to evaluate its health condition. This makes it possible to evaluate the health condition of livestock by analyzing their excrement.

[0071] The analysis unit adds an emotion estimation function to the feces and urine image analysis technology, and is able to make health management suggestions based on the user's emotional state. The analysis unit, for example, adds an emotion estimation function to the feces and urine image analysis technology, and develops a system that estimates the user's emotional state based on the analysis results. For example, if the state of excrement is poor, stress or anxiety can be estimated and health management suggestions can be made. In addition, the emotion estimation function can estimate the emotional state using a machine learning algorithm. Thus, by adding the emotion estimation function, it becomes possible to make health management suggestions based on the user's emotional state.

[0072] The analysis unit measures the water level fluctuations in the toilet bowl, allowing for more accurate measurement of the discharge amount. For example, the analysis unit installs a water level sensor in the toilet bowl and measures water level fluctuations caused by waste. This allows for the development of a system that accurately measures the discharge amount. For example, the amount of water level fluctuation is analyzed and the amount of waste is calculated. The water level sensor can also measure the water level using an ultrasonic sensor or a pressure sensor. This allows for more accurate measurement of the discharge amount by measuring the water level fluctuations.

[0073] The analysis unit can evaluate the health condition by combining the temperature changes inside the toilet bowl and analyzing the temperature data of the waste. For example, the analysis unit installs a temperature sensor inside the toilet bowl and collects temperature data of the waste. The analysis unit analyzes the collected temperature data and evaluates the health condition. For example, the temperature changes of the waste are analyzed to detect abnormalities in the digestive system. The temperature sensor can also measure temperature using an infrared sensor or a thermistor. This allows the health condition to be evaluated by analyzing the temperature data.

[0074] The analysis unit uses the emotion estimation function to associate fluctuations in emission amount with the user's emotional state and detect signs of stress or poor health. The analysis unit develops a system that estimates the user's emotional state based on emission data, for example. For example, if emission amount is low, stress or poor health is estimated and reflected in the health status evaluation. The emotion estimation function can also estimate the emotional state using a machine learning algorithm. As a result, by using the emotion estimation function, it is possible to associate fluctuations in emission amount with the emotional state and detect signs of stress or poor health.

[0075] The analysis unit can apply the technology for determining the amount of waste generated to a kitchen waste disposal system, automatically measuring and managing the amount of waste. The analysis unit, for example, develops a system that applies the technology for determining the amount of waste generated to a kitchen waste disposal system. For example, sensors can be installed in trash cans to automatically measure and manage the amount of waste. The analysis unit can also monitor the amount of waste in real time and optimize the timing of waste collection. This makes it possible to automatically measure and manage the amount of waste, thereby enabling efficient waste disposal.

[0076] The analysis unit applies the technology to determine the amount of waste generated to a factory's waste management system, enabling real-time monitoring of the amount of waste. The analysis unit, for example, develops a system that applies the technology to determine the amount of waste generated to a factory's waste management system. For example, sensors can be installed in waste containers to monitor the amount of waste in real time. The analysis unit can also optimize waste disposal schedules based on the amount of waste. This makes it possible to monitor the amount of waste in real time, enabling efficient waste disposal.

[0077] The analysis unit can add an emotion estimation function and analyze fluctuations in emission amounts based on the user's emotional state, which can be useful for health management. The analysis unit, for example, develops a system that estimates the user's emotional state based on emission data. For example, if emission amounts are low, it can estimate stress or poor health, which can be useful for health management. In addition, the emotion estimation function can estimate the emotional state using a machine learning algorithm. By adding this emotion estimation function, it is possible to associate fluctuations in emission amounts with the emotional state, which can be useful for health management.

[0078] A voice assistant function can be added to the data linkage system, allowing users to check their health status and receive alerts via voice. For example, a voice assistant function can be added to the data linkage system, allowing users to check their health status and receive alerts via voice. For example, if users inquire about health data via voice, the voice assistant will respond. The data linkage system can also issue a voice alert if an abnormality is detected. This means that by adding the voice assistant function, users can check their health status and receive alerts via voice.

[0079] The data linkage system can link with smartwatches and fitness trackers to manage comprehensive health data. For example, a data linkage system can be developed that links with smartwatches and fitness trackers to manage comprehensive health data. For example, it can link heart rate and step count data to comprehensively evaluate health status. The data linkage system can also make health management suggestions based on data obtained from smartwatches and fitness trackers. This allows comprehensive health data to be managed by linking with smartwatches and fitness trackers.

[0080] The data linkage system can use an emotion estimation function to grasp the user's emotional state in real time and provide appropriate alerts and suggestions. For example, the data linkage system can add an emotion estimation function to develop a system that grasps the user's emotional state in real time. For example, it can detect stress levels and suggest relaxation. The emotion estimation function can also estimate the emotional state using a machine learning algorithm. This makes it possible to grasp the user's emotional state in real time and provide appropriate alerts and suggestions.

[0081] The data linkage system can be expanded to the entire smart home, allowing for health management and life checks for the entire house. For example, a system can be developed that can be expanded to the entire smart home to manage the health of the entire house and check the health of the entire family. For example, sensors can be installed in each room to monitor the health of all family members. The data linkage system can also issue an alert if an abnormality is detected. This makes it possible to manage the health of the entire house and check the health of the entire family by expanding the system to the entire smart home.

[0082] The data linkage system can be introduced into nursing care facilities to centrally manage the health status of multiple residents. For example, a data linkage system can be introduced into nursing care facilities to develop a system that centrally manages the health status of multiple residents. For example, it can monitor each resident's health data in real time and issue an alert if an abnormality is detected. The data linkage system can also optimize nursing care plans based on the resident's health status. As a result, introducing the system into nursing care facilities will enable centralized management of the health status of multiple residents.

[0083] The data linkage system can add an emotion estimation function to make customized health care suggestions based on the user's emotional state. For example, a data linkage system can be developed that adds an emotion estimation function to make customized health care suggestions based on the user's emotional state. For example, it can detect stress levels and make relaxation suggestions. Furthermore, the emotion estimation function can estimate the emotional state using a machine learning algorithm. Adding the emotion estimation function thus makes it possible to make customized health care suggestions based on the user's emotional state.

[0084] The medical collaboration system will add a remote medical consultation function, allowing doctors to check patients' health data in real time and make diagnoses. For example, the medical collaboration system will add a remote medical consultation function and develop a system that allows doctors to check patients' health data in real time. For example, a diagnosis will be made via video call and necessary prescriptions will be given. The medical collaboration system can also provide support for doctors to make diagnoses based on the patient's health data. By adding the remote medical consultation function, doctors will be able to check patients' health data in real time and make diagnoses.

[0085] The medical collaboration system can add an AI diagnostic assistance function and make diagnostic suggestions based on the analysis results of Gemini. For example, the medical collaboration system can add an AI diagnostic assistance function and develop a system that makes diagnostic suggestions based on the analysis results of Gemini. For example, AI can assist in diagnosis based on the analysis results and support the doctor's diagnosis. In addition, the AI ​​diagnostic assistance function can make diagnostic suggestions using machine learning algorithms. As a result, by adding the AI ​​diagnostic assistance function, it becomes possible to make diagnostic suggestions based on the analysis results of Gemini.

[0086] The medical collaboration system uses an emotion estimation function to provide doctors with information about a patient's emotional state, which can be useful in determining diagnoses and treatment plans. For example, the medical collaboration system may add an emotion estimation function to develop a system that provides doctors with information about a patient's emotional state. For example, the system may detect stress levels and notify doctors, which can be useful in determining diagnoses and treatment plans. The emotion estimation function can also estimate emotional states using machine learning algorithms. This allows the emotional estimation function to provide doctors with information about a patient's emotional state, which can be useful in determining diagnoses and treatment plans.

[0087] The medical collaboration system can be introduced into fitness gyms and sports clubs, allowing trainers to manage the health status of members. For example, a system can be developed for introducing the medical collaboration system into fitness gyms and sports clubs, allowing trainers to manage the health status of members. For example, the system can monitor training data in real time and provide appropriate training plans. The medical collaboration system can also allow trainers to create individual fitness plans based on members' health data. This allows trainers to manage the health status of members by introducing the system into fitness gyms and sports clubs.

[0088] The medical collaboration system can be applied to health management systems in schools and companies to centrally manage the health status of students and employees. For example, the medical collaboration system can be applied to health management systems in schools and companies to develop a system that centrally manages the health status of students and employees. For example, it can monitor health data in real time and issue alerts if there are any abnormalities. The medical collaboration system can also make health management suggestions based on the health data of students and employees. This means that by applying it to health management systems in schools and companies, the health status of students and employees can be centrally managed.

[0089] The medical collaboration system can add an emotion estimation function to make customized treatment suggestions based on the patient's emotional state. For example, the medical collaboration system can add an emotion estimation function to develop a system that makes customized treatment suggestions based on the patient's emotional state. For example, it can detect stress levels and make relaxation suggestions. In addition, the emotion estimation function can estimate the emotional state using a machine learning algorithm. By adding the emotion estimation function, it becomes possible to make customized treatment suggestions based on the patient's emotional state.

[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 health management system also includes a voice recognition unit, allowing users to check their health status and receive alerts by voice. For example, when a user asks, "How is my health today?", the voice recognition unit responds with the analysis results by voice. If an abnormality is detected, a voice alert can also be issued. Thus, by adding the voice recognition unit, users can check their health status without using their hands.

[0092] The health management system further includes a nutrition management section that can collect the user's dietary data and reflect it in the evaluation of the user's health condition. For example, when the user inputs the details of their diet, the nutrition management section analyzes the balance of nutrients and uses this information to evaluate the user's health condition. The nutrition management section can also suggest meals based on the user's health condition. Thus, adding the nutrition management section enables more comprehensive health management.

[0093] The health management system further includes an exercise management unit that can collect the user's exercise data and reflect it in the evaluation of the user's health condition. For example, when the user inputs the details of the exercise, the exercise management unit analyzes the amount of exercise and calories burned, and uses this information to evaluate the user's health condition. The exercise management unit can also propose an exercise plan based on the user's health condition. Thus, adding the exercise management unit enables more comprehensive health management.

[0094] The health management system further includes a sleep management unit that can collect the user's sleep data and reflect it in the evaluation of their health condition. For example, when the user inputs the amount of sleep and quality, the sleep management unit analyzes the data and uses it to evaluate their health condition. The sleep management unit can also make suggestions for improving sleep according to the user's health condition. Thus, adding a sleep management unit enables more comprehensive health management.

[0095] The health management system also includes a stress management unit that can monitor the user's stress level and reflect it in the evaluation of their health condition. For example, when the user inputs their stress level, the stress management unit analyzes the data and uses it to evaluate their health condition. The stress management unit can also suggest stress reduction measures based on the user's health condition. Thus, adding a stress management unit enables more comprehensive health management.

[0096] The health management system can further use an emotion estimation function to suggest health management based on the user's emotional state. For example, if the user is under stress, it can suggest relaxation. The emotion estimation function can also monitor the user's emotional state in real time and reflect this in the evaluation of the user's health condition. This makes it possible to use the emotion estimation function to suggest health management based on the user's emotional state.

[0097] The health management system can further use the emotion estimation function to suggest an exercise plan based on the user's emotional state. For example, if the user is under stress, it can suggest an exercise that has a relaxing effect. The emotion estimation function can also monitor the user's emotional state in real time and use this information to help optimize the exercise plan. This makes it possible to suggest an exercise plan based on the user's emotional state.

[0098] The health management system can further use the emotion estimation function to suggest a meal plan based on the user's emotional state. For example, if the user is under stress, it can suggest ingredients that have a relaxing effect. The emotion estimation function can also monitor the user's emotional state in real time and use this information to help optimize the meal plan. This makes it possible to suggest a meal plan based on the user's emotional state.

[0099] The health management system can further use the emotion estimation function to suggest sleep improvement measures based on the user's emotional state. For example, if the user is under stress, the system can suggest a relaxing sleep environment. The emotion estimation function can also monitor the user's emotional state in real time and help optimize sleep improvement. This makes it possible to use the emotion estimation function to suggest sleep improvement measures based on the user's emotional state.

[0100] The health management system can further use the emotion estimation function to suggest stress reduction based on the user's emotional state. For example, if the user is under stress, the system can suggest relaxation. The emotion estimation function can also monitor the user's emotional state in real time and help optimize stress reduction. This makes it possible to use the emotion estimation function to suggest stress reduction based on the user's emotional state.

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

[0102] Step 1: The camera is placed inside the toilet bowl to capture images of feces and urine. For example, the camera is placed inside the toilet bowl and can capture high-resolution images. It is also waterproof and can withstand the environment inside the toilet bowl. Step 2: The buttock pressure measurement system identifies individuals by measuring their weight and pressure (buttock pressure) using the toilet seat and footrest. For example, it measures weight using a pressure sensor built into the toilet seat and measures pressure using a pressure sensor installed in the footrest. It then uses an algorithm to identify individuals based on the measurement data. Step 3: The analysis unit analyzes the images of the stool and urine captured by the camera. For example, it uses an image analysis algorithm to analyze the shape and color of the stool and the transparency of the urine, and evaluates the health status based on the image data of the stool and urine. It also records the data on a daily / weekly / monthly basis so that the user can review it. Step 4: The notification unit notifies the user of the results obtained by the analysis unit. For example, the analysis results can be notified to the user via a smartphone app, and an alert can be issued if an abnormality is detected. Notifications can also be sent via messaging apps such as LINE.

[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 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, the 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 area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," 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 types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[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 camera installed in the toilet, A buttock pressure measurement system that identifies individuals by measuring their weight and pressure (buttock pressure) using the toilet seat and footrest, an analysis unit that analyzes the images of feces and urine acquired by the camera; a notification unit that notifies the results obtained by the analysis unit. A system characterized by:

2. The buttock pressure measuring system includes: Measure the temperature change of the toilet seat and add body temperature data to improve the accuracy of individual identification 2. The system of claim 1.

3. The buttock pressure measuring system includes: Apply it to an office chair or car seat and perform personal authentication just by sitting on it.

2. The system of claim 1.

4. Analyzing the voice data in the toilet bowl and evaluating the health condition from the discharge sound 2. The system of claim 1.

5. Measures the water level fluctuations in the toilet bowl to measure the discharge amount more accurately.

2. The system of claim 1.

6. A voice assistant function has been added to the data linkage system, allowing users to check their health status and receive alerts by voice.

2. The system of claim 1.

7. Emotion estimation function provides doctors with information about the patient's emotional state to help them make diagnoses and decide on treatment options.

2. The system of claim 1.

8. The buttock pressure measuring system includes: Using the emotion estimation function, the user's emotional state is estimated from weight fluctuations and pressure changes when sitting on the toilet seat, and this is used for personal identification.

2. The system of claim 1.

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