System

Abstract

An object of a system according to an embodiment is to analyze health data of a user and perform appropriate health management.SOLUTION: A system according to an embodiment includes a health data collector, an abnormality tracker, a schedule suggester, and an advice provider. The health data collection unit collects health data of a user. The anomaly tracker analyzes the health data collected by the health data collector and tracks changes and signs of anomalies. The schedule proposal unit proposes an appropriate check-up schedule based on the result tracked by the abnormality tracking unit. The advice providing unit provides advice related to health on the basis of a result of the check-up.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of not being able to efficiently collect and analyze users' health data and provide appropriate health management.

[0005] The system according to the embodiment aims to analyze health data of a user and provide appropriate health management. [Means for solving the problem]

[0006] The system according to the embodiment includes a health data collection unit, an abnormality tracking unit, a schedule proposal unit, and an advice provision unit. The health data collection unit collects health data of a user. The abnormality tracking unit analyzes the health data collected by the health data collection unit and tracks signs of changes or abnormalities. The schedule proposal unit proposes an appropriate check-up schedule based on the results tracked by the abnormality tracking unit. The advice provision unit provides health advice based on the results of the check-up. [Effects of the Invention]

[0007] The system according to the embodiment can analyze the health data of a user and provide appropriate health management. [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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[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 check system according to an embodiment of the present invention collects user health data, uses a generative AI to track changes and signs of abnormalities, and provides regular health checks. This allows the health check system to continuously monitor the user's health status, detect abnormalities early, and take appropriate measures.

[0029] A health check system according to an embodiment includes a health data collection unit, an anomaly tracking unit, a schedule suggestion unit, and an advice providing unit. The health data collection unit collects a user's health data. For example, the health data collection unit acquires data such as heart rate, step count, and sleep patterns from a wearable device such as a smartwatch or fitness tracker. The health data collection unit also collects data such as weight, blood pressure, and dietary details manually entered by the user. For example, the health data collection unit acquires heart rate data from the smartwatch. The health data collection unit can also acquire step count data from the fitness tracker. The health data collection unit can also collect weight data manually entered by the user. The anomaly tracking unit analyzes the health data collected by the health data collection unit and tracks signs of changes or abnormalities. For example, the anomaly tracking unit can detect abnormal increases in heart rate. The anomaly tracking unit can also detect disturbances in sleep patterns. The anomaly tracking unit can also detect sudden changes in blood pressure. The schedule suggestion unit suggests an appropriate check-up schedule based on the results tracked by the anomaly tracking unit. For example, the schedule suggestion unit can suggest regular blood pressure measurements. The schedule suggestion unit can also suggest an annual health check. The schedule suggestion unit can also suggest a check-up at the optimal time according to the user's lifestyle. The advice providing unit provides health advice based on the check-up results. For example, the advice providing unit can suggest an exercise plan to overcome a lack of exercise. The advice providing unit can also suggest dietary improvements. The advice providing unit can also suggest stress management methods. This allows the health check system according to the embodiment to continuously monitor the user's health condition, detect abnormalities early, and take measures. For example, the output unit displays the scoring results to students and teachers via a web application or a mobile application. If students or teachers wish to receive feedback on paper, the results are printed using a printer. Sending the results via email allows for quick feedback by sending the results directly to students and parents.

[0030] The health data collection unit can collect data from IoT devices in the home in addition to data from wearable devices. For example, the health data collection unit collects weight and body fat percentage data from a smart scale and integrates it with data from the wearable device. This allows for detailed tracking of changes in the user's weight and body fat percentage. The health data collection unit also uses a smart mirror to periodically record changes in the user's posture and body shape and collects this as health data. For example, it can detect deterioration in posture and changes in body shape. The health data collection unit also collects food consumption data from a smart refrigerator in the home to gain a detailed understanding of the user's eating habits. For example, it can monitor the amount of vegetables and fruits consumed. This allows for the provision of more detailed health information.

[0031] The health data collection unit can analyze the impact of environmental factors on health based on the user's living environment. For example, the health data collection unit collects air quality data from smart home devices and correlates it with the user's respiratory health. For example, it monitors PM2.5 and CO2 concentrations. The health data collection unit also uses a noise sensor to measure noise levels within the home and correlates them with the user's sleep patterns. For example, it analyzes the impact of nighttime noise on sleep quality. The health data collection unit also collects indoor temperature and humidity data and correlates it with the user's thermoregulation and comfort level. For example, it evaluates the impact of an appropriate temperature and humidity environment on health. This enables health management that takes environmental factors into account.

[0032] The health data collection unit can manage the health of the entire family by linking the pet's health data with the data of other members of the household. The health data collection unit, for example, collects health data from the pet's wearable device and integrates it with the health data of the entire family. For example, it monitors the pet's activity level and food intake. The health data collection unit also collects health data from other members of the household and centrally manages the health status of the entire family. For example, it integrates weight and blood pressure data for all family members. The health data collection unit also sets common health goals based on the health data of the entire family and monitors the progress of these goals. For example, it sets a walking goal for the entire family. This makes it possible to manage the health of the entire family.

[0033] The health data collection unit can collect health data using the portable device and the mobile app so that it can also be used during travel and business trips. The health data collection unit uses, for example, a portable device to collect health data even during travel and business trips. For example, a portable blood pressure monitor and heart rate monitor are used. The health data collection unit also uses a mobile app to allow health data to be entered even during travel and business trips. For example, the contents of meals and the amount of exercise are recorded in the app. The health data collection unit also uses a GPS function to collect environmental data at the travel destination or business trip destination and associate it with the health data. For example, the air quality and temperature of the place of stay are monitored. This allows health data to be collected even during travel and business trips.

[0034] The anomaly tracking unit can perform more accurate anomaly detection based on past medical records and the genetic information. For example, the anomaly tracking unit retrieves the user's past medical records from a database and integrates them with current health data for analysis. For example, past medical history and treatment history are taken into consideration. The anomaly tracking unit also analyzes the user's genetic information and performs anomaly detection taking into account genetic risk factors. For example, it performs risk assessment based on family history. The anomaly tracking unit also performs individualized health risk assessment based on past medical records and genetic information to detect signs of anomalies early. For example, it suggests preventive measures based on specific genetic risks. This enables highly accurate anomaly detection.

[0035] When the anomaly tracking unit detects signs of an abnormality, the AI ​​can automatically collaborate with the medical institution and make the appointment for the necessary test or consultation. For example, when the anomaly tracking unit detects signs of an abnormality, the AI ​​automatically notifies the user's family doctor and makes an appointment for the necessary test or consultation. For example, if an abnormal heart rate is detected, an electrocardiogram test is scheduled. Furthermore, when the AI ​​detects signs of an abnormality, the anomaly tracking unit selects an appropriate medical institution based on the user's health insurance information and makes an appointment. For example, it automatically searches for a nearby specialist. Furthermore, when the anomaly tracking unit detects signs of an abnormality, the AI ​​takes the user's schedule into consideration and makes an appointment for the test or consultation at the optimal date and time. For example, it sets an appointment based on the user's free time. This allows for prompt collaboration with a medical institution when signs of an abnormality are detected.

[0036] The anomaly tracking unit can compare the data of the other users and identify the anomaly based on the collective health trend. The anomaly tracking unit, for example, compares the health data of the other users to identify signs of an abnormality. For example, it detects an abnormality by comparing with the average heart rate of users of the same age. The anomaly tracking unit also analyzes collective health trends to identify signs of an abnormality. For example, it detects an abnormality based on the spread of seasonal influenza. The anomaly tracking unit also identifies signs of an abnormality early based on the data of other users. For example, it analyzes data of users in the same area to detect health risks specific to the area. This makes it possible to identify an abnormality based on collective health trends.

[0037] When detecting the abnormality sign, the anomaly tracking unit can analyze the user's lifestyle habits and behavioral patterns and propose specific improvement measures. For example, when detecting the abnormality sign, the anomaly tracking unit can analyze the user's dietary content and propose specific improvement measures. For example, it can propose improving nutritional balance. The anomaly tracking unit can also analyze the user's exercise habits and propose specific improvement measures for the abnormality sign. For example, it can provide an exercise plan to eliminate lack of exercise. The anomaly tracking unit can also analyze the user's sleep patterns and propose specific improvement measures for the abnormality sign. For example, it can provide advice to improve sleep quality. In this way, specific improvement measures can be proposed.

[0038] The schedule suggestion unit can suggest the optimal timing for the check-up based on the user's past health data and lifestyle rhythm. The schedule suggestion unit, for example, analyzes the user's past health data and suggests the optimal timing for the check-up. For example, it determines the next measurement date based on past blood pressure measurement data. The schedule suggestion unit also analyzes the user's lifestyle rhythm and suggests the optimal timing for the check-up. For example, it suggests the optimal date and time for a health check based on the user's sleep pattern. The schedule suggestion unit also integrates the past health data and lifestyle rhythm to suggest an individualized check-up schedule. For example, it suggests regular fitness checks taking into account the user's exercise habits. This makes it possible to suggest check-ups at the optimal times.

[0039] The schedule suggestion unit can perform the customization according to the user's occupation and lifestyle. The schedule suggestion unit proposes an optimal check-up schedule based on, for example, the user's occupation information. For example, it proposes a daytime health check for a user whose occupation involves many night shifts. The schedule suggestion unit also analyzes the user's lifestyle data and proposes a customized check-up schedule. For example, it proposes a health check at the destination of a business trip for a user who frequently travels for work. The schedule suggestion unit also proposes check-ups for specific health risks according to the user's occupation and lifestyle. For example, it proposes regular posture checks for a user who does a lot of desk work. This makes it possible to customize the schedule according to the user's occupation and lifestyle.

[0040] The schedule suggestion unit can share the check-up schedule with the entire family or coworkers, thereby promoting the health management as a group. The schedule suggestion unit, for example, proposes a common check-up schedule based on health data of the entire family, and the whole family manages their health together. For example, it proposes a health check date for the whole family. The schedule suggestion unit also shares health data with coworkers and proposes a common check-up schedule. For example, it sets a health check date for the whole workplace. The schedule suggestion unit also shares the check-up schedule with family and coworkers, thereby promoting group health management. For example, doing health checks together with family and coworkers can increase motivation. This can promote group health management.

[0041] The schedule suggestion unit can link the check-up schedule with the travel or business trip schedule, allowing the user to manage their health even while traveling. The schedule suggestion unit, for example, suggests the optimal timing for a check-up based on the user's travel or business trip schedule. For example, it suggests a health check at the business trip destination. The schedule suggestion unit also suggests a check-up using a portable device so that health can be managed even while traveling or on a business trip. For example, it uses a portable blood pressure monitor or heart rate monitor. The schedule suggestion unit also links with the user's travel schedule so that health can be managed even while traveling. For example, it suggests a simple health check that can be done while traveling. This allows health management even while traveling.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The health check system can also include a data sharing unit that anonymizes the user's health data and shares it with research institutions and medical institutions. For example, with the user's consent, the data sharing unit can provide the anonymized health data to research institutions to help them conduct research to gain new insights into health. The data sharing unit can also collaborate with medical institutions to analyze local health trends based on the anonymized data and contribute to improving the health of the entire region. Furthermore, the data sharing unit can anonymize the user's health data and share it with insurance companies to propose insurance plans based on individual health risks. This allows the user's health data to contribute to a wide range of health research and the improvement of local medical care.

[0044] The health check system may further include a coaching unit that provides a virtual health coach based on the user's health data. For example, the coaching unit may analyze the user's exercise data and propose an individual fitness plan. The coaching unit may also provide a nutritionally balanced meal plan based on the user's dietary data. The coaching unit may also analyze the user's sleep data and provide advice on improving sleep quality. This allows the user to manage their health while receiving individual advice from the virtual health coach.

[0045] The health check system may further include a prediction unit that predicts health risks based on the user's health data. For example, the prediction unit may analyze the user's past health data and predict future health risks. The prediction unit may also predict health risks taking into account genetic risk factors based on the user's genetic information. Furthermore, the prediction unit may analyze the user's lifestyle data and predict the risk of lifestyle-related diseases. This allows the user to understand future health risks in advance and take preventive measures.

[0046] The health check system may further include a goal setting unit that sets health goals based on the user's health data and monitors the progress toward those goals. For example, the goal setting unit may set a daily step goal based on the user's exercise data. The goal setting unit may also set a daily calorie intake goal based on the user's dietary data. The goal setting unit may also set a daily sleep duration goal based on the user's sleep data. This allows the user to set specific health goals and manage their health while monitoring the progress toward those goals.

[0047] The health check system may further include an education unit that provides health-related educational content based on the user's health data. For example, the education unit may analyze the user's health data and provide information on individual health risks. The education unit may also provide advice on healthy lifestyle habits based on the user's lifestyle data. Furthermore, the education unit may provide the latest health-related research results and news based on the user's health data. This allows the user to deepen their health knowledge and manage their health more effectively.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The health data collection unit collects the user's health data. For example, it obtains data such as heart rate, number of steps, and sleep patterns from wearable devices such as smartwatches and fitness trackers. It also collects data such as weight, blood pressure, and dietary information manually entered by the user. Step 2: The anomaly tracking unit analyzes the health data collected by the health data collection unit and tracks signs of changes or abnormalities, such as abnormally high heart rates, disturbed sleep patterns, or sudden changes in blood pressure. Step 3: The schedule suggestion unit proposes an appropriate check-up schedule based on the results of tracking by the anomaly tracking unit. For example, it suggests regular blood pressure measurements, annual health check-ups, and check-ups at optimal times based on the user's lifestyle. Step 4: The advice section provides health advice based on the results of the check-up, such as an exercise plan to address lack of exercise, suggestions for improving your diet, and methods for managing stress.

[0050] (Example 2) A health check system according to an embodiment of the present invention collects user health data, uses a generative AI to track changes and signs of abnormalities, and provides regular health checks. This allows the health check system to continuously monitor the user's health status, detect abnormalities early, and take appropriate measures.

[0051] A health check system according to an embodiment includes a health data collection unit, an anomaly tracking unit, a schedule suggestion unit, and an advice providing unit. The health data collection unit collects a user's health data. For example, the health data collection unit acquires data such as heart rate, step count, and sleep patterns from a wearable device such as a smartwatch or fitness tracker. The health data collection unit also collects data such as weight, blood pressure, and dietary details manually entered by the user. For example, the health data collection unit acquires heart rate data from the smartwatch. The health data collection unit can also acquire step count data from the fitness tracker. The health data collection unit can also collect weight data manually entered by the user. The anomaly tracking unit analyzes the health data collected by the health data collection unit and tracks signs of changes or abnormalities. For example, the anomaly tracking unit can detect abnormal increases in heart rate. The anomaly tracking unit can also detect disturbances in sleep patterns. The anomaly tracking unit can also detect sudden changes in blood pressure. The schedule suggestion unit suggests an appropriate check-up schedule based on the results tracked by the anomaly tracking unit. For example, the schedule suggestion unit can suggest regular blood pressure measurements. The schedule suggestion unit can also suggest an annual health check. The schedule suggestion unit can also suggest a check-up at the optimal time according to the user's lifestyle. The advice providing unit provides health advice based on the check-up results. For example, the advice providing unit can suggest an exercise plan to overcome a lack of exercise. The advice providing unit can also suggest dietary improvements. The advice providing unit can also suggest stress management methods. This allows the health check system according to the embodiment to continuously monitor the user's health condition, detect abnormalities early, and take measures. For example, the output unit displays the scoring results to students and teachers via a web application or a mobile application. If students or teachers wish to receive feedback on paper, the results are printed using a printer. Sending the results via email allows for quick feedback by sending the results directly to students and parents.

[0052] The health data collection unit can collect data from IoT devices in the home in addition to data from wearable devices. For example, the health data collection unit collects weight and body fat percentage data from a smart scale and integrates it with data from the wearable device. This allows for detailed tracking of changes in the user's weight and body fat percentage. The health data collection unit also uses a smart mirror to periodically record changes in the user's posture and body shape and collects this as health data. For example, it can detect deterioration in posture and changes in body shape. The health data collection unit also collects food consumption data from a smart refrigerator in the home to gain a detailed understanding of the user's eating habits. For example, it can monitor the amount of vegetables and fruits consumed. This allows for the provision of more detailed health information.

[0053] The health data collection unit estimates the user's emotional state and can also incorporate stress levels and mood fluctuations as health data. The health data collection unit, for example, analyzes the user's voice data to estimate the emotional state. For example, it evaluates stress levels based on changes in voice tone and speaking style. The health data collection unit also captures the user's facial expressions with a camera and estimates the emotional state using generative AI. For example, it analyzes changes in facial expressions such as smiling and furrowing the brow. The health data collection unit also analyzes the content of the user's social media posts to estimate the emotional state. For example, it determines that the user is in a good mood if there are many positive posts. This enables health management that takes the user's emotional state into account.

[0054] The health data collection unit can analyze the impact of environmental factors on health based on the user's living environment. For example, the health data collection unit collects air quality data from smart home devices and correlates it with the user's respiratory health. For example, it monitors PM2.5 and CO2 concentrations. The health data collection unit also uses a noise sensor to measure noise levels within the home and correlates them with the user's sleep patterns. For example, it analyzes the impact of nighttime noise on sleep quality. The health data collection unit also collects indoor temperature and humidity data and correlates it with the user's thermoregulation and comfort level. For example, it evaluates the impact of an appropriate temperature and humidity environment on health. This enables health management that takes environmental factors into account.

[0055] The health data collection unit can manage the health of the entire family by linking the pet's health data with the data of other members of the household. The health data collection unit, for example, collects health data from the pet's wearable device and integrates it with the health data of the entire family. For example, it monitors the pet's activity level and food intake. The health data collection unit also collects health data from other members of the household and centrally manages the health status of the entire family. For example, it integrates weight and blood pressure data for all family members. The health data collection unit also sets common health goals based on the health data of the entire family and monitors the progress of these goals. For example, it sets a walking goal for the entire family. This makes it possible to manage the health of the entire family.

[0056] The health data collection unit can collect health data using the portable device and the mobile app so that it can also be used during travel and business trips. The health data collection unit uses, for example, a portable device to collect health data even during travel and business trips. For example, a portable blood pressure monitor and heart rate monitor are used. The health data collection unit also uses a mobile app to allow health data to be entered even during travel and business trips. For example, the contents of meals and the amount of exercise are recorded in the app. The health data collection unit also uses a GPS function to collect environmental data at the travel destination or business trip destination and associate it with the health data. For example, the air quality and temperature of the place of stay are monitored. This allows health data to be collected even during travel and business trips.

[0057] The health data collection unit can use an emotion estimation function to estimate the emotion of the user when entering health data in real time and provide the positive feedback. For example, the health data collection unit analyzes the user's facial expression when entering health data to estimate the user's emotional state. For example, if the user smiles frequently, positive feedback is provided. The health data collection unit also uses voice analysis to estimate the user's emotional state and provide positive feedback. For example, if the user enters health data in a cheerful voice, an encouraging message is displayed. The health data collection unit also provides feedback in real time based on the emotion estimation data when entering health data, and provides advice to strengthen positive emotions. For example, appropriate encouragement or praise is displayed according to the input content. This can increase motivation for data entry.

[0058] The anomaly tracking unit can perform more accurate anomaly detection based on past medical records and the genetic information. For example, the anomaly tracking unit retrieves the user's past medical records from a database and integrates them with current health data for analysis. For example, past medical history and treatment history are taken into consideration. The anomaly tracking unit also analyzes the user's genetic information and performs anomaly detection taking into account genetic risk factors. For example, it performs risk assessment based on family history. The anomaly tracking unit also performs individualized health risk assessment based on past medical records and genetic information to detect signs of anomalies early. For example, it suggests preventive measures based on specific genetic risks. This enables highly accurate anomaly detection.

[0059] The anomaly tracking unit can use a generative AI to analyze the user's emotional state and track the impact of the emotional fluctuations on the health condition. The anomaly tracking unit, for example, analyzes the user's voice data to estimate the emotional state. For example, it evaluates the stress level based on changes in voice tone and speaking style. The anomaly tracking unit also captures the user's facial expressions with a camera and estimates the emotional state using a generative AI. For example, it analyzes changes in facial expressions such as smiling and furrowing the brow. The anomaly tracking unit also analyzes the content of the user's social media posts to estimate the emotional state. For example, it determines that the user is in a good mood if there are many positive posts. This makes it possible to track the impact of emotional fluctuations on the health condition.

[0060] When the anomaly tracking unit detects signs of an abnormality, the AI ​​can automatically collaborate with the medical institution and make the appointment for the necessary test or consultation. For example, when the anomaly tracking unit detects signs of an abnormality, the AI ​​automatically notifies the user's family doctor and makes an appointment for the necessary test or consultation. For example, if an abnormal heart rate is detected, an electrocardiogram test is scheduled. Furthermore, when the AI ​​detects signs of an abnormality, the anomaly tracking unit selects an appropriate medical institution based on the user's health insurance information and makes an appointment. For example, it automatically searches for a nearby specialist. Furthermore, when the anomaly tracking unit detects signs of an abnormality, the AI ​​takes the user's schedule into consideration and makes an appointment for the test or consultation at the optimal date and time. For example, it sets an appointment based on the user's free time. This allows for prompt collaboration with a medical institution when signs of an abnormality are detected.

[0061] The anomaly tracking unit can compare the data of the other users and identify the anomaly based on the collective health trend. The anomaly tracking unit, for example, compares the health data of the other users to identify signs of an abnormality. For example, it detects an abnormality by comparing with the average heart rate of users of the same age. The anomaly tracking unit also analyzes collective health trends to identify signs of an abnormality. For example, it detects an abnormality based on the spread of seasonal influenza. The anomaly tracking unit also identifies signs of an abnormality early based on the data of other users. For example, it analyzes data of users in the same area to detect health risks specific to the area. This makes it possible to identify an abnormality based on collective health trends.

[0062] When detecting the abnormality sign, the anomaly tracking unit can analyze the user's lifestyle habits and behavioral patterns and propose specific improvement measures. For example, when detecting the abnormality sign, the anomaly tracking unit can analyze the user's dietary content and propose specific improvement measures. For example, it can propose improving nutritional balance. The anomaly tracking unit can also analyze the user's exercise habits and propose specific improvement measures for the abnormality sign. For example, it can provide an exercise plan to eliminate lack of exercise. The anomaly tracking unit can also analyze the user's sleep patterns and propose specific improvement measures for the abnormality sign. For example, it can provide advice to improve sleep quality. In this way, specific improvement measures can be proposed.

[0063] The schedule suggestion unit can suggest the optimal timing for the check-up based on the user's past health data and lifestyle rhythm. The schedule suggestion unit, for example, analyzes the user's past health data and suggests the optimal timing for the check-up. For example, it determines the next measurement date based on past blood pressure measurement data. The schedule suggestion unit also analyzes the user's lifestyle rhythm and suggests the optimal timing for the check-up. For example, it suggests the optimal date and time for a health check based on the user's sleep pattern. The schedule suggestion unit also integrates the past health data and lifestyle rhythm to suggest an individualized check-up schedule. For example, it suggests regular fitness checks taking into account the user's exercise habits. This makes it possible to suggest check-ups at the optimal times.

[0064] The schedule suggestion unit can analyze the emotional state of the user using the generation AI and suggest the check-up during times when the user is less stressed. The schedule suggestion unit, for example, analyzes the user's voice data to estimate the user's emotional state. For example, it evaluates the stress level based on changes in voice tone and speaking style and suggests a check-up during times when the user is less stressed. The schedule suggestion unit also captures the user's facial expressions with a camera and estimates the emotional state using the generation AI. For example, it suggests a health check-up during times when the user smiles a lot. The schedule suggestion unit also analyzes the content of the user's SNS posts to estimate the emotional state. For example, it suggests regular health check-ups during times when the user posts a lot of positive things. This makes it possible to suggest a check-up during times when the user is less stressed.

[0065] The schedule suggestion unit can perform the customization according to the user's occupation and lifestyle. The schedule suggestion unit proposes an optimal check-up schedule based on, for example, the user's occupation information. For example, it proposes a daytime health check for a user whose occupation involves many night shifts. The schedule suggestion unit also analyzes the user's lifestyle data and proposes a customized check-up schedule. For example, it proposes a health check at the destination of a business trip for a user who frequently travels for work. The schedule suggestion unit also proposes check-ups for specific health risks according to the user's occupation and lifestyle. For example, it proposes regular posture checks for a user who does a lot of desk work. This makes it possible to customize the schedule according to the user's occupation and lifestyle.

[0066] The schedule suggestion unit can share the check-up schedule with the entire family or coworkers, thereby promoting the health management as a group. The schedule suggestion unit, for example, proposes a common check-up schedule based on health data of the entire family, and the whole family manages their health together. For example, it proposes a health check date for the whole family. The schedule suggestion unit also shares health data with coworkers and proposes a common check-up schedule. For example, it sets a health check date for the whole workplace. The schedule suggestion unit also shares the check-up schedule with family and coworkers, thereby promoting group health management. For example, doing health checks together with family and coworkers can increase motivation. This can promote group health management.

[0067] The schedule suggestion unit can link the check-up schedule with the travel or business trip schedule, allowing the user to manage their health even while traveling. The schedule suggestion unit, for example, suggests the optimal timing for a check-up based on the user's travel or business trip schedule. For example, it suggests a health check at the business trip destination. The schedule suggestion unit also suggests a check-up using a portable device so that health can be managed even while traveling or on a business trip. For example, it uses a portable blood pressure monitor or heart rate monitor. The schedule suggestion unit also links with the user's travel schedule so that health can be managed even while traveling. For example, it suggests a simple health check that can be done while traveling. This allows health management even while traveling.

[0068] The schedule suggestion unit can use the emotion estimation function to consider the user's emotions when proposing the check-up schedule and make suggestions that will elicit the positive emotions. For example, the schedule suggestion unit analyzes the user's facial expressions and estimates their emotional state when proposing the check-up schedule. For example, it may suggest a health check-up when there are many smiles. The schedule suggestion unit also uses voice analysis to estimate the user's emotional state and make suggestions that will elicit positive emotions. For example, it may suggest a health check-up when there are many positive voices talking. The schedule suggestion unit also analyzes the content of the user's SNS posts and estimates their emotional state. For example, it may suggest regular health check-ups when there are many positive posts. This makes it possible to make suggestions that will elicit positive emotions.

[0069] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0070] The health check system can also include a data sharing unit that anonymizes the user's health data and shares it with research institutions and medical institutions. For example, with the user's consent, the data sharing unit can provide the anonymized health data to research institutions to help them conduct research to gain new insights into health. The data sharing unit can also collaborate with medical institutions to analyze local health trends based on the anonymized data and contribute to improving the health of the entire region. Furthermore, the data sharing unit can anonymize the user's health data and share it with insurance companies to propose insurance plans based on individual health risks. This allows the user's health data to contribute to a wide range of health research and the improvement of local medical care.

[0071] The health check system may further include a coaching unit that provides a virtual health coach based on the user's health data. For example, the coaching unit may analyze the user's exercise data and propose an individual fitness plan. The coaching unit may also provide a nutritionally balanced meal plan based on the user's dietary data. The coaching unit may also analyze the user's sleep data and provide advice on improving sleep quality. This allows the user to manage their health while receiving individual advice from the virtual health coach.

[0072] The health check system may further include a prediction unit that predicts health risks based on the user's health data. For example, the prediction unit may analyze the user's past health data and predict future health risks. The prediction unit may also predict health risks taking into account genetic risk factors based on the user's genetic information. Furthermore, the prediction unit may analyze the user's lifestyle data and predict the risk of lifestyle-related diseases. This allows the user to understand future health risks in advance and take preventive measures.

[0073] The health check system may further include a goal setting unit that sets health goals based on the user's health data and monitors the progress toward those goals. For example, the goal setting unit may set a daily step goal based on the user's exercise data. The goal setting unit may also set a daily calorie intake goal based on the user's dietary data. The goal setting unit may also set a daily sleep duration goal based on the user's sleep data. This allows the user to set specific health goals and manage their health while monitoring the progress toward those goals.

[0074] The health check system may further include an education unit that provides health-related educational content based on the user's health data. For example, the education unit may analyze the user's health data and provide information on individual health risks. The education unit may also provide advice on healthy lifestyle habits based on the user's lifestyle data. Furthermore, the education unit may provide the latest health-related research results and news based on the user's health data. This allows the user to deepen their health knowledge and manage their health more effectively.

[0075] The health check system may further include a relaxation suggestion unit that suggests relaxation methods based on the user's emotional state. For example, the relaxation suggestion unit may analyze the user's voice data, evaluate the user's stress level, and suggest relaxation methods. For example, it may suggest deep breathing or meditation techniques. The relaxation suggestion unit may also analyze the user's facial expressions, estimate the user's emotional state, and suggest relaxation methods. For example, it may suggest exercises to increase smiling. Furthermore, the relaxation suggestion unit may analyze the content of the user's social media posts, estimate the user's emotional state, and suggest relaxation methods. For example, it may suggest hobbies or activities that bring out positive emotions. This allows the user to practice relaxation methods that correspond to their emotional state and reduce stress.

[0076] The health check system can further include an exercise suggestion unit that suggests an appropriate exercise plan based on the user's emotional state. For example, the exercise suggestion unit analyzes the user's voice data, estimates the user's emotional state, and suggests an exercise plan. For example, during times of high stress, the exercise suggestion unit suggests yoga, which has a relaxing effect. The exercise suggestion unit can also analyze the user's facial expressions, estimate the user's emotional state, and suggest an exercise plan. For example, it can suggest light walking when the user is feeling low. Furthermore, the exercise suggestion unit can analyze the content of the user's social media posts, estimate the user's emotional state, and suggest an exercise plan. For example, it can suggest dance exercises to bring out positive emotions. This allows the user to practice an exercise plan that suits their emotional state and maintain their health.

[0077] The health check system can further include a meal suggestion unit that suggests an appropriate meal plan based on the user's emotional state. For example, the meal suggestion unit analyzes the user's voice data, infers the user's emotional state, and suggests a meal plan. For example, during times of high stress, it suggests recipes using ingredients that have a relaxing effect. The meal suggestion unit can also analyze the user's facial expressions, infers the user's emotional state, and suggest a meal plan. For example, during times of low energy, it suggests a nutritionally balanced meal. Furthermore, the meal suggestion unit can analyze the content of the user's social media posts, infers the user's emotional state, and suggest a meal plan. For example, it can suggest a dessert recipe that elicits positive emotions. This allows the user to implement a meal plan that suits their emotional state and maintain their health.

[0078] The health check system can further include a sleep suggestion unit that suggests an appropriate sleeping environment based on the user's emotional state. For example, the sleep suggestion unit analyzes the user's voice data, estimates the user's emotional state, and suggests a sleeping environment. For example, during times of high stress, it suggests music with a relaxing effect. The sleep suggestion unit can also analyze the user's facial expressions, estimate the user's emotional state, and suggest a sleeping environment. For example, it can suggest comfortable bedding during times of low energy. Furthermore, the sleep suggestion unit can analyze the content of the user's social media posts, estimate the user's emotional state, and suggest a sleeping environment. For example, it can suggest aromatherapy to elicit positive emotions. This allows the user to create a sleeping environment that suits their emotional state and achieve high-quality sleep.

[0079] The health check system can further include a mental health suggestion unit that suggests appropriate mental health care based on the user's emotional state. For example, the mental health suggestion unit analyzes the user's voice data, estimates the user's emotional state, and suggests mental health care. For example, it may suggest counseling during times of high stress. The mental health suggestion unit can also analyze the user's facial expressions, estimate the user's emotional state, and suggest mental health care. For example, it may suggest a relaxation session during times of low energy. Furthermore, the mental health suggestion unit can analyze the content of the user's social media posts, estimate the user's emotional state, and suggest mental health care. For example, it may suggest a hobby activity that will bring out positive emotions. This allows the user to practice mental health care according to their emotional state and maintain their mental health.

[0080] The processing flow of the second embodiment will be briefly explained below.

[0081] Step 1: The health data collection unit collects the user's health data. For example, it obtains data such as heart rate, number of steps, and sleep patterns from wearable devices such as smartwatches and fitness trackers. It also collects data such as weight, blood pressure, and dietary information manually entered by the user. Step 2: The anomaly tracking unit analyzes the health data collected by the health data collection unit and tracks signs of changes or abnormalities, such as abnormally high heart rates, disturbed sleep patterns, or sudden changes in blood pressure. Step 3: The schedule suggestion unit proposes an appropriate check-up schedule based on the results of tracking by the anomaly tracking unit. For example, it suggests regular blood pressure measurements, annual health check-ups, and check-ups at optimal times based on the user's lifestyle. Step 4: The advice section provides health advice based on the results of the check-up, such as an exercise plan to address lack of exercise, suggestions for improving your diet, and methods for managing stress.

[0082] 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.

[0083] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<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.

[0084] 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.

[0085] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0086] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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).

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0095] 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. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0101] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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).

[0106] 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.

[0107] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0108] 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.

[0109] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0110] In the headset type terminal 314, 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 headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0126] In the robot 414, 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. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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."

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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]

[0149] 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 health data collection unit that collects health data of a user; an abnormality tracking unit that analyzes the health data collected by the health data collecting unit and tracks changes or signs of abnormalities; a schedule suggestion unit that suggests an appropriate check-up schedule based on the results of tracking by the anomaly tracking unit; an advice providing unit that provides health advice based on the results of the check-up. A system characterized by:

2. The health data collection unit: In addition to data from wearable devices, data from IoT devices in the home will also be collected.

2. The system of claim 1.

3. The health data collection unit: The emotional state of the user is estimated, and stress levels and mood fluctuations are also captured as health data.

2. The system of claim 1.

4. The health data collection unit: Analyzing the impact of environmental factors on health based on the user's living environment 2. The system of claim 1.

5. The health data collection unit: Linking pet health data with that of other household members to manage the health of the entire family 2. The system of claim 1.

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