System

Abstract

A system is provided.SOLUTION: Means for collecting data of sleeping hours, heart rate, blood oxygen concentration, electrocardiogram, and skin temperature by using a wearable device for constantly measuring biological information of a user, means for automatically collecting weather information on the Internet and acquiring data such as temperature, atmospheric pressure, and humidity, and means for displaying a question related to a daily physical condition of a user by using a generated AI; This system includes a data collecting means for allowing a user to digitize and answer, a means for integrating and analyzing collected biological information, weather information and the answer data of the user, and for predicting a situation that the user is likely to feel a bad physical condition, and a means for notifying the user of the information of preventive measures or improvement measures based on the predicted result.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] In modern society, many people suffer from lifestyle-related diseases and sudden onsets of poor health. These diseases are difficult to predict and prevent because they arise from a complex interplay of multiple factors, including individual lifestyles, biometric information, and weather conditions. Furthermore, conventional health management systems are unable to collect biometric information in real time and perform integrated analysis of weather information, limiting their response to the event. As a result, they are unable to provide specific preventative measures or deliver medications in advance, effectively supporting users' health management. Therefore, there is a need for a system that can collect and analyze multiple data in real time and provide users with preventative and improvement measures. [Means for solving the problem]

[0005] This invention provides a system that uses a wearable device and a generative AI to continuously measure a user's biometric information. Specifically, it includes a means for collecting data on sleep time, heart rate, blood oxygen level, electrocardiogram, and skin temperature. Furthermore, comprehensive data is collected by automatically collecting weather information from the Internet and combining it with a means for acquiring data such as temperature, barometric pressure, and humidity. This provides a data collection means in which the generative AI displays questions about the user's daily health condition, and the user quantifies and answers the questions. It also includes a means for integrating and analyzing the collected biometric information, weather information, and user response data to predict situations in which the user is likely to feel unwell. It also provides a means for notifying the user of information on preventive and remedial measures based on the prediction results. The system also proposes a means for a doctor to suggest preventive measures and for the advance delivery of necessary medications based on the predicted state of poor health.

[0006] A "wearable device" is an electronic device that is always attached to the user's body and is used to measure and record biometric information.

[0007] "Biometric information" refers to data that indicates a user's biological condition, such as heart rate, sleep time, blood oxygen concentration, electrocardiogram, and skin temperature.

[0008] "Weather information" is data that indicates the weather conditions in a specific area or time, such as temperature, air pressure, and humidity.

[0009] "Generative AI" is artificial intelligence used to analyze collected data and make predictions and suggestions.

[0010] "Data collection means" refers to mechanisms and devices for collecting biometric information, meteorological information, and user input information.

[0011] "Data analysis" is the act or process of processing and synthesizing collected data to derive patterns and trends.

[0012] "Prediction" is the act of predicting future events or conditions based on collected and analyzed data.

[0013] "Preventive measures" are specific actions or suggestions taken to prevent predicted illnesses or problems.

[0014] "Improvements" are specific actions or suggestions offered to improve a current situation or condition.

[0015] A "notification means" is a mechanism or device for conveying specific information to a user.

[0016] "Pre-delivery of medicines" refers to the act of delivering necessary medicines to a user in advance when poor health is predicted. [Brief explanation of the drawings]

[0017] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9]1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0018] 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.

[0019] First, the terms used in the following description will be explained.

[0020] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0021] 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.

[0022] 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.

[0023] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0024] 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."

[0025] [First embodiment]

[0026] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0027] 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.

[0028] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0029] 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.

[0030] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0031] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0032] 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.

[0033] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 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.

[0035] 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.

[0036] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0037] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0038] The present invention is a system that combines a wearable device, generative AI, and a smartphone, and aims to efficiently manage the user's physical condition. This system is implemented by the following means.

[0039] Wearable device data collection

[0040] Device behavior

[0041] Wearable devices constantly measure the user's biometric information, such as heart rate, sleep time, blood oxygen level, electrocardiogram, and skin temperature, and periodically transmit this data to a smartphone. For example, heart rate and sleep patterns are measured every hour and transmitted to the smartphone via Bluetooth or other communication methods.

[0042] Automatic collection of weather information

[0043] Server Operation

[0044] The server automatically collects real-time temperature, pressure, and humidity data from weather data providers on the Internet, and stores this data in a database for later analysis.

[0045] Collecting User Input

[0046] Device behavior

[0047] The smartphone sends a notification to the user at a specific time each day asking about their health. The user answers by rating their health on a scale of 1 to 10, and the information is saved on the smartphone. The saved data is then sent to a server and stored in a database.

[0048] Data integration and analysis

[0049] Server Operation

[0050] The server combines biometric data from the wearable device, weather information, and the user's health assessment data, and analyzes this data using a generative AI that identifies specific patterns and trends for each user and predicts the likelihood of poor health.

[0051] Prevention and Notification

[0052] Server Operation

[0053] Based on the AI's predictions, the server identifies situations in which users are likely to feel unwell and sends a notification to their smartphone containing specific preventative and remedial measures, allowing users to proactively manage their health.

[0054] Physician precautions and advance medication delivery

[0055] Server Operation

[0056] If illness is predicted, the server will send a notification to the connected medical service, and doctors can use this information to suggest appropriate preventive measures and, if necessary, arrange for the advance delivery of medication.

[0057] Specific examples

[0058] For example, if a user's wearable device indicates that their heart rate remains higher than normal, and weather data predicts that the temperature and humidity will continue to be high, the generative AI will determine that the user is at high risk of developing poor health. The server will then notify the user's smartphone of this information and recommend preventative measures such as "staying hydrated." The server will also notify a linked medical service and, in some cases, arrange for medication to be delivered to the user in advance.

[0059] As a result, the present invention enables users to manage their physical condition based on weather conditions and biological information, and prevent poor physical condition from occurring.

[0060] The processing flow will be explained below.

[0061] Step 1:

[0062] Terminal (wearable device) operation

[0063] Wearable devices constantly measure the user's biometric information, such as heart rate, sleep time, blood oxygen level, electrocardiogram, and skin temperature, and this data is temporarily stored in the device.

[0064] Step 2:

[0065] Terminal (wearable device) operation

[0066] The measured data is transferred to a smartphone at regular intervals (for example, every hour) using wireless communication means such as Bluetooth.

[0067] Step 3:

[0068] Device (smartphone) operation

[0069] The smartphone receives the biometric data received from the wearable device and stores it locally, where it is ready to be sent to a server.

[0070] Step 4:

[0071] Server Operation

[0072] The server periodically obtains real-time weather information such as temperature, pressure, and humidity from weather data providers on the Internet, and stores the obtained weather information in a database.

[0073] Step 5:

[0074] Device (smartphone) operation

[0075] At a specific time each day (e.g., 8:00 a.m.), the user will receive a notification from their smartphone asking the question, "How are you feeling today?"

[0076] Step 6:

[0077] User Actions

[0078] The user answers questions about their physical condition using a number from 1 to 10. For example, if they feel good, they enter "8," and if they feel bad, they enter "3."

[0079] Step 7:

[0080] Device (smartphone) operation

[0081] The physical condition evaluation data entered by the user is stored in the smartphone and then sent to the server.

[0082] Step 8:

[0083] Server Operation

[0084] The server receives the biometric information, weather information, and user's physical condition evaluation data sent from the wearable device and stores them in a database.

[0085] Step 9:

[0086] Server Operation

[0087] The generative AI uses the collected data to analyze it, comparing it with past data to identify patterns and trends in the user's poor health.

[0088] Step 10:

[0089] Server Operation

[0090] Based on the prediction results, the generating AI identifies days and situations when there is a high possibility of poor health and notifies the user's smartphone of this information.

[0091] Step 11:

[0092] Device (smartphone) operation

[0093] The smartphone receives notifications from the server and provides the user with specific preventative and improvement measures, such as advice like "Drink more water today."

[0094] Step 12:

[0095] Server Operation

[0096] Based on the predicted results of poor health, notifications are sent to linked medical services (e.g., HELPO).

[0097] Step 13:

[0098] Server Operation

[0099] Based on the information provided, a medical service professional (e.g., a doctor) will suggest appropriate preventive measures to the user and arrange for the advance delivery of necessary medications.

[0100] Step 14:

[0101] User Actions

[0102] The user checks the preventive and remedial measures displayed on their smartphone, puts them into practice, and, if necessary, receives and takes the medicine delivered.

[0103] By using the above processing steps, the present invention can efficiently manage the user's physical condition and prevent poor physical condition from occurring.

[0104] Example 1

[0105] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0106] In modern society, many people lead busy lives, making it difficult to properly manage their health. Furthermore, because it is difficult to predict the impact of weather conditions, daily lifestyle habits, stress, and other factors on physical condition, it is difficult to take appropriate preventive measures to prevent illness before it occurs. Furthermore, even if illness is predicted, there is no way to seek medical advice or obtain necessary medications in advance, making it difficult to respond quickly.

[0107] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0108] In this invention, the server includes: a means for collecting data on sleep time, heart rate, blood oxygen concentration, electrocardiogram, and skin temperature using a wearable device for constantly measuring the user's biometric information; a means for wirelessly transferring the collected biometric information to a smartphone and sending it to the server via the Internet; a means for automatically collecting weather information from the Internet and acquiring data such as temperature, air pressure, and humidity; a data collection means for displaying questions about the user's daily health using a generation AI and allowing the user to quantify and respond; a means for integrating and analyzing the collected biometric information, weather information, and user response data to predict situations in which the user is likely to feel unwell; and a means for notifying the user of information on preventive and remedial measures based on the prediction results. This allows the user to understand their own health condition and take appropriate preventive measures. Furthermore, if a health condition is predicted, the user can receive appropriate advice from a doctor or obtain necessary medication in advance, enabling a prompt response.

[0109] A "wearable device" is a device that is worn on the user's body to continuously measure biometric information.

[0110] "Biometric information" refers to data that indicates the user's health status, such as heart rate, sleep time, blood oxygen concentration, electrocardiogram, and skin temperature.

[0111] "Wireless communication" is a method of sending and receiving data between devices using wireless technologies such as Bluetooth and Wi-Fi.

[0112] "Weather information" refers to information about atmospheric conditions such as temperature, air pressure, and humidity, and is obtained from data providing services on the Internet.

[0113] "Generative AI" is an algorithm that uses techniques such as machine learning and deep learning to analyze data and generate patterns and predictions.

[0114] A "smartphone" is a portable information terminal that can connect to the Internet and is equipped with various sensors and communication functions.

[0115] The "data collection means" is a system that uses wearable devices or smartphones to collect biometric information, weather information, and user health evaluation data.

[0116] "Data integration" is the process of combining data collected from multiple sources into a single data set.

[0117] "Preventive measures" are specific actions or advice taken to reduce the risk of ill health.

[0118] "Means of notification" refers to a method of providing information to users by displaying a message on a device such as a smartphone.

[0119] "Medical services" are health management and treatment support services provided by professionals such as doctors and pharmacists.

[0120] A "cloud environment" is an infrastructure for using data storage and computing resources provided over the Internet.

[0121] "Data analysis" is the process of analyzing collected data using statistical or computational methods to derive meaningful information or predictive results.

[0122] The present invention is a system for efficiently managing a user's physical condition, which is configured by combining a wearable device, a generation AI, and a smartphone. This system is implemented by the following means.

[0123] Wearable device deployment and data collection

[0124] First, the user puts on a wearable device. This device has the function of constantly measuring biometric information such as heart rate, sleep time, blood oxygen level, electrocardiogram, and skin temperature. For example, heart rate is measured periodically every hour and temporarily stored in the device's memory. The data is then periodically transferred to a smartphone via Bluetooth communication.

[0125] Receiving and transferring data via smartphone

[0126] The smartphone receives the biometric information sent from the wearable device. This data is stored in the smartphone's local storage and simultaneously sent to a server via the Internet. The smartphone periodically uploads the biometric information to the server, enabling centralized data management.

[0127] Automatic collection of weather information

[0128] The server accesses a weather data provider on the Internet and automatically obtains real-time weather data (temperature, air pressure, humidity, etc.) The obtained data is stored in a database on the server and used for subsequent data analysis.

[0129] Collecting User Input

[0130] The smartphone sends a notification asking about the user's physical condition at a specific time every day. The user rates their physical condition on a scale of 1 to 10, and this data is saved on the smartphone. The saved data is periodically sent to a server and stored in a database.

[0131] Data integration and generation AI analysis

[0132] The server combines biometric data from the wearable device, weather information, and the user's health assessment data. This combined data set is then fed into a generative AI model for further analysis. The generative AI detects specific patterns and trends and predicts the risk of poor health.

[0133] Preventive measures and notifications

[0134] Based on the analysis results of the AI, the server generates appropriate preventive measures for cases where there is a high risk of illness. For example, specific advice such as "stay hydrated" and "take adequate rest" is included. These preventive measures are notified to the user's smartphone, allowing them to take measures in real time.

[0135] Physician precautions and advance medication delivery

[0136] If an illness is predicted, the server will send a notification to the linked medical service. Based on this information, doctors can suggest appropriate preventive measures to the user and, if necessary, arrange for advance delivery of medication. This allows users to reduce the risk of illness in advance.

[0137] Examples of concrete examples and prompts

[0138] For example, if a user's wearable device indicates that their heart rate remains higher than normal, and weather data predicts that the temperature and humidity will continue to be high, the generative AI may determine that the user is at high risk of becoming ill. The server then notifies the user's smartphone of this information and suggests preventative measures such as "staying hydrated." It also notifies medical services and arranges for medication to be delivered in advance if necessary.

[0139] An example of a prompt for the generative AI model is, "If the user's heart rate is higher than normal and weather data indicates that the temperature and humidity will be high for several days in a row, please suggest specific preventive measures for managing their health."

[0140] As described above, the present invention provides a system for efficiently managing a user's physical condition based on the user's biological information and weather conditions, thereby preventing poor physical condition before it occurs.

[0141] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0142] Step 1:

[0143] Collecting biometric information using wearable devices

[0144] Device behavior

[0145] Wearable devices constantly measure biometric information such as heart rate, sleep time, blood oxygen level, electrocardiogram, skin temperature, etc. The measured data is stored in the device's internal memory.

[0146] Input: User's biological information (heart rate, sleep time, blood oxygen level, electrocardiogram, skin temperature)

[0147] Output: Stored biometric data

[0148] Step 2:

[0149] Transferring data to a smartphone

[0150] Device behavior

[0151] The wearable device periodically transmits the collected data via Bluetooth to a smartphone, which receives the data and stores it in its internal storage.

[0152] Input: Biometric data sent from a wearable device

[0153] Output: Biometric data stored on a smartphone

[0154] Step 3:

[0155] Automatic collection of weather information

[0156] Server Operation

[0157] The server periodically obtains real-time weather data (temperature, air pressure, humidity) from a weather data provider on the Internet and stores it in a database.

[0158] Input: Weather information from an online weather data provider

[0159] Output: Weather data stored on the server

[0160] Step 4:

[0161] Collecting User Input

[0162] Device behavior

[0163] The smartphone will prompt the user with questions about their health at a specific time each day. The user will rate their health on a scale of 1 to 10, which will be saved on the smartphone. This data will then be periodically sent to a server.

[0164] Input: Health evaluation data entered by the user into their smartphone

[0165] Output: Health evaluation data sent to the server

[0166] Step 5:

[0167] Data integration and generation AI analysis

[0168] Server Operation

[0169] The server integrates biometric data from the wearable device, weather information, and the user's health assessment data. This data is then input into a generative AI model for analysis. The generative AI detects specific patterns and trends and predicts the risk of poor health.

[0170] Input: Biometric information from wearable devices, weather information, and user's physical condition evaluation data

[0171] Output: Poor health risk prediction results by generative AI

[0172] Step 6:

[0173] Preventive measures and notifications

[0174] Server Operation

[0175] The server generates appropriate preventive and remedial measures based on the predictions made by the AI. These preventive measures are then sent to the user's smartphone. For example, specific advice such as "stay hydrated" is provided.

[0176] Input: Prediction results by generative AI

[0177] Output: Preventive measures notified to your smartphone

[0178] Step 7:

[0179] Physician precautions and advance medication delivery

[0180] Server Operation

[0181] If the server predicts a high risk of illness, it notifies the medical service, who can then use the information to suggest preventative measures and, if necessary, arrange for the advance delivery of medication.

[0182] Input: Prediction results by the generation AI, user situation information

[0183] Output: Proposal of preventive measures by medical services and advance delivery of medicines

[0184] (Application example 1)

[0185] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0186] Employee illness in brick-and-mortar stores is a problem that leads to reduced work efficiency and increased safety risks. While there is a need for methods and systems to continuously monitor employee health and prevent illness before it occurs, current methods make it difficult to grasp the situation in real time or provide appropriate preventive measures. Furthermore, personalized advice for individual employees is often not provided, and only general warnings are issued. Therefore, the challenge is to develop a system that can efficiently manage employee health and provide preventive measures immediately.

[0187] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0188] In this invention, the server includes: means for collecting data on sleep time, heart rate, blood oxygen concentration, electrocardiogram, and skin temperature using a wearable device for constantly measuring a user's biometric information; means for automatically collecting weather information from the Internet and acquiring data such as temperature, barometric pressure, and humidity; means for displaying questions about the user's daily health using a generative AI and for the user to quantify and answer; means for integrating and analyzing the collected biometric information, weather information, and user response data to predict situations in which the user is likely to feel unwell; means for notifying the user of information on preventive and remedial measures based on the prediction results; and means for monitoring the health of employees in physical stores in real time and providing proactive alerts and personalized human care. This enables efficient management of employee health and the provision of appropriate preventive measures in real time.

[0189] A "wearable device" is a device worn by a user that constantly measures biometric information.

[0190] "Biometric information" refers to information such as the user's heart rate, sleep time, blood oxygen concentration, electrocardiogram, and skin temperature.

[0191] "Weather information" refers to data such as temperature, air pressure, and humidity obtained from weather data services on the Internet.

[0192] "Generative AI" is artificial intelligence that uses collected data to analyze and predict.

[0193] The "questions about health" are questions to quantify the user's daily health condition.

[0194] "Data collection means" refers to the means of collecting biometric information, weather information, and user response data using wearable devices and generative AI.

[0195] The "integration and analysis method" is a method that integrates collected biometric information, weather information, and user response data, and uses generative AI to predict situations in which users are likely to feel unwell.

[0196] "Information on preventive measures and improvement measures" refers to specific measures and advice provided when a situation is predicted in which the user is likely to feel unwell.

[0197] A "physical store" is a face-to-face business facility that sells goods or provides services.

[0198] "Employee health monitoring" refers to the continuous real-time monitoring of the biometric information of employees working in physical stores.

[0199] A "proactive alert" is a notification that warns you in advance when poor health is predicted.

[0200] "Personalized human care" refers to providing care and advice tailored to each employee's individual health condition.

[0201] "Doctor's recommendations for preventive measures" are advice on preventive measures provided by a medical professional when an illness is predicted.

[0202] "Pre-delivery of medicines" means delivering necessary medicines to the user in advance.

[0203] A "cloud environment" is a virtual environment that stores and manages data via the Internet.

[0204] The "data analysis means" is a means for analyzing collected data and predicting poor health.

[0205] The present invention is a system for efficiently managing the health of employees in brick-and-mortar stores in real time. This system is composed of a wearable device, a generative AI, and a smartphone. Specific embodiments of this system are described below.

[0206] Wearable device data collection

[0207] Device behavior

[0208] The server constantly measures biometric information from the wearable device worn by the user, including heart rate, sleep time, blood oxygen level, electrocardiogram, and skin temperature. This data is periodically transferred to a smartphone via Bluetooth or other communication methods.

[0209] Automatic collection of weather information

[0210] Server Operation

[0211] The server automatically collects real-time temperature, pressure, and humidity data from weather data providers on the Internet, and stores the collected weather information in a database for later analysis.

[0212] Collecting User Input

[0213] Device behavior

[0214] The device sends a notification to employees at a specific time each day asking them questions about their health. Employees rate their health on a scale of 1 to 10, and the information is saved on their smartphone. The saved data is sent to a server and stored in a database.

[0215] Data integration and analysis

[0216] Server Operation

[0217] The server combines biometric data from the wearable devices, weather information, and user health assessment data, and analyzes this data using Generative AI, which identifies specific patterns and tendencies for each employee and predicts the likelihood of poor health.

[0218] Prevention and Notification

[0219] Server Operation

[0220] Based on the predictions of the generating AI, the server identifies situations in which employees are likely to feel unwell and sends a notification to their smartphone containing specific preventative and remedial measures, allowing employees to proactively manage their health.

[0221] Physician precautions and advance medication delivery

[0222] Server Operation

[0223] If illness is predicted, the server will send a notification to the connected medical service, and doctors can use this information to suggest appropriate preventive measures and, if necessary, arrange for the advance delivery of medication.

[0224] Specific examples

[0225] For example, if an employee's wearable device indicates that their heart rate remains higher than normal, and weather data predicts that the temperature and humidity will continue to be high, the generative AI will determine that the employee is at high risk of becoming ill. The server will then notify the employee's smartphone of this information and provide preventative measures such as taking breaks and staying hydrated. It will also notify a collaborative medical service and, in some cases, arrange for medication to be delivered to the employee in advance.

[0226] Prompt Sentence Examples

[0227] Predict health risks based on heart rate and weather data and suggest appropriate preventive measures.

[0228] In this way, the present invention integrates the biometric information of employees in physical stores with weather information and utilizes generative AI to achieve more efficient and safe health management.

[0229] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0230] Step 1:

[0231] The server periodically collects biometric information such as heart rate, sleep time, blood oxygen level, electrocardiogram, and skin temperature from the wearable device worn by the user. The collected biometric information is transferred to a smartphone using a communication method such as Bluetooth. The smartphone receives the information and sends it to the server.

[0232] Input: Biometric information obtained from a wearable device

[0233] Output: Biometric information sent to the smartphone and server

[0234] Step 2:

[0235] The server periodically obtains real-time weather information such as temperature, pressure, and humidity from a weather data provider. The obtained weather information is stored in a database and used for later analysis.

[0236] Input: Weather information from an internet weather data provider

[0237] Output: Weather information stored in the database on the server

[0238] Step 3:

[0239] The device sends a notification to the user at a specific time each day asking about their health. The user answers by rating their health on a scale of 1 to 10, and the information is saved on the smartphone. The saved data is sent to a server and stored in a database.

[0240] Input: User's physical condition rating

[0241] Output: User's health evaluation data stored on the server

[0242] Step 4:

[0243] The server integrates the collected biometric information, weather information, and the user's health assessment data, and analyzes the data using a generation AI. The generation AI uses a specific algorithm to input each piece of data and predict the situations in which the user is likely to feel unwell.

[0244] Input: Biometric information, weather information, user's physical condition evaluation data

[0245] Output: Risk assessment of poor health

[0246] Step 5:

[0247] The server sends notifications to the user's smartphone with preventive and remedial measures based on the risk of illness predicted by the generative AI. The notifications include specific instructions for action (e.g., taking a break, drinking water, etc.).

[0248] Input: Risk assessment results of the generated AI

[0249] Output: Notifications of preventive and remedial measures sent to your smartphone

[0250] Step 6:

[0251] If a high risk of illness is predicted, the server will send a notification to the associated medical service, and medical professionals will use the predicted information to suggest preventative measures and, if necessary, arrange for the advance delivery of medication.

[0252] Input: Generative AI high-risk assessment results

[0253] Output: Notification sent to medical services and precautions taken by doctors, drug delivery arrangements

[0254] Step 7:

[0255] The server stores all collected data in a cloud environment, allowing the generative AI to analyze this data, which will improve the accuracy of future predictions.

[0256] Input: All collected data

[0257] Output: Data stored in the cloud environment

[0258] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0259] The present invention is a system that combines a wearable device, generative AI, an emotion engine, and a smartphone, and aims to efficiently manage the user's physical condition and emotions. This system is implemented by the following means.

[0260] Wearable device data collection

[0261] Device behavior

[0262] Wearable devices constantly measure the user's biometric information, such as heart rate, sleep time, blood oxygen level, electrocardiogram, and skin temperature, and periodically transmit this data to a smartphone. For example, heart rate and sleep patterns are measured every hour and transmitted to the smartphone via Bluetooth or other communication methods.

[0263] Automatic collection of weather information

[0264] Server Operation

[0265] The server automatically collects real-time temperature, pressure, and humidity data from weather data providers on the Internet, and stores this data in a database for later analysis.

[0266] Collecting User Input

[0267] Device behavior

[0268] The smartphone sends a notification to the user at a specific time each day asking about their health. The user answers by rating their health on a scale of 1 to 10, and the information is saved on the smartphone. The saved data is then sent to a server and stored in a database.

[0269] Sentiment analysis with emotion engine

[0270] Server Operation

[0271] The server uses an emotion engine to analyze the user's emotional state based on the collected biometric information and the user's physical condition assessment data. For example, it can infer stress or fatigue from fluctuations in heart rate or abnormal sleep patterns.

[0272] Data integration and analysis

[0273] Server Operation

[0274] The server combines biometric information, weather information, and the user's health assessment data sent from the wearable device, as well as emotional data from the emotion engine, and analyzes this data using a generative AI. The generative AI identifies specific patterns and tendencies for each user and predicts the likelihood of poor health.

[0275] Prevention and Notification

[0276] Server Operation

[0277] Based on the AI's predictions, the server identifies situations in which the user is likely to feel unwell and sends a notification to their smartphone. The notification includes specific preventive and remedial measures, allowing users to proactively manage their health. Furthermore, preventive measures, including stress management and psychological support based on emotional data, are also provided.

[0278] Physician precautions and advance medication delivery

[0279] Server Operation

[0280] If illness is predicted, the server will send a notification to the connected medical service, and doctors can use this information to suggest appropriate preventive measures and, if necessary, arrange for the advance delivery of medication.

[0281] Specific examples

[0282] For example, a user's wearable device may indicate that their heart rate remains higher than normal, and weather data predicts that the temperature and humidity will continue to be high. In this case, the generative AI determines that there is a high risk of illness. The emotion engine further analyzes the user's emotional state and detects a high stress level. The server then notifies the user's smartphone of this information and offers preventative measures, such as "Drink more fluids and take more breaks today." The server also sends a notification to a connected medical service, and in some cases arranges for medication to be delivered to the user in advance.

[0283] As a result, the present invention enables the user to manage their physical condition and emotions based on weather conditions, emotional state, and biological information, and prevent poor physical condition from occurring.

[0284] The processing flow will be explained below.

[0285] Step 1:

[0286] Terminal (wearable device) operation

[0287] Wearable devices constantly measure the user's biometric information, such as heart rate, sleep time, blood oxygen level, electrocardiogram, and skin temperature, and temporarily store this data within the device.

[0288] Step 2:

[0289] Terminal (wearable device) operation

[0290] The measured data is transferred to a smartphone at regular intervals (for example, every hour) using wireless communication means such as Bluetooth.

[0291] Step 3:

[0292] Device (smartphone) operation

[0293] The smartphone receives the biometric data received from the wearable device and stores it locally, where it is prepared for transmission to a server.

[0294] Step 4:

[0295] Server Operation

[0296] The server periodically obtains real-time weather information such as temperature, pressure, and humidity from weather data providers on the Internet, and stores the obtained weather information in a database.

[0297] Step 5:

[0298] Device (smartphone) operation

[0299] At a specific time each day (e.g., 8:00 a.m.), the user will receive a notification from their smartphone asking the question, "How are you feeling today?"

[0300] Step 6:

[0301] User Actions

[0302] The user answers questions about their physical condition using a number from 1 to 10. For example, if they feel good, they enter "8," and if they feel bad, they enter "3."

[0303] Step 7:

[0304] Device (smartphone) operation

[0305] The physical condition evaluation data entered by the user is stored in the smartphone and then sent to the server.

[0306] Step 8:

[0307] Server Operation

[0308] The server receives the biometric information, weather information, and user's physical condition evaluation data sent from the wearable device and stores them in a database.

[0309] Step 9:

[0310] Server Operation

[0311] The server analyzes the user's emotional state based on the collected biometric information and the user's physical condition assessment data using an emotion engine. For example, it can infer stress or fatigue from fluctuations in heart rate or abnormal sleep patterns.

[0312] Step 10:

[0313] Server Operation

[0314] The server combines biometric information sent from the wearable device, weather information, the user's health assessment data, and emotional data from the emotion engine, and analyzes this data using generative AI, which identifies specific patterns and tendencies for each user and predicts the possibility of poor health.

[0315] Step 11:

[0316] Server Operation

[0317] Based on the prediction results, the generating AI identifies days and situations when there is a high possibility of poor health and notifies the user's smartphone of this information.

[0318] Step 12:

[0319] Device (smartphone) operation

[0320] The smartphone receives notifications from the server and provides the user with specific preventative and improvement measures, such as advice like "Drink more water today."

[0321] Step 13:

[0322] Server Operation

[0323] Based on the predicted results of poor health, notifications are sent to linked medical services (e.g., HELPO).

[0324] Step 14:

[0325] Server Operation

[0326] Based on the information provided, a medical service professional (e.g., a doctor) will suggest appropriate preventive measures to the user and arrange for the advance delivery of necessary medications.

[0327] Step 15:

[0328] User Actions

[0329] The user checks the preventive and remedial measures displayed on their smartphone, puts them into practice, and, if necessary, receives and takes the medicine delivered.

[0330] By using the above processing steps, the present invention can efficiently manage the user's physical condition and emotions, and prevent poor physical condition from occurring.

[0331] Example 2

[0332] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0333] Conventional health management systems lack the functionality to comprehensively manage a user's biometric information, weather conditions, and emotional state, predict illness, and provide preventative measures. In particular, they do not provide preventative measures that take into account the user's emotional state or medical cooperation, which often leaves users unable to respond appropriately to sudden illnesses. Therefore, there is a need for a system that comprehensively manages a user's biometric information, weather conditions, and emotional state, predicts illness, and provides preventative measures in cooperation with medical cooperation.

[0334] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for collecting data on sleep time, heart rate, blood oxygen concentration, electrocardiogram, and skin temperature using a wearable device for constantly measuring the user's biological information; means for automatically collecting weather information on the Internet and acquiring data such as temperature, atmospheric pressure, and humidity; data collection means for displaying questions about the user's daily physical condition using a smartphone and for the user to quantify and answer; means for integrating and analyzing the collected biological information, weather information, and user's answer data to predict situations in which the user is likely to feel unwell; means for analyzing the user's emotional state using an emotion engine based on the collected biological information and the user's physical condition evaluation data; means for notifying the user of information on preventive measures and improvement measures based on the predicted risk of poor health using a generation AI; and means for sending a notification to a coordinated medical service when poor health is predicted and arranging for instructions from a doctor or delivery of medicine. This will enable integrated management of the user's biometric information, weather conditions, and emotional state, making it possible to predict illness and provide appropriate preventive measures through medical cooperation.

[0335] "Biometric information" refers to data obtained from inside or on the surface of a user's body, and includes heart rate, sleep time, blood oxygen concentration, electrocardiogram, skin temperature, etc.

[0336] A "wearable device" is a device worn by a user that can constantly measure their biometric information.

[0337] "Weather information" refers to data related to weather, such as temperature, air pressure, and humidity, which is automatically collected from external services provided on the Internet.

[0338] A "smartphone" is a portable information terminal with telephone functions, and is a device that interacts with users using applications.

[0339] "Data collection means" refers to a method or device that displays questions about the user's daily physical condition and collects the answers as numerical data.

[0340] "Generative AI" refers to artificial intelligence technology that performs advanced analysis on collected data to identify specific patterns and trends.

[0341] "Emotion engine" is a general term for algorithms and software that analyze a user's emotional state based on collected biometric information and the user's physical condition evaluation data.

[0342] "Notification means" refers to a method or device for communicating information on preventive or improvement measures to users based on the prediction results.

[0343] "Medical services" is a general term for services aimed at providing medical examinations and treatment by doctors, medicines, etc.

[0344] "Cloud environment" refers to computing resources and data storage provided via the Internet, and refers to the infrastructure for storing and processing data.

[0345] This invention is a system that combines a wearable device, a smartphone, a generative AI, and an emotion engine to manage the user's physical condition and emotions. The specific hardware and software operations required to realize each step of this system are explained in order.

[0346] First, wearable devices as terminals constantly measure the user's biometric information, such as heart rate, sleep time, blood oxygen level, electrocardiogram, and skin temperature. This data is periodically transferred to a smartphone via Bluetooth communication. For example, heart rate and sleep patterns are measured every hour and sent to the smartphone.

[0347] The server then automatically collects real-time temperature, pressure, and humidity data from an internet weather data provider, which is then stored in a database for later analysis.

[0348] The smartphone device also sends users a question about their health at a specific time each day. Users rate their health on a scale of 1 to 10, and the information is saved on the smartphone. This data is sent to a server and stored in a database.

[0349] The server uses an emotion engine to analyze the user's emotional state based on the collected biometric information and the user's physical condition evaluation data. For example, it can infer stress or fatigue from fluctuations in heart rate or abnormal sleep patterns.

[0350] The server then combines biometric information sent from the wearable device, weather information, the user's health assessment data, and emotional data from an emotion engine. This combined data is then analyzed by generative AI to identify specific patterns and trends for each user and predict the likelihood of poor health.

[0351] Finally, based on the predictions of the generative AI, the server identifies situations in which the user is likely to feel unwell and notifies the user of this information via their smartphone. The notification includes specific preventive and remedial measures, allowing the user to proactively manage their health. Furthermore, if medical collaboration is required, the server sends a notification to the collaborative medical service and arranges for advance delivery of doctor's instructions and medication.

[0352] Specific examples

[0353] For example, if a user's wearable device indicates a sustained higher-than-normal heart rate and weather data predicts a continued period of high temperatures and humidity, the generative AI will determine that there is a high risk of illness. The emotion engine then analyzes the user's emotional state and detects high stress levels. The server then notifies the user's smartphone of this information and offers preventative measures, such as "Drink more fluids and take more breaks today." If necessary, the system will also notify a medical service, which will suggest preventative measures and, in some cases, arrange for medication to be delivered to the user.

[0354] Prompt Sentence Examples

[0355] Below is an example of a prompt to input to the generative AI model.

[0356] ---

[0357] "A user's heart rate has been consistently higher than normal, and weather data predicts a period of high temperatures and humidity. Please assess the user's risk of illness in this situation and suggest necessary preventative measures."

[0358] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0359] Step 1:

[0360] Device behavior

[0361] The wearable device measures the user's biometric information every hour, including heart rate, sleep time, blood oxygen level, electrocardiogram, and skin temperature. The measurement results are then transferred to a smartphone via Bluetooth, where the data is temporarily stored.

[0362] Input: User's biological information (heart rate, sleep time, blood oxygen level, electrocardiogram, skin temperature)

[0363] Output: Biometric information sent to a smartphone

[0364] Step 2:

[0365] Server Operation

[0366] The server periodically polls weather data providers on the Internet to collect temperature, pressure, and humidity data. This data is then stored in a database on the server. The data collection frequency is, for example, every 15 minutes.

[0367] Input: Real-time data from weather data provider (temperature, pressure, humidity)

[0368] Output: Weather information stored in a database

[0369] Step 3:

[0370] Device behavior

[0371] The smartphone will send notifications to users at specific times, asking them questions about their health. For example, every morning at 8:00, a question like "Please rate how you feel this morning on a scale of 1 to 10" will be displayed. When the user enters a number, the data is stored on the smartphone and later sent to a server.

[0372] Input: User's physical condition evaluation data (numerical input)

[0373] Output: Physical condition evaluation data stored on a smartphone

[0374] Step 4:

[0375] Server Operation

[0376] The server receives the biometric information and physical condition evaluation data sent from the smartphone, stores them in a database, and then analyzes the data using an emotion engine to evaluate the user's emotional state (e.g., stress level).

[0377] Input: Biometric information, physical condition evaluation data

[0378] Output: Emotion data stored in a database

[0379] Step 5:

[0380] Server Operation

[0381] The collected biometric information, weather information, health assessment data, and emotional data are combined into a single dataset and input into the Generative AI. The Generative AI analyzes this data and identifies specific patterns and trends for each user (for example, a correlation between high humidity and stress levels). Based on the results of this analysis, the risk of poor health is predicted.

[0382] Input: Integrated dataset (biometric information, weather information, physical condition assessment data, emotional data)

[0383] Output: Analysis results (prediction of risk of poor health)

[0384] Step 6:

[0385] Server Operation

[0386] Based on the prediction results, the generative AI generates specific preventative and improvement measures. For example, it might create a message such as, "Drink water frequently and take plenty of breaks today." This message is then sent to the user's smartphone.

[0387] Input: Generative AI prediction results

[0388] Output: Preventive measures message sent to your smartphone

[0389] Step 7:

[0390] Server Operation

[0391] If a user's condition is predicted to worsen, the server sends a notification to the connected medical service. This notification includes information on the user's biometrics, weather information, emotional data, etc. Based on the information received, a doctor at the medical service will provide appropriate preventive measures and, if necessary, arrange for the advance delivery of medication.

[0392] Input: Predicted illness data

[0393] Output: Notifying medical services and suggesting preventative measures, arranging for medication delivery

[0394] Through the above steps, the system comprehensively manages the user's biometric information, weather conditions, and emotional state, enabling prediction of poor health and provision of appropriate preventive measures.

[0395] (Application example 2)

[0396] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0397] Managing employee health is particularly important in factory working environments, but conventional methods have made it difficult to grasp the health status of individual employees in real time and provide appropriate preventive measures. Furthermore, there has been a lack of automated means for quickly responding to predicted illnesses. This has created challenges for the productivity of the entire factory and for managing employee health.

[0398] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for collecting data on sleep time, heart rate, blood oxygen concentration, electrocardiogram, and skin temperature using a wearable device for constantly measuring a user's biometric information; means for automatically collecting weather information from the Internet and acquiring data such as temperature, barometric pressure, and humidity; means for displaying questions about the user's daily health using a generation AI and allowing the user to quantify and respond; means for integrating and analyzing the collected biometric information, weather information, and user response data to predict situations in which the user is likely to feel unwell; means for a factory robot to collect biometric data measured by factory employees using a wearable device and analyze the data using a generation AI to manage their health; and means for notifying users of information on preventive and remedial measures based on the prediction results. This makes it possible to grasp the health of factory employees in real time and quickly provide appropriate preventive measures.

[0399] A "wearable device" is a device that constantly monitors the user's physical condition and measures and collects biometric information such as heart rate, blood oxygen concentration, and skin temperature.

[0400] "Weather information" includes environmental data such as temperature, air pressure, and humidity, and is information obtained via the Internet.

[0401] "Generative AI" is an artificial intelligence technology that predicts and analyzes a user's physical and emotional state based on collected data.

[0402] "Data collection means" refers to a means for integrating and managing biometric information and weather information collected from wearable devices and the Internet.

[0403] The "means for predicting poor health" is a system in which a generative AI analyzes collected data and determines whether the user is likely to be in poor health.

[0404] "Means for notifying preventive and improvement measures" refers to a means for providing users with appropriate health management information based on the risks predicted by the generative AI.

[0405] "Factory robots" are automated machines that operate within factories, collect and analyze data from employees' wearable devices, and support their health management.

[0406] This invention is a system for supporting employee health management, and aims to efficiently monitor and manage employee health by combining wearable devices, generative AI, emotion engines, and factory robots.

[0407] Wearable device data collection

[0408] Device behavior

[0409] The wearable device has the function of constantly measuring employees' biometric information, such as heart rate, blood oxygen concentration, and skin temperature, and periodically transmitting this information to factory robots. For example, heart rate and skin temperature are measured every hour and transmitted to the factory robot via communication means such as Bluetooth.

[0410] Automatic collection of weather information

[0411] Server Operation

[0412] The server automatically collects real-time temperature, pressure, and humidity data from an online weather data provider and stores it in a database. This data is then used for analysis to identify factors that affect employees' physical condition.

[0413] Data collection methods

[0414] Factory robot operation

[0415] Factory robots collect and manage measurement data from wearable devices worn by employees, which is sent to generative AI and analyzed to understand the employee's physical condition in real time.

[0416] Sentiment analysis with emotion engine

[0417] Server Operation

[0418] The server uses an emotion engine to analyze employees' emotional states based on collected biometric and weather data, for example, inferring stress or fatigue from abnormal heart rates and fluctuations in skin temperature.

[0419] Data integration and analysis

[0420] Server Operation

[0421] The server combines biometric data from wearable devices and environmental sensors with weather information, and then analyzes this data using generative AI, which identifies specific patterns and tendencies for each employee and predicts the likelihood of illness.

[0422] Prevention and Notification

[0423] Factory robot operation

[0424] Based on the predictions of generative AI, the factory robot identifies situations in which employees are likely to feel unwell and notifies them via voice or display, including specific advice on taking breaks and staying hydrated.

[0425] Physician precautions and advance medication delivery

[0426] Server Operation

[0427] If necessary, the factory's partner medical institutions will be notified to arrange for preventative measures to support employee health management and the advance delivery of necessary medications.

[0428] Specific examples

[0429] For example, let's say an employee's wearable device indicates a persistently higher-than-normal heart rate. Weather data predicts that the temperature and humidity in the factory will remain high. In this case, the generative AI determines that there is a high risk of illness. The emotion engine further analyzes the employee's emotional state and detects a high stress level. Based on this information, the factory robot will issue a voice notification to the employee advising them to take a break and hydrate. If necessary, it will also notify affiliated medical institutions and arrange for preventive measures and necessary medications to be delivered in advance.

[0430] Prompt Sentence Examples

[0431] "If the heart rate is over 90 and the skin temperature is over 36.5 degrees, notify the employee to take a break and drink fluids."

[0432] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0433] Step 1:

[0434] The wearable device measures the employee's heart rate, blood oxygen level, and skin temperature, and stores this biometric information internally. The input is various types of biometric data, and the output is the aggregated measurement data.

[0435] Step 2:

[0436] The wearable device transmits the measured biometric information to the factory robot via Bluetooth. The input is the measured data mentioned above, and the output is the transmitted biometric data.

[0437] Step 3:

[0438] The server automatically retrieves weather data such as temperature, pressure, and humidity from a weather information database on the Internet. The input is information retrieved from the weather data provider service, and the output is the latest weather data.

[0439] Step 4:

[0440] The factory robot receives the biometric data transmitted from the wearable device and the meteorological data obtained from the server, which forms an integrated data set. The inputs are the biometric data and meteorological data, and the output is the integrated data set.

[0441] Step 5:

[0442] The server uses generative AI to analyze the employee's physical condition from the integrated data set. Specifically, it detects abnormalities in heart rate and skin temperature, and predicts the risk of poor health by taking weather conditions into account. The input is the integrated data set, and the output is the physical condition risk assessment result.

[0443] Step 6:

[0444] The server uses an emotion engine to analyze the employee's emotional state from biometric data. It infers stress and fatigue from changes in heart rate and skin temperature. The input is biometric data, and the output is the emotion analysis results.

[0445] Step 7:

[0446] The server integrates the risk assessment results from the generative AI and the emotion analysis results from the emotion engine to comprehensively determine the risk of poor health and emotional state.The inputs are the health risk assessment results and the emotion analysis results, and the output is a comprehensive health assessment result.

[0447] Step 8:

[0448] Based on the health assessment results received from the server, the factory robot notifies employees of preventive measures and break instructions via voice and display. Specific notification content may include "take breaks and hydrate." The input is the health assessment results, and the output is the notification to the employee.

[0449] Step 9:

[0450] The server sends notifications to the factory's affiliated medical institutions as needed, recommends preventive measures, and arranges for the advance delivery of necessary medicines.The input is the health assessment results, and the output is notifications to affiliated medical institutions.

[0451] 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.

[0452] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0453] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0454] [Second embodiment]

[0455] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0456] 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.

[0457] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0458] 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.

[0459] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0460] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0461] 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.

[0462] 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.

[0463] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0464] 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.

[0465] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0466] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0467] The present invention is a system that combines a wearable device, generative AI, and a smartphone, and aims to efficiently manage the user's physical condition. This system is implemented by the following means.

[0468] Wearable device data collection

[0469] Device behavior

[0470] Wearable devices constantly measure the user's biometric information, such as heart rate, sleep time, blood oxygen level, electrocardiogram, and skin temperature, and periodically transmit this data to a smartphone. For example, heart rate and sleep patterns are measured every hour and transmitted to the smartphone via Bluetooth or other communication methods.

[0471] Automatic collection of weather information

[0472] Server Operation

[0473] The server automatically collects real-time temperature, pressure, and humidity data from weather data providers on the Internet, and stores this data in a database for later analysis.

[0474] Collecting User Input

[0475] Device behavior

[0476] The smartphone sends a notification to the user at a specific time each day asking about their health. The user answers by rating their health on a scale of 1 to 10, and the information is saved on the smartphone. The saved data is then sent to a server and stored in a database.

[0477] Data integration and analysis

[0478] Server Operation

[0479] The server combines biometric data from the wearable device, weather information, and the user's health assessment data, and analyzes this data using a generative AI that identifies specific patterns and trends for each user and predicts the likelihood of poor health.

[0480] Prevention and Notification

[0481] Server Operation

[0482] Based on the AI's predictions, the server identifies situations in which users are likely to feel unwell and sends a notification to their smartphone containing specific preventative and remedial measures, allowing users to proactively manage their health.

[0483] Physician precautions and advance medication delivery

[0484] Server Operation

[0485] If illness is predicted, the server will send a notification to the connected medical service, and doctors can use this information to suggest appropriate preventive measures and, if necessary, arrange for the advance delivery of medication.

[0486] Specific examples

[0487] For example, if a user's wearable device indicates that their heart rate remains higher than normal, and weather data predicts that the temperature and humidity will continue to be high, the generative AI will determine that the user is at high risk of developing poor health. The server will then notify the user's smartphone of this information and recommend preventative measures such as "staying hydrated." The server will also notify a linked medical service and, in some cases, arrange for medication to be delivered to the user in advance.

[0488] As a result, the present invention enables users to manage their physical condition based on weather conditions and biological information, and prevent poor physical condition from occurring.

[0489] The processing flow will be explained below.

[0490] Step 1:

[0491] Terminal (wearable device) operation

[0492] Wearable devices constantly measure the user's biometric information, such as heart rate, sleep time, blood oxygen level, electrocardiogram, and skin temperature, and this data is temporarily stored in the device.

[0493] Step 2:

[0494] Terminal (wearable device) operation

[0495] The measured data is transferred to a smartphone at regular intervals (for example, every hour) using wireless communication means such as Bluetooth.

[0496] Step 3:

[0497] Device (smartphone) operation

[0498] The smartphone receives the biometric data received from the wearable device and stores it locally, where it is ready to be sent to a server.

[0499] Step 4:

[0500] Server Operation

[0501] The server periodically obtains real-time weather information such as temperature, pressure, and humidity from weather data providers on the Internet, and stores the obtained weather information in a database.

[0502] Step 5:

[0503] Device (smartphone) operation

[0504] At a specific time each day (e.g., 8:00 a.m.), the user will receive a notification from their smartphone asking the question, "How are you feeling today?"

[0505] Step 6:

[0506] User Actions

[0507] The user answers questions about their physical condition using a number from 1 to 10. For example, if they feel good, they enter "8," and if they feel bad, they enter "3."

[0508] Step 7:

[0509] Device (smartphone) operation

[0510] The physical condition evaluation data entered by the user is stored in the smartphone and then sent to the server.

[0511] Step 8:

[0512] Server Operation

[0513] The server receives the biometric information, weather information, and user's physical condition evaluation data sent from the wearable device and stores them in a database.

[0514] Step 9:

[0515] Server Operation

[0516] The generative AI uses the collected data to analyze it, comparing it with past data to identify patterns and trends in the user's poor health.

[0517] Step 10:

[0518] Server Operation

[0519] Based on the prediction results, the generating AI identifies days and situations when there is a high possibility of poor health and notifies the user's smartphone of this information.

[0520] Step 11:

[0521] Device (smartphone) operation

[0522] The smartphone receives notifications from the server and provides the user with specific preventative and improvement measures, such as advice like "Drink more water today."

[0523] Step 12:

[0524] Server Operation

[0525] Based on the predicted results of poor health, notifications are sent to linked medical services (e.g., HELPO).

[0526] Step 13:

[0527] Server Operation

[0528] Based on the information provided, a medical service professional (e.g., a doctor) will suggest appropriate preventive measures to the user and arrange for the advance delivery of necessary medications.

[0529] Step 14:

[0530] User Actions

[0531] The user checks the preventive and remedial measures displayed on their smartphone, puts them into practice, and, if necessary, receives and takes the medicine delivered.

[0532] By using the above processing steps, the present invention can efficiently manage the user's physical condition and prevent poor physical condition from occurring.

[0533] Example 1

[0534] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0535] In modern society, many people lead busy lives, making it difficult to properly manage their health. Furthermore, because it is difficult to predict the impact of weather conditions, daily lifestyle habits, stress, and other factors on physical condition, it is difficult to take appropriate preventive measures to prevent illness before it occurs. Furthermore, even if illness is predicted, there is no way to seek medical advice or obtain necessary medications in advance, making it difficult to respond quickly.

[0536] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0537] In this invention, the server includes: a means for collecting data on sleep time, heart rate, blood oxygen concentration, electrocardiogram, and skin temperature using a wearable device for constantly measuring the user's biometric information; a means for wirelessly transferring the collected biometric information to a smartphone and sending it to the server via the Internet; a means for automatically collecting weather information from the Internet and acquiring data such as temperature, air pressure, and humidity; a data collection means for displaying questions about the user's daily health using a generation AI and allowing the user to quantify and respond; a means for integrating and analyzing the collected biometric information, weather information, and user response data to predict situations in which the user is likely to feel unwell; and a means for notifying the user of information on preventive and remedial measures based on the prediction results. This allows the user to understand their own health condition and take appropriate preventive measures. Furthermore, if a health condition is predicted, the user can receive appropriate advice from a doctor or obtain necessary medication in advance, enabling a prompt response.

[0538] A "wearable device" is a device that is worn on the user's body to continuously measure biometric information.

[0539] "Biometric information" refers to data that indicates the user's health status, such as heart rate, sleep time, blood oxygen concentration, electrocardiogram, and skin temperature.

[0540] "Wireless communication" is a method of sending and receiving data between devices using wireless technologies such as Bluetooth and Wi-Fi.

[0541] "Weather information" refers to information about atmospheric conditions such as temperature, air pressure, and humidity, and is obtained from data providing services on the Internet.

[0542] "Generative AI" is an algorithm that uses techniques such as machine learning and deep learning to analyze data and generate patterns and predictions.

[0543] A "smartphone" is a portable information terminal that can connect to the Internet and is equipped with various sensors and communication functions.

[0544] The "data collection means" is a system that uses wearable devices or smartphones to collect biometric information, weather information, and user health evaluation data.

[0545] "Data integration" is the process of combining data collected from multiple sources into a single data set.

[0546] "Preventive measures" are specific actions or advice taken to reduce the risk of ill health.

[0547] "Means of notification" refers to a method of providing information to users by displaying a message on a device such as a smartphone.

[0548] "Medical services" are health management and treatment support services provided by professionals such as doctors and pharmacists.

[0549] A "cloud environment" is an infrastructure for using data storage and computing resources provided over the Internet.

[0550] "Data analysis" is the process of analyzing collected data using statistical or computational methods to derive meaningful information or predictive results.

[0551] The present invention is a system for efficiently managing a user's physical condition, which is configured by combining a wearable device, a generation AI, and a smartphone. This system is implemented by the following means.

[0552] Wearable device deployment and data collection

[0553] First, the user puts on a wearable device. This device has the function of constantly measuring biometric information such as heart rate, sleep time, blood oxygen level, electrocardiogram, and skin temperature. For example, heart rate is measured periodically every hour and temporarily stored in the device's memory. The data is then periodically transferred to a smartphone via Bluetooth communication.

[0554] Receiving and transferring data via smartphone

[0555] The smartphone receives the biometric information sent from the wearable device. This data is stored in the smartphone's local storage and simultaneously sent to a server via the Internet. The smartphone periodically uploads the biometric information to the server, enabling centralized data management.

[0556] Automatic collection of weather information

[0557] The server accesses a weather data provider on the Internet and automatically obtains real-time weather data (temperature, air pressure, humidity, etc.) The obtained data is stored in a database on the server and used for subsequent data analysis.

[0558] Collecting User Input

[0559] The smartphone sends a notification asking about the user's physical condition at a specific time every day. The user rates their physical condition on a scale of 1 to 10, and this data is saved on the smartphone. The saved data is periodically sent to a server and stored in a database.

[0560] Data integration and generation AI analysis

[0561] The server combines biometric data from the wearable device, weather information, and the user's health assessment data. This combined data set is then fed into a generative AI model for further analysis. The generative AI detects specific patterns and trends and predicts the risk of poor health.

[0562] Preventive measures and notifications

[0563] Based on the analysis results of the AI, the server generates appropriate preventive measures for cases where there is a high risk of illness. For example, specific advice such as "stay hydrated" and "take adequate rest" is included. These preventive measures are notified to the user's smartphone, allowing them to take measures in real time.

[0564] Physician precautions and advance medication delivery

[0565] If an illness is predicted, the server will send a notification to the linked medical service. Based on this information, doctors can suggest appropriate preventive measures to the user and, if necessary, arrange for advance delivery of medication. This allows users to reduce the risk of illness in advance.

[0566] Examples of concrete examples and prompts

[0567] For example, if a user's wearable device indicates that their heart rate remains higher than normal, and weather data predicts that the temperature and humidity will continue to be high, the generative AI may determine that the user is at high risk of becoming ill. The server then notifies the user's smartphone of this information and suggests preventative measures such as "staying hydrated." It also notifies medical services and arranges for medication to be delivered in advance if necessary.

[0568] An example of a prompt for the generative AI model is, "If the user's heart rate is higher than normal and weather data indicates that the temperature and humidity will be high for several days in a row, please suggest specific preventive measures for managing their health."

[0569] As described above, the present invention provides a system for efficiently managing a user's physical condition based on the user's biological information and weather conditions, thereby preventing poor physical condition before it occurs.

[0570] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0571] Step 1:

[0572] Collecting biometric information using wearable devices

[0573] Device behavior

[0574] Wearable devices constantly measure biometric information such as heart rate, sleep time, blood oxygen level, electrocardiogram, skin temperature, etc. The measured data is stored in the device's internal memory.

[0575] Input: User's biological information (heart rate, sleep time, blood oxygen level, electrocardiogram, skin temperature)

[0576] Output: Stored biometric data

[0577] Step 2:

[0578] Transferring data to a smartphone

[0579] Device behavior

[0580] The wearable device periodically transmits the collected data via Bluetooth to a smartphone, which receives the data and stores it in its internal storage.

[0581] Input: Biometric data sent from a wearable device

[0582] Output: Biometric data stored on a smartphone

[0583] Step 3:

[0584] Automatic collection of weather information

[0585] Server Operation

[0586] The server periodically obtains real-time weather data (temperature, air pressure, humidity) from a weather data provider on the Internet and stores it in a database.

[0587] Input: Weather information from an online weather data provider

[0588] Output: Weather data stored on the server

[0589] Step 4:

[0590] Collecting User Input

[0591] Device behavior

[0592] The smartphone will prompt the user with questions about their health at a specific time each day. The user will rate their health on a scale of 1 to 10, which will be saved on the smartphone. This data will then be periodically sent to a server.

[0593] Input: Health evaluation data entered by the user into their smartphone

[0594] Output: Health evaluation data sent to the server

[0595] Step 5:

[0596] Data integration and generation AI analysis

[0597] Server Operation

[0598] The server integrates biometric data from the wearable device, weather information, and the user's health assessment data. This data is then input into a generative AI model for analysis. The generative AI detects specific patterns and trends and predicts the risk of poor health.

[0599] Input: Biometric information from wearable devices, weather information, and user's physical condition evaluation data

[0600] Output: Poor health risk prediction results by generative AI

[0601] Step 6:

[0602] Preventive measures and notifications

[0603] Server Operation

[0604] The server generates appropriate preventive and remedial measures based on the predictions made by the AI. These preventive measures are then sent to the user's smartphone. For example, specific advice such as "stay hydrated" is provided.

[0605] Input: Prediction results by generative AI

[0606] Output: Preventive measures notified to your smartphone

[0607] Step 7:

[0608] Physician precautions and advance medication delivery

[0609] Server Operation

[0610] If the server predicts a high risk of illness, it notifies the medical service, who can then use the information to suggest preventative measures and, if necessary, arrange for the advance delivery of medication.

[0611] Input: Prediction results by the generation AI, user situation information

[0612] Output: Proposal of preventive measures by medical services and advance delivery of medicines

[0613] (Application example 1)

[0614] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0615] Employee illness in brick-and-mortar stores is a problem that leads to reduced work efficiency and increased safety risks. While there is a need for methods and systems to continuously monitor employee health and prevent illness before it occurs, current methods make it difficult to grasp the situation in real time or provide appropriate preventive measures. Furthermore, personalized advice for individual employees is often not provided, and only general warnings are issued. Therefore, the challenge is to develop a system that can efficiently manage employee health and provide preventive measures immediately.

[0616] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0617] In this invention, the server includes: means for collecting data on sleep time, heart rate, blood oxygen concentration, electrocardiogram, and skin temperature using a wearable device for constantly measuring a user's biometric information; means for automatically collecting weather information from the Internet and acquiring data such as temperature, barometric pressure, and humidity; means for displaying questions about the user's daily health using a generative AI and for the user to quantify and answer; means for integrating and analyzing the collected biometric information, weather information, and user response data to predict situations in which the user is likely to feel unwell; means for notifying the user of information on preventive and remedial measures based on the prediction results; and means for monitoring the health of employees in physical stores in real time and providing proactive alerts and personalized human care. This enables efficient management of employee health and the provision of appropriate preventive measures in real time.

[0618] A "wearable device" is a device worn by a user that constantly measures biometric information.

[0619] "Biometric information" refers to information such as the user's heart rate, sleep time, blood oxygen concentration, electrocardiogram, and skin temperature.

[0620] "Weather information" refers to data such as temperature, air pressure, and humidity obtained from weather data services on the Internet.

[0621] "Generative AI" is artificial intelligence that uses collected data to analyze and predict.

[0622] The "questions about health" are questions to quantify the user's daily health condition.

[0623] "Data collection means" refers to the means of collecting biometric information, weather information, and user response data using wearable devices and generative AI.

[0624] The "integration and analysis method" is a method that integrates collected biometric information, weather information, and user response data, and uses generative AI to predict situations in which users are likely to feel unwell.

[0625] "Information on preventive measures and improvement measures" refers to specific measures and advice provided when a situation is predicted in which the user is likely to feel unwell.

[0626] A "physical store" is a face-to-face business facility that sells goods or provides services.

[0627] "Employee health monitoring" refers to the continuous real-time monitoring of the biometric information of employees working in physical stores.

[0628] A "proactive alert" is a notification that warns you in advance when poor health is predicted.

[0629] "Personalized human care" refers to providing care and advice tailored to each employee's individual health condition.

[0630] "Doctor's recommendations for preventive measures" are advice on preventive measures provided by a medical professional when an illness is predicted.

[0631] "Pre-delivery of medicines" means delivering necessary medicines to the user in advance.

[0632] A "cloud environment" is a virtual environment that stores and manages data via the Internet.

[0633] The "data analysis means" is a means for analyzing collected data and predicting poor health.

[0634] The present invention is a system for efficiently managing the health of employees in brick-and-mortar stores in real time. This system is composed of a wearable device, a generative AI, and a smartphone. Specific embodiments of this system are described below.

[0635] Wearable device data collection

[0636] Device behavior

[0637] The server constantly measures biometric information from the wearable device worn by the user, including heart rate, sleep time, blood oxygen level, electrocardiogram, and skin temperature. This data is periodically transferred to a smartphone via Bluetooth or other communication methods.

[0638] Automatic collection of weather information

[0639] Server Operation

[0640] The server automatically collects real-time temperature, pressure, and humidity data from weather data providers on the Internet, and stores the collected weather information in a database for later analysis.

[0641] Collecting User Input

[0642] Device behavior

[0643] The device sends a notification to employees at a specific time each day asking them questions about their health. Employees rate their health on a scale of 1 to 10, and the information is saved on their smartphone. The saved data is sent to a server and stored in a database.

[0644] Data integration and analysis

[0645] Server Operation

[0646] The server combines biometric data from the wearable devices, weather information, and user health assessment data, and analyzes this data using Generative AI, which identifies specific patterns and tendencies for each employee and predicts the likelihood of poor health.

[0647] Prevention and Notification

[0648] Server Operation

[0649] Based on the predictions of the generating AI, the server identifies situations in which employees are likely to feel unwell and sends a notification to their smartphone containing specific preventative and remedial measures, allowing employees to proactively manage their health.

[0650] Physician precautions and advance medication delivery

[0651] Server Operation

[0652] If illness is predicted, the server will send a notification to the connected medical service, and doctors can use this information to suggest appropriate preventive measures and, if necessary, arrange for the advance delivery of medication.

[0653] Specific examples

[0654] For example, if an employee's wearable device indicates that their heart rate remains higher than normal, and weather data predicts that the temperature and humidity will continue to be high, the generative AI will determine that the employee is at high risk of becoming ill. The server will then notify the employee's smartphone of this information and provide preventative measures such as taking breaks and staying hydrated. It will also notify a collaborative medical service and, in some cases, arrange for medication to be delivered to the employee in advance.

[0655] Prompt Sentence Examples

[0656] Predict health risks based on heart rate and weather data and suggest appropriate preventive measures.

[0657] In this way, the present invention integrates the biometric information of employees in physical stores with weather information and utilizes generative AI to achieve more efficient and safe health management.

[0658] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0659] Step 1:

[0660] The server periodically collects biometric information such as heart rate, sleep time, blood oxygen level, electrocardiogram, and skin temperature from the wearable device worn by the user. The collected biometric information is transferred to a smartphone using a communication method such as Bluetooth. The smartphone receives the information and sends it to the server.

[0661] Input: Biometric information obtained from a wearable device

[0662] Output: Biometric information sent to the smartphone and server

[0663] Step 2:

[0664] The server periodically obtains real-time weather information such as temperature, pressure, and humidity from a weather data provider. The obtained weather information is stored in a database and used for later analysis.

[0665] Input: Weather information from an internet weather data provider

[0666] Output: Weather information stored in the database on the server

[0667] Step 3:

[0668] The device sends a notification to the user at a specific time each day asking about their health. The user answers by rating their health on a scale of 1 to 10, and the information is saved on the smartphone. The saved data is sent to a server and stored in a database.

[0669] Input: User's physical condition rating

[0670] Output: User's health evaluation data stored on the server

[0671] Step 4:

[0672] The server integrates the collected biometric information, weather information, and the user's health assessment data, and analyzes the data using a generation AI. The generation AI uses a specific algorithm to input each piece of data and predict the situations in which the user is likely to feel unwell.

[0673] Input: Biometric information, weather information, user's physical condition evaluation data

[0674] Output: Risk assessment of poor health

[0675] Step 5:

[0676] The server sends notifications to the user's smartphone with preventive and remedial measures based on the risk of illness predicted by the generative AI. The notifications include specific instructions for action (e.g., taking a break, drinking water, etc.).

[0677] Input: Risk assessment results of the generated AI

[0678] Output: Notifications of preventive and remedial measures sent to your smartphone

[0679] Step 6:

[0680] If a high risk of illness is predicted, the server will send a notification to the associated medical service, and medical professionals will use the predicted information to suggest preventative measures and, if necessary, arrange for the advance delivery of medication.

[0681] Input: Generative AI high-risk assessment results

[0682] Output: Notification sent to medical services and precautions taken by doctors, drug delivery arrangements

[0683] Step 7:

[0684] The server stores all collected data in a cloud environment, allowing the generative AI to analyze this data, which will improve the accuracy of future predictions.

[0685] Input: All collected data

[0686] Output: Data stored in the cloud environment

[0687] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0688] The present invention is a system that combines a wearable device, generative AI, an emotion engine, and a smartphone, and aims to efficiently manage the user's physical condition and emotions. This system is implemented by the following means.

[0689] Wearable device data collection

[0690] Device behavior

[0691] Wearable devices constantly measure the user's biometric information, such as heart rate, sleep time, blood oxygen level, electrocardiogram, and skin temperature, and periodically transmit this data to a smartphone. For example, heart rate and sleep patterns are measured every hour and transmitted to the smartphone via Bluetooth or other communication methods.

[0692] Automatic collection of weather information

[0693] Server Operation

[0694] The server automatically collects real-time temperature, pressure, and humidity data from weather data providers on the Internet, and stores this data in a database for later analysis.

[0695] Collecting User Input

[0696] Device behavior

[0697] The smartphone sends a notification to the user at a specific time each day asking about their health. The user answers by rating their health on a scale of 1 to 10, and the information is saved on the smartphone. The saved data is then sent to a server and stored in a database.

[0698] Sentiment analysis with emotion engine

[0699] Server Operation

[0700] The server uses an emotion engine to analyze the user's emotional state based on the collected biometric information and the user's physical condition assessment data. For example, it can infer stress or fatigue from fluctuations in heart rate or abnormal sleep patterns.

[0701] Data integration and analysis

[0702] Server Operation

[0703] The server combines biometric information, weather information, and the user's health assessment data sent from the wearable device, as well as emotional data from the emotion engine, and analyzes this data using a generative AI. The generative AI identifies specific patterns and tendencies for each user and predicts the likelihood of poor health.

[0704] Prevention and Notification

[0705] Server Operation

[0706] Based on the AI's predictions, the server identifies situations in which the user is likely to feel unwell and sends a notification to their smartphone. The notification includes specific preventive and remedial measures, allowing users to proactively manage their health. Furthermore, preventive measures, including stress management and psychological support based on emotional data, are also provided.

[0707] Physician precautions and advance medication delivery

[0708] Server Operation

[0709] If illness is predicted, the server will send a notification to the connected medical service, and doctors can use this information to suggest appropriate preventive measures and, if necessary, arrange for the advance delivery of medication.

[0710] Specific examples

[0711] For example, a user's wearable device may indicate that their heart rate remains higher than normal, and weather data predicts that the temperature and humidity will continue to be high. In this case, the generative AI determines that there is a high risk of illness. The emotion engine further analyzes the user's emotional state and detects a high stress level. The server then notifies the user's smartphone of this information and offers preventative measures, such as "Drink more fluids and take more breaks today." The server also sends a notification to a connected medical service, and in some cases arranges for medication to be delivered to the user in advance.

[0712] As a result, the present invention enables the user to manage their physical condition and emotions based on weather conditions, emotional state, and biological information, and prevent poor physical condition from occurring.

[0713] The processing flow will be explained below.

[0714] Step 1:

[0715] Terminal (wearable device) operation

[0716] Wearable devices constantly measure the user's biometric information, such as heart rate, sleep time, blood oxygen level, electrocardiogram, and skin temperature, and temporarily store this data within the device.

[0717] Step 2:

[0718] Terminal (wearable device) operation

[0719] The measured data is transferred to a smartphone at regular intervals (for example, every hour) using wireless communication means such as Bluetooth.

[0720] Step 3:

[0721] Device (smartphone) operation

[0722] The smartphone receives the biometric data received from the wearable device and stores it locally, where it is prepared for transmission to a server.

[0723] Step 4:

[0724] Server Operation

[0725] The server periodically obtains real-time weather information such as temperature, pressure, and humidity from weather data providers on the Internet, and stores the obtained weather information in a database.

[0726] Step 5:

[0727] Device (smartphone) operation

[0728] At a specific time each day (e.g., 8:00 a.m.), the user will receive a notification from their smartphone asking the question, "How are you feeling today?"

[0729] Step 6:

[0730] User Actions

[0731] The user answers questions about their physical condition using a number from 1 to 10. For example, if they feel good, they enter "8," and if they feel bad, they enter "3."

[0732] Step 7:

[0733] Device (smartphone) operation

[0734] The physical condition evaluation data entered by the user is stored in the smartphone and then sent to the server.

[0735] Step 8:

[0736] Server Operation

[0737] The server receives the biometric information, weather information, and user's physical condition evaluation data sent from the wearable device and stores them in a database.

[0738] Step 9:

[0739] Server Operation

[0740] The server analyzes the user's emotional state based on the collected biometric information and the user's physical condition assessment data using an emotion engine. For example, it can infer stress or fatigue from fluctuations in heart rate or abnormal sleep patterns.

[0741] Step 10:

[0742] Server Operation

[0743] The server combines biometric information sent from the wearable device, weather information, the user's health assessment data, and emotional data from the emotion engine, and analyzes this data using generative AI, which identifies specific patterns and tendencies for each user and predicts the possibility of poor health.

[0744] Step 11:

[0745] Server Operation

[0746] Based on the prediction results, the generating AI identifies days and situations when there is a high possibility of poor health and notifies the user's smartphone of this information.

[0747] Step 12:

[0748] Device (smartphone) operation

[0749] The smartphone receives notifications from the server and provides the user with specific preventative and improvement measures, such as advice like "Drink more water today."

[0750] Step 13:

[0751] Server Operation

[0752] Based on the predicted results of poor health, notifications are sent to linked medical services (e.g., HELPO).

[0753] Step 14:

[0754] Server Operation

[0755] Based on the information provided, a medical service professional (e.g., a doctor) will suggest appropriate preventive measures to the user and arrange for the advance delivery of necessary medications.

[0756] Step 15:

[0757] User Actions

[0758] The user checks the preventive and remedial measures displayed on their smartphone, puts them into practice, and, if necessary, receives and takes the medicine delivered.

[0759] By using the above processing steps, the present invention can efficiently manage the user's physical condition and emotions, and prevent poor physical condition from occurring.

[0760] Example 2

[0761] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0762] Conventional health management systems lack the functionality to comprehensively manage a user's biometric information, weather conditions, and emotional state, predict illness, and provide preventative measures. In particular, they do not provide preventative measures that take into account the user's emotional state or medical cooperation, which often leaves users unable to respond appropriately to sudden illnesses. Therefore, there is a need for a system that comprehensively manages a user's biometric information, weather conditions, and emotional state, predicts illness, and provides preventative measures in cooperation with medical cooperation.

[0763] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for collecting data on sleep time, heart rate, blood oxygen concentration, electrocardiogram, and skin temperature using a wearable device for constantly measuring the user's biological information; means for automatically collecting weather information on the Internet and acquiring data such as temperature, atmospheric pressure, and humidity; data collection means for displaying questions about the user's daily physical condition using a smartphone and for the user to quantify and answer; means for integrating and analyzing the collected biological information, weather information, and user's answer data to predict situations in which the user is likely to feel unwell; means for analyzing the user's emotional state using an emotion engine based on the collected biological information and the user's physical condition evaluation data; means for notifying the user of information on preventive measures and improvement measures based on the predicted risk of poor health using a generation AI; and means for sending a notification to a coordinated medical service when poor health is predicted and arranging for instructions from a doctor or delivery of medicine. This will enable integrated management of the user's biometric information, weather conditions, and emotional state, making it possible to predict illness and provide appropriate preventive measures through medical cooperation.

[0764] "Biometric information" refers to data obtained from inside or on the surface of a user's body, and includes heart rate, sleep time, blood oxygen concentration, electrocardiogram, skin temperature, etc.

[0765] A "wearable device" is a device worn by a user that can constantly measure their biometric information.

[0766] "Weather information" refers to data related to weather, such as temperature, air pressure, and humidity, which is automatically collected from external services provided on the Internet.

[0767] A "smartphone" is a portable information terminal with telephone functions, and is a device that interacts with users using applications.

[0768] "Data collection means" refers to a method or device that displays questions about the user's daily physical condition and collects the answers as numerical data.

[0769] "Generative AI" refers to artificial intelligence technology that performs advanced analysis on collected data to identify specific patterns and trends.

[0770] "Emotion engine" is a general term for algorithms and software that analyze a user's emotional state based on collected biometric information and the user's physical condition evaluation data.

[0771] "Notification means" refers to a method or device for communicating information on preventive or improvement measures to users based on the prediction results.

[0772] "Medical services" is a general term for services aimed at providing medical examinations and treatment by doctors, medicines, etc.

[0773] "Cloud environment" refers to computing resources and data storage provided via the Internet, and refers to the infrastructure for storing and processing data.

[0774] This invention is a system that combines a wearable device, a smartphone, a generative AI, and an emotion engine to manage the user's physical condition and emotions. The specific hardware and software operations required to realize each step of this system are explained in order.

[0775] First, wearable devices as terminals constantly measure the user's biometric information, such as heart rate, sleep time, blood oxygen level, electrocardiogram, and skin temperature. This data is periodically transferred to a smartphone via Bluetooth communication. For example, heart rate and sleep patterns are measured every hour and sent to the smartphone.

[0776] The server then automatically collects real-time temperature, pressure, and humidity data from an internet weather data provider, which is then stored in a database for later analysis.

[0777] The smartphone device also sends users a question about their health at a specific time each day. Users rate their health on a scale of 1 to 10, and the information is saved on the smartphone. This data is sent to a server and stored in a database.

[0778] The server uses an emotion engine to analyze the user's emotional state based on the collected biometric information and the user's physical condition evaluation data. For example, it can infer stress or fatigue from fluctuations in heart rate or abnormal sleep patterns.

[0779] The server then combines biometric information sent from the wearable device, weather information, the user's health assessment data, and emotional data from an emotion engine. This combined data is then analyzed by generative AI to identify specific patterns and trends for each user and predict the likelihood of poor health.

[0780] Finally, based on the predictions of the generative AI, the server identifies situations in which the user is likely to feel unwell and notifies the user of this information via their smartphone. The notification includes specific preventive and remedial measures, allowing the user to proactively manage their health. Furthermore, if medical collaboration is required, the server sends a notification to the collaborative medical service and arranges for advance delivery of doctor's instructions and medication.

[0781] Specific examples

[0782] For example, if a user's wearable device indicates a sustained higher-than-normal heart rate and weather data predicts a continued period of high temperatures and humidity, the generative AI will determine that there is a high risk of illness. The emotion engine then analyzes the user's emotional state and detects high stress levels. The server then notifies the user's smartphone of this information and offers preventative measures, such as "Drink more fluids and take more breaks today." If necessary, the system will also notify a medical service, which will suggest preventative measures and, in some cases, arrange for medication to be delivered to the user.

[0783] Prompt Sentence Examples

[0784] Below is an example of a prompt to input to the generative AI model.

[0785] ---

[0786] "A user's heart rate has been consistently higher than normal, and weather data predicts a period of high temperatures and humidity. Please assess the user's risk of illness in this situation and suggest necessary preventative measures."

[0787] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0788] Step 1:

[0789] Device behavior

[0790] The wearable device measures the user's biometric information every hour, including heart rate, sleep time, blood oxygen level, electrocardiogram, and skin temperature. The measurement results are then transferred to a smartphone via Bluetooth, where the data is temporarily stored.

[0791] Input: User's biological information (heart rate, sleep time, blood oxygen level, electrocardiogram, skin temperature)

[0792] Output: Biometric information sent to a smartphone

[0793] Step 2:

[0794] Server Operation

[0795] The server periodically polls weather data providers on the Internet to collect temperature, pressure, and humidity data. This data is then stored in a database on the server. The data collection frequency is, for example, every 15 minutes.

[0796] Input: Real-time data from weather data provider (temperature, pressure, humidity)

[0797] Output: Weather information stored in a database

[0798] Step 3:

[0799] Device behavior

[0800] The smartphone will send notifications to users at specific times, asking them questions about their health. For example, every morning at 8:00, a question like "Please rate how you feel this morning on a scale of 1 to 10" will be displayed. When the user enters a number, the data is stored on the smartphone and later sent to a server.

[0801] Input: User's physical condition evaluation data (numerical input)

[0802] Output: Physical condition evaluation data stored on a smartphone

[0803] Step 4:

[0804] Server Operation

[0805] The server receives the biometric information and physical condition evaluation data sent from the smartphone, stores them in a database, and then analyzes the data using an emotion engine to evaluate the user's emotional state (e.g., stress level).

[0806] Input: Biometric information, physical condition evaluation data

[0807] Output: Emotion data stored in a database

[0808] Step 5:

[0809] Server Operation

[0810] The collected biometric information, weather information, health assessment data, and emotional data are combined into a single dataset and input into the Generative AI. The Generative AI analyzes this data and identifies specific patterns and trends for each user (for example, a correlation between high humidity and stress levels). Based on the results of this analysis, the risk of poor health is predicted.

[0811] Input: Integrated dataset (biometric information, weather information, physical condition assessment data, emotional data)

[0812] Output: Analysis results (prediction of risk of poor health)

[0813] Step 6:

[0814] Server Operation

[0815] Based on the prediction results, the generative AI generates specific preventative and improvement measures. For example, it might create a message such as, "Drink water frequently and take plenty of breaks today." This message is then sent to the user's smartphone.

[0816] Input: Generative AI prediction results

[0817] Output: Preventive measures message sent to your smartphone

[0818] Step 7:

[0819] Server Operation

[0820] If a user's condition is predicted to worsen, the server sends a notification to the connected medical service. This notification includes information on the user's biometrics, weather information, emotional data, etc. Based on the information received, a doctor at the medical service will provide appropriate preventive measures and, if necessary, arrange for the advance delivery of medication.

[0821] Input: Predicted illness data

[0822] Output: Notifying medical services and suggesting preventative measures, arranging for medication delivery

[0823] Through the above steps, the system comprehensively manages the user's biometric information, weather conditions, and emotional state, enabling prediction of poor health and provision of appropriate preventive measures.

[0824] (Application example 2)

[0825] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0826] Managing employee health is particularly important in factory working environments, but conventional methods have made it difficult to grasp the health status of individual employees in real time and provide appropriate preventive measures. Furthermore, there has been a lack of automated means for quickly responding to predicted illnesses. This has created challenges for the productivity of the entire factory and for managing employee health.

[0827] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for collecting data on sleep time, heart rate, blood oxygen concentration, electrocardiogram, and skin temperature using a wearable device for constantly measuring a user's biometric information; means for automatically collecting weather information from the Internet and acquiring data such as temperature, barometric pressure, and humidity; means for displaying questions about the user's daily health using a generation AI and allowing the user to quantify and respond; means for integrating and analyzing the collected biometric information, weather information, and user response data to predict situations in which the user is likely to feel unwell; means for a factory robot to collect biometric data measured by factory employees using a wearable device and analyze the data using a generation AI to manage their health; and means for notifying users of information on preventive and remedial measures based on the prediction results. This makes it possible to grasp the health of factory employees in real time and quickly provide appropriate preventive measures.

[0828] A "wearable device" is a device that constantly monitors the user's physical condition and measures and collects biometric information such as heart rate, blood oxygen concentration, and skin temperature.

[0829] "Weather information" includes environmental data such as temperature, air pressure, and humidity, and is information obtained via the Internet.

[0830] "Generative AI" is an artificial intelligence technology that predicts and analyzes a user's physical and emotional state based on collected data.

[0831] "Data collection means" refers to a means for integrating and managing biometric information and weather information collected from wearable devices and the Internet.

[0832] The "means for predicting poor health" is a system in which a generative AI analyzes collected data and determines whether the user is likely to be in poor health.

[0833] "Means for notifying preventive and improvement measures" refers to a means for providing users with appropriate health management information based on the risks predicted by the generative AI.

[0834] "Factory robots" are automated machines that operate within factories, collect and analyze data from employees' wearable devices, and support their health management.

[0835] This invention is a system for supporting employee health management, and aims to efficiently monitor and manage employee health by combining wearable devices, generative AI, emotion engines, and factory robots.

[0836] Wearable device data collection

[0837] Device behavior

[0838] The wearable device has the function of constantly measuring employees' biometric information, such as heart rate, blood oxygen concentration, and skin temperature, and periodically transmitting this information to factory robots. For example, heart rate and skin temperature are measured every hour and transmitted to the factory robot via communication means such as Bluetooth.

[0839] Automatic collection of weather information

[0840] Server Operation

[0841] The server automatically collects real-time temperature, pressure, and humidity data from an online weather data provider and stores it in a database. This data is then used for analysis to identify factors that affect employees' physical condition.

[0842] Data collection methods

[0843] Factory robot operation

[0844] Factory robots collect and manage measurement data from wearable devices worn by employees, which is sent to generative AI and analyzed to understand the employee's physical condition in real time.

[0845] Sentiment analysis with emotion engine

[0846] Server Operation

[0847] The server uses an emotion engine to analyze employees' emotional states based on collected biometric and weather data, for example, inferring stress or fatigue from abnormal heart rates and fluctuations in skin temperature.

[0848] Data integration and analysis

[0849] Server Operation

[0850] The server combines biometric data from wearable devices and environmental sensors with weather information, and then analyzes this data using generative AI, which identifies specific patterns and tendencies for each employee and predicts the likelihood of illness.

[0851] Prevention and Notification

[0852] Factory robot operation

[0853] Based on the predictions of generative AI, the factory robot identifies situations in which employees are likely to feel unwell and notifies them via voice or display, including specific advice on taking breaks and staying hydrated.

[0854] Physician precautions and advance medication delivery

[0855] Server Operation

[0856] If necessary, the factory's partner medical institutions will be notified to arrange for preventative measures to support employee health management and the advance delivery of necessary medications.

[0857] Specific examples

[0858] For example, let's say an employee's wearable device indicates a persistently higher-than-normal heart rate. Weather data predicts that the temperature and humidity in the factory will remain high. In this case, the generative AI determines that there is a high risk of illness. The emotion engine further analyzes the employee's emotional state and detects a high stress level. Based on this information, the factory robot will issue a voice notification to the employee advising them to take a break and hydrate. If necessary, it will also notify affiliated medical institutions and arrange for preventive measures and necessary medications to be delivered in advance.

[0859] Prompt Sentence Examples

[0860] "If the heart rate is over 90 and the skin temperature is over 36.5 degrees, notify the employee to take a break and drink fluids."

[0861] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0862] Step 1:

[0863] The wearable device measures the employee's heart rate, blood oxygen level, and skin temperature, and stores this biometric information internally. The input is various types of biometric data, and the output is the aggregated measurement data.

[0864] Step 2:

[0865] The wearable device transmits the measured biometric information to the factory robot via Bluetooth. The input is the measured data mentioned above, and the output is the transmitted biometric data.

[0866] Step 3:

[0867] The server automatically retrieves weather data such as temperature, pressure, and humidity from a weather information database on the Internet. The input is information retrieved from the weather data provider service, and the output is the latest weather data.

[0868] Step 4:

[0869] The factory robot receives the biometric data transmitted from the wearable device and the meteorological data obtained from the server, which forms an integrated data set. The inputs are the biometric data and meteorological data, and the output is the integrated data set.

[0870] Step 5:

[0871] The server uses generative AI to analyze the employee's physical condition from the integrated data set. Specifically, it detects abnormalities in heart rate and skin temperature, and predicts the risk of poor health by taking weather conditions into account. The input is the integrated data set, and the output is the physical condition risk assessment result.

[0872] Step 6:

[0873] The server uses an emotion engine to analyze the employee's emotional state from biometric data. It infers stress and fatigue from changes in heart rate and skin temperature. The input is biometric data, and the output is the emotion analysis results.

[0874] Step 7:

[0875] The server integrates the risk assessment results from the generative AI and the emotion analysis results from the emotion engine to comprehensively determine the risk of poor health and emotional state.The inputs are the health risk assessment results and the emotion analysis results, and the output is a comprehensive health assessment result.

[0876] Step 8:

[0877] Based on the health assessment results received from the server, the factory robot notifies employees of preventive measures and break instructions via voice and display. Specific notification content may include "take breaks and hydrate." The input is the health assessment results, and the output is the notification to the employee.

[0878] Step 9:

[0879] The server sends notifications to the factory's affiliated medical institutions as needed, recommends preventive measures, and arranges for the advance delivery of necessary medicines.The input is the health assessment results, and the output is notifications to affiliated medical institutions.

[0880] 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.

[0881] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0882] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0883] [Third embodiment]

[0884] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0885] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0886] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0887] 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.

[0888] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0889] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0890] 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.

[0891] 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.

[0892] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0893] 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.

[0894] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0895] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0896] The present invention is a system that combines a wearable device, generative AI, and a smartphone, and aims to efficiently manage the user's physical condition. This system is implemented by the following means.

[0897] Wearable device data collection

[0898] Device behavior

[0899] Wearable devices constantly measure the user's biometric information, such as heart rate, sleep time, blood oxygen level, electrocardiogram, and skin temperature, and periodically transmit this data to a smartphone. For example, heart rate and sleep patterns are measured every hour and transmitted to the smartphone via Bluetooth or other communication methods.

[0900] Automatic collection of weather information

[0901] Server Operation

[0902] The server automatically collects real-time temperature, pressure, and humidity data from weather data providers on the Internet, and stores this data in a database for later analysis.

[0903] Collecting User Input

[0904] Device behavior

[0905] The smartphone sends a notification to the user at a specific time each day asking about their health. The user answers by rating their health on a scale of 1 to 10, and the information is saved on the smartphone. The saved data is then sent to a server and stored in a database.

[0906] Data integration and analysis

[0907] Server Operation

[0908] The server combines biometric data from the wearable device, weather information, and the user's health assessment data, and analyzes this data using a generative AI that identifies specific patterns and trends for each user and predicts the likelihood of poor health.

[0909] Prevention and Notification

[0910] Server Operation

[0911] Based on the AI's predictions, the server identifies situations in which users are likely to feel unwell and sends a notification to their smartphone containing specific preventative and remedial measures, allowing users to proactively manage their health.

[0912] Physician precautions and advance medication delivery

[0913] Server Operation

[0914] If illness is predicted, the server will send a notification to the connected medical service, and doctors can use this information to suggest appropriate preventive measures and, if necessary, arrange for the advance delivery of medication.

[0915] Specific examples

[0916] For example, if a user's wearable device indicates that their heart rate remains higher than normal, and weather data predicts that the temperature and humidity will continue to be high, the generative AI will determine that the user is at high risk of developing poor health. The server will then notify the user's smartphone of this information and recommend preventative measures such as "staying hydrated." The server will also notify a linked medical service and, in some cases, arrange for medication to be delivered to the user in advance.

[0917] As a result, the present invention enables users to manage their physical condition based on weather conditions and biological information, and prevent poor physical condition from occurring.

[0918] The processing flow will be explained below.

[0919] Step 1:

[0920] Terminal (wearable device) operation

[0921] Wearable devices constantly measure the user's biometric information, such as heart rate, sleep time, blood oxygen level, electrocardiogram, and skin temperature, and this data is temporarily stored in the device.

[0922] Step 2:

[0923] Terminal (wearable device) operation

[0924] The measured data is transferred to a smartphone at regular intervals (for example, every hour) using wireless communication means such as Bluetooth.

[0925] Step 3:

[0926] Device (smartphone) operation

[0927] The smartphone receives the biometric data received from the wearable device and stores it locally, where it is ready to be sent to a server.

[0928] Step 4:

[0929] Server Operation

[0930] The server periodically obtains real-time weather information such as temperature, pressure, and humidity from weather data providers on the Internet, and stores the obtained weather information in a database.

[0931] Step 5:

[0932] Device (smartphone) operation

[0933] At a specific time each day (e.g., 8:00 a.m.), the user will receive a notification from their smartphone asking the question, "How are you feeling today?"

[0934] Step 6:

[0935] User Actions

[0936] The user answers questions about their physical condition using a number from 1 to 10. For example, if they feel good, they enter "8," and if they feel bad, they enter "3."

[0937] Step 7:

[0938] Device (smartphone) operation

[0939] The physical condition evaluation data entered by the user is stored in the smartphone and then sent to the server.

[0940] Step 8:

[0941] Server Operation

[0942] The server receives the biometric information, weather information, and user's physical condition evaluation data sent from the wearable device and stores them in a database.

[0943] Step 9:

[0944] Server Operation

[0945] The generative AI uses the collected data to analyze it, comparing it with past data to identify patterns and trends in the user's poor health.

[0946] Step 10:

[0947] Server Operation

[0948] Based on the prediction results, the generating AI identifies days and situations when there is a high possibility of poor health and notifies the user's smartphone of this information.

[0949] Step 11:

[0950] Device (smartphone) operation

[0951] The smartphone receives notifications from the server and provides the user with specific preventative and improvement measures, such as advice like "Drink more water today."

[0952] Step 12:

[0953] Server Operation

[0954] Based on the predicted results of poor health, notifications are sent to linked medical services (e.g., HELPO).

[0955] Step 13:

[0956] Server Operation

[0957] Based on the information provided, a medical service professional (e.g., a doctor) will suggest appropriate preventive measures to the user and arrange for the advance delivery of necessary medications.

[0958] Step 14:

[0959] User Actions

[0960] The user checks the preventive and remedial measures displayed on their smartphone, puts them into practice, and, if necessary, receives and takes the medicine delivered.

[0961] By using the above processing steps, the present invention can efficiently manage the user's physical condition and prevent poor physical condition from occurring.

[0962] Example 1

[0963] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0964] In modern society, many people lead busy lives, making it difficult to properly manage their health. Furthermore, because it is difficult to predict the impact of weather conditions, daily lifestyle habits, stress, and other factors on physical condition, it is difficult to take appropriate preventive measures to prevent illness before it occurs. Furthermore, even if illness is predicted, there is no way to seek medical advice or obtain necessary medications in advance, making it difficult to respond quickly.

[0965] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0966] In this invention, the server includes: a means for collecting data on sleep time, heart rate, blood oxygen concentration, electrocardiogram, and skin temperature using a wearable device for constantly measuring the user's biometric information; a means for wirelessly transferring the collected biometric information to a smartphone and sending it to the server via the Internet; a means for automatically collecting weather information from the Internet and acquiring data such as temperature, air pressure, and humidity; a data collection means for displaying questions about the user's daily health using a generation AI and allowing the user to quantify and respond; a means for integrating and analyzing the collected biometric information, weather information, and user response data to predict situations in which the user is likely to feel unwell; and a means for notifying the user of information on preventive and remedial measures based on the prediction results. This allows the user to understand their own health condition and take appropriate preventive measures. Furthermore, if a health condition is predicted, the user can receive appropriate advice from a doctor or obtain necessary medication in advance, enabling a prompt response.

[0967] A "wearable device" is a device that is worn on the user's body to continuously measure biometric information.

[0968] "Biometric information" refers to data that indicates the user's health status, such as heart rate, sleep time, blood oxygen concentration, electrocardiogram, and skin temperature.

[0969] "Wireless communication" is a method of sending and receiving data between devices using wireless technologies such as Bluetooth and Wi-Fi.

[0970] "Weather information" refers to information about atmospheric conditions such as temperature, air pressure, and humidity, and is obtained from data providing services on the Internet.

[0971] "Generative AI" is an algorithm that uses techniques such as machine learning and deep learning to analyze data and generate patterns and predictions.

[0972] A "smartphone" is a portable information terminal that can connect to the Internet and is equipped with various sensors and communication functions.

[0973] The "data collection means" is a system that uses wearable devices or smartphones to collect biometric information, weather information, and user health evaluation data.

[0974] "Data integration" is the process of combining data collected from multiple sources into a single data set.

[0975] "Preventive measures" are specific actions or advice taken to reduce the risk of ill health.

[0976] "Means of notification" refers to a method of providing information to users by displaying a message on a device such as a smartphone.

[0977] "Medical services" are health management and treatment support services provided by professionals such as doctors and pharmacists.

[0978] A "cloud environment" is an infrastructure for using data storage and computing resources provided over the Internet.

[0979] "Data analysis" is the process of analyzing collected data using statistical or computational methods to derive meaningful information or predictive results.

[0980] The present invention is a system for efficiently managing a user's physical condition, which is configured by combining a wearable device, a generation AI, and a smartphone. This system is implemented by the following means.

[0981] Wearable device deployment and data collection

[0982] First, the user puts on a wearable device. This device has the function of constantly measuring biometric information such as heart rate, sleep time, blood oxygen level, electrocardiogram, and skin temperature. For example, heart rate is measured periodically every hour and temporarily stored in the device's memory. The data is then periodically transferred to a smartphone via Bluetooth communication.

[0983] Receiving and transferring data via smartphone

[0984] The smartphone receives the biometric information sent from the wearable device. This data is stored in the smartphone's local storage and simultaneously sent to a server via the Internet. The smartphone periodically uploads the biometric information to the server, enabling centralized data management.

[0985] Automatic collection of weather information

[0986] The server accesses a weather data provider on the Internet and automatically obtains real-time weather data (temperature, air pressure, humidity, etc.) The obtained data is stored in a database on the server and used for subsequent data analysis.

[0987] Collecting User Input

[0988] The smartphone sends a notification asking about the user's physical condition at a specific time every day. The user rates their physical condition on a scale of 1 to 10, and this data is saved on the smartphone. The saved data is periodically sent to a server and stored in a database.

[0989] Data integration and generation AI analysis

[0990] The server combines biometric data from the wearable device, weather information, and the user's health assessment data. This combined data set is then fed into a generative AI model for further analysis. The generative AI detects specific patterns and trends and predicts the risk of poor health.

[0991] Preventive measures and notifications

[0992] Based on the analysis results of the AI, the server generates appropriate preventive measures for cases where there is a high risk of illness. For example, specific advice such as "stay hydrated" and "take adequate rest" is included. These preventive measures are notified to the user's smartphone, allowing them to take measures in real time.

[0993] Physician precautions and advance medication delivery

[0994] If an illness is predicted, the server will send a notification to the linked medical service. Based on this information, doctors can suggest appropriate preventive measures to the user and, if necessary, arrange for advance delivery of medication. This allows users to reduce the risk of illness in advance.

[0995] Examples of concrete examples and prompts

[0996] For example, if a user's wearable device indicates that their heart rate remains higher than normal, and weather data predicts that the temperature and humidity will continue to be high, the generative AI may determine that the user is at high risk of becoming ill. The server then notifies the user's smartphone of this information and suggests preventative measures such as "staying hydrated." It also notifies medical services and arranges for medication to be delivered in advance if necessary.

[0997] An example of a prompt for the generative AI model is, "If the user's heart rate is higher than normal and weather data indicates that the temperature and humidity will be high for several days in a row, please suggest specific preventive measures for managing their health."

[0998] As described above, the present invention provides a system for efficiently managing a user's physical condition based on the user's biological information and weather conditions, thereby preventing poor physical condition before it occurs.

[0999] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1000] Step 1:

[1001] Collecting biometric information using wearable devices

[1002] Device behavior

[1003] Wearable devices constantly measure biometric information such as heart rate, sleep time, blood oxygen level, electrocardiogram, skin temperature, etc. The measured data is stored in the device's internal memory.

[1004] Input: User's biological information (heart rate, sleep time, blood oxygen level, electrocardiogram, skin temperature)

[1005] Output: Stored biometric data

[1006] Step 2:

[1007] Transferring data to a smartphone

[1008] Device behavior

[1009] The wearable device periodically transmits the collected data via Bluetooth to a smartphone, which receives the data and stores it in its internal storage.

[1010] Input: Biometric data sent from a wearable device

[1011] Output: Biometric data stored on a smartphone

[1012] Step 3:

[1013] Automatic collection of weather information

[1014] Server Operation

[1015] The server periodically obtains real-time weather data (temperature, air pressure, humidity) from a weather data provider on the Internet and stores it in a database.

[1016] Input: Weather information from an online weather data provider

[1017] Output: Weather data stored on the server

[1018] Step 4:

[1019] Collecting User Input

[1020] Device behavior

[1021] The smartphone will prompt the user with questions about their health at a specific time each day. The user will rate their health on a scale of 1 to 10, which will be saved on the smartphone. This data will then be periodically sent to a server.

[1022] Input: Health evaluation data entered by the user into their smartphone

[1023] Output: Health evaluation data sent to the server

[1024] Step 5:

[1025] Data integration and generation AI analysis

[1026] Server Operation

[1027] The server integrates biometric data from the wearable device, weather information, and the user's health assessment data. This data is then input into a generative AI model for analysis. The generative AI detects specific patterns and trends and predicts the risk of poor health.

[1028] Input: Biometric information from wearable devices, weather information, and user's physical condition evaluation data

[1029] Output: Poor health risk prediction results by generative AI

[1030] Step 6:

[1031] Preventive measures and notifications

[1032] Server Operation

[1033] The server generates appropriate preventive and remedial measures based on the predictions made by the AI. These preventive measures are then sent to the user's smartphone. For example, specific advice such as "stay hydrated" is provided.

[1034] Input: Prediction results by generative AI

[1035] Output: Preventive measures notified to your smartphone

[1036] Step 7:

[1037] Physician precautions and advance medication delivery

[1038] Server Operation

[1039] If the server predicts a high risk of illness, it notifies the medical service, who can then use the information to suggest preventative measures and, if necessary, arrange for the advance delivery of medication.

[1040] Input: Prediction results by the generation AI, user situation information

[1041] Output: Proposal of preventive measures by medical services and advance delivery of medicines

[1042] (Application example 1)

[1043] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1044] Employee illness in brick-and-mortar stores is a problem that leads to reduced work efficiency and increased safety risks. While there is a need for methods and systems to continuously monitor employee health and prevent illness before it occurs, current methods make it difficult to grasp the situation in real time or provide appropriate preventive measures. Furthermore, personalized advice for individual employees is often not provided, and only general warnings are issued. Therefore, the challenge is to develop a system that can efficiently manage employee health and provide preventive measures immediately.

[1045] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1046] In this invention, the server includes: means for collecting data on sleep time, heart rate, blood oxygen concentration, electrocardiogram, and skin temperature using a wearable device for constantly measuring a user's biometric information; means for automatically collecting weather information from the Internet and acquiring data such as temperature, barometric pressure, and humidity; means for displaying questions about the user's daily health using a generative AI and for the user to quantify and answer; means for integrating and analyzing the collected biometric information, weather information, and user response data to predict situations in which the user is likely to feel unwell; means for notifying the user of information on preventive and remedial measures based on the prediction results; and means for monitoring the health of employees in physical stores in real time and providing proactive alerts and personalized human care. This enables efficient management of employee health and the provision of appropriate preventive measures in real time.

[1047] A "wearable device" is a device worn by a user that constantly measures biometric information.

[1048] "Biometric information" refers to information such as the user's heart rate, sleep time, blood oxygen concentration, electrocardiogram, and skin temperature.

[1049] "Weather information" refers to data such as temperature, air pressure, and humidity obtained from weather data services on the Internet.

[1050] "Generative AI" is artificial intelligence that uses collected data to analyze and predict.

[1051] The "questions about health" are questions to quantify the user's daily health condition.

[1052] "Data collection means" refers to the means of collecting biometric information, weather information, and user response data using wearable devices and generative AI.

[1053] The "integration and analysis method" is a method that integrates collected biometric information, weather information, and user response data, and uses generative AI to predict situations in which users are likely to feel unwell.

[1054] "Information on preventive measures and improvement measures" refers to specific measures and advice provided when a situation is predicted in which the user is likely to feel unwell.

[1055] A "physical store" is a face-to-face business facility that sells goods or provides services.

[1056] "Employee health monitoring" refers to the continuous real-time monitoring of the biometric information of employees working in physical stores.

[1057] A "proactive alert" is a notification that warns you in advance when poor health is predicted.

[1058] "Personalized human care" refers to providing care and advice tailored to each employee's individual health condition.

[1059] "Doctor's recommendations for preventive measures" are advice on preventive measures provided by a medical professional when an illness is predicted.

[1060] "Pre-delivery of medicines" means delivering necessary medicines to the user in advance.

[1061] A "cloud environment" is a virtual environment that stores and manages data via the Internet.

[1062] The "data analysis means" is a means for analyzing collected data and predicting poor health.

[1063] The present invention is a system for efficiently managing the health of employees in brick-and-mortar stores in real time. This system is composed of a wearable device, a generative AI, and a smartphone. Specific embodiments of this system are described below.

[1064] Wearable device data collection

[1065] Device behavior

[1066] The server constantly measures biometric information from the wearable device worn by the user, including heart rate, sleep time, blood oxygen level, electrocardiogram, and skin temperature. This data is periodically transferred to a smartphone via Bluetooth or other communication methods.

[1067] Automatic collection of weather information

[1068] Server Operation

[1069] The server automatically collects real-time temperature, pressure, and humidity data from weather data providers on the Internet, and stores the collected weather information in a database for later analysis.

[1070] Collecting User Input

[1071] Device behavior

[1072] The device sends a notification to employees at a specific time each day asking them questions about their health. Employees rate their health on a scale of 1 to 10, and the information is saved on their smartphone. The saved data is sent to a server and stored in a database.

[1073] Data integration and analysis

[1074] Server Operation

[1075] The server combines biometric data from the wearable devices, weather information, and user health assessment data, and analyzes this data using Generative AI, which identifies specific patterns and tendencies for each employee and predicts the likelihood of poor health.

[1076] Prevention and Notification

[1077] Server Operation

[1078] Based on the predictions of the generating AI, the server identifies situations in which employees are likely to feel unwell and sends a notification to their smartphone containing specific preventative and remedial measures, allowing employees to proactively manage their health.

[1079] Physician precautions and advance medication delivery

[1080] Server Operation

[1081] If illness is predicted, the server will send a notification to the connected medical service, and doctors can use this information to suggest appropriate preventive measures and, if necessary, arrange for the advance delivery of medication.

[1082] Specific examples

[1083] For example, if an employee's wearable device indicates that their heart rate remains higher than normal, and weather data predicts that the temperature and humidity will continue to be high, the generative AI will determine that the employee is at high risk of becoming ill. The server will then notify the employee's smartphone of this information and provide preventative measures such as taking breaks and staying hydrated. It will also notify a collaborative medical service and, in some cases, arrange for medication to be delivered to the employee in advance.

[1084] Prompt Sentence Examples

[1085] Predict health risks based on heart rate and weather data and suggest appropriate preventive measures.

[1086] In this way, the present invention integrates the biometric information of employees in physical stores with weather information and utilizes generative AI to achieve more efficient and safe health management.

[1087] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1088] Step 1:

[1089] The server periodically collects biometric information such as heart rate, sleep time, blood oxygen level, electrocardiogram, and skin temperature from the wearable device worn by the user. The collected biometric information is transferred to a smartphone using a communication method such as Bluetooth. The smartphone receives the information and sends it to the server.

[1090] Input: Biometric information obtained from a wearable device

[1091] Output: Biometric information sent to the smartphone and server

[1092] Step 2:

[1093] The server periodically obtains real-time weather information such as temperature, pressure, and humidity from a weather data provider. The obtained weather information is stored in a database and used for later analysis.

[1094] Input: Weather information from an internet weather data provider

[1095] Output: Weather information stored in the database on the server

[1096] Step 3:

[1097] The device sends a notification to the user at a specific time each day asking about their health. The user answers by rating their health on a scale of 1 to 10, and the information is saved on the smartphone. The saved data is sent to a server and stored in a database.

[1098] Input: User's physical condition rating

[1099] Output: User's health evaluation data stored on the server

[1100] Step 4:

[1101] The server integrates the collected biometric information, weather information, and the user's health assessment data, and analyzes the data using a generation AI. The generation AI uses a specific algorithm to input each piece of data and predict the situations in which the user is likely to feel unwell.

[1102] Input: Biometric information, weather information, user's physical condition evaluation data

[1103] Output: Risk assessment of poor health

[1104] Step 5:

[1105] The server sends notifications to the user's smartphone with preventive and remedial measures based on the risk of illness predicted by the generative AI. The notifications include specific instructions for action (e.g., taking a break, drinking water, etc.).

[1106] Input: Risk assessment results of the generated AI

[1107] Output: Notifications of preventive and remedial measures sent to your smartphone

[1108] Step 6:

[1109] If a high risk of illness is predicted, the server will send a notification to the associated medical service, and medical professionals will use the predicted information to suggest preventative measures and, if necessary, arrange for the advance delivery of medication.

[1110] Input: Generative AI high-risk assessment results

[1111] Output: Notification sent to medical services and precautions taken by doctors, drug delivery arrangements

[1112] Step 7:

[1113] The server stores all collected data in a cloud environment, allowing the generative AI to analyze this data, which will improve the accuracy of future predictions.

[1114] Input: All collected data

[1115] Output: Data stored in the cloud environment

[1116] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1117] The present invention is a system that combines a wearable device, generative AI, an emotion engine, and a smartphone, and aims to efficiently manage the user's physical condition and emotions. This system is implemented by the following means.

[1118] Wearable device data collection

[1119] Device behavior

[1120] Wearable devices constantly measure the user's biometric information, such as heart rate, sleep time, blood oxygen level, electrocardiogram, and skin temperature, and periodically transmit this data to a smartphone. For example, heart rate and sleep patterns are measured every hour and transmitted to the smartphone via Bluetooth or other communication methods.

[1121] Automatic collection of weather information

[1122] Server Operation

[1123] The server automatically collects real-time temperature, pressure, and humidity data from weather data providers on the Internet, and stores this data in a database for later analysis.

[1124] Collecting User Input

[1125] Device behavior

[1126] The smartphone sends a notification to the user at a specific time each day asking about their health. The user answers by rating their health on a scale of 1 to 10, and the information is saved on the smartphone. The saved data is then sent to a server and stored in a database.

[1127] Sentiment analysis with emotion engine

[1128] Server Operation

[1129] The server uses an emotion engine to analyze the user's emotional state based on the collected biometric information and the user's physical condition assessment data. For example, it can infer stress or fatigue from fluctuations in heart rate or abnormal sleep patterns.

[1130] Data integration and analysis

[1131] Server Operation

[1132] The server combines biometric information, weather information, and the user's health assessment data sent from the wearable device, as well as emotional data from the emotion engine, and analyzes this data using a generative AI. The generative AI identifies specific patterns and tendencies for each user and predicts the likelihood of poor health.

[1133] Prevention and Notification

[1134] Server Operation

[1135] Based on the AI's predictions, the server identifies situations in which the user is likely to feel unwell and sends a notification to their smartphone. The notification includes specific preventive and remedial measures, allowing users to proactively manage their health. Furthermore, preventive measures, including stress management and psychological support based on emotional data, are also provided.

[1136] Physician precautions and advance medication delivery

[1137] Server Operation

[1138] If illness is predicted, the server will send a notification to the connected medical service, and doctors can use this information to suggest appropriate preventive measures and, if necessary, arrange for the advance delivery of medication.

[1139] Specific examples

[1140] For example, a user's wearable device may indicate that their heart rate remains higher than normal, and weather data predicts that the temperature and humidity will continue to be high. In this case, the generative AI determines that there is a high risk of illness. The emotion engine further analyzes the user's emotional state and detects a high stress level. The server then notifies the user's smartphone of this information and offers preventative measures, such as "Drink more fluids and take more breaks today." The server also sends a notification to a connected medical service, and in some cases arranges for medication to be delivered to the user in advance.

[1141] As a result, the present invention enables the user to manage their physical condition and emotions based on weather conditions, emotional state, and biological information, and prevent poor physical condition from occurring.

[1142] The processing flow will be explained below.

[1143] Step 1:

[1144] Terminal (wearable device) operation

[1145] Wearable devices constantly measure the user's biometric information, such as heart rate, sleep time, blood oxygen level, electrocardiogram, and skin temperature, and temporarily store this data within the device.

[1146] Step 2:

[1147] Terminal (wearable device) operation

[1148] The measured data is transferred to a smartphone at regular intervals (for example, every hour) using wireless communication means such as Bluetooth.

[1149] Step 3:

[1150] Device (smartphone) operation

[1151] The smartphone receives the biometric data received from the wearable device and stores it locally, where it is prepared for transmission to a server.

[1152] Step 4:

[1153] Server Operation

[1154] The server periodically obtains real-time weather information such as temperature, pressure, and humidity from weather data providers on the Internet, and stores the obtained weather information in a database.

[1155] Step 5:

[1156] Device (smartphone) operation

[1157] At a specific time each day (e.g., 8:00 a.m.), the user will receive a notification from their smartphone asking the question, "How are you feeling today?"

[1158] Step 6:

[1159] User Actions

[1160] The user answers questions about their physical condition using a number from 1 to 10. For example, if they feel good, they enter "8," and if they feel bad, they enter "3."

[1161] Step 7:

[1162] Device (smartphone) operation

[1163] The physical condition evaluation data entered by the user is stored in the smartphone and then sent to the server.

[1164] Step 8:

[1165] Server Operation

[1166] The server receives the biometric information, weather information, and user's physical condition evaluation data sent from the wearable device and stores them in a database.

[1167] Step 9:

[1168] Server Operation

[1169] The server analyzes the user's emotional state based on the collected biometric information and the user's physical condition assessment data using an emotion engine. For example, it can infer stress or fatigue from fluctuations in heart rate or abnormal sleep patterns.

[1170] Step 10:

[1171] Server Operation

[1172] The server combines biometric information sent from the wearable device, weather information, the user's health assessment data, and emotional data from the emotion engine, and analyzes this data using generative AI, which identifies specific patterns and tendencies for each user and predicts the possibility of poor health.

[1173] Step 11:

[1174] Server Operation

[1175] Based on the prediction results, the generating AI identifies days and situations when there is a high possibility of poor health and notifies the user's smartphone of this information.

[1176] Step 12:

[1177] Device (smartphone) operation

[1178] The smartphone receives notifications from the server and provides the user with specific preventative and improvement measures, such as advice like "Drink more water today."

[1179] Step 13:

[1180] Server Operation

[1181] Based on the predicted results of poor health, notifications are sent to linked medical services (e.g., HELPO).

[1182] Step 14:

[1183] Server Operation

[1184] Based on the information provided, a medical service professional (e.g., a doctor) will suggest appropriate preventive measures to the user and arrange for the advance delivery of necessary medications.

[1185] Step 15:

[1186] User Actions

[1187] The user checks the preventive and remedial measures displayed on their smartphone, puts them into practice, and, if necessary, receives and takes the medicine delivered.

[1188] By using the above processing steps, the present invention can efficiently manage the user's physical condition and emotions, and prevent poor physical condition from occurring.

[1189] Example 2

[1190] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1191] Conventional health management systems lack the functionality to comprehensively manage a user's biometric information, weather conditions, and emotional state, predict illness, and provide preventative measures. In particular, they do not provide preventative measures that take into account the user's emotional state or medical cooperation, which often leaves users unable to respond appropriately to sudden illnesses. Therefore, there is a need for a system that comprehensively manages a user's biometric information, weather conditions, and emotional state, predicts illness, and provides preventative measures in cooperation with medical cooperation.

[1192] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for collecting data on sleep time, heart rate, blood oxygen concentration, electrocardiogram, and skin temperature using a wearable device for constantly measuring the user's biological information; means for automatically collecting weather information on the Internet and acquiring data such as temperature, atmospheric pressure, and humidity; data collection means for displaying questions about the user's daily physical condition using a smartphone and for the user to quantify and answer; means for integrating and analyzing the collected biological information, weather information, and user's answer data to predict situations in which the user is likely to feel unwell; means for analyzing the user's emotional state using an emotion engine based on the collected biological information and the user's physical condition evaluation data; means for notifying the user of information on preventive measures and improvement measures based on the predicted risk of poor health using a generation AI; and means for sending a notification to a coordinated medical service when poor health is predicted and arranging for instructions from a doctor or delivery of medicine. This will enable integrated management of the user's biometric information, weather conditions, and emotional state, making it possible to predict illness and provide appropriate preventive measures through medical cooperation.

[1193] "Biometric information" refers to data obtained from inside or on the surface of a user's body, and includes heart rate, sleep time, blood oxygen concentration, electrocardiogram, skin temperature, etc.

[1194] A "wearable device" is a device worn by a user that can constantly measure their biometric information.

[1195] "Weather information" refers to data related to weather, such as temperature, air pressure, and humidity, which is automatically collected from external services provided on the Internet.

[1196] A "smartphone" is a portable information terminal with telephone functions, and is a device that interacts with users using applications.

[1197] "Data collection means" refers to a method or device that displays questions about the user's daily physical condition and collects the answers as numerical data.

[1198] "Generative AI" refers to artificial intelligence technology that performs advanced analysis on collected data to identify specific patterns and trends.

[1199] "Emotion engine" is a general term for algorithms and software that analyze a user's emotional state based on collected biometric information and the user's physical condition evaluation data.

[1200] "Notification means" refers to a method or device for communicating information on preventive or improvement measures to users based on the prediction results.

[1201] "Medical services" is a general term for services aimed at providing medical examinations and treatment by doctors, medicines, etc.

[1202] "Cloud environment" refers to computing resources and data storage provided via the Internet, and refers to the infrastructure for storing and processing data.

[1203] This invention is a system that combines a wearable device, a smartphone, a generative AI, and an emotion engine to manage the user's physical condition and emotions. The specific hardware and software operations required to realize each step of this system are explained in order.

[1204] First, wearable devices as terminals constantly measure the user's biometric information, such as heart rate, sleep time, blood oxygen level, electrocardiogram, and skin temperature. This data is periodically transferred to a smartphone via Bluetooth communication. For example, heart rate and sleep patterns are measured every hour and sent to the smartphone.

[1205] The server then automatically collects real-time temperature, pressure, and humidity data from an internet weather data provider, which is then stored in a database for later analysis.

[1206] The smartphone device also sends users a question about their health at a specific time each day. Users rate their health on a scale of 1 to 10, and the information is saved on the smartphone. This data is sent to a server and stored in a database.

[1207] The server uses an emotion engine to analyze the user's emotional state based on the collected biometric information and the user's physical condition evaluation data. For example, it can infer stress or fatigue from fluctuations in heart rate or abnormal sleep patterns.

[1208] The server then combines biometric information sent from the wearable device, weather information, the user's health assessment data, and emotional data from an emotion engine. This combined data is then analyzed by generative AI to identify specific patterns and trends for each user and predict the likelihood of poor health.

[1209] Finally, based on the predictions of the generative AI, the server identifies situations in which the user is likely to feel unwell and notifies the user of this information via their smartphone. The notification includes specific preventive and remedial measures, allowing the user to proactively manage their health. Furthermore, if medical collaboration is required, the server sends a notification to the collaborative medical service and arranges for advance delivery of doctor's instructions and medication.

[1210] Specific examples

[1211] For example, if a user's wearable device indicates a sustained higher-than-normal heart rate and weather data predicts a continued period of high temperatures and humidity, the generative AI will determine that there is a high risk of illness. The emotion engine then analyzes the user's emotional state and detects high stress levels. The server then notifies the user's smartphone of this information and offers preventative measures, such as "Drink more fluids and take more breaks today." If necessary, the system will also notify a medical service, which will suggest preventative measures and, in some cases, arrange for medication to be delivered to the user.

[1212] Prompt Sentence Examples

[1213] Below is an example of a prompt to input to the generative AI model.

[1214] ---

[1215] "A user's heart rate has been consistently higher than normal, and weather data predicts a period of high temperatures and humidity. Please assess the user's risk of illness in this situation and suggest necessary preventative measures."

[1216] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1217] Step 1:

[1218] Device behavior

[1219] The wearable device measures the user's biometric information every hour, including heart rate, sleep time, blood oxygen level, electrocardiogram, and skin temperature. The measurement results are then transferred to a smartphone via Bluetooth, where the data is temporarily stored.

[1220] Input: User's biological information (heart rate, sleep time, blood oxygen level, electrocardiogram, skin temperature)

[1221] Output: Biometric information sent to a smartphone

[1222] Step 2:

[1223] Server Operation

[1224] The server periodically polls weather data providers on the Internet to collect temperature, pressure, and humidity data. This data is then stored in a database on the server. The data collection frequency is, for example, every 15 minutes.

[1225] Input: Real-time data from weather data provider (temperature, pressure, humidity)

[1226] Output: Weather information stored in a database

[1227] Step 3:

[1228] Device behavior

[1229] The smartphone will send notifications to users at specific times, asking them questions about their health. For example, every morning at 8:00, a question like "Please rate how you feel this morning on a scale of 1 to 10" will be displayed. When the user enters a number, the data is stored on the smartphone and later sent to a server.

[1230] Input: User's physical condition evaluation data (numerical input)

[1231] Output: Physical condition evaluation data stored on a smartphone

[1232] Step 4:

[1233] Server Operation

[1234] The server receives the biometric information and physical condition evaluation data sent from the smartphone, stores them in a database, and then analyzes the data using an emotion engine to evaluate the user's emotional state (e.g., stress level).

[1235] Input: Biometric information, physical condition evaluation data

[1236] Output: Emotion data stored in a database

[1237] Step 5:

[1238] Server Operation

[1239] The collected biometric information, weather information, health assessment data, and emotional data are combined into a single dataset and input into the Generative AI. The Generative AI analyzes this data and identifies specific patterns and trends for each user (for example, a correlation between high humidity and stress levels). Based on the results of this analysis, the risk of poor health is predicted.

[1240] Input: Integrated dataset (biometric information, weather information, physical condition assessment data, emotional data)

[1241] Output: Analysis results (prediction of risk of poor health)

[1242] Step 6:

[1243] Server Operation

[1244] Based on the prediction results, the generative AI generates specific preventative and improvement measures. For example, it might create a message such as, "Drink water frequently and take plenty of breaks today." This message is then sent to the user's smartphone.

[1245] Input: Generative AI prediction results

[1246] Output: Preventive measures message sent to your smartphone

[1247] Step 7:

[1248] Server Operation

[1249] If a user's condition is predicted to worsen, the server sends a notification to the connected medical service. This notification includes information on the user's biometrics, weather information, emotional data, etc. Based on the information received, a doctor at the medical service will provide appropriate preventive measures and, if necessary, arrange for the advance delivery of medication.

[1250] Input: Predicted illness data

[1251] Output: Notifying medical services and suggesting preventative measures, arranging for medication delivery

[1252] Through the above steps, the system comprehensively manages the user's biometric information, weather conditions, and emotional state, enabling prediction of poor health and provision of appropriate preventive measures.

[1253] (Application example 2)

[1254] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1255] Managing employee health is particularly important in factory working environments, but conventional methods have made it difficult to grasp the health status of individual employees in real time and provide appropriate preventive measures. Furthermore, there has been a lack of automated means for quickly responding to predicted illnesses. This has created challenges for the productivity of the entire factory and for managing employee health.

[1256] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for collecting data on sleep time, heart rate, blood oxygen concentration, electrocardiogram, and skin temperature using a wearable device for constantly measuring a user's biometric information; means for automatically collecting weather information from the Internet and acquiring data such as temperature, barometric pressure, and humidity; means for displaying questions about the user's daily health using a generation AI and allowing the user to quantify and respond; means for integrating and analyzing the collected biometric information, weather information, and user response data to predict situations in which the user is likely to feel unwell; means for a factory robot to collect biometric data measured by factory employees using a wearable device and analyze the data using a generation AI to manage their health; and means for notifying users of information on preventive and remedial measures based on the prediction results. This makes it possible to grasp the health of factory employees in real time and quickly provide appropriate preventive measures.

[1257] A "wearable device" is a device that constantly monitors the user's physical condition and measures and collects biometric information such as heart rate, blood oxygen concentration, and skin temperature.

[1258] "Weather information" includes environmental data such as temperature, air pressure, and humidity, and is information obtained via the Internet.

[1259] "Generative AI" is an artificial intelligence technology that predicts and analyzes a user's physical and emotional state based on collected data.

[1260] "Data collection means" refers to a means for integrating and managing biometric information and weather information collected from wearable devices and the Internet.

[1261] The "means for predicting poor health" is a system in which a generative AI analyzes collected data and determines whether the user is likely to be in poor health.

[1262] "Means for notifying preventive and improvement measures" refers to a means for providing users with appropriate health management information based on the risks predicted by the generative AI.

[1263] "Factory robots" are automated machines that operate within factories, collect and analyze data from employees' wearable devices, and support their health management.

[1264] This invention is a system for supporting employee health management, and aims to efficiently monitor and manage employee health by combining wearable devices, generative AI, emotion engines, and factory robots.

[1265] Wearable device data collection

[1266] Device behavior

[1267] The wearable device has the function of constantly measuring employees' biometric information, such as heart rate, blood oxygen concentration, and skin temperature, and periodically transmitting this information to factory robots. For example, heart rate and skin temperature are measured every hour and transmitted to the factory robot via communication means such as Bluetooth.

[1268] Automatic collection of weather information

[1269] Server Operation

[1270] The server automatically collects real-time temperature, pressure, and humidity data from an online weather data provider and stores it in a database. This data is then used for analysis to identify factors that affect employees' physical condition.

[1271] Data collection methods

[1272] Factory robot operation

[1273] Factory robots collect and manage measurement data from wearable devices worn by employees, which is sent to generative AI and analyzed to understand the employee's physical condition in real time.

[1274] Sentiment analysis with emotion engine

[1275] Server Operation

[1276] The server uses an emotion engine to analyze employees' emotional states based on collected biometric and weather data, for example, inferring stress or fatigue from abnormal heart rates and fluctuations in skin temperature.

[1277] Data integration and analysis

[1278] Server Operation

[1279] The server combines biometric data from wearable devices and environmental sensors with weather information, and then analyzes this data using generative AI, which identifies specific patterns and tendencies for each employee and predicts the likelihood of illness.

[1280] Prevention and Notification

[1281] Factory robot operation

[1282] Based on the predictions of generative AI, the factory robot identifies situations in which employees are likely to feel unwell and notifies them via voice or display, including specific advice on taking breaks and staying hydrated.

[1283] Physician precautions and advance medication delivery

[1284] Server Operation

[1285] If necessary, the factory's partner medical institutions will be notified to arrange for preventative measures to support employee health management and the advance delivery of necessary medications.

[1286] Specific examples

[1287] For example, let's say an employee's wearable device indicates a persistently higher-than-normal heart rate. Weather data predicts that the temperature and humidity in the factory will remain high. In this case, the generative AI determines that there is a high risk of illness. The emotion engine further analyzes the employee's emotional state and detects a high stress level. Based on this information, the factory robot will issue a voice notification to the employee advising them to take a break and hydrate. If necessary, it will also notify affiliated medical institutions and arrange for preventive measures and necessary medications to be delivered in advance.

[1288] Prompt Sentence Examples

[1289] "If the heart rate is over 90 and the skin temperature is over 36.5 degrees, notify the employee to take a break and drink fluids."

[1290] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1291] Step 1:

[1292] The wearable device measures the employee's heart rate, blood oxygen level, and skin temperature, and stores this biometric information internally. The input is various types of biometric data, and the output is the aggregated measurement data.

[1293] Step 2:

[1294] The wearable device transmits the measured biometric information to the factory robot via Bluetooth. The input is the measured data mentioned above, and the output is the transmitted biometric data.

[1295] Step 3:

[1296] The server automatically retrieves weather data such as temperature, pressure, and humidity from a weather information database on the Internet. The input is information retrieved from the weather data provider service, and the output is the latest weather data.

[1297] Step 4:

[1298] The factory robot receives the biometric data transmitted from the wearable device and the meteorological data obtained from the server, which forms an integrated data set. The inputs are the biometric data and meteorological data, and the output is the integrated data set.

[1299] Step 5:

[1300] The server uses generative AI to analyze the employee's physical condition from the integrated data set. Specifically, it detects abnormalities in heart rate and skin temperature, and predicts the risk of poor health by taking weather conditions into account. The input is the integrated data set, and the output is the physical condition risk assessment result.

[1301] Step 6:

[1302] The server uses an emotion engine to analyze the employee's emotional state from biometric data. It infers stress and fatigue from changes in heart rate and skin temperature. The input is biometric data, and the output is the emotion analysis results.

[1303] Step 7:

[1304] The server integrates the risk assessment results from the generative AI and the emotion analysis results from the emotion engine to comprehensively determine the risk of poor health and emotional state.The inputs are the health risk assessment results and the emotion analysis results, and the output is a comprehensive health assessment result.

[1305] Step 8:

[1306] Based on the health assessment results received from the server, the factory robot notifies employees of preventive measures and break instructions via voice and display. Specific notification content may include "take breaks and hydrate." The input is the health assessment results, and the output is the notification to the employee.

[1307] Step 9:

[1308] The server sends notifications to the factory's affiliated medical institutions as needed, recommends preventive measures, and arranges for the advance delivery of necessary medicines.The input is the health assessment results, and the output is notifications to affiliated medical institutions.

[1309] 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.

[1310] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1311] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1312] [Fourth embodiment]

[1313] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1314] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1315] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1316] 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.

[1317] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1318] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1319] 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.

[1320] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1321] 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.

[1322] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1323] 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.

[1324] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1325] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1326] The present invention is a system that combines a wearable device, generative AI, and a smartphone, and aims to efficiently manage the user's physical condition. This system is implemented by the following means.

[1327] Wearable device data collection

[1328] Device behavior

[1329] Wearable devices constantly measure the user's biometric information, such as heart rate, sleep time, blood oxygen level, electrocardiogram, and skin temperature, and periodically transmit this data to a smartphone. For example, heart rate and sleep patterns are measured every hour and transmitted to the smartphone via Bluetooth or other communication methods.

[1330] Automatic collection of weather information

[1331] Server Operation

[1332] The server automatically collects real-time temperature, pressure, and humidity data from weather data providers on the Internet, and stores this data in a database for later analysis.

[1333] Collecting User Input

[1334] Device behavior

[1335] The smartphone sends a notification to the user at a specific time each day asking about their health. The user answers by rating their health on a scale of 1 to 10, and the information is saved on the smartphone. The saved data is then sent to a server and stored in a database.

[1336] Data integration and analysis

[1337] Server Operation

[1338] The server combines biometric data from the wearable device, weather information, and the user's health assessment data, and analyzes this data using a generative AI that identifies specific patterns and trends for each user and predicts the likelihood of poor health.

[1339] Prevention and Notification

[1340] Server Operation

[1341] Based on the AI's predictions, the server identifies situations in which users are likely to feel unwell and sends a notification to their smartphone containing specific preventative and remedial measures, allowing users to proactively manage their health.

[1342] Physician precautions and advance medication delivery

[1343] Server Operation

[1344] If illness is predicted, the server will send a notification to the connected medical service, and doctors can use this information to suggest appropriate preventive measures and, if necessary, arrange for the advance delivery of medication.

[1345] Specific examples

[1346] For example, if a user's wearable device indicates that their heart rate remains higher than normal, and weather data predicts that the temperature and humidity will continue to be high, the generative AI will determine that the user is at high risk of developing poor health. The server will then notify the user's smartphone of this information and recommend preventative measures such as "staying hydrated." The server will also notify a linked medical service and, in some cases, arrange for medication to be delivered to the user in advance.

[1347] As a result, the present invention enables users to manage their physical condition based on weather conditions and biological information, and prevent poor physical condition from occurring.

[1348] The processing flow will be explained below.

[1349] Step 1:

[1350] Terminal (wearable device) operation

[1351] Wearable devices constantly measure the user's biometric information, such as heart rate, sleep time, blood oxygen level, electrocardiogram, and skin temperature, and this data is temporarily stored in the device.

[1352] Step 2:

[1353] Terminal (wearable device) operation

[1354] The measured data is transferred to a smartphone at regular intervals (for example, every hour) using wireless communication means such as Bluetooth.

[1355] Step 3:

[1356] Device (smartphone) operation

[1357] The smartphone receives the biometric data received from the wearable device and stores it locally, where it is ready to be sent to a server.

[1358] Step 4:

[1359] Server Operation

[1360] The server periodically obtains real-time weather information such as temperature, pressure, and humidity from weather data providers on the Internet, and stores the obtained weather information in a database.

[1361] Step 5:

[1362] Device (smartphone) operation

[1363] At a specific time each day (e.g., 8:00 a.m.), the user will receive a notification from their smartphone asking the question, "How are you feeling today?"

[1364] Step 6:

[1365] User Actions

[1366] The user answers questions about their physical condition using a number from 1 to 10. For example, if they feel good, they enter "8," and if they feel bad, they enter "3."

[1367] Step 7:

[1368] Device (smartphone) operation

[1369] The physical condition evaluation data entered by the user is stored in the smartphone and then sent to the server.

[1370] Step 8:

[1371] Server Operation

[1372] The server receives the biometric information, weather information, and user's physical condition evaluation data sent from the wearable device and stores them in a database.

[1373] Step 9:

[1374] Server Operation

[1375] The generative AI uses the collected data to analyze it, comparing it with past data to identify patterns and trends in the user's poor health.

[1376] Step 10:

[1377] Server Operation

[1378] Based on the prediction results, the generating AI identifies days and situations when there is a high possibility of poor health and notifies the user's smartphone of this information.

[1379] Step 11:

[1380] Device (smartphone) operation

[1381] The smartphone receives notifications from the server and provides the user with specific preventative and improvement measures, such as advice like "Drink more water today."

[1382] Step 12:

[1383] Server Operation

[1384] Based on the predicted results of poor health, notifications are sent to linked medical services (e.g., HELPO).

[1385] Step 13:

[1386] Server Operation

[1387] Based on the information provided, a medical service professional (e.g., a doctor) will suggest appropriate preventive measures to the user and arrange for the advance delivery of necessary medications.

[1388] Step 14:

[1389] User Actions

[1390] The user checks the preventive and remedial measures displayed on their smartphone, puts them into practice, and, if necessary, receives and takes the medicine delivered.

[1391] By using the above processing steps, the present invention can efficiently manage the user's physical condition and prevent poor physical condition from occurring.

[1392] Example 1

[1393] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1394] In modern society, many people lead busy lives, making it difficult to properly manage their health. Furthermore, because it is difficult to predict the impact of weather conditions, daily lifestyle habits, stress, and other factors on physical condition, it is difficult to take appropriate preventive measures to prevent illness before it occurs. Furthermore, even if illness is predicted, there is no way to seek medical advice or obtain necessary medications in advance, making it difficult to respond quickly.

[1395] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1396] In this invention, the server includes: a means for collecting data on sleep time, heart rate, blood oxygen concentration, electrocardiogram, and skin temperature using a wearable device for constantly measuring the user's biometric information; a means for wirelessly transferring the collected biometric information to a smartphone and sending it to the server via the Internet; a means for automatically collecting weather information from the Internet and acquiring data such as temperature, air pressure, and humidity; a data collection means for displaying questions about the user's daily health using a generation AI and allowing the user to quantify and respond; a means for integrating and analyzing the collected biometric information, weather information, and user response data to predict situations in which the user is likely to feel unwell; and a means for notifying the user of information on preventive and remedial measures based on the prediction results. This allows the user to understand their own health condition and take appropriate preventive measures. Furthermore, if a health condition is predicted, the user can receive appropriate advice from a doctor or obtain necessary medication in advance, enabling a prompt response.

[1397] A "wearable device" is a device that is worn on the user's body to continuously measure biometric information.

[1398] "Biometric information" refers to data that indicates the user's health status, such as heart rate, sleep time, blood oxygen concentration, electrocardiogram, and skin temperature.

[1399] "Wireless communication" is a method of sending and receiving data between devices using wireless technologies such as Bluetooth and Wi-Fi.

[1400] "Weather information" refers to information about atmospheric conditions such as temperature, air pressure, and humidity, and is obtained from data providing services on the Internet.

[1401] "Generative AI" is an algorithm that uses techniques such as machine learning and deep learning to analyze data and generate patterns and predictions.

[1402] A "smartphone" is a portable information terminal that can connect to the Internet and is equipped with various sensors and communication functions.

[1403] The "data collection means" is a system that uses wearable devices or smartphones to collect biometric information, weather information, and user health evaluation data.

[1404] "Data integration" is the process of combining data collected from multiple sources into a single data set.

[1405] "Preventive measures" are specific actions or advice taken to reduce the risk of ill health.

[1406] "Means of notification" refers to a method of providing information to users by displaying a message on a device such as a smartphone.

[1407] "Medical services" are health management and treatment support services provided by professionals such as doctors and pharmacists.

[1408] A "cloud environment" is an infrastructure for using data storage and computing resources provided over the Internet.

[1409] "Data analysis" is the process of analyzing collected data using statistical or computational methods to derive meaningful information or predictive results.

[1410] The present invention is a system for efficiently managing a user's physical condition, which is configured by combining a wearable device, a generation AI, and a smartphone. This system is implemented by the following means.

[1411] Wearable device deployment and data collection

[1412] First, the user puts on a wearable device. This device has the function of constantly measuring biometric information such as heart rate, sleep time, blood oxygen level, electrocardiogram, and skin temperature. For example, heart rate is measured periodically every hour and temporarily stored in the device's memory. The data is then periodically transferred to a smartphone via Bluetooth communication.

[1413] Receiving and transferring data via smartphone

[1414] The smartphone receives the biometric information sent from the wearable device. This data is stored in the smartphone's local storage and simultaneously sent to a server via the Internet. The smartphone periodically uploads the biometric information to the server, enabling centralized data management.

[1415] Automatic collection of weather information

[1416] The server accesses a weather data provider on the Internet and automatically obtains real-time weather data (temperature, air pressure, humidity, etc.) The obtained data is stored in a database on the server and used for subsequent data analysis.

[1417] Collecting User Input

[1418] The smartphone sends a notification asking about the user's physical condition at a specific time every day. The user rates their physical condition on a scale of 1 to 10, and this data is saved on the smartphone. The saved data is periodically sent to a server and stored in a database.

[1419] Data integration and generation AI analysis

[1420] The server combines biometric data from the wearable device, weather information, and the user's health assessment data. This combined data set is then fed into a generative AI model for further analysis. The generative AI detects specific patterns and trends and predicts the risk of poor health.

[1421] Preventive measures and notifications

[1422] Based on the analysis results of the AI, the server generates appropriate preventive measures for cases where there is a high risk of illness. For example, specific advice such as "stay hydrated" and "take adequate rest" is included. These preventive measures are notified to the user's smartphone, allowing them to take measures in real time.

[1423] Physician precautions and advance medication delivery

[1424] If an illness is predicted, the server will send a notification to the linked medical service. Based on this information, doctors can suggest appropriate preventive measures to the user and, if necessary, arrange for advance delivery of medication. This allows users to reduce the risk of illness in advance.

[1425] Examples of concrete examples and prompts

[1426] For example, if a user's wearable device indicates that their heart rate remains higher than normal, and weather data predicts that the temperature and humidity will continue to be high, the generative AI may determine that the user is at high risk of becoming ill. The server then notifies the user's smartphone of this information and suggests preventative measures such as "staying hydrated." It also notifies medical services and arranges for medication to be delivered in advance if necessary.

[1427] An example of a prompt for the generative AI model is, "If the user's heart rate is higher than normal and weather data indicates that the temperature and humidity will be high for several days in a row, please suggest specific preventive measures for managing their health."

[1428] As described above, the present invention provides a system for efficiently managing a user's physical condition based on the user's biological information and weather conditions, thereby preventing poor physical condition before it occurs.

[1429] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1430] Step 1:

[1431] Collecting biometric information using wearable devices

[1432] Device behavior

[1433] Wearable devices constantly measure biometric information such as heart rate, sleep time, blood oxygen level, electrocardiogram, skin temperature, etc. The measured data is stored in the device's internal memory.

[1434] Input: User's biological information (heart rate, sleep time, blood oxygen level, electrocardiogram, skin temperature)

[1435] Output: Stored biometric data

[1436] Step 2:

[1437] Transferring data to a smartphone

[1438] Device behavior

[1439] The wearable device periodically transmits the collected data via Bluetooth to a smartphone, which receives the data and stores it in its internal storage.

[1440] Input: Biometric data sent from a wearable device

[1441] Output: Biometric data stored on a smartphone

[1442] Step 3:

[1443] Automatic collection of weather information

[1444] Server Operation

[1445] The server periodically obtains real-time weather data (temperature, air pressure, humidity) from a weather data provider on the Internet and stores it in a database.

[1446] Input: Weather information from an online weather data provider

[1447] Output: Weather data stored on the server

[1448] Step 4:

[1449] Collecting User Input

[1450] Device behavior

[1451] The smartphone will prompt the user with questions about their health at a specific time each day. The user will rate their health on a scale of 1 to 10, which will be saved on the smartphone. This data will then be periodically sent to a server.

[1452] Input: Health evaluation data entered by the user into their smartphone

[1453] Output: Health evaluation data sent to the server

[1454] Step 5:

[1455] Data integration and generation AI analysis

[1456] Server Operation

[1457] The server integrates biometric data from the wearable device, weather information, and the user's health assessment data. This data is then input into a generative AI model for analysis. The generative AI detects specific patterns and trends and predicts the risk of poor health.

[1458] Input: Biometric information from wearable devices, weather information, and user's physical condition evaluation data

[1459] Output: Poor health risk prediction results by generative AI

[1460] Step 6:

[1461] Preventive measures and notifications

[1462] Server Operation

[1463] The server generates appropriate preventive and remedial measures based on the predictions made by the AI. These preventive measures are then sent to the user's smartphone. For example, specific advice such as "stay hydrated" is provided.

[1464] Input: Prediction results by generative AI

[1465] Output: Preventive measures notified to your smartphone

[1466] Step 7:

[1467] Physician precautions and advance medication delivery

[1468] Server Operation

[1469] If the server predicts a high risk of illness, it notifies the medical service, who can then use the information to suggest preventative measures and, if necessary, arrange for the advance delivery of medication.

[1470] Input: Prediction results by the generation AI, user situation information

[1471] Output: Proposal of preventive measures by medical services and advance delivery of medicines

[1472] (Application example 1)

[1473] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1474] Employee illness in brick-and-mortar stores is a problem that leads to reduced work efficiency and increased safety risks. While there is a need for methods and systems to continuously monitor employee health and prevent illness before it occurs, current methods make it difficult to grasp the situation in real time or provide appropriate preventive measures. Furthermore, personalized advice for individual employees is often not provided, and only general warnings are issued. Therefore, the challenge is to develop a system that can efficiently manage employee health and provide preventive measures immediately.

[1475] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1476] In this invention, the server includes: means for collecting data on sleep time, heart rate, blood oxygen concentration, electrocardiogram, and skin temperature using a wearable device for constantly measuring a user's biometric information; means for automatically collecting weather information from the Internet and acquiring data such as temperature, barometric pressure, and humidity; means for displaying questions about the user's daily health using a generative AI and for the user to quantify and answer; means for integrating and analyzing the collected biometric information, weather information, and user response data to predict situations in which the user is likely to feel unwell; means for notifying the user of information on preventive and remedial measures based on the prediction results; and means for monitoring the health of employees in physical stores in real time and providing proactive alerts and personalized human care. This enables efficient management of employee health and the provision of appropriate preventive measures in real time.

[1477] A "wearable device" is a device worn by a user that constantly measures biometric information.

[1478] "Biometric information" refers to information such as the user's heart rate, sleep time, blood oxygen concentration, electrocardiogram, and skin temperature.

[1479] "Weather information" refers to data such as temperature, air pressure, and humidity obtained from weather data services on the Internet.

[1480] "Generative AI" is artificial intelligence that uses collected data to analyze and predict.

[1481] The "questions about health" are questions to quantify the user's daily health condition.

[1482] "Data collection means" refers to the means of collecting biometric information, weather information, and user response data using wearable devices and generative AI.

[1483] The "integration and analysis method" is a method that integrates collected biometric information, weather information, and user response data, and uses generative AI to predict situations in which users are likely to feel unwell.

[1484] "Information on preventive measures and improvement measures" refers to specific measures and advice provided when a situation is predicted in which the user is likely to feel unwell.

[1485] A "physical store" is a face-to-face business facility that sells goods or provides services.

[1486] "Employee health monitoring" refers to the continuous real-time monitoring of the biometric information of employees working in physical stores.

[1487] A "proactive alert" is a notification that warns you in advance when poor health is predicted.

[1488] "Personalized human care" refers to providing care and advice tailored to each employee's individual health condition.

[1489] "Doctor's recommendations for preventive measures" are advice on preventive measures provided by a medical professional when an illness is predicted.

[1490] "Pre-delivery of medicines" means delivering necessary medicines to the user in advance.

[1491] A "cloud environment" is a virtual environment that stores and manages data via the Internet.

[1492] The "data analysis means" is a means for analyzing collected data and predicting poor health.

[1493] The present invention is a system for efficiently managing the health of employees in brick-and-mortar stores in real time. This system is composed of a wearable device, a generative AI, and a smartphone. Specific embodiments of this system are described below.

[1494] Wearable device data collection

[1495] Device behavior

[1496] The server constantly measures biometric information from the wearable device worn by the user, including heart rate, sleep time, blood oxygen level, electrocardiogram, and skin temperature. This data is periodically transferred to a smartphone via Bluetooth or other communication methods.

[1497] Automatic collection of weather information

[1498] Server Operation

[1499] The server automatically collects real-time temperature, pressure, and humidity data from weather data providers on the Internet, and stores the collected weather information in a database for later analysis.

[1500] Collecting User Input

[1501] Device behavior

[1502] The device sends a notification to employees at a specific time each day asking them questions about their health. Employees rate their health on a scale of 1 to 10, and the information is saved on their smartphone. The saved data is sent to a server and stored in a database.

[1503] Data integration and analysis

[1504] Server Operation

[1505] The server combines biometric data from the wearable devices, weather information, and user health assessment data, and analyzes this data using Generative AI, which identifies specific patterns and tendencies for each employee and predicts the likelihood of poor health.

[1506] Prevention and Notification

[1507] Server Operation

[1508] Based on the predictions of the generating AI, the server identifies situations in which employees are likely to feel unwell and sends a notification to their smartphone containing specific preventative and remedial measures, allowing employees to proactively manage their health.

[1509] Physician precautions and advance medication delivery

[1510] Server Operation

[1511] If illness is predicted, the server will send a notification to the connected medical service, and doctors can use this information to suggest appropriate preventive measures and, if necessary, arrange for the advance delivery of medication.

[1512] Specific examples

[1513] For example, if an employee's wearable device indicates that their heart rate remains higher than normal, and weather data predicts that the temperature and humidity will continue to be high, the generative AI will determine that the employee is at high risk of becoming ill. The server will then notify the employee's smartphone of this information and provide preventative measures such as taking breaks and staying hydrated. It will also notify a collaborative medical service and, in some cases, arrange for medication to be delivered to the employee in advance.

[1514] Prompt Sentence Examples

[1515] Predict health risks based on heart rate and weather data and suggest appropriate preventive measures.

[1516] In this way, the present invention integrates the biometric information of employees in physical stores with weather information and utilizes generative AI to achieve more efficient and safe health management.

[1517] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1518] Step 1:

[1519] The server periodically collects biometric information such as heart rate, sleep time, blood oxygen level, electrocardiogram, and skin temperature from the wearable device worn by the user. The collected biometric information is transferred to a smartphone using a communication method such as Bluetooth. The smartphone receives the information and sends it to the server.

[1520] Input: Biometric information obtained from a wearable device

[1521] Output: Biometric information sent to the smartphone and server

[1522] Step 2:

[1523] The server periodically obtains real-time weather information such as temperature, pressure, and humidity from a weather data provider. The obtained weather information is stored in a database and used for later analysis.

[1524] Input: Weather information from an internet weather data provider

[1525] Output: Weather information stored in the database on the server

[1526] Step 3:

[1527] The device sends a notification to the user at a specific time each day asking about their health. The user answers by rating their health on a scale of 1 to 10, and the information is saved on the smartphone. The saved data is sent to a server and stored in a database.

[1528] Input: User's physical condition rating

[1529] Output: User's health evaluation data stored on the server

[1530] Step 4:

[1531] The server integrates the collected biometric information, weather information, and the user's health assessment data, and analyzes the data using a generation AI. The generation AI uses a specific algorithm to input each piece of data and predict the situations in which the user is likely to feel unwell.

[1532] Input: Biometric information, weather information, user's physical condition evaluation data

[1533] Output: Risk assessment of poor health

[1534] Step 5:

[1535] The server sends notifications to the user's smartphone with preventive and remedial measures based on the risk of illness predicted by the generative AI. The notifications include specific instructions for action (e.g., taking a break, drinking water, etc.).

[1536] Input: Risk assessment results of the generated AI

[1537] Output: Notifications of preventive and remedial measures sent to your smartphone

[1538] Step 6:

[1539] If a high risk of illness is predicted, the server will send a notification to the associated medical service, and medical professionals will use the predicted information to suggest preventative measures and, if necessary, arrange for the advance delivery of medication.

[1540] Input: Generative AI high-risk assessment results

[1541] Output: Notification sent to medical services and precautions taken by doctors, drug delivery arrangements

[1542] Step 7:

[1543] The server stores all collected data in a cloud environment, allowing the generative AI to analyze this data, which will improve the accuracy of future predictions.

[1544] Input: All collected data

[1545] Output: Data stored in the cloud environment

[1546] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1547] The present invention is a system that combines a wearable device, generative AI, an emotion engine, and a smartphone, and aims to efficiently manage the user's physical condition and emotions. This system is implemented by the following means.

[1548] Wearable device data collection

[1549] Device behavior

[1550] Wearable devices constantly measure the user's biometric information, such as heart rate, sleep time, blood oxygen level, electrocardiogram, and skin temperature, and periodically transmit this data to a smartphone. For example, heart rate and sleep patterns are measured every hour and transmitted to the smartphone via Bluetooth or other communication methods.

[1551] Automatic collection of weather information

[1552] Server Operation

[1553] The server automatically collects real-time temperature, pressure, and humidity data from weather data providers on the Internet, and stores this data in a database for later analysis.

[1554] Collecting User Input

[1555] Device behavior

[1556] The smartphone sends a notification to the user at a specific time each day asking about their health. The user answers by rating their health on a scale of 1 to 10, and the information is saved on the smartphone. The saved data is then sent to a server and stored in a database.

[1557] Sentiment analysis with emotion engine

[1558] Server Operation

[1559] The server uses an emotion engine to analyze the user's emotional state based on the collected biometric information and the user's physical condition assessment data. For example, it can infer stress or fatigue from fluctuations in heart rate or abnormal sleep patterns.

[1560] Data integration and analysis

[1561] Server Operation

[1562] The server combines biometric information, weather information, and the user's health assessment data sent from the wearable device, as well as emotional data from the emotion engine, and analyzes this data using a generative AI. The generative AI identifies specific patterns and tendencies for each user and predicts the likelihood of poor health.

[1563] Prevention and Notification

[1564] Server Operation

[1565] Based on the AI's predictions, the server identifies situations in which the user is likely to feel unwell and sends a notification to their smartphone. The notification includes specific preventive and remedial measures, allowing users to proactively manage their health. Furthermore, preventive measures, including stress management and psychological support based on emotional data, are also provided.

[1566] Physician precautions and advance medication delivery

[1567] Server Operation

[1568] If illness is predicted, the server will send a notification to the connected medical service, and doctors can use this information to suggest appropriate preventive measures and, if necessary, arrange for the advance delivery of medication.

[1569] Specific examples

[1570] For example, a user's wearable device may indicate that their heart rate remains higher than normal, and weather data predicts that the temperature and humidity will continue to be high. In this case, the generative AI determines that there is a high risk of illness. The emotion engine further analyzes the user's emotional state and detects a high stress level. The server then notifies the user's smartphone of this information and offers preventative measures, such as "Drink more fluids and take more breaks today." The server also sends a notification to a connected medical service, and in some cases arranges for medication to be delivered to the user in advance.

[1571] As a result, the present invention enables the user to manage their physical condition and emotions based on weather conditions, emotional state, and biological information, and prevent poor physical condition from occurring.

[1572] The processing flow will be explained below.

[1573] Step 1:

[1574] Terminal (wearable device) operation

[1575] Wearable devices constantly measure the user's biometric information, such as heart rate, sleep time, blood oxygen level, electrocardiogram, and skin temperature, and temporarily store this data within the device.

[1576] Step 2:

[1577] Terminal (wearable device) operation

[1578] The measured data is transferred to a smartphone at regular intervals (for example, every hour) using wireless communication means such as Bluetooth.

[1579] Step 3:

[1580] Device (smartphone) operation

[1581] The smartphone receives the biometric data received from the wearable device and stores it locally, where it is prepared for transmission to a server.

[1582] Step 4:

[1583] Server Operation

[1584] The server periodically obtains real-time weather information such as temperature, pressure, and humidity from weather data providers on the Internet, and stores the obtained weather information in a database.

[1585] Step 5:

[1586] Device (smartphone) operation

[1587] At a specific time each day (e.g., 8:00 a.m.), the user will receive a notification from their smartphone asking the question, "How are you feeling today?"

[1588] Step 6:

[1589] User Actions

[1590] The user answers questions about their physical condition using a number from 1 to 10. For example, if they feel good, they enter "8," and if they feel bad, they enter "3."

[1591] Step 7:

[1592] Device (smartphone) operation

[1593] The physical condition evaluation data entered by the user is stored in the smartphone and then sent to the server.

[1594] Step 8:

[1595] Server Operation

[1596] The server receives the biometric information, weather information, and user's physical condition evaluation data sent from the wearable device and stores them in a database.

[1597] Step 9:

[1598] Server Operation

[1599] The server analyzes the user's emotional state based on the collected biometric information and the user's physical condition assessment data using an emotion engine. For example, it can infer stress or fatigue from fluctuations in heart rate or abnormal sleep patterns.

[1600] Step 10:

[1601] Server Operation

[1602] The server combines biometric information sent from the wearable device, weather information, the user's health assessment data, and emotional data from the emotion engine, and analyzes this data using generative AI, which identifies specific patterns and tendencies for each user and predicts the possibility of poor health.

[1603] Step 11:

[1604] Server Operation

[1605] Based on the prediction results, the generating AI identifies days and situations when there is a high possibility of poor health and notifies the user's smartphone of this information.

[1606] Step 12:

[1607] Device (smartphone) operation

[1608] The smartphone receives notifications from the server and provides the user with specific preventative and improvement measures, such as advice like "Drink more water today."

[1609] Step 13:

[1610] Server Operation

[1611] Based on the predicted results of poor health, notifications are sent to linked medical services (e.g., HELPO).

[1612] Step 14:

[1613] Server Operation

[1614] Based on the information provided, a medical service professional (e.g., a doctor) will suggest appropriate preventive measures to the user and arrange for the advance delivery of necessary medications.

[1615] Step 15:

[1616] User Actions

[1617] The user checks the preventive and remedial measures displayed on their smartphone, puts them into practice, and, if necessary, receives and takes the medicine delivered.

[1618] By using the above processing steps, the present invention can efficiently manage the user's physical condition and emotions, and prevent poor physical condition from occurring.

[1619] Example 2

[1620] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1621] Conventional health management systems lack the functionality to comprehensively manage a user's biometric information, weather conditions, and emotional state, predict illness, and provide preventative measures. In particular, they do not provide preventative measures that take into account the user's emotional state or medical cooperation, which often leaves users unable to respond appropriately to sudden illnesses. Therefore, there is a need for a system that comprehensively manages a user's biometric information, weather conditions, and emotional state, predicts illness, and provides preventative measures in cooperation with medical cooperation.

[1622] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for collecting data on sleep time, heart rate, blood oxygen concentration, electrocardiogram, and skin temperature using a wearable device for constantly measuring the user's biological information; means for automatically collecting weather information on the Internet and acquiring data such as temperature, atmospheric pressure, and humidity; data collection means for displaying questions about the user's daily physical condition using a smartphone and for the user to quantify and answer; means for integrating and analyzing the collected biological information, weather information, and user's answer data to predict situations in which the user is likely to feel unwell; means for analyzing the user's emotional state using an emotion engine based on the collected biological information and the user's physical condition evaluation data; means for notifying the user of information on preventive measures and improvement measures based on the predicted risk of poor health using a generation AI; and means for sending a notification to a coordinated medical service when poor health is predicted and arranging for instructions from a doctor or delivery of medicine. This will enable integrated management of the user's biometric information, weather conditions, and emotional state, making it possible to predict illness and provide appropriate preventive measures through medical cooperation.

[1623] "Biometric information" refers to data obtained from inside or on the surface of a user's body, and includes heart rate, sleep time, blood oxygen concentration, electrocardiogram, skin temperature, etc.

[1624] A "wearable device" is a device worn by a user that can constantly measure their biometric information.

[1625] "Weather information" refers to data related to weather, such as temperature, air pressure, and humidity, which is automatically collected from external services provided on the Internet.

[1626] A "smartphone" is a portable information terminal with telephone functions, and is a device that interacts with users using applications.

[1627] "Data collection means" refers to a method or device that displays questions about the user's daily physical condition and collects the answers as numerical data.

[1628] "Generative AI" refers to artificial intelligence technology that performs advanced analysis on collected data to identify specific patterns and trends.

[1629] "Emotion engine" is a general term for algorithms and software that analyze a user's emotional state based on collected biometric information and the user's physical condition evaluation data.

[1630] "Notification means" refers to a method or device for communicating information on preventive or improvement measures to users based on the prediction results.

[1631] "Medical services" is a general term for services aimed at providing medical examinations and treatment by doctors, medicines, etc.

[1632] "Cloud environment" refers to computing resources and data storage provided via the Internet, and refers to the infrastructure for storing and processing data.

[1633] This invention is a system that combines a wearable device, a smartphone, a generative AI, and an emotion engine to manage the user's physical condition and emotions. The specific hardware and software operations required to realize each step of this system are explained in order.

[1634] First, wearable devices as terminals constantly measure the user's biometric information, such as heart rate, sleep time, blood oxygen level, electrocardiogram, and skin temperature. This data is periodically transferred to a smartphone via Bluetooth communication. For example, heart rate and sleep patterns are measured every hour and sent to the smartphone.

[1635] The server then automatically collects real-time temperature, pressure, and humidity data from an internet weather data provider, which is then stored in a database for later analysis.

[1636] The smartphone device also sends users a question about their health at a specific time each day. Users rate their health on a scale of 1 to 10, and the information is saved on the smartphone. This data is sent to a server and stored in a database.

[1637] The server uses an emotion engine to analyze the user's emotional state based on the collected biometric information and the user's physical condition evaluation data. For example, it can infer stress or fatigue from fluctuations in heart rate or abnormal sleep patterns.

[1638] The server then combines biometric information sent from the wearable device, weather information, the user's health assessment data, and emotional data from an emotion engine. This combined data is then analyzed by generative AI to identify specific patterns and trends for each user and predict the likelihood of poor health.

[1639] Finally, based on the predictions of the generative AI, the server identifies situations in which the user is likely to feel unwell and notifies the user of this information via their smartphone. The notification includes specific preventive and remedial measures, allowing the user to proactively manage their health. Furthermore, if medical collaboration is required, the server sends a notification to the collaborative medical service and arranges for advance delivery of doctor's instructions and medication.

[1640] Specific examples

[1641] For example, if a user's wearable device indicates a sustained higher-than-normal heart rate and weather data predicts a continued period of high temperatures and humidity, the generative AI will determine that there is a high risk of illness. The emotion engine then analyzes the user's emotional state and detects high stress levels. The server then notifies the user's smartphone of this information and offers preventative measures, such as "Drink more fluids and take more breaks today." If necessary, the system will also notify a medical service, which will suggest preventative measures and, in some cases, arrange for medication to be delivered to the user.

[1642] Prompt Sentence Examples

[1643] Below is an example of a prompt to input to the generative AI model.

[1644] ---

[1645] "A user's heart rate has been consistently higher than normal, and weather data predicts a period of high temperatures and humidity. Please assess the user's risk of illness in this situation and suggest necessary preventative measures."

[1646] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1647] Step 1:

[1648] Device behavior

[1649] The wearable device measures the user's biometric information every hour, including heart rate, sleep time, blood oxygen level, electrocardiogram, and skin temperature. The measurement results are then transferred to a smartphone via Bluetooth, where the data is temporarily stored.

[1650] Input: User's biological information (heart rate, sleep time, blood oxygen level, electrocardiogram, skin temperature)

[1651] Output: Biometric information sent to a smartphone

[1652] Step 2:

[1653] Server Operation

[1654] The server periodically polls weather data providers on the Internet to collect temperature, pressure, and humidity data. This data is then stored in a database on the server. The data collection frequency is, for example, every 15 minutes.

[1655] Input: Real-time data from weather data provider (temperature, pressure, humidity)

[1656] Output: Weather information stored in a database

[1657] Step 3:

[1658] Device behavior

[1659] The smartphone will send notifications to users at specific times, asking them questions about their health. For example, every morning at 8:00, a question like "Please rate how you feel this morning on a scale of 1 to 10" will be displayed. When the user enters a number, the data is stored on the smartphone and later sent to a server.

[1660] Input: User's physical condition evaluation data (numerical input)

[1661] Output: Physical condition evaluation data stored on a smartphone

[1662] Step 4:

[1663] Server Operation

[1664] The server receives the biometric information and physical condition evaluation data sent from the smartphone, stores them in a database, and then analyzes the data using an emotion engine to evaluate the user's emotional state (e.g., stress level).

[1665] Input: Biometric information, physical condition evaluation data

[1666] Output: Emotion data stored in a database

[1667] Step 5:

[1668] Server Operation

[1669] The collected biometric information, weather information, health assessment data, and emotional data are combined into a single dataset and input into the Generative AI. The Generative AI analyzes this data and identifies specific patterns and trends for each user (for example, a correlation between high humidity and stress levels). Based on the results of this analysis, the risk of poor health is predicted.

[1670] Input: Integrated dataset (biometric information, weather information, physical condition assessment data, emotional data)

[1671] Output: Analysis results (prediction of risk of poor health)

[1672] Step 6:

[1673] Server Operation

[1674] Based on the prediction results, the generative AI generates specific preventative and improvement measures. For example, it might create a message such as, "Drink water frequently and take plenty of breaks today." This message is then sent to the user's smartphone.

[1675] Input: Generative AI prediction results

[1676] Output: Preventive measures message sent to your smartphone

[1677] Step 7:

[1678] Server Operation

[1679] If a user's condition is predicted to worsen, the server sends a notification to the connected medical service. This notification includes information on the user's biometrics, weather information, emotional data, etc. Based on the information received, a doctor at the medical service will provide appropriate preventive measures and, if necessary, arrange for the advance delivery of medication.

[1680] Input: Predicted illness data

[1681] Output: Notifying medical services and suggesting preventative measures, arranging for medication delivery

[1682] Through the above steps, the system comprehensively manages the user's biometric information, weather conditions, and emotional state, enabling prediction of poor health and provision of appropriate preventive measures.

[1683] (Application example 2)

[1684] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1685] Managing employee health is particularly important in factory working environments, but conventional methods have made it difficult to grasp the health status of individual employees in real time and provide appropriate preventive measures. Furthermore, there has been a lack of automated means for quickly responding to predicted illnesses. This has created challenges for the productivity of the entire factory and for managing employee health.

[1686] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for collecting data on sleep time, heart rate, blood oxygen concentration, electrocardiogram, and skin temperature using a wearable device for constantly measuring a user's biometric information; means for automatically collecting weather information from the Internet and acquiring data such as temperature, barometric pressure, and humidity; means for displaying questions about the user's daily health using a generation AI and allowing the user to quantify and respond; means for integrating and analyzing the collected biometric information, weather information, and user response data to predict situations in which the user is likely to feel unwell; means for a factory robot to collect biometric data measured by factory employees using a wearable device and analyze the data using a generation AI to manage their health; and means for notifying users of information on preventive and remedial measures based on the prediction results. This makes it possible to grasp the health of factory employees in real time and quickly provide appropriate preventive measures.

[1687] A "wearable device" is a device that constantly monitors the user's physical condition and measures and collects biometric information such as heart rate, blood oxygen concentration, and skin temperature.

[1688] "Weather information" includes environmental data such as temperature, air pressure, and humidity, and is information obtained via the Internet.

[1689] "Generative AI" is an artificial intelligence technology that predicts and analyzes a user's physical and emotional state based on collected data.

[1690] "Data collection means" refers to a means for integrating and managing biometric information and weather information collected from wearable devices and the Internet.

[1691] The "means for predicting poor health" is a system in which a generative AI analyzes collected data and determines whether the user is likely to be in poor health.

[1692] "Means for notifying preventive and improvement measures" refers to a means for providing users with appropriate health management information based on the risks predicted by the generative AI.

[1693] "Factory robots" are automated machines that operate within factories, collect and analyze data from employees' wearable devices, and support their health management.

[1694] This invention is a system for supporting employee health management, and aims to efficiently monitor and manage employee health by combining wearable devices, generative AI, emotion engines, and factory robots.

[1695] Wearable device data collection

[1696] Device behavior

[1697] The wearable device has the function of constantly measuring employees' biometric information, such as heart rate, blood oxygen concentration, and skin temperature, and periodically transmitting this information to factory robots. For example, heart rate and skin temperature are measured every hour and transmitted to the factory robot via communication means such as Bluetooth.

[1698] Automatic collection of weather information

[1699] Server Operation

[1700] The server automatically collects real-time temperature, pressure, and humidity data from an online weather data provider and stores it in a database. This data is then used for analysis to identify factors that affect employees' physical condition.

[1701] Data collection methods

[1702] Factory robot operation

[1703] Factory robots collect and manage measurement data from wearable devices worn by employees, which is sent to generative AI and analyzed to understand the employee's physical condition in real time.

[1704] Sentiment analysis with emotion engine

[1705] Server Operation

[1706] The server uses an emotion engine to analyze employees' emotional states based on collected biometric and weather data, for example, inferring stress or fatigue from abnormal heart rates and fluctuations in skin temperature.

[1707] Data integration and analysis

[1708] Server Operation

[1709] The server combines biometric data from wearable devices and environmental sensors with weather information, and then analyzes this data using generative AI, which identifies specific patterns and tendencies for each employee and predicts the likelihood of illness.

[1710] Prevention and Notification

[1711] Factory robot operation

[1712] Based on the predictions of generative AI, the factory robot identifies situations in which employees are likely to feel unwell and notifies them via voice or display, including specific advice on taking breaks and staying hydrated.

[1713] Physician precautions and advance medication delivery

[1714] Server Operation

[1715] If necessary, the factory's partner medical institutions will be notified to arrange for preventative measures to support employee health management and the advance delivery of necessary medications.

[1716] Specific examples

[1717] For example, let's say an employee's wearable device indicates a persistently higher-than-normal heart rate. Weather data predicts that the temperature and humidity in the factory will remain high. In this case, the generative AI determines that there is a high risk of illness. The emotion engine further analyzes the employee's emotional state and detects a high stress level. Based on this information, the factory robot will issue a voice notification to the employee advising them to take a break and hydrate. If necessary, it will also notify affiliated medical institutions and arrange for preventive measures and necessary medications to be delivered in advance.

[1718] Prompt Sentence Examples

[1719] "If the heart rate is over 90 and the skin temperature is over 36.5 degrees, notify the employee to take a break and drink fluids."

[1720] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1721] Step 1:

[1722] The wearable device measures the employee's heart rate, blood oxygen level, and skin temperature, and stores this biometric information internally. The input is various types of biometric data, and the output is the aggregated measurement data.

[1723] Step 2:

[1724] The wearable device transmits the measured biometric information to the factory robot via Bluetooth. The input is the measured data mentioned above, and the output is the transmitted biometric data.

[1725] Step 3:

[1726] The server automatically retrieves weather data such as temperature, pressure, and humidity from a weather information database on the Internet. The input is information retrieved from the weather data provider service, and the output is the latest weather data.

[1727] Step 4:

[1728] The factory robot receives the biometric data transmitted from the wearable device and the meteorological data obtained from the server, which forms an integrated data set. The inputs are the biometric data and meteorological data, and the output is the integrated data set.

[1729] Step 5:

[1730] The server uses generative AI to analyze the employee's physical condition from the integrated data set. Specifically, it detects abnormalities in heart rate and skin temperature, and predicts the risk of poor health by taking weather conditions into account. The input is the integrated data set, and the output is the physical condition risk assessment result.

[1731] Step 6:

[1732] The server uses an emotion engine to analyze the employee's emotional state from biometric data. It infers stress and fatigue from changes in heart rate and skin temperature. The input is biometric data, and the output is the emotion analysis results.

[1733] Step 7:

[1734] The server integrates the risk assessment results from the generative AI and the emotion analysis results from the emotion engine to comprehensively determine the risk of poor health and emotional state.The inputs are the health risk assessment results and the emotion analysis results, and the output is a comprehensive health assessment result.

[1735] Step 8:

[1736] Based on the health assessment results received from the server, the factory robot notifies employees of preventive measures and break instructions via voice and display. Specific notification content may include "take breaks and hydrate." The input is the health assessment results, and the output is the notification to the employee.

[1737] Step 9:

[1738] The server sends notifications to the factory's affiliated medical institutions as needed, recommends preventive measures, and arranges for the advance delivery of necessary medicines.The input is the health assessment results, and the output is notifications to affiliated medical institutions.

[1739] 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.

[1740] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1741] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1742] 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.

[1743] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1744] 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.

[1745] 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).

[1746] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1747] 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."

[1748] 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.

[1749] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1750] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1751] 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.

[1752] 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.

[1753] 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.

[1754] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1755] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1756] 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.

[1757] 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.

[1758] 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.

[1759] 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.

[1760] The following is further disclosed regarding the above embodiment.

[1761] (Claim 1)

[1762] A means for collecting data on sleep time, heart rate, blood oxygen concentration, electrocardiogram, and skin temperature using a wearable device for constantly measuring a user's biological information;

[1763] A means to automatically collect weather information from the Internet and obtain data such as temperature, pressure, and humidity.

[1764] A data collection method that uses generative AI to display questions about the user's daily health condition and allows the user to answer numerically.

[1765] A means of integrating and analyzing collected biometric information, weather information, and user response data to predict situations in which users are likely to feel unwell;

[1766] A means for notifying users of information on preventive and remedial measures based on the prediction results;

[1767] A system including:

[1768] (Claim 2)

[1769] 10. The system of claim 1, further comprising means for a doctor to suggest preventive measures and for pre-delivery of necessary medicines based on the predicted state of poor health.

[1770] (Claim 3)

[1771] The system of claim 1, further comprising means for storing the collected biometric and meteorological information in a cloud environment and for the generating AI to perform data analysis.

[1772] "Example 1"

[1773] (Claim 1)

[1774] A means for collecting data on sleep time, heart rate, blood oxygen concentration, electrocardiogram, and skin temperature using a wearable device for constantly measuring a user's biological information;

[1775] a means for transferring the collected biometric information to a smartphone via wireless communication and transmitting the information to a server via the Internet;

[1776] A means to automatically collect weather information from the Internet and obtain data such as temperature, pressure, and humidity.

[1777] A data collection method that uses generative AI to display questions about the user's daily health condition and allows the user to answer numerically.

[1778] A means of integrating and analyzing collected biometric information, weather information, and user response data to predict situations in which users are likely to feel unwell;

[1779] A means for notifying users of information on preventive and remedial measures based on the prediction results;

[1780] A system including:

[1781] (Claim 2)

[1782] 10. The system of claim 1, further comprising means for a doctor to suggest preventive measures and for pre-delivery of necessary medicines based on the predicted state of poor health.

[1783] (Claim 3)

[1784] The system of claim 1, further comprising means for storing the collected biometric and meteorological information in a cloud environment and for the generating AI to perform data analysis.

[1785] "Application Example 1"

[1786] (Claim 1)

[1787] A means for collecting data on sleep time, heart rate, blood oxygen concentration, electrocardiogram, and skin temperature using a wearable device for constantly measuring a user's biological information;

[1788] A means to automatically collect weather information from the Internet and obtain data such as temperature, pressure, and humidity.

[1789] A data collection method that uses generative AI to display questions about the user's daily health condition and allows the user to answer numerically.

[1790] A means of integrating and analyzing collected biometric information, weather information, and user response data to predict situations in which users are likely to feel unwell;

[1791] A means for notifying users of information on preventive and remedial measures based on the prediction results;

[1792] A means to monitor the physical health of store employees in real time and provide proactive alerts and personalized human care;

[1793] A system including:

[1794] (Claim 2)

[1795] The system of claim 1 further includes a means for ensuring the safety of employees at physical stores by having a doctor suggest preventive measures and delivering necessary medicines in advance based on the predicted state of illness.

[1796] (Claim 3)

[1797] The system of claim 1 includes a means for storing the collected biometric and meteorological information in a cloud environment, and for the generating AI to perform data analysis to optimize the health management of employees in the physical store.

[1798] "Example 2: Combining Emotion Engines"

[1799] (Claim 1)

[1800] A means for collecting data on sleep time, heart rate, blood oxygen concentration, electrocardiogram, and skin temperature using a wearable device for constantly measuring a user's biological information;

[1801] A means to automatically collect weather information from the Internet and obtain data such as temperature, pressure, and humidity.

[1802] A data collection means for displaying questions about the user's daily physical condition using a smartphone and for the user to answer numerically;

[1803] A means of integrating and analyzing collected biometric information, weather information, and user response data to predict situations in which users are likely to feel unwell;

[1804] a means for analyzing the emotional state of the user using an emotion engine based on the collected biometric information and the user's physical condition evaluation data;

[1805] A method for notifying users of preventive and remedial measures based on predicted risks of poor health using generative AI;

[1806] A means to notify linked medical services if illness is predicted and arrange for delivery of doctor's orders and medication;

[1807] A system including:

[1808] (Claim 2)

[1809] 2. The system according to claim 1, wherein a doctor suggests preventive measures and delivers necessary medicines in advance based on the predicted state of poor health.

[1810] (Claim 3)

[1811] The system of claim 1, wherein the collected biometric information and meteorological information are stored in a cloud environment and the generating AI performs data analysis.

[1812] "Application example 2 when combining emotion engines"

[1813] (Claim 1)

[1814] A means for collecting data on sleep time, heart rate, blood oxygen concentration, electrocardiogram, and skin temperature using a wearable device for constantly measuring a user's biological information;

[1815] A means to automatically collect weather information from the Internet and obtain data such as temperature, pressure, and humidity.

[1816] A data collection method that uses generative AI to display questions about the user's daily health condition and allows the user to answer numerically.

[1817] A means of integrating and analyzing collected biometric information, weather information, and user response data to predict situations in which users are likely to feel unwell;

[1818] Factory robots collect biometric data measured by factory employees using wearable devices, and the data is analyzed by AI to manage their health.

[1819] A means for notifying users of information on preventive and remedial measures based on the prediction results;

[1820] A system including:

[1821] (Claim 2)

[1822] 10. The system of claim 1, further comprising means for a doctor to suggest preventive measures and for pre-delivery of necessary medicines based on the predicted state of poor health.

[1823] (Claim 3)

[1824] The system of claim 1, further comprising means for storing the collected biometric and meteorological information in a cloud environment and for the generating AI to perform data analysis. [Explanation of symbols]

[1825] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for collecting data on sleep time, heart rate, blood oxygen concentration, electrocardiogram, and skin temperature using a wearable device for constantly measuring a user's biological information; A means to automatically collect weather information from the Internet and obtain data such as temperature, pressure, and humidity. A data collection method that uses generative AI to display questions about the user's daily health condition and allows the user to answer numerically. A means for predicting situations in which a user is likely to feel unwell by integrating and analyzing collected biometric information, weather information, and user response data; and A means for notifying users of information on preventive and remedial measures based on the prediction results; A system including:

2. 2. The system according to claim 1, further comprising means for suggesting preventive measures by a doctor and delivering necessary medicines in advance based on the predicted state of poor health.

3. The system of claim 1, further comprising means for storing collected biometric information and meteorological information in a cloud environment and for the generating AI to perform data analysis.

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