system

Abstract

Provide a system. [Solution] means for acquiring data from a plurality of different health monitoring devices of a user; a means for integrating the acquired data; means for transmitting the integrated data to an analytical device; means for providing health advice generated by the analytical device; A system including:

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] Health management is becoming increasingly important in modern society, but it is difficult to centrally manage data obtained from different health monitoring devices and obtain comprehensive health advice based on that data. This makes it difficult for users to accurately understand their own health status and take appropriate measures. Furthermore, integrating and analyzing data from multiple devices requires advanced technical knowledge, which is a hurdle for the average user. To solve these issues, a system is needed that can integrate health data from different devices and provide individualized, specific health advice based on that data. [Means for solving the problem]

[0005] This invention solves the above-mentioned problems by providing a system including means for acquiring data from a user's multiple different health monitoring devices, means for integrating the acquired data, means for transmitting the integrated data to an analysis device, and means for providing health advice generated by the analysis device. Specifically, the health monitoring devices include a smartwatch, a blood pressure monitor, and a weight scale. Data collected from these devices is integrated and transmitted to an analysis device that uses a chat generation algorithm. Based on the received data, the analysis device generates and provides specific health advice to the user, enabling the user to comprehensively understand their health status and take appropriate health management measures.

[0006] A "user" is an individual who wishes to manage their health using multiple health monitoring devices.

[0007] A "health monitoring device" is a device that measures and records health-related data such as heart rate, blood pressure, and weight.

[0008] "Data" means information obtained from a health monitoring device.

[0009] "Integration" means combining acquired data from multiple devices into a single data set.

[0010] An "analysis device" is a device that generates health advice based on the data it receives.

[0011] "Health advice" refers to specific, individualized advice based on the user's health data.

[0012] "System" refers to a set of means and devices that acquire, integrate, analyze, and provide health advice to users.

[0013] A "smartwatch" is a portable device that measures the user's physical activity data, such as heart rate and number of steps taken.

[0014] A "sphygmomanometer" is a device that measures systolic and diastolic blood pressure.

[0015] A "weighing scale" is a device that measures weight and body fat percentage.

[0016] A "chat generation algorithm" is an algorithm that analyzes data and generates responses such as advice in natural language. [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] System Overview

[0039] This invention is a system that acquires, integrates, and analyzes data from different health monitoring devices such as smartwatches, blood pressure monitors, and weight scales to provide health advice to users. Specific embodiments of this system are described below.

[0040] Inter-device communication and data integration

[0041] The server collects data from multiple health monitoring devices, such as a user's smartwatch, blood pressure monitor, and weight scale. Each device sends data through its own API endpoint. The server converts the data from the devices into a unified format and combines it into a single dataset.

[0042] Data analysis

[0043] The server sends the integrated data set to an analysis device equipped with a chat generation algorithm. The analysis device analyzes the data (heart rate, blood pressure, weight, body fat percentage, etc.) acquired from each device. Based on the analysis results, it generates individualized, specific health advice and sends it back to the server.

[0044] Providing health advice

[0045] The server provides the health advice returned from the analyzer to the user. For example, the advice is displayed in text format to the user via a device such as a smartphone or PC.

[0046] Specific examples

[0047] Health data examples

[0048] 1. Smartwatch: Heart rate 85, steps 7000

[0049] 2. Sphygmomanometer: systolic blood pressure 130, diastolic blood pressure 85

[0050] 3. Scale: Weight 70kg, body fat percentage 22%

[0051] Advice from chat generation algorithms

[0052] When a user steps on the scale, uses a smartwatch, or measures their blood pressure with a blood pressure monitor, this data is automatically sent to the server, which then converts the acquired data into a unified format and sends it to the analyzer, which analyzes the data and generates health advice such as:

[0053] "Your heart rate has been a little high recently, and your blood pressure is also a little higher than normal. These may be due to stress or lack of exercise. I recommend incorporating about 30 minutes of aerobic exercise every day and making time to relax. Also, although your weight is within the appropriate range, it is important to manage your body fat percentage, so make sure to eat a balanced diet."

[0054] This health advice is displayed on the user's terminal via the server, and the user can use it as a reference to improve their daily health management.

[0055] In this way, this invention centrally manages data obtained from different health monitoring devices and provides individual, specific health advice based on that data, thereby providing multifaceted support for users' health management.

[0056] The processing flow will be explained below.

[0057] Step 1:

[0058] The server sends an HTTP GET request to the API endpoint of the smartwatch to obtain heart rate and step count data from the user's smartwatch. The server receives the obtained data in JSON format, analyzes it, and extracts the necessary data.

[0059] Step 2:

[0060] The server sends an HTTP GET request to the API endpoint of the blood pressure monitor to retrieve blood pressure data from the user's blood pressure monitor. The server receives the retrieved data in JSON format and parses it to extract systolic and diastolic blood pressure data.

[0061] Step 3:

[0062] The server sends an HTTP GET request to the scale's API endpoint to retrieve weight and body fat percentage data from the user's scale, receives the retrieved data in JSON format, and parses it to extract the appropriate data.

[0063] Step 4:

[0064] The server integrates the data acquired in steps 1 to 3 into a single dataset. It converts the data acquired from each health monitoring device into a unified format and combines them into a single dataset.

[0065] Step 5:

[0066] The server sends an HTTP POST request to the API endpoint of the analysis device to send the integrated dataset to the analysis device equipped with the chat generation algorithm. The dataset is sent in JSON format.

[0067] Step 6:

[0068] The chat generation algorithm analyzes the data set received from the server, including heart rate, blood pressure, weight, and body fat percentage, to generate personalized health advice for each user.

[0069] Step 7:

[0070] The analyzer sends the generated health advice back to the server in JSON format, which then analyzes the data and extracts the advice.

[0071] Step 8:

[0072] The server sends the extracted health advice to the user's terminal for display, and the terminal presents the received advice to the user in text format.

[0073] Through these steps, users can receive personalized and specific health advice based on data collected from multiple health monitoring devices, helping them manage their daily health more effectively.

[0074] Example 1

[0075] 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."

[0076] Effective health management is required to combat the increasing number of lifestyle-related diseases and health risks in modern society. However, the process of collecting, integrating, and analyzing data from multiple different health monitoring devices is complex and time-consuming. In addition, there is a lack of appropriate analytical methods to provide useful health advice from the collected data. To solve this problem, a system is needed that can comprehensively and efficiently manage health data and provide accurate health advice.

[0077] 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.

[0078] In this invention, the server includes means for acquiring data from multiple different health monitoring devices of a user, means for converting the acquired data into an integrated format and integrating them into a single data set, means for transmitting the integrated data set to an analysis device, and means for providing health advice generated by the analysis device, thereby enabling efficient integration and analysis of data from multiple devices and providing useful health advice to the user in a timely manner.

[0079] A "health monitoring device" is a device that measures a user's biometric information and collects that data.

[0080] A "integrated format" is a data structure or format for converting data captured in multiple different formats into a consistent format.

[0081] A "dataset" is a collection of data obtained from health monitoring devices and converted into a unified format.

[0082] An "analyzer" is a computer and software for analyzing a data set and generating health advice.

[0083] A "generative AI model" is an artificial intelligence model that generates output in natural language format from input data.

[0084] "Health advice" refers to specific instructions or recommendations for improving lifestyle habits or managing health for a user, which are generated by the analysis device based on a data set.

[0085] MODE FOR CARRYING OUT THE INVENTION

[0086] This invention is a system that acquires data from multiple health monitoring devices of a user, integrates it, and provides health advice through analysis. Next, the program processing is explained in natural language, and the details of the hardware and software used, as well as data processing and data calculation, are clearly stated.

[0087] System configuration and operation

[0088] The server collects data from users' health monitoring devices, such as smartwatches, blood pressure monitors, and weight scales, and receives data from these devices via communication methods such as Bluetooth and Wi-Fi.

[0089] Data Integration

[0090] The server converts the data acquired from each device into a unified format (JSON format) and integrates it into a single data set, allowing data acquired from different devices to be handled consistently.

[0091] Data analysis

[0092] The server then sends the combined data set to an analysis device, which incorporates a generative AI model (e.g., GPT-3®) to analyze the data and generate health advice based on biometric data such as heart rate, blood pressure, weight, and body fat percentage.

[0093] Generating health advice

[0094] The generative AI model generates health advice in natural language based on the input prompt. Here are some examples of specific prompts:

[0095] "Generate health advice based on the following data: heart rate 85, systolic blood pressure 130, diastolic blood pressure 85, weight 70kg, body fat percentage 22%."

[0096] Providing advice to users

[0097] The server then sends the health advice received from the analysis device to the user's device. The user receives the advice via a device such as a smartphone or computer. This allows the user to understand their own health condition and take appropriate measures to improve their lifestyle habits.

[0098] Specific examples

[0099] For example, if a user provides the following health monitoring data:

[0100] Smartwatch: Heart rate 85, steps 7000

[0101] Sphygmomanometer: systolic blood pressure 130, diastolic blood pressure 85

[0102] Scale: Weight 70kg, body fat percentage 22%

[0103] The server aggregates this data and sends it to an analysis device, which generates health advice such as:

[0104] "Your heart rate has been a little high recently, and your blood pressure is also a little higher than normal. These may be due to stress or lack of exercise. I recommend incorporating about 30 minutes of aerobic exercise every day and making time to relax. Also, although your weight is within the appropriate range, it is important to manage your body fat percentage, so make sure to eat a balanced diet."

[0105] This advice is displayed on the user's device via the server, allowing the user to refer to it and improve their daily health management.By using this system, it is possible to efficiently integrate and analyze health data obtained from multiple devices and provide users with accurate and timely health advice.

[0106] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0107] Step 1:

[0108] Collecting data from devices

[0109] The server collects data from different health monitoring devices such as users' smartwatches, blood pressure monitors, and weight scales, which transmit data via communication methods such as Bluetooth and Wi-Fi.

[0110] Input: Biometric data sent from smartwatches, blood pressure monitors, and weight scales

[0111] Output: Biometric data stored on the server

[0112] Specific operation: The smartwatch sends heart rate and step count data to the server, the blood pressure monitor sends systolic and diastolic blood pressure data, and the scale sends weight and body fat percentage data to the server.

[0113] Step 2:

[0114] Data integration and format conversion

[0115] The server converts the data received from each device into a unified format (e.g., JSON format) and integrates it into a single data set.

[0116] Input: Unintegrated biometric data captured from each device

[0117] Output: Dataset converted to a unified format (JSON format)

[0118] Specific operation: The server converts the data from the smartwatch, blood pressure monitor, and weight scale into JSON format and combines them into one.

[0119] Step 3:

[0120] Data analysis

[0121] The server sends the combined dataset to an analyzer that incorporates a chat generation algorithm, which incorporates a generative AI model (e.g., GPT-3).

[0122] Input: Dataset converted to unified format (JSON format)

[0123] Output: Health advice based on the analysis results

[0124] How it works: The server sends the integrated data set to the analysis device, which then uses the generative AI model to analyze the data. For example, if your heart rate is high, it will generate relevant advice.

[0125] Step 4:

[0126] Generating health advice

[0127] The analysis device generates health advice in natural language format based on the results of the analysis of the data.

[0128] Input: Data analysis results by the analysis device

[0129] Output: Health advice summarized in natural language format

[0130] Specific operation: The analysis device uses the generative AI model to input the following prompt: "Please generate health advice based on the following data: heart rate 85, systolic blood pressure 130, diastolic blood pressure 85, weight 70 kg, and body fat percentage 22%." Based on this, the generative AI model creates advice in natural language.

[0131] Step 5:

[0132] Providing advice to users

[0133] The server sends the health advice returned by the analysis device to the user's device, where the user can check the advice via a smartphone, computer, or other device and use it to help manage their health.

[0134] Input: Health advice in natural language form returned by the analyzer

[0135] Output: Health advice displayed on the user's device

[0136] Specific operation: The server sends the generated health advice to the user's smartphone or computer, where the user can review it and use it to help manage their daily health.

[0137] (Application example 1)

[0138] 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."

[0139] Conventional health monitoring systems are primarily limited to monitoring the health status of individuals, and lack the means to comprehensively monitor the health status of equipment used in industrial settings such as factories and manage optimal maintenance. In addition, the technology to comprehensively analyze different types of health indicators and provide effective advice is underdeveloped.

[0140] 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.

[0141] In this invention, the server includes means for acquiring data from a plurality of different health monitoring devices of a user, means for integrating the acquired data, means for transmitting the integrated data to an analysis device, means for providing health advice generated by the analysis device, means for collecting health indicators from factory automation equipment, and means for presenting an optimal maintenance schedule based on the collected health indicators, thereby enabling both personal health management and optimal maintenance management of factory equipment.

[0142] "Health monitoring device" is a general term for devices used to collect a user's health data, including smartwatches, blood pressure monitors, and weighing scales.

[0143] "Data integration" is the process of converting data obtained from multiple different health monitoring devices into a single unified format and managing it centrally.

[0144] An "analysis device" is a device or system that performs analysis based on integrated data obtained through data integration and generates output such as health advice.

[0145] "Health Advice" means a health care recommendation or advice generated by an analytical device and provided to a user.

[0146] "Factory automation equipment" is a general term for automated machinery and systems used in factories and manufacturing sites, including robotic arms, assembly lines, and sensor devices.

[0147] "Health indicators" is a general term for data measured to indicate the health status of equipment or the human body, and includes operating time, wear level, internal temperature, vibration level, etc.

[0148] An "optimal maintenance schedule" is a plan that indicates the optimal timing and procedures for performing equipment maintenance based on collected health indicator data.

[0149] This invention is a system that collects data from multiple different health monitoring devices of a user, integrates and analyzes it, provides individualized health advice, collects health indicators of factory automation equipment, and presents optimal maintenance schedules.

[0150] System Overview

[0151] First, the server collects data from different health monitoring devices such as a user's smartwatch, blood pressure monitor, weight scale, etc. Each device sends data through its own API endpoint, and the server converts this data into a unified format and combines it into a single dataset.

[0152] The server then sends the combined data set to an analyzer, which uses algorithms called generative AI models to analyze the data and generate personalized health advice, which is then sent back to the server.

[0153] In addition, the server collects health indicator data from factory automation equipment (e.g., operating hours, wear level, internal temperature, vibration level, etc.) This data is also sent to the server and converted into a unified format.

[0154] Providing health advice and maintenance schedules

[0155] The server provides the user with the health advice returned from the analyzer. For example, the advice is displayed in text format on a smartphone, PC, or other device. It also provides optimal maintenance schedules and operational precautions to factory managers based on the health index data of factory automation equipment.

[0156] Hardware and software used

[0157] The hardware used includes automated devices equipped with various sensors, such as smartwatches, blood pressure monitors, weight scales, and factory robots. The software uses Python 3.8 or higher and the requests library. The server manages the entire process, including data collection, integration, transmission, and return of analysis results.

[0158] A specific example is the process of collecting, analyzing, and providing advice on health data.

[0159] For example, if the following data is sent to the server from a smartwatch: heart rate 85, step count 7000, systolic blood pressure 130, diastolic blood pressure 85, weight 70 kg, and body fat percentage 22% from a scale, the generative AI model will generate the following advice:

[0160] "Your heart rate has been a little high recently, and your blood pressure is also a little higher than normal. These may be due to stress or lack of exercise. I recommend incorporating about 30 minutes of aerobic exercise every day and making time to relax. Also, although your weight is within the appropriate range, it is important to manage your body fat percentage, so make sure to eat a balanced diet."

[0161] For example, for factory automation equipment, data such as 5,000 hours of operation, 15% wear level, 45°C internal temperature, and low vibration levels can be sent to a server, and the following maintenance advice can be provided:

[0162] "The robot has been operating for a long time and its wear level is low. The internal temperature is normal, but caution is needed for continued operation in the future. Regular maintenance is recommended."

[0163] Example prompt sentence:

[0164] "What is the health advice for a factory robot that has 5000 operating hours, is currently at 15% wear, has an internal temperature of 45 degrees, and has low vibration levels?"

[0165] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0166] Step 1:

[0167] The server collects data from multiple different health monitoring devices of the user. Specifically, it collects data such as heart rate and step count from a smartwatch, blood pressure from a blood pressure monitor, and weight and body fat percentage from a weighing scale through API endpoints. The input is raw data obtained from each device's API, and the output is pre-processed data for integration.

[0168] Step 2:

[0169] The server converts the acquired data into a unified format and integrates it into a single dataset. Data processing includes converting data from different formats into a common format and adding timestamps. The input is the preprocessed raw data, and the output is an integrated dataset converted into a unified format.

[0170] Step 3:

[0171] The server sends the integrated dataset to the analysis device. Specifically, it generates an analysis request and sends the data to the analysis device using an HTTP request. The input is the integrated dataset, and the output is an HTTP response to the analysis request.

[0172] Step 4:

[0173] The analysis device performs data analysis based on the integrated data sent. Using a generative AI model, it analyzes various health data and generates individual, specific health advice. The input is the integrated data set sent from the server, and the output is text data containing health advice.

[0174] Step 5:

[0175] The analysis device returns the generated health advice to the server. The input is text data containing the health advice, and the output is an HTTP response to the server.

[0176] Step 6:

[0177] The server provides the returned health advice to the user. Specifically, the advice is displayed in text format on a device such as a smartphone or PC. The input is the received health advice, and the output is the text advice displayed on the user's device.

[0178] Step 7:

[0179] The server collects health indicators from factory automation equipment, specifically, data such as operating hours, wear level, internal temperature, and vibration level obtained through sensors. The input is raw data from the sensors, and the output is preprocessed health indicator data.

[0180] Step 8:

[0181] The server presents an optimal maintenance schedule based on the collected health indicators. Data analysis is performed through an analyzer to generate optimal maintenance timing and operational precautions. The input is preprocessed health indicator data, and the output is text data including the maintenance schedule and advice.

[0182] Step 9:

[0183] The server provides the presented maintenance schedule to the factory manager. Specifically, it displays the schedule and advice on the manager's terminal. The input is text data including the maintenance schedule and advice, and the output is the content displayed on the manager's terminal.

[0184] 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.

[0185] System Overview

[0186] This invention is a system that acquires data from different health monitoring devices such as smartwatches, blood pressure monitors, and weight scales, and combines it with an emotion engine that recognizes the user's emotions to integrate and analyze the data and provide health advice. Specific embodiments of this system are described below.

[0187] Inter-device communication and data integration

[0188] The server sends an HTTP GET request to the API endpoint of the smartwatch to obtain heart rate and step count data from the user's smartwatch. The server sends an HTTP GET request to the API endpoint of the blood pressure monitor to obtain blood pressure data from the blood pressure monitor. The server also sends an HTTP GET request to the API endpoint of the weight scale to obtain weight and body fat percentage data from the weight scale. The server converts the data obtained from each device into a unified format and integrates it into a single dataset.

[0189] Acquiring and analyzing emotion data

[0190] The device uses voice input, facial expression recognition using a camera, or text analysis to recognize the user's emotions, thereby acquiring the user's emotion data, and then transmits the acquired emotion data to a server.

[0191] Data analysis

[0192] The server sends the integrated data set obtained from the health monitoring device and the emotion data obtained from the emotion engine to the analysis device. The analysis device performs analysis taking into account the user's emotional state in addition to data such as heart rate, blood pressure, weight, and body fat percentage. This allows the analysis device to generate individualized health advice that includes stress management and psychological advice in addition to general health advice.

[0193] Providing health advice

[0194] The server sends the health advice returned from the analyzer to the user's device and displays it. For example, the advice may be presented to the user in text format via a device such as a smartphone or PC.

[0195] Specific examples

[0196] Health data examples

[0197] 1. Smartwatch: Heart rate 85, steps 7000

[0198] 2. Sphygmomanometer: systolic blood pressure 130, diastolic blood pressure 85

[0199] 3. Scale: Weight 70kg, body fat percentage 22%

[0200] Emotion data example

[0201] Facial expression recognition result: Feeling stressed

[0202] Voice analysis results: Fatigue

[0203] Text analysis results: Feeling anxious

[0204] Advice from chat generation algorithms

[0205] When a user steps on a scale, uses a smartwatch, or measures their blood pressure with a blood pressure monitor, this data is automatically sent to the server. In addition, the device captures emotional data from the user's facial expressions, voice, and text, and sends this data to the server. The server then integrates all the collected data and sends it to an analysis device. The analysis device analyzes the data and generates health advice such as:

[0206] "Your heart rate has been a little high recently, and your blood pressure is also a little higher than normal. These may be due to stress or lack of exercise. I recommend that you incorporate about 30 minutes of aerobic exercise every day and make time to relax. Also, although your weight is within the appropriate range, it is important to manage your body fat percentage, so be sure to eat a balanced diet. Regarding the stress and anxiety you are currently feeling, try taking a deep breath and relaxing. If necessary, please consider seeking psychological counseling."

[0207] This health advice is displayed on the user's terminal via the server, and the user can use it as a reference to improve their daily health management.

[0208] In this way, this invention centrally manages data and emotional data obtained from different health monitoring devices, and provides individual, specific health advice based on this data, thereby providing multifaceted support for users' health management.

[0209] The processing flow will be explained below.

[0210] Step 1:

[0211] The server sends an HTTP GET request to the API endpoint of the smartwatch to obtain heart rate and step count data from the user's smartwatch. The server then analyzes the JSON format data returned as a response and extracts the heart rate and step count data.

[0212] Step 2:

[0213] The server sends an HTTP GET request to the API endpoint of the blood pressure monitor to obtain blood pressure data from the user's blood pressure monitor. It then parses the JSON-formatted data returned as a response and extracts the systolic and diastolic blood pressure data.

[0214] Step 3:

[0215] The server sends an HTTP GET request to the scale's API endpoint to obtain the user's weight and body fat percentage data from the scale. The server then parses the JSON format data returned as a response and extracts the weight and body fat percentage data.

[0216] Step 4:

[0217] The server combines the data acquired in steps 1 to 3 into a single integrated data set, converting the data from each device into a unified format to complete the overall data set.

[0218] Step 5:

[0219] To recognize the user's emotions, the device uses sensors such as a camera and microphone to collect facial and voice data, and analyzes this emotional data in real time to determine the user's emotional state.

[0220] Step 6:

[0221] The device then sends the analyzed emotional data, including information about stress levels and emotional states, to the server.

[0222] Step 7:

[0223] To send the integrated dataset and emotion data to the analysis device, the server sends an HTTP POST request to the analysis device's API endpoint, which sends the dataset and emotion data in JSON format.

[0224] Step 8:

[0225] The analyzer analyzes the received health and emotional data, taking into account the user's heart rate, blood pressure, weight, body fat percentage, and emotional state to generate personalized health advice.

[0226] Step 9:

[0227] The analyzer sends the generated health advice back to the server in JSON format, which then analyzes the received data and extracts the advice.

[0228] Step 10:

[0229] The server then sends the extracted health advice to the user's device, which includes specific health management suggestions and psychological support suggestions.

[0230] Step 11:

[0231] The device then presents the received advice to the user in text format, allowing the user to receive comprehensive health advice based on their own health condition.

[0232] Through these steps, users can receive personalized and specific health advice based on multiple health monitoring devices and emotional data, enabling them to comprehensively manage their daily health.

[0233] Example 2

[0234] 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."

[0235] In modern society, user health management is becoming an increasingly important issue. Conventional health monitoring devices often collect data individually, making it difficult to centrally manage and analyze that data. Furthermore, while a user's emotional state is an important factor in health management, existing systems lack the means to integrate and analyze emotional state and health data, making it difficult to provide comprehensive health advice. To solve these problems, a system is needed that can perform an integrated analysis of health data and emotional data and provide individual, specific advice.

[0236] 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 acquiring data from multiple different health monitoring devices of the user, means for integrating the acquired data, means for acquiring emotional data of the user, means for transmitting the integrated data to an analytical device, and means for providing health advice generated by the analytical device. This makes it possible to centrally manage health data and emotional data and provide comprehensive and individually specific health advice to the user.

[0237] A "health monitoring device" is a device for measuring and recording a user's health status, and examples include smart watches, blood pressure monitors, and weight scales.

[0238] "Means for acquiring data" refers to the processes or technologies that have the ability to transmit and receive data collected from the health monitoring device to the server.

[0239] "Data integration measures" refers to the processes and techniques used to convert data from multiple health monitoring devices into a unified format and compile it into a single data set.

[0240] "Means for acquiring emotional data" refers to the process or technology for collecting a user's emotions using voice input, facial expression recognition, text analysis, etc., and transmitting that data to a server.

[0241] "Analysis equipment" refers to devices or software that analyze the integrated data and emotional data sent from the server and generate health advice for the user.

[0242] "Means for providing health advice" refers to the process or technology for transmitting the health advice generated by the analytical device to the user's terminal and displaying it.

[0243] "Generative algorithm" refers to a computational method or program for generating personalized, specific health advice based on integrated health and emotional data.

[0244] System Overview

[0245] This invention is a system that provides comprehensive health advice by collecting data from a user's different health monitoring devices, integrating and analyzing it together with the user's emotional data. The hardware used includes health monitoring devices such as smartwatches, blood pressure monitors, and weight scales, and the software includes API endpoint utilization, an emotional analysis engine, and a data analysis device.

[0246] Inter-device communication and data integration

[0247] The server acquires data from each health monitoring device in the following procedure.

[0248] 1. Send an HTTP GET request to the smartwatch's API endpoint to retrieve heart rate and step count data from the smartwatch.

[0249] 2. To retrieve blood pressure data from the blood pressure monitor, send an HTTP GET request to the blood pressure monitor's API endpoint.

[0250] 3. Send an HTTP GET request to the scale's API endpoint to retrieve weight and body fat percentage data from the scale.

[0251] The data acquired from each device is converted into a unified format by the server and compiled into a single integrated data set.

[0252] Acquiring and analyzing emotion data

[0253] The terminal acquires the user's emotion data in the following manner.

[0254] 1. Sentiment analysis using voice input

[0255] The device records the user's voice and analyzes the emotional data using a voice analysis engine.

[0256] 2. Camera-based facial expression recognition

[0257] The device captures the user's face with a camera and acquires emotional data using a facial expression analysis engine.

[0258] 3. Text Analysis

[0259] The device analyzes the text entered by the user and generates emotion data.

[0260] The acquired emotion data is transmitted to the server through the terminal.

[0261] Data analysis

[0262] The server performs data analysis in the following procedure.

[0263] 1. Send the combined health dataset and emotion data to the analyzer, specifically by sending the dataset as an API request.

[0264] 2. The analyzer analyzes health data such as heart rate, blood pressure, weight, and body fat percentage, as well as the user's emotional data. This analysis is then used with a generative algorithm to generate personalized health advice for the user.

[0265] Providing health advice

[0266] The server sends the health advice returned from the analyzer to the user's device, and the device displays the advice to the user. For example, if the device is a smartphone, the advice is displayed in text format on the screen.

[0267] Examples and prompts

[0268] For example, the following data is obtained:

[0269] Smartwatch: Heart rate 85, steps 7000

[0270] Sphygmomanometer: systolic blood pressure 130, diastolic blood pressure 85

[0271] Scale: Weight 70kg, body fat percentage 22%

[0272] Emotional data: stress, fatigue, anxiety

[0273] Based on this data, the following advice is given to the user:

[0274] "Your heart rate has been a little high recently, and your blood pressure is also a little higher than normal. These may be due to stress or lack of exercise. I recommend that you incorporate about 30 minutes of aerobic exercise every day and make time to relax. Also, although your weight is within the appropriate range, it is important to manage your body fat percentage, so be sure to eat a balanced diet. Regarding the stress and anxiety you are currently feeling, try taking a deep breath and relaxing. If necessary, please consider seeking psychological counseling."

[0275] Example prompt for a generative AI model:

[0276] "Please provide specific health advice based on the health data (heart rate 85, steps 7000, systolic blood pressure 130, diastolic blood pressure 85, weight 70 kg, body fat percentage 22%) and emotional data (stress, fatigue, anxiety) obtained from the user."

[0277] Using the above methods, the system can grasp the user's health condition from multiple angles and support an appropriate healthy lifestyle.

[0278] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0279] Step 1: Obtaining health data

[0280] Input: Data from smartwatches, blood pressure monitors, and scales (e.g., heart rate, steps, blood pressure, weight, body fat percentage)

[0281] Output: A set of acquired health data

[0282] Specific operation:

[0283] The server sends an HTTP GET request to the smartwatch's API endpoint to retrieve heart rate and step count data, for example, to the URL GET https: / / api.smartwatch.com / userdata?metrics=heart_rate,steps.

[0284] The server sends an HTTP GET request to the API endpoint of the blood pressure monitor to retrieve blood pressure data, for example, to the URL GET https: / / api.bloodpressuremonitor.com / data?metrics=systolic,diastolic.

[0285] The server sends an HTTP GET request to the scale's API endpoint to retrieve weight and body fat percentage data, for example, to the URL GET https: / / api.weighingscale.com / data?metrics=weight,body_fat.

[0286] Step 2: Integrate and transform data

[0287] Input: Individually acquired health data (heart rate, steps, blood pressure, weight, body fat percentage)

[0288] Output: Unified dataset converted to a unified format

[0289] Specific operation:

[0290] The server converts the individually acquired data into a unified format, for example, {"heart_rate": 85, "steps": 7000, "blood_pressure": {"systolic": 130, "diastolic": 85}, "weight": 70, "body_fat": 22}.

[0291] Step 3: Obtaining emotion data

[0292] Input: User's voice, facial expressions, and text data

[0293] Output: A set of retrieved emotion data

[0294] Specific operation:

[0295] The device records the user's voice and uses a voice analysis engine to extract emotional data. For example, if the user says, "I'm a little tired today," the device can extract the emotional data "fatigue."

[0296] The device captures the user's face with a camera and uses a facial expression analysis engine to obtain emotional data. For example, it can extract emotional data indicating that the user is feeling "stressed" from the user's facial image.

[0297] The device analyzes the text entered by the user and extracts emotional data. For example, the device extracts the emotional data "anxiety" from the text "I'm anxious because things aren't going well at work."

[0298] Step 4: Data analysis

[0299] Input: Integrated health dataset, emotion data

[0300] Output: Analysis results and health advice

[0301] Specific operation:

[0302] The server sends the combined health dataset and emotion data to the analyzer, for example, by sending the dataset via API request POST https: / / api.analysisengine.com / analyze.

[0303] The analyzer analyzes the transmitted data and uses a generation algorithm to generate personalized health advice, such as heart rate 85, steps 7000, blood pressure 130 / 85, weight 70kg, body fat percentage 22%, and emotional data such as stress, fatigue, and anxiety.

[0304] Step 5: Providing health advice

[0305] Input: Generated health advice

[0306] Output: Advice displayed on the user's terminal

[0307] Specific operation:

[0308] The server sends the health advice returned from the analyzer to the user's device, for example, by sending an API request POST https: / / api.userterminal.com / advice { "advice": "Your heart rate has been a little high recently..."}.

[0309] The device displays the received advice to the user. For example, the device may display the following text on the user's smartphone: "Your heart rate has been a little high recently, and your blood pressure is also a little above normal..."

[0310] (Application example 2)

[0311] 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."

[0312] There is a need to improve work efficiency and safety by monitoring the health and psychological states of employees in factories in real time and providing appropriate health advice. However, existing systems lack the means to effectively integrate data collected from multiple health monitoring devices and provide specific health advice that takes into account employees' emotional states.

[0313] The specific processing by the specific 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 acquiring data from multiple different health monitoring devices of the user, means for integrating the acquired data and emotional data, and means for transmitting the integrated data to the analysis device. This makes it possible to integrate the data and emotional data collected from the health monitoring devices and provide health advice generated using the analysis device in real time.

[0314] A "health monitoring device" is a device for measuring and recording a user's health data, such as a smart watch, blood pressure monitor, or weight scale.

[0315] "Emotional data" is data that indicates a user's emotional state, obtained using voice input, facial recognition using a camera, or text analysis.

[0316] "Server" is a computing system for collecting and integrating data from health monitoring devices and emotion engines and transmitting it to an analysis device.

[0317] "Integration" is the process of bringing together data from multiple health monitoring devices and emotion engines and converting it into an analyzable format.

[0318] An "analysis device" is an electronic device or software used to analyze the integrated health and emotional data and generate health advice.

[0319] "Health advice" is specific behavioral suggestions or advice generated by the analysis device based on the user's health and emotional state.

[0320] The "chat generation algorithm" is a computer program that uses natural language processing technology to generate health advice to provide to users.

[0321] "Employee" refers to an individual worker working at a production site such as a factory.

[0322] This invention is a system that monitors the health and emotional states of factory workers in real time and provides appropriate health advice. This system aims to improve employee work efficiency and safety by integrating factory robots, employee health monitoring devices, and an emotion analysis engine.

[0323] Hardware used

[0324] 1. Factory robots: Collecting employee health and emotional data.

[0325] 2. Employee smartwatches: measure heart rate, steps, etc.

[0326] 3. Employee blood pressure monitor: Captures blood pressure data.

[0327] 4. Employee scale: Measures weight and body fat percentage.

[0328] 5. Camera: Acquires emotion data through facial expression recognition.

[0329] 6. Microphone: Acquires emotion data through voice analysis.

[0330] Software used

[0331] 1. Robot control software: Control software that allows the robot to operate in conjunction with other hardware.

[0332] 2. Health Data Collection API: An interface for acquiring data from smartwatches, blood pressure monitors, and weight scales.

[0333] 3. Emotion analysis engine: Software that uses cameras and microphones to analyze employees' emotional states.

[0334] 4. Data analysis device: A device that runs on the server and analyzes the integrated data to generate health advice.

[0335] Program processing explanation

[0336] The server first collects health data from employee smartwatches, blood pressure monitors, and weight scales by sending HTTP GET requests to the API endpoints of each device, converting the collected data into a unified format, and then integrating it into a single dataset.

[0337] The device uses an emotion analysis engine to capture employee emotional data, analyzing their emotional state using voice input, facial recognition via a camera, or text analysis, and also sends this data to the server.

[0338] The server sends the integrated health data and emotional data to a data analysis device, which analyzes the data and generates personalized health advice based on the employee's health and emotional state. The generated health advice is displayed on the employee's device via the server.

[0339] This system allows employees to use health monitoring devices to collect emotional data, allowing them to understand their health status in real time and receive appropriate advice.

[0340] Specific examples

[0341] Health data examples

[0342] 1. Smartwatch: Heart rate 90, steps 5000

[0343] 2. Sphygmomanometer: systolic blood pressure 140, diastolic blood pressure 90

[0344] 3. Scale: Weight 68 kg, body fat percentage 25%

[0345] Emotion data example

[0346] Facial expression recognition result: Feeling tired

[0347] Voice analysis results: Feeling stressed

[0348] Text analysis results: Impatience

[0349] Prompt Sentence Examples

[0350] "Based on the latest employee health and sentiment data, determine what health advice is appropriate for the employee in their current work performance and generate specific advice."

[0351] In this way, the present invention makes it possible to improve work efficiency and safety in factories by monitoring the health and emotional state of employees in real time and providing individualized health advice.

[0352] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0353] Step 1:

[0354] The server collects health data from employees' smartwatches, blood pressure monitors, and scales. Specifically, it sends an HTTP GET request to the API endpoint of each device and receives data on heart rate, steps, blood pressure, weight, and body fat percentage in response. The input is raw data from each device, and the output is health data converted into a unified format.

[0355] Step 2:

[0356] The device uses an emotion analysis engine to obtain employee emotional data. This can be done using voice input, camera-based facial recognition, or text analysis. The input is the employee's facial expression, voice, and text data, and the output is analyzed data indicating their emotional state.

[0357] Step 3:

[0358] The server integrates the data acquired in steps 1 and 2. Specifically, it compiles the heart rate, step count, blood pressure, weight, body fat percentage data, and emotional data into a unified format based on the same timestamp. The input is health data and emotional data, and the output is an integrated dataset.

[0359] Step 4:

[0360] The server sends the integrated data to a data analyzer, which processes the integrated data and generates personalized health advice based on the employee's health and emotional state. The input is the integrated dataset, and the output is the health advice generated by the generative AI model.

[0361] Step 5:

[0362] The server sends the generated health advice to the employee's device and displays it. Specifically, the advice is provided to the employee using the device's display and notification function. The input is the health advice data, and the output is the specific advice displayed on the employee's device.

[0363] Step 6:

[0364] Employees receive health advice displayed on the device and modify or improve their behavior based on it. For example, specific actions are recommended, such as taking regular breaks or practicing deep breathing to reduce stress. The input is the health advice displayed on the device, and the output is the change in employee behavior.

[0365] 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.

[0366] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (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.

[0367] 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.

[0368] [Second embodiment]

[0369] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0370] 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.

[0371] 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).

[0372] 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.

[0373] 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.

[0374] 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).

[0375] 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.

[0376] 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.

[0377] 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.

[0378] 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.

[0379] 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.

[0380] 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."

[0381] System Overview

[0382] This invention is a system that acquires, integrates, and analyzes data from different health monitoring devices such as smartwatches, blood pressure monitors, and weight scales to provide health advice to users. Specific embodiments of this system are described below.

[0383] Inter-device communication and data integration

[0384] The server collects data from multiple health monitoring devices, such as a user's smartwatch, blood pressure monitor, and weight scale. Each device sends data through its own API endpoint. The server converts the data from the devices into a unified format and combines it into a single dataset.

[0385] Data analysis

[0386] The server sends the integrated data set to an analysis device equipped with a chat generation algorithm. The analysis device analyzes the data (heart rate, blood pressure, weight, body fat percentage, etc.) acquired from each device. Based on the analysis results, it generates individualized, specific health advice and sends it back to the server.

[0387] Providing health advice

[0388] The server provides the health advice returned from the analyzer to the user. For example, the advice is displayed in text format to the user via a device such as a smartphone or PC.

[0389] Specific examples

[0390] Health data examples

[0391] 1. Smartwatch: Heart rate 85, steps 7000

[0392] 2. Sphygmomanometer: systolic blood pressure 130, diastolic blood pressure 85

[0393] 3. Scale: Weight 70kg, body fat percentage 22%

[0394] Advice from chat generation algorithms

[0395] When a user steps on the scale, uses a smartwatch, or measures their blood pressure with a blood pressure monitor, this data is automatically sent to the server, which then converts the acquired data into a unified format and sends it to the analyzer, which analyzes the data and generates health advice such as:

[0396] "Your heart rate has been a little high recently, and your blood pressure is also a little higher than normal. These may be due to stress or lack of exercise. I recommend incorporating about 30 minutes of aerobic exercise every day and making time to relax. Also, although your weight is within the appropriate range, it is important to manage your body fat percentage, so make sure to eat a balanced diet."

[0397] This health advice is displayed on the user's terminal via the server, and the user can use it as a reference to improve their daily health management.

[0398] In this way, this invention centrally manages data obtained from different health monitoring devices and provides individual, specific health advice based on that data, thereby providing multifaceted support for users' health management.

[0399] The processing flow will be explained below.

[0400] Step 1:

[0401] The server sends an HTTP GET request to the API endpoint of the smartwatch to obtain heart rate and step count data from the user's smartwatch. The server receives the obtained data in JSON format, analyzes it, and extracts the necessary data.

[0402] Step 2:

[0403] The server sends an HTTP GET request to the API endpoint of the blood pressure monitor to retrieve blood pressure data from the user's blood pressure monitor. The server receives the retrieved data in JSON format and parses it to extract systolic and diastolic blood pressure data.

[0404] Step 3:

[0405] The server sends an HTTP GET request to the scale's API endpoint to retrieve weight and body fat percentage data from the user's scale, receives the retrieved data in JSON format, and parses it to extract the appropriate data.

[0406] Step 4:

[0407] The server integrates the data acquired in steps 1 to 3 into a single dataset. It converts the data acquired from each health monitoring device into a unified format and combines them into a single dataset.

[0408] Step 5:

[0409] The server sends an HTTP POST request to the API endpoint of the analysis device to send the integrated dataset to the analysis device equipped with the chat generation algorithm. The dataset is sent in JSON format.

[0410] Step 6:

[0411] The chat generation algorithm analyzes the data set received from the server, including heart rate, blood pressure, weight, and body fat percentage, to generate personalized health advice for each user.

[0412] Step 7:

[0413] The analyzer sends the generated health advice back to the server in JSON format, which then analyzes the data and extracts the advice.

[0414] Step 8:

[0415] The server sends the extracted health advice to the user's terminal for display, and the terminal presents the received advice to the user in text format.

[0416] Through these steps, users can receive personalized and specific health advice based on data collected from multiple health monitoring devices, helping them manage their daily health more effectively.

[0417] Example 1

[0418] 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."

[0419] Effective health management is required to combat the increasing number of lifestyle-related diseases and health risks in modern society. However, the process of collecting, integrating, and analyzing data from multiple different health monitoring devices is complex and time-consuming. In addition, there is a lack of appropriate analytical methods to provide useful health advice from the collected data. To solve this problem, a system is needed that can comprehensively and efficiently manage health data and provide accurate health advice.

[0420] 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.

[0421] In this invention, the server includes means for acquiring data from multiple different health monitoring devices of a user, means for converting the acquired data into an integrated format and integrating them into a single data set, means for transmitting the integrated data set to an analysis device, and means for providing health advice generated by the analysis device, thereby enabling efficient integration and analysis of data from multiple devices and providing useful health advice to the user in a timely manner.

[0422] A "health monitoring device" is a device that measures a user's biometric information and collects that data.

[0423] A "integrated format" is a data structure or format for converting data captured in multiple different formats into a consistent format.

[0424] A "dataset" is a collection of data obtained from health monitoring devices and converted into a unified format.

[0425] An "analyzer" is a computer and software for analyzing a data set and generating health advice.

[0426] A "generative AI model" is an artificial intelligence model that generates output in natural language format from input data.

[0427] "Health advice" refers to specific instructions or recommendations for improving lifestyle habits or managing health for a user, which are generated by the analysis device based on a data set.

[0428] MODE FOR CARRYING OUT THE INVENTION

[0429] This invention is a system that acquires data from multiple health monitoring devices of a user, integrates it, and provides health advice through analysis. Next, the program processing is explained in natural language, and the details of the hardware and software used, as well as data processing and data calculation, are clearly stated.

[0430] System configuration and operation

[0431] The server collects data from users' health monitoring devices, such as smartwatches, blood pressure monitors, and weight scales, and receives data from these devices via communication methods such as Bluetooth and Wi-Fi.

[0432] Data Integration

[0433] The server converts the data acquired from each device into a unified format (JSON format) and integrates it into a single data set, allowing data acquired from different devices to be handled consistently.

[0434] Data analysis

[0435] The server sends the combined data set to an analysis device, which incorporates a generative AI model (e.g., GPT-3) to analyze the data and generate health advice based on biometric data such as heart rate, blood pressure, weight, and body fat percentage.

[0436] Generating health advice

[0437] The generative AI model generates health advice in natural language based on the input prompt. Here are some examples of specific prompts:

[0438] "Generate health advice based on the following data: heart rate 85, systolic blood pressure 130, diastolic blood pressure 85, weight 70kg, body fat percentage 22%."

[0439] Providing advice to users

[0440] The server then sends the health advice received from the analysis device to the user's device. The user receives the advice via a device such as a smartphone or computer. This allows the user to understand their own health condition and take appropriate measures to improve their lifestyle habits.

[0441] Specific examples

[0442] For example, if a user provides the following health monitoring data:

[0443] Smartwatch: Heart rate 85, steps 7000

[0444] Sphygmomanometer: systolic blood pressure 130, diastolic blood pressure 85

[0445] Scale: Weight 70kg, body fat percentage 22%

[0446] The server aggregates this data and sends it to an analysis device, which generates health advice such as:

[0447] "Your heart rate has been a little high recently, and your blood pressure is also a little higher than normal. These may be due to stress or lack of exercise. I recommend incorporating about 30 minutes of aerobic exercise every day and making time to relax. Also, although your weight is within the appropriate range, it is important to manage your body fat percentage, so make sure to eat a balanced diet."

[0448] This advice is displayed on the user's device via the server, allowing the user to refer to it and improve their daily health management.By using this system, it is possible to efficiently integrate and analyze health data obtained from multiple devices and provide users with accurate and timely health advice.

[0449] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0450] Step 1:

[0451] Collecting data from devices

[0452] The server collects data from different health monitoring devices such as users' smartwatches, blood pressure monitors, and weight scales, which transmit data via communication methods such as Bluetooth and Wi-Fi.

[0453] Input: Biometric data sent from smartwatches, blood pressure monitors, and weight scales

[0454] Output: Biometric data stored on the server

[0455] Specific operation: The smartwatch sends heart rate and step count data to the server, the blood pressure monitor sends systolic and diastolic blood pressure data, and the scale sends weight and body fat percentage data to the server.

[0456] Step 2:

[0457] Data integration and format conversion

[0458] The server converts the data received from each device into a unified format (e.g., JSON format) and integrates it into a single data set.

[0459] Input: Unintegrated biometric data captured from each device

[0460] Output: Dataset converted to a unified format (JSON format)

[0461] Specific operation: The server converts the data from the smartwatch, blood pressure monitor, and weight scale into JSON format and combines them into one.

[0462] Step 3:

[0463] Data analysis

[0464] The server sends the combined dataset to an analyzer that incorporates a chat generation algorithm, which incorporates a generative AI model (e.g., GPT-3).

[0465] Input: Dataset converted to unified format (JSON format)

[0466] Output: Health advice based on the analysis results

[0467] How it works: The server sends the integrated data set to the analysis device, which then uses the generative AI model to analyze the data. For example, if your heart rate is high, it will generate relevant advice.

[0468] Step 4:

[0469] Generating health advice

[0470] The analysis device generates health advice in natural language format based on the results of the analysis of the data.

[0471] Input: Data analysis results by the analysis device

[0472] Output: Health advice summarized in natural language format

[0473] Specific operation: The analysis device uses the generative AI model to input the following prompt: "Please generate health advice based on the following data: heart rate 85, systolic blood pressure 130, diastolic blood pressure 85, weight 70 kg, and body fat percentage 22%." Based on this, the generative AI model creates advice in natural language.

[0474] Step 5:

[0475] Providing advice to users

[0476] The server sends the health advice returned by the analysis device to the user's device, where the user can check the advice via a smartphone, computer, or other device and use it to help manage their health.

[0477] Input: Health advice in natural language form returned by the analyzer

[0478] Output: Health advice displayed on the user's device

[0479] Specific operation: The server sends the generated health advice to the user's smartphone or computer, where the user can review it and use it to help manage their daily health.

[0480] (Application example 1)

[0481] 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."

[0482] Conventional health monitoring systems are primarily limited to monitoring the health status of individuals, and lack the means to comprehensively monitor the health status of equipment used in industrial settings such as factories and manage optimal maintenance. In addition, the technology to comprehensively analyze different types of health indicators and provide effective advice is underdeveloped.

[0483] 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.

[0484] In this invention, the server includes means for acquiring data from a plurality of different health monitoring devices of a user, means for integrating the acquired data, means for transmitting the integrated data to an analysis device, means for providing health advice generated by the analysis device, means for collecting health indicators from factory automation equipment, and means for presenting an optimal maintenance schedule based on the collected health indicators, thereby enabling both personal health management and optimal maintenance management of factory equipment.

[0485] "Health monitoring device" is a general term for devices used to collect a user's health data, including smartwatches, blood pressure monitors, and weighing scales.

[0486] "Data integration" is the process of converting data obtained from multiple different health monitoring devices into a single unified format and managing it centrally.

[0487] An "analysis device" is a device or system that performs analysis based on integrated data obtained through data integration and generates output such as health advice.

[0488] "Health Advice" means a health care recommendation or advice generated by an analytical device and provided to a user.

[0489] "Factory automation equipment" is a general term for automated machinery and systems used in factories and manufacturing sites, including robotic arms, assembly lines, and sensor devices.

[0490] "Health indicators" is a general term for data measured to indicate the health status of equipment or the human body, and includes operating time, wear level, internal temperature, vibration level, etc.

[0491] An "optimal maintenance schedule" is a plan that indicates the optimal timing and procedures for performing equipment maintenance based on collected health indicator data.

[0492] This invention is a system that collects data from multiple different health monitoring devices of a user, integrates and analyzes it, provides individualized health advice, collects health indicators of factory automation equipment, and presents optimal maintenance schedules.

[0493] System Overview

[0494] First, the server collects data from different health monitoring devices such as a user's smartwatch, blood pressure monitor, weight scale, etc. Each device sends data through its own API endpoint, and the server converts this data into a unified format and combines it into a single dataset.

[0495] The server then sends the combined data set to an analyzer, which uses algorithms called generative AI models to analyze the data and generate personalized health advice, which is then sent back to the server.

[0496] In addition, the server collects health indicator data from factory automation equipment (e.g., operating hours, wear level, internal temperature, vibration level, etc.) This data is also sent to the server and converted into a unified format.

[0497] Providing health advice and maintenance schedules

[0498] The server provides the user with the health advice returned from the analyzer. For example, the advice is displayed in text format on a smartphone, PC, or other device. It also provides optimal maintenance schedules and operational precautions to factory managers based on the health index data of factory automation equipment.

[0499] Hardware and software used

[0500] The hardware used includes automated devices equipped with various sensors, such as smartwatches, blood pressure monitors, weight scales, and factory robots. The software uses Python 3.8 or higher and the requests library. The server manages the entire process, including data collection, integration, transmission, and return of analysis results.

[0501] A specific example is the process of collecting, analyzing, and providing advice on health data.

[0502] For example, if the following data is sent to the server from a smartwatch: heart rate 85, step count 7000, systolic blood pressure 130, diastolic blood pressure 85, weight 70 kg, and body fat percentage 22% from a scale, the generative AI model will generate the following advice:

[0503] "Your heart rate has been a little high recently, and your blood pressure is also a little higher than normal. These may be due to stress or lack of exercise. I recommend incorporating about 30 minutes of aerobic exercise every day and making time to relax. Also, although your weight is within the appropriate range, it is important to manage your body fat percentage, so make sure to eat a balanced diet."

[0504] For example, for factory automation equipment, data such as 5,000 hours of operation, 15% wear level, 45°C internal temperature, and low vibration levels can be sent to a server, and the following maintenance advice can be provided:

[0505] "The robot has been operating for a long time and its wear level is low. The internal temperature is normal, but caution is needed for continued operation in the future. Regular maintenance is recommended."

[0506] Example prompt sentence:

[0507] "What is the health advice for a factory robot that has 5000 operating hours, is currently at 15% wear, has an internal temperature of 45 degrees, and has low vibration levels?"

[0508] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0509] Step 1:

[0510] The server collects data from multiple different health monitoring devices of the user. Specifically, it collects data such as heart rate and step count from a smartwatch, blood pressure from a blood pressure monitor, and weight and body fat percentage from a weighing scale through API endpoints. The input is raw data obtained from each device's API, and the output is pre-processed data for integration.

[0511] Step 2:

[0512] The server converts the acquired data into a unified format and integrates it into a single dataset. Data processing includes converting data from different formats into a common format and adding timestamps. The input is the preprocessed raw data, and the output is an integrated dataset converted into a unified format.

[0513] Step 3:

[0514] The server sends the integrated dataset to the analysis device. Specifically, it generates an analysis request and sends the data to the analysis device using an HTTP request. The input is the integrated dataset, and the output is an HTTP response to the analysis request.

[0515] Step 4:

[0516] The analysis device performs data analysis based on the integrated data sent. Using a generative AI model, it analyzes various health data and generates individual, specific health advice. The input is the integrated data set sent from the server, and the output is text data containing health advice.

[0517] Step 5:

[0518] The analysis device returns the generated health advice to the server. The input is text data containing the health advice, and the output is an HTTP response to the server.

[0519] Step 6:

[0520] The server provides the returned health advice to the user. Specifically, the advice is displayed in text format on a device such as a smartphone or PC. The input is the received health advice, and the output is the text advice displayed on the user's device.

[0521] Step 7:

[0522] The server collects health indicators from factory automation equipment, specifically, data such as operating hours, wear level, internal temperature, and vibration level obtained through sensors. The input is raw data from the sensors, and the output is preprocessed health indicator data.

[0523] Step 8:

[0524] The server presents an optimal maintenance schedule based on the collected health indicators. Data analysis is performed through an analyzer to generate optimal maintenance timing and operational precautions. The input is preprocessed health indicator data, and the output is text data including the maintenance schedule and advice.

[0525] Step 9:

[0526] The server provides the presented maintenance schedule to the factory manager. Specifically, it displays the schedule and advice on the manager's terminal. The input is text data including the maintenance schedule and advice, and the output is the content displayed on the manager's terminal.

[0527] 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.

[0528] System Overview

[0529] This invention is a system that acquires data from different health monitoring devices such as smartwatches, blood pressure monitors, and weight scales, and combines it with an emotion engine that recognizes the user's emotions to integrate and analyze the data and provide health advice. Specific embodiments of this system are described below.

[0530] Inter-device communication and data integration

[0531] The server sends an HTTP GET request to the API endpoint of the smartwatch to obtain heart rate and step count data from the user's smartwatch. The server sends an HTTP GET request to the API endpoint of the blood pressure monitor to obtain blood pressure data from the blood pressure monitor. The server also sends an HTTP GET request to the API endpoint of the weight scale to obtain weight and body fat percentage data from the weight scale. The server converts the data obtained from each device into a unified format and integrates it into a single dataset.

[0532] Acquiring and analyzing emotion data

[0533] The device uses voice input, facial expression recognition using a camera, or text analysis to recognize the user's emotions, thereby acquiring the user's emotion data, and then transmits the acquired emotion data to a server.

[0534] Data analysis

[0535] The server sends the integrated data set obtained from the health monitoring device and the emotion data obtained from the emotion engine to the analysis device. The analysis device performs analysis taking into account the user's emotional state in addition to data such as heart rate, blood pressure, weight, and body fat percentage. This allows the analysis device to generate individualized health advice that includes stress management and psychological advice in addition to general health advice.

[0536] Providing health advice

[0537] The server sends the health advice returned from the analyzer to the user's device and displays it. For example, the advice may be presented to the user in text format via a device such as a smartphone or PC.

[0538] Specific examples

[0539] Health data examples

[0540] 1. Smartwatch: Heart rate 85, steps 7000

[0541] 2. Sphygmomanometer: systolic blood pressure 130, diastolic blood pressure 85

[0542] 3. Scale: Weight 70kg, body fat percentage 22%

[0543] Emotion data example

[0544] Facial expression recognition result: Feeling stressed

[0545] Voice analysis results: Fatigue

[0546] Text analysis results: Feeling anxious

[0547] Advice from chat generation algorithms

[0548] When a user steps on a scale, uses a smartwatch, or measures their blood pressure with a blood pressure monitor, this data is automatically sent to the server. In addition, the device captures emotional data from the user's facial expressions, voice, and text, and sends this data to the server. The server then integrates all the collected data and sends it to an analysis device. The analysis device analyzes the data and generates health advice such as:

[0549] "Your heart rate has been a little high recently, and your blood pressure is also a little higher than normal. These may be due to stress or lack of exercise. I recommend that you incorporate about 30 minutes of aerobic exercise every day and make time to relax. Also, although your weight is within the appropriate range, it is important to manage your body fat percentage, so be sure to eat a balanced diet. Regarding the stress and anxiety you are currently feeling, try taking a deep breath and relaxing. If necessary, please consider seeking psychological counseling."

[0550] This health advice is displayed on the user's terminal via the server, and the user can use it as a reference to improve their daily health management.

[0551] In this way, this invention centrally manages data and emotional data obtained from different health monitoring devices, and provides individual, specific health advice based on this data, thereby providing multifaceted support for users' health management.

[0552] The processing flow will be explained below.

[0553] Step 1:

[0554] The server sends an HTTP GET request to the API endpoint of the smartwatch to obtain heart rate and step count data from the user's smartwatch. The server then analyzes the JSON format data returned as a response and extracts the heart rate and step count data.

[0555] Step 2:

[0556] The server sends an HTTP GET request to the API endpoint of the blood pressure monitor to obtain blood pressure data from the user's blood pressure monitor. It then parses the JSON-formatted data returned as a response and extracts the systolic and diastolic blood pressure data.

[0557] Step 3:

[0558] The server sends an HTTP GET request to the scale's API endpoint to obtain the user's weight and body fat percentage data from the scale. The server then parses the JSON format data returned as a response and extracts the weight and body fat percentage data.

[0559] Step 4:

[0560] The server combines the data acquired in steps 1 to 3 into a single integrated data set, converting the data from each device into a unified format to complete the overall data set.

[0561] Step 5:

[0562] To recognize the user's emotions, the device uses sensors such as a camera and microphone to collect facial and voice data, and analyzes this emotional data in real time to determine the user's emotional state.

[0563] Step 6:

[0564] The device then sends the analyzed emotional data, including information about stress levels and emotional states, to the server.

[0565] Step 7:

[0566] To send the integrated dataset and emotion data to the analysis device, the server sends an HTTP POST request to the analysis device's API endpoint, which sends the dataset and emotion data in JSON format.

[0567] Step 8:

[0568] The analyzer analyzes the received health and emotional data, taking into account the user's heart rate, blood pressure, weight, body fat percentage, and emotional state to generate personalized health advice.

[0569] Step 9:

[0570] The analyzer sends the generated health advice back to the server in JSON format, which then analyzes the received data and extracts the advice.

[0571] Step 10:

[0572] The server then sends the extracted health advice to the user's device, which includes specific health management suggestions and psychological support suggestions.

[0573] Step 11:

[0574] The device then presents the received advice to the user in text format, allowing the user to receive comprehensive health advice based on their own health condition.

[0575] Through these steps, users can receive personalized and specific health advice based on multiple health monitoring devices and emotional data, enabling them to comprehensively manage their daily health.

[0576] Example 2

[0577] 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."

[0578] In modern society, user health management is becoming an increasingly important issue. Conventional health monitoring devices often collect data individually, making it difficult to centrally manage and analyze that data. Furthermore, while a user's emotional state is an important factor in health management, existing systems lack the means to integrate and analyze emotional state and health data, making it difficult to provide comprehensive health advice. To solve these problems, a system is needed that can perform an integrated analysis of health data and emotional data and provide individual, specific advice.

[0579] 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 acquiring data from multiple different health monitoring devices of the user, means for integrating the acquired data, means for acquiring emotional data of the user, means for transmitting the integrated data to an analytical device, and means for providing health advice generated by the analytical device. This makes it possible to centrally manage health data and emotional data and provide comprehensive and individually specific health advice to the user.

[0580] A "health monitoring device" is a device for measuring and recording a user's health status, and examples include smart watches, blood pressure monitors, and weight scales.

[0581] "Means for acquiring data" refers to the processes or technologies that have the ability to transmit and receive data collected from the health monitoring device to the server.

[0582] "Data integration measures" refers to the processes and techniques used to convert data from multiple health monitoring devices into a unified format and compile it into a single data set.

[0583] "Means for acquiring emotional data" refers to the process or technology for collecting a user's emotions using voice input, facial expression recognition, text analysis, etc., and transmitting that data to a server.

[0584] "Analysis equipment" refers to devices or software that analyze the integrated data and emotional data sent from the server and generate health advice for the user.

[0585] "Means for providing health advice" refers to the process or technology for transmitting the health advice generated by the analytical device to the user's terminal and displaying it.

[0586] "Generative algorithm" refers to a computational method or program for generating personalized, specific health advice based on integrated health and emotional data.

[0587] System Overview

[0588] This invention is a system that provides comprehensive health advice by collecting data from a user's different health monitoring devices, integrating and analyzing it together with the user's emotional data. The hardware used includes health monitoring devices such as smartwatches, blood pressure monitors, and weight scales, and the software includes API endpoint utilization, an emotional analysis engine, and a data analysis device.

[0589] Inter-device communication and data integration

[0590] The server acquires data from each health monitoring device in the following procedure.

[0591] 1. Send an HTTP GET request to the smartwatch's API endpoint to retrieve heart rate and step count data from the smartwatch.

[0592] 2. To retrieve blood pressure data from the blood pressure monitor, send an HTTP GET request to the blood pressure monitor's API endpoint.

[0593] 3. Send an HTTP GET request to the scale's API endpoint to retrieve weight and body fat percentage data from the scale.

[0594] The data acquired from each device is converted into a unified format by the server and compiled into a single integrated data set.

[0595] Acquiring and analyzing emotion data

[0596] The terminal acquires the user's emotion data in the following manner.

[0597] 1. Sentiment analysis using voice input

[0598] The device records the user's voice and analyzes the emotional data using a voice analysis engine.

[0599] 2. Camera-based facial expression recognition

[0600] The device captures the user's face with a camera and acquires emotional data using a facial expression analysis engine.

[0601] 3. Text Analysis

[0602] The device analyzes the text entered by the user and generates emotion data.

[0603] The acquired emotion data is transmitted to the server through the terminal.

[0604] Data analysis

[0605] The server performs data analysis in the following procedure.

[0606] 1. Send the combined health dataset and emotion data to the analyzer, specifically by sending the dataset as an API request.

[0607] 2. The analyzer analyzes health data such as heart rate, blood pressure, weight, and body fat percentage, as well as the user's emotional data. This analysis is then used with a generative algorithm to generate personalized health advice for the user.

[0608] Providing health advice

[0609] The server sends the health advice returned from the analyzer to the user's device, and the device displays the advice to the user. For example, if the device is a smartphone, the advice is displayed in text format on the screen.

[0610] Examples and prompts

[0611] For example, the following data is obtained:

[0612] Smartwatch: Heart rate 85, steps 7000

[0613] Sphygmomanometer: systolic blood pressure 130, diastolic blood pressure 85

[0614] Scale: Weight 70kg, body fat percentage 22%

[0615] Emotional data: stress, fatigue, anxiety

[0616] Based on this data, the following advice is given to the user:

[0617] "Your heart rate has been a little high recently, and your blood pressure is also a little higher than normal. These may be due to stress or lack of exercise. I recommend that you incorporate about 30 minutes of aerobic exercise every day and make time to relax. Also, although your weight is within the appropriate range, it is important to manage your body fat percentage, so be sure to eat a balanced diet. Regarding the stress and anxiety you are currently feeling, try taking a deep breath and relaxing. If necessary, please consider seeking psychological counseling."

[0618] Example prompt for a generative AI model:

[0619] "Please provide specific health advice based on the health data (heart rate 85, steps 7000, systolic blood pressure 130, diastolic blood pressure 85, weight 70 kg, body fat percentage 22%) and emotional data (stress, fatigue, anxiety) obtained from the user."

[0620] Using the above methods, the system can grasp the user's health condition from multiple angles and support an appropriate healthy lifestyle.

[0621] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0622] Step 1: Obtaining health data

[0623] Input: Data from smartwatches, blood pressure monitors, and scales (e.g., heart rate, steps, blood pressure, weight, body fat percentage)

[0624] Output: A set of acquired health data

[0625] Specific operation:

[0626] The server sends an HTTP GET request to the smartwatch's API endpoint to retrieve heart rate and step count data, for example, to the URL GET https: / / api.smartwatch.com / userdata?metrics=heart_rate,steps.

[0627] The server sends an HTTP GET request to the API endpoint of the blood pressure monitor to retrieve blood pressure data, for example, to the URL GET https: / / api.bloodpressuremonitor.com / data?metrics=systolic,diastolic.

[0628] The server sends an HTTP GET request to the scale's API endpoint to retrieve weight and body fat percentage data, for example, to the URL GET https: / / api.weighingscale.com / data?metrics=weight,body_fat.

[0629] Step 2: Integrate and transform data

[0630] Input: Individually acquired health data (heart rate, steps, blood pressure, weight, body fat percentage)

[0631] Output: Unified dataset converted to a unified format

[0632] Specific operation:

[0633] The server converts the individually acquired data into a unified format, for example, {"heart_rate": 85, "steps": 7000, "blood_pressure": {"systolic": 130, "diastolic": 85}, "weight": 70, "body_fat": 22}.

[0634] Step 3: Obtaining emotion data

[0635] Input: User's voice, facial expressions, and text data

[0636] Output: A set of retrieved emotion data

[0637] Specific operation:

[0638] The device records the user's voice and uses a voice analysis engine to extract emotional data. For example, if the user says, "I'm a little tired today," the device can extract the emotional data "fatigue."

[0639] The device captures the user's face with a camera and uses a facial expression analysis engine to obtain emotional data. For example, it can extract emotional data indicating that the user is feeling "stressed" from the user's facial image.

[0640] The device analyzes the text entered by the user and extracts emotional data. For example, the device extracts the emotional data "anxiety" from the text "I'm anxious because things aren't going well at work."

[0641] Step 4: Data analysis

[0642] Input: Integrated health dataset, emotion data

[0643] Output: Analysis results and health advice

[0644] Specific operation:

[0645] The server sends the combined health dataset and emotion data to the analyzer, for example, by sending the dataset via API request POST https: / / api.analysisengine.com / analyze.

[0646] The analyzer analyzes the transmitted data and uses a generation algorithm to generate personalized health advice, such as heart rate 85, steps 7000, blood pressure 130 / 85, weight 70kg, body fat percentage 22%, and emotional data such as stress, fatigue, and anxiety.

[0647] Step 5: Providing health advice

[0648] Input: Generated health advice

[0649] Output: Advice displayed on the user's terminal

[0650] Specific operation:

[0651] The server sends the health advice returned from the analyzer to the user's device, for example, by sending an API request POST https: / / api.userterminal.com / advice { "advice": "Your heart rate has been a little high recently..."}.

[0652] The device displays the received advice to the user. For example, the device may display the following text on the user's smartphone: "Your heart rate has been a little high recently, and your blood pressure is also a little above normal..."

[0653] (Application example 2)

[0654] 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."

[0655] There is a need to improve work efficiency and safety by monitoring the health and psychological states of employees in factories in real time and providing appropriate health advice. However, existing systems lack the means to effectively integrate data collected from multiple health monitoring devices and provide specific health advice that takes into account employees' emotional states.

[0656] The specific processing by the specific 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 acquiring data from multiple different health monitoring devices of the user, means for integrating the acquired data and emotional data, and means for transmitting the integrated data to the analysis device. This makes it possible to integrate the data and emotional data collected from the health monitoring devices and provide health advice generated using the analysis device in real time.

[0657] A "health monitoring device" is a device for measuring and recording a user's health data, such as a smart watch, blood pressure monitor, or weight scale.

[0658] "Emotional data" is data that indicates a user's emotional state, obtained using voice input, facial recognition using a camera, or text analysis.

[0659] "Server" is a computing system for collecting and integrating data from health monitoring devices and emotion engines and transmitting it to an analysis device.

[0660] "Integration" is the process of bringing together data from multiple health monitoring devices and emotion engines and converting it into an analyzable format.

[0661] An "analysis device" is an electronic device or software used to analyze the integrated health and emotional data and generate health advice.

[0662] "Health advice" is specific behavioral suggestions or advice generated by the analysis device based on the user's health and emotional state.

[0663] The "chat generation algorithm" is a computer program that uses natural language processing technology to generate health advice to provide to users.

[0664] "Employee" refers to an individual worker working at a production site such as a factory.

[0665] This invention is a system that monitors the health and emotional states of factory workers in real time and provides appropriate health advice. This system aims to improve employee work efficiency and safety by integrating factory robots, employee health monitoring devices, and an emotion analysis engine.

[0666] Hardware used

[0667] 1. Factory robots: Collecting employee health and emotional data.

[0668] 2. Employee smartwatches: measure heart rate, steps, etc.

[0669] 3. Employee blood pressure monitor: Captures blood pressure data.

[0670] 4. Employee scale: Measures weight and body fat percentage.

[0671] 5. Camera: Acquires emotion data through facial expression recognition.

[0672] 6. Microphone: Acquires emotion data through voice analysis.

[0673] Software used

[0674] 1. Robot control software: Control software that allows the robot to operate in conjunction with other hardware.

[0675] 2. Health Data Collection API: An interface for acquiring data from smartwatches, blood pressure monitors, and weight scales.

[0676] 3. Emotion analysis engine: Software that uses cameras and microphones to analyze employees' emotional states.

[0677] 4. Data analysis device: A device that runs on the server and analyzes the integrated data to generate health advice.

[0678] Program processing explanation

[0679] The server first collects health data from employee smartwatches, blood pressure monitors, and weight scales by sending HTTP GET requests to the API endpoints of each device, converting the collected data into a unified format, and then integrating it into a single dataset.

[0680] The device uses an emotion analysis engine to capture employee emotional data, analyzing their emotional state using voice input, facial recognition via a camera, or text analysis, and also sends this data to the server.

[0681] The server sends the integrated health data and emotional data to a data analysis device, which analyzes the data and generates personalized health advice based on the employee's health and emotional state. The generated health advice is displayed on the employee's device via the server.

[0682] This system allows employees to use health monitoring devices to collect emotional data, allowing them to understand their health status in real time and receive appropriate advice.

[0683] Specific examples

[0684] Health data examples

[0685] 1. Smartwatch: Heart rate 90, steps 5000

[0686] 2. Sphygmomanometer: systolic blood pressure 140, diastolic blood pressure 90

[0687] 3. Scale: Weight 68 kg, body fat percentage 25%

[0688] Emotion data example

[0689] Facial expression recognition result: Feeling tired

[0690] Voice analysis results: Feeling stressed

[0691] Text analysis results: Impatience

[0692] Prompt Sentence Examples

[0693] "Based on the latest employee health and sentiment data, determine what health advice is appropriate for the employee in their current work performance and generate specific advice."

[0694] In this way, the present invention makes it possible to improve work efficiency and safety in factories by monitoring the health and emotional state of employees in real time and providing individualized health advice.

[0695] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0696] Step 1:

[0697] The server collects health data from employees' smartwatches, blood pressure monitors, and scales. Specifically, it sends an HTTP GET request to the API endpoint of each device and receives data on heart rate, steps, blood pressure, weight, and body fat percentage in response. The input is raw data from each device, and the output is health data converted into a unified format.

[0698] Step 2:

[0699] The device uses an emotion analysis engine to obtain employee emotional data. This can be done using voice input, camera-based facial recognition, or text analysis. The input is the employee's facial expression, voice, and text data, and the output is analyzed data indicating their emotional state.

[0700] Step 3:

[0701] The server integrates the data acquired in steps 1 and 2. Specifically, it compiles the heart rate, step count, blood pressure, weight, body fat percentage data, and emotional data into a unified format based on the same timestamp. The input is health data and emotional data, and the output is an integrated dataset.

[0702] Step 4:

[0703] The server sends the integrated data to a data analyzer, which processes the integrated data and generates personalized health advice based on the employee's health and emotional state. The input is the integrated dataset, and the output is the health advice generated by the generative AI model.

[0704] Step 5:

[0705] The server sends the generated health advice to the employee's device and displays it. Specifically, the advice is provided to the employee using the device's display and notification function. The input is the health advice data, and the output is the specific advice displayed on the employee's device.

[0706] Step 6:

[0707] Employees receive health advice displayed on the device and modify or improve their behavior based on it. For example, specific actions are recommended, such as taking regular breaks or practicing deep breathing to reduce stress. The input is the health advice displayed on the device, and the output is the change in employee behavior.

[0708] 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.

[0709] 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.

[0710] 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.

[0711] [Third embodiment]

[0712] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0713] 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.

[0714] 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).

[0715] 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.

[0716] 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.

[0717] 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).

[0718] 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.

[0719] 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.

[0720] 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.

[0721] 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.

[0722] 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.

[0723] 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."

[0724] System Overview

[0725] This invention is a system that acquires, integrates, and analyzes data from different health monitoring devices such as smartwatches, blood pressure monitors, and weight scales to provide health advice to users. Specific embodiments of this system are described below.

[0726] Inter-device communication and data integration

[0727] The server collects data from multiple health monitoring devices, such as a user's smartwatch, blood pressure monitor, and weight scale. Each device sends data through its own API endpoint. The server converts the data from the devices into a unified format and combines it into a single dataset.

[0728] Data analysis

[0729] The server sends the integrated data set to an analysis device equipped with a chat generation algorithm. The analysis device analyzes the data (heart rate, blood pressure, weight, body fat percentage, etc.) acquired from each device. Based on the analysis results, it generates individualized, specific health advice and sends it back to the server.

[0730] Providing health advice

[0731] The server provides the health advice returned from the analyzer to the user. For example, the advice is displayed in text format to the user via a device such as a smartphone or PC.

[0732] Specific examples

[0733] Health data examples

[0734] 1. Smartwatch: Heart rate 85, steps 7000

[0735] 2. Sphygmomanometer: systolic blood pressure 130, diastolic blood pressure 85

[0736] 3. Scale: Weight 70kg, body fat percentage 22%

[0737] Advice from chat generation algorithms

[0738] When a user steps on the scale, uses a smartwatch, or measures their blood pressure with a blood pressure monitor, this data is automatically sent to the server, which then converts the acquired data into a unified format and sends it to the analyzer, which analyzes the data and generates health advice such as:

[0739] "Your heart rate has been a little high recently, and your blood pressure is also a little higher than normal. These may be due to stress or lack of exercise. I recommend incorporating about 30 minutes of aerobic exercise every day and making time to relax. Also, although your weight is within the appropriate range, it is important to manage your body fat percentage, so make sure to eat a balanced diet."

[0740] This health advice is displayed on the user's terminal via the server, and the user can use it as a reference to improve their daily health management.

[0741] In this way, this invention centrally manages data obtained from different health monitoring devices and provides individual, specific health advice based on that data, thereby providing multifaceted support for users' health management.

[0742] The processing flow will be explained below.

[0743] Step 1:

[0744] The server sends an HTTP GET request to the API endpoint of the smartwatch to obtain heart rate and step count data from the user's smartwatch. The server receives the obtained data in JSON format, analyzes it, and extracts the necessary data.

[0745] Step 2:

[0746] The server sends an HTTP GET request to the API endpoint of the blood pressure monitor to retrieve blood pressure data from the user's blood pressure monitor. The server receives the retrieved data in JSON format and parses it to extract systolic and diastolic blood pressure data.

[0747] Step 3:

[0748] The server sends an HTTP GET request to the scale's API endpoint to retrieve weight and body fat percentage data from the user's scale, receives the retrieved data in JSON format, and parses it to extract the appropriate data.

[0749] Step 4:

[0750] The server integrates the data acquired in steps 1 to 3 into a single dataset. It converts the data acquired from each health monitoring device into a unified format and combines them into a single dataset.

[0751] Step 5:

[0752] The server sends an HTTP POST request to the API endpoint of the analysis device to send the integrated dataset to the analysis device equipped with the chat generation algorithm. The dataset is sent in JSON format.

[0753] Step 6:

[0754] The chat generation algorithm analyzes the data set received from the server, including heart rate, blood pressure, weight, and body fat percentage, to generate personalized health advice for each user.

[0755] Step 7:

[0756] The analyzer sends the generated health advice back to the server in JSON format, which then analyzes the data and extracts the advice.

[0757] Step 8:

[0758] The server sends the extracted health advice to the user's terminal for display, and the terminal presents the received advice to the user in text format.

[0759] Through these steps, users can receive personalized and specific health advice based on data collected from multiple health monitoring devices, helping them manage their daily health more effectively.

[0760] Example 1

[0761] 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."

[0762] Effective health management is required to combat the increasing number of lifestyle-related diseases and health risks in modern society. However, the process of collecting, integrating, and analyzing data from multiple different health monitoring devices is complex and time-consuming. In addition, there is a lack of appropriate analytical methods to provide useful health advice from the collected data. To solve this problem, a system is needed that can comprehensively and efficiently manage health data and provide accurate health advice.

[0763] 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.

[0764] In this invention, the server includes means for acquiring data from multiple different health monitoring devices of a user, means for converting the acquired data into an integrated format and integrating them into a single data set, means for transmitting the integrated data set to an analysis device, and means for providing health advice generated by the analysis device, thereby enabling efficient integration and analysis of data from multiple devices and providing useful health advice to the user in a timely manner.

[0765] A "health monitoring device" is a device that measures a user's biometric information and collects that data.

[0766] A "integrated format" is a data structure or format for converting data captured in multiple different formats into a consistent format.

[0767] A "dataset" is a collection of data obtained from health monitoring devices and converted into a unified format.

[0768] An "analyzer" is a computer and software for analyzing a data set and generating health advice.

[0769] A "generative AI model" is an artificial intelligence model that generates output in natural language format from input data.

[0770] "Health advice" refers to specific instructions or recommendations for improving lifestyle habits or managing health for a user, which are generated by the analysis device based on a data set.

[0771] MODE FOR CARRYING OUT THE INVENTION

[0772] This invention is a system that acquires data from multiple health monitoring devices of a user, integrates it, and provides health advice through analysis. Next, the program processing is explained in natural language, and the details of the hardware and software used, as well as data processing and data calculation, are clearly stated.

[0773] System configuration and operation

[0774] The server collects data from users' health monitoring devices, such as smartwatches, blood pressure monitors, and weight scales, and receives data from these devices via communication methods such as Bluetooth and Wi-Fi.

[0775] Data Integration

[0776] The server converts the data acquired from each device into a unified format (JSON format) and integrates it into a single data set, allowing data acquired from different devices to be handled consistently.

[0777] Data analysis

[0778] The server sends the combined data set to an analysis device, which incorporates a generative AI model (e.g., GPT-3) to analyze the data and generate health advice based on biometric data such as heart rate, blood pressure, weight, and body fat percentage.

[0779] Generating health advice

[0780] The generative AI model generates health advice in natural language based on the input prompt. Here are some examples of specific prompts:

[0781] "Generate health advice based on the following data: heart rate 85, systolic blood pressure 130, diastolic blood pressure 85, weight 70kg, body fat percentage 22%."

[0782] Providing advice to users

[0783] The server then sends the health advice received from the analysis device to the user's device. The user receives the advice via a device such as a smartphone or computer. This allows the user to understand their own health condition and take appropriate measures to improve their lifestyle habits.

[0784] Specific examples

[0785] For example, if a user provides the following health monitoring data:

[0786] Smartwatch: Heart rate 85, steps 7000

[0787] Sphygmomanometer: systolic blood pressure 130, diastolic blood pressure 85

[0788] Scale: Weight 70kg, body fat percentage 22%

[0789] The server aggregates this data and sends it to an analysis device, which generates health advice such as:

[0790] "Your heart rate has been a little high recently, and your blood pressure is also a little higher than normal. These may be due to stress or lack of exercise. I recommend incorporating about 30 minutes of aerobic exercise every day and making time to relax. Also, although your weight is within the appropriate range, it is important to manage your body fat percentage, so make sure to eat a balanced diet."

[0791] This advice is displayed on the user's device via the server, allowing the user to refer to it and improve their daily health management.By using this system, it is possible to efficiently integrate and analyze health data obtained from multiple devices and provide users with accurate and timely health advice.

[0792] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0793] Step 1:

[0794] Collecting data from devices

[0795] The server collects data from different health monitoring devices such as users' smartwatches, blood pressure monitors, and weight scales, which transmit data via communication methods such as Bluetooth and Wi-Fi.

[0796] Input: Biometric data sent from smartwatches, blood pressure monitors, and weight scales

[0797] Output: Biometric data stored on the server

[0798] Specific operation: The smartwatch sends heart rate and step count data to the server, the blood pressure monitor sends systolic and diastolic blood pressure data, and the scale sends weight and body fat percentage data to the server.

[0799] Step 2:

[0800] Data integration and format conversion

[0801] The server converts the data received from each device into a unified format (e.g., JSON format) and integrates it into a single data set.

[0802] Input: Unintegrated biometric data captured from each device

[0803] Output: Dataset converted to a unified format (JSON format)

[0804] Specific operation: The server converts the data from the smartwatch, blood pressure monitor, and weight scale into JSON format and combines them into one.

[0805] Step 3:

[0806] Data analysis

[0807] The server sends the combined dataset to an analyzer that incorporates a chat generation algorithm, which incorporates a generative AI model (e.g., GPT-3).

[0808] Input: Dataset converted to unified format (JSON format)

[0809] Output: Health advice based on the analysis results

[0810] How it works: The server sends the integrated data set to the analysis device, which then uses the generative AI model to analyze the data. For example, if your heart rate is high, it will generate relevant advice.

[0811] Step 4:

[0812] Generating health advice

[0813] The analysis device generates health advice in natural language format based on the results of the analysis of the data.

[0814] Input: Data analysis results by the analysis device

[0815] Output: Health advice summarized in natural language format

[0816] Specific operation: The analysis device uses the generative AI model to input the following prompt: "Please generate health advice based on the following data: heart rate 85, systolic blood pressure 130, diastolic blood pressure 85, weight 70 kg, and body fat percentage 22%." Based on this, the generative AI model creates advice in natural language.

[0817] Step 5:

[0818] Providing advice to users

[0819] The server sends the health advice returned by the analysis device to the user's device, where the user can check the advice via a smartphone, computer, or other device and use it to help manage their health.

[0820] Input: Health advice in natural language form returned by the analyzer

[0821] Output: Health advice displayed on the user's device

[0822] Specific operation: The server sends the generated health advice to the user's smartphone or computer, where the user can review it and use it to help manage their daily health.

[0823] (Application example 1)

[0824] 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."

[0825] Conventional health monitoring systems are primarily limited to monitoring the health status of individuals, and lack the means to comprehensively monitor the health status of equipment used in industrial settings such as factories and manage optimal maintenance. In addition, the technology to comprehensively analyze different types of health indicators and provide effective advice is underdeveloped.

[0826] 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.

[0827] In this invention, the server includes means for acquiring data from a plurality of different health monitoring devices of a user, means for integrating the acquired data, means for transmitting the integrated data to an analysis device, means for providing health advice generated by the analysis device, means for collecting health indicators from factory automation equipment, and means for presenting an optimal maintenance schedule based on the collected health indicators, thereby enabling both personal health management and optimal maintenance management of factory equipment.

[0828] "Health monitoring device" is a general term for devices used to collect a user's health data, including smartwatches, blood pressure monitors, and weighing scales.

[0829] "Data integration" is the process of converting data obtained from multiple different health monitoring devices into a single unified format and managing it centrally.

[0830] An "analysis device" is a device or system that performs analysis based on integrated data obtained through data integration and generates output such as health advice.

[0831] "Health Advice" means a health care recommendation or advice generated by an analytical device and provided to a user.

[0832] "Factory automation equipment" is a general term for automated machinery and systems used in factories and manufacturing sites, including robotic arms, assembly lines, and sensor devices.

[0833] "Health indicators" is a general term for data measured to indicate the health status of equipment or the human body, and includes operating time, wear level, internal temperature, vibration level, etc.

[0834] An "optimal maintenance schedule" is a plan that indicates the optimal timing and procedures for performing equipment maintenance based on collected health indicator data.

[0835] This invention is a system that collects data from multiple different health monitoring devices of a user, integrates and analyzes it, provides individualized health advice, collects health indicators of factory automation equipment, and presents optimal maintenance schedules.

[0836] System Overview

[0837] First, the server collects data from different health monitoring devices such as a user's smartwatch, blood pressure monitor, weight scale, etc. Each device sends data through its own API endpoint, and the server converts this data into a unified format and combines it into a single dataset.

[0838] The server then sends the combined data set to an analyzer, which uses algorithms called generative AI models to analyze the data and generate personalized health advice, which is then sent back to the server.

[0839] In addition, the server collects health indicator data from factory automation equipment (e.g., operating hours, wear level, internal temperature, vibration level, etc.) This data is also sent to the server and converted into a unified format.

[0840] Providing health advice and maintenance schedules

[0841] The server provides the user with the health advice returned from the analyzer. For example, the advice is displayed in text format on a smartphone, PC, or other device. It also provides optimal maintenance schedules and operational precautions to factory managers based on the health index data of factory automation equipment.

[0842] Hardware and software used

[0843] The hardware used includes automated devices equipped with various sensors, such as smartwatches, blood pressure monitors, weight scales, and factory robots. The software uses Python 3.8 or higher and the requests library. The server manages the entire process, including data collection, integration, transmission, and return of analysis results.

[0844] A specific example is the process of collecting, analyzing, and providing advice on health data.

[0845] For example, if the following data is sent to the server from a smartwatch: heart rate 85, step count 7000, systolic blood pressure 130, diastolic blood pressure 85, weight 70 kg, and body fat percentage 22% from a scale, the generative AI model will generate the following advice:

[0846] "Your heart rate has been a little high recently, and your blood pressure is also a little higher than normal. These may be due to stress or lack of exercise. I recommend incorporating about 30 minutes of aerobic exercise every day and making time to relax. Also, although your weight is within the appropriate range, it is important to manage your body fat percentage, so make sure to eat a balanced diet."

[0847] For example, for factory automation equipment, data such as 5,000 hours of operation, 15% wear level, 45°C internal temperature, and low vibration levels can be sent to a server, and the following maintenance advice can be provided:

[0848] "The robot has been operating for a long time and its wear level is low. The internal temperature is normal, but caution is needed for continued operation in the future. Regular maintenance is recommended."

[0849] Example prompt sentence:

[0850] "What is the health advice for a factory robot that has 5000 operating hours, is currently at 15% wear, has an internal temperature of 45 degrees, and has low vibration levels?"

[0851] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0852] Step 1:

[0853] The server collects data from multiple different health monitoring devices of the user. Specifically, it collects data such as heart rate and step count from a smartwatch, blood pressure from a blood pressure monitor, and weight and body fat percentage from a weighing scale through API endpoints. The input is raw data obtained from each device's API, and the output is pre-processed data for integration.

[0854] Step 2:

[0855] The server converts the acquired data into a unified format and integrates it into a single dataset. Data processing includes converting data from different formats into a common format and adding timestamps. The input is the preprocessed raw data, and the output is an integrated dataset converted into a unified format.

[0856] Step 3:

[0857] The server sends the integrated dataset to the analysis device. Specifically, it generates an analysis request and sends the data to the analysis device using an HTTP request. The input is the integrated dataset, and the output is an HTTP response to the analysis request.

[0858] Step 4:

[0859] The analysis device performs data analysis based on the integrated data sent. Using a generative AI model, it analyzes various health data and generates individual, specific health advice. The input is the integrated data set sent from the server, and the output is text data containing health advice.

[0860] Step 5:

[0861] The analysis device returns the generated health advice to the server. The input is text data containing the health advice, and the output is an HTTP response to the server.

[0862] Step 6:

[0863] The server provides the returned health advice to the user. Specifically, the advice is displayed in text format on a device such as a smartphone or PC. The input is the received health advice, and the output is the text advice displayed on the user's device.

[0864] Step 7:

[0865] The server collects health indicators from factory automation equipment, specifically, data such as operating hours, wear level, internal temperature, and vibration level obtained through sensors. The input is raw data from the sensors, and the output is preprocessed health indicator data.

[0866] Step 8:

[0867] The server presents an optimal maintenance schedule based on the collected health indicators. Data analysis is performed through an analyzer to generate optimal maintenance timing and operational precautions. The input is preprocessed health indicator data, and the output is text data including the maintenance schedule and advice.

[0868] Step 9:

[0869] The server provides the presented maintenance schedule to the factory manager. Specifically, it displays the schedule and advice on the manager's terminal. The input is text data including the maintenance schedule and advice, and the output is the content displayed on the manager's terminal.

[0870] 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.

[0871] System Overview

[0872] This invention is a system that acquires data from different health monitoring devices such as smartwatches, blood pressure monitors, and weight scales, and combines it with an emotion engine that recognizes the user's emotions to integrate and analyze the data and provide health advice. Specific embodiments of this system are described below.

[0873] Inter-device communication and data integration

[0874] The server sends an HTTP GET request to the API endpoint of the smartwatch to obtain heart rate and step count data from the user's smartwatch. The server sends an HTTP GET request to the API endpoint of the blood pressure monitor to obtain blood pressure data from the blood pressure monitor. The server also sends an HTTP GET request to the API endpoint of the weight scale to obtain weight and body fat percentage data from the weight scale. The server converts the data obtained from each device into a unified format and integrates it into a single dataset.

[0875] Acquiring and analyzing emotion data

[0876] The device uses voice input, facial expression recognition using a camera, or text analysis to recognize the user's emotions, thereby acquiring the user's emotion data, and then transmits the acquired emotion data to a server.

[0877] Data analysis

[0878] The server sends the integrated data set obtained from the health monitoring device and the emotion data obtained from the emotion engine to the analysis device. The analysis device performs analysis taking into account the user's emotional state in addition to data such as heart rate, blood pressure, weight, and body fat percentage. This allows the analysis device to generate individualized health advice that includes stress management and psychological advice in addition to general health advice.

[0879] Providing health advice

[0880] The server sends the health advice returned from the analyzer to the user's device and displays it. For example, the advice may be presented to the user in text format via a device such as a smartphone or PC.

[0881] Specific examples

[0882] Health data examples

[0883] 1. Smartwatch: Heart rate 85, steps 7000

[0884] 2. Sphygmomanometer: systolic blood pressure 130, diastolic blood pressure 85

[0885] 3. Scale: Weight 70kg, body fat percentage 22%

[0886] Emotion data example

[0887] Facial expression recognition result: Feeling stressed

[0888] Voice analysis results: Fatigue

[0889] Text analysis results: Feeling anxious

[0890] Advice from chat generation algorithms

[0891] When a user steps on a scale, uses a smartwatch, or measures their blood pressure with a blood pressure monitor, this data is automatically sent to the server. In addition, the device captures emotional data from the user's facial expressions, voice, and text, and sends this data to the server. The server then integrates all the collected data and sends it to an analysis device. The analysis device analyzes the data and generates health advice such as:

[0892] "Your heart rate has been a little high recently, and your blood pressure is also a little higher than normal. These may be due to stress or lack of exercise. I recommend that you incorporate about 30 minutes of aerobic exercise every day and make time to relax. Also, although your weight is within the appropriate range, it is important to manage your body fat percentage, so be sure to eat a balanced diet. Regarding the stress and anxiety you are currently feeling, try taking a deep breath and relaxing. If necessary, please consider seeking psychological counseling."

[0893] This health advice is displayed on the user's terminal via the server, and the user can use it as a reference to improve their daily health management.

[0894] In this way, this invention centrally manages data and emotional data obtained from different health monitoring devices, and provides individual, specific health advice based on this data, thereby providing multifaceted support for users' health management.

[0895] The processing flow will be explained below.

[0896] Step 1:

[0897] The server sends an HTTP GET request to the API endpoint of the smartwatch to obtain heart rate and step count data from the user's smartwatch. The server then analyzes the JSON format data returned as a response and extracts the heart rate and step count data.

[0898] Step 2:

[0899] The server sends an HTTP GET request to the API endpoint of the blood pressure monitor to obtain blood pressure data from the user's blood pressure monitor. It then parses the JSON-formatted data returned as a response and extracts the systolic and diastolic blood pressure data.

[0900] Step 3:

[0901] The server sends an HTTP GET request to the scale's API endpoint to obtain the user's weight and body fat percentage data from the scale. The server then parses the JSON format data returned as a response and extracts the weight and body fat percentage data.

[0902] Step 4:

[0903] The server combines the data acquired in steps 1 to 3 into a single integrated data set, converting the data from each device into a unified format to complete the overall data set.

[0904] Step 5:

[0905] To recognize the user's emotions, the device uses sensors such as a camera and microphone to collect facial and voice data, and analyzes this emotional data in real time to determine the user's emotional state.

[0906] Step 6:

[0907] The device then sends the analyzed emotional data, including information about stress levels and emotional states, to the server.

[0908] Step 7:

[0909] To send the integrated dataset and emotion data to the analysis device, the server sends an HTTP POST request to the analysis device's API endpoint, which sends the dataset and emotion data in JSON format.

[0910] Step 8:

[0911] The analyzer analyzes the received health and emotional data, taking into account the user's heart rate, blood pressure, weight, body fat percentage, and emotional state to generate personalized health advice.

[0912] Step 9:

[0913] The analyzer sends the generated health advice back to the server in JSON format, which then analyzes the received data and extracts the advice.

[0914] Step 10:

[0915] The server then sends the extracted health advice to the user's device, which includes specific health management suggestions and psychological support suggestions.

[0916] Step 11:

[0917] The device then presents the received advice to the user in text format, allowing the user to receive comprehensive health advice based on their own health condition.

[0918] Through these steps, users can receive personalized and specific health advice based on multiple health monitoring devices and emotional data, enabling them to comprehensively manage their daily health.

[0919] Example 2

[0920] 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."

[0921] In modern society, user health management is becoming an increasingly important issue. Conventional health monitoring devices often collect data individually, making it difficult to centrally manage and analyze that data. Furthermore, while a user's emotional state is an important factor in health management, existing systems lack the means to integrate and analyze emotional state and health data, making it difficult to provide comprehensive health advice. To solve these problems, a system is needed that can perform an integrated analysis of health data and emotional data and provide individual, specific advice.

[0922] 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 acquiring data from multiple different health monitoring devices of the user, means for integrating the acquired data, means for acquiring emotional data of the user, means for transmitting the integrated data to an analytical device, and means for providing health advice generated by the analytical device. This makes it possible to centrally manage health data and emotional data and provide comprehensive and individually specific health advice to the user.

[0923] A "health monitoring device" is a device for measuring and recording a user's health status, and examples include smart watches, blood pressure monitors, and weight scales.

[0924] "Means for acquiring data" refers to the processes or technologies that have the ability to transmit and receive data collected from the health monitoring device to the server.

[0925] "Data integration measures" refers to the processes and techniques used to convert data from multiple health monitoring devices into a unified format and compile it into a single data set.

[0926] "Means for acquiring emotional data" refers to the process or technology for collecting a user's emotions using voice input, facial expression recognition, text analysis, etc., and transmitting that data to a server.

[0927] "Analysis equipment" refers to devices or software that analyze the integrated data and emotional data sent from the server and generate health advice for the user.

[0928] "Means for providing health advice" refers to the process or technology for transmitting the health advice generated by the analytical device to the user's terminal and displaying it.

[0929] "Generative algorithm" refers to a computational method or program for generating personalized, specific health advice based on integrated health and emotional data.

[0930] System Overview

[0931] This invention is a system that provides comprehensive health advice by collecting data from a user's different health monitoring devices, integrating and analyzing it together with the user's emotional data. The hardware used includes health monitoring devices such as smartwatches, blood pressure monitors, and weight scales, and the software includes API endpoint utilization, an emotional analysis engine, and a data analysis device.

[0932] Inter-device communication and data integration

[0933] The server acquires data from each health monitoring device in the following procedure.

[0934] 1. Send an HTTP GET request to the smartwatch's API endpoint to retrieve heart rate and step count data from the smartwatch.

[0935] 2. To retrieve blood pressure data from the blood pressure monitor, send an HTTP GET request to the blood pressure monitor's API endpoint.

[0936] 3. Send an HTTP GET request to the scale's API endpoint to retrieve weight and body fat percentage data from the scale.

[0937] The data acquired from each device is converted into a unified format by the server and compiled into a single integrated data set.

[0938] Acquiring and analyzing emotion data

[0939] The terminal acquires the user's emotion data in the following manner.

[0940] 1. Sentiment analysis using voice input

[0941] The device records the user's voice and analyzes the emotional data using a voice analysis engine.

[0942] 2. Camera-based facial expression recognition

[0943] The device captures the user's face with a camera and acquires emotional data using a facial expression analysis engine.

[0944] 3. Text Analysis

[0945] The device analyzes the text entered by the user and generates emotion data.

[0946] The acquired emotion data is transmitted to the server through the terminal.

[0947] Data analysis

[0948] The server performs data analysis in the following procedure.

[0949] 1. Send the combined health dataset and emotion data to the analyzer, specifically by sending the dataset as an API request.

[0950] 2. The analyzer analyzes health data such as heart rate, blood pressure, weight, and body fat percentage, as well as the user's emotional data. This analysis is then used with a generative algorithm to generate personalized health advice for the user.

[0951] Providing health advice

[0952] The server sends the health advice returned from the analyzer to the user's device, and the device displays the advice to the user. For example, if the device is a smartphone, the advice is displayed in text format on the screen.

[0953] Examples and prompts

[0954] For example, the following data is obtained:

[0955] Smartwatch: Heart rate 85, steps 7000

[0956] Sphygmomanometer: systolic blood pressure 130, diastolic blood pressure 85

[0957] Scale: Weight 70kg, body fat percentage 22%

[0958] Emotional data: stress, fatigue, anxiety

[0959] Based on this data, the following advice is given to the user:

[0960] "Your heart rate has been a little high recently, and your blood pressure is also a little higher than normal. These may be due to stress or lack of exercise. I recommend that you incorporate about 30 minutes of aerobic exercise every day and make time to relax. Also, although your weight is within the appropriate range, it is important to manage your body fat percentage, so be sure to eat a balanced diet. Regarding the stress and anxiety you are currently feeling, try taking a deep breath and relaxing. If necessary, please consider seeking psychological counseling."

[0961] Example prompt for a generative AI model:

[0962] "Please provide specific health advice based on the health data (heart rate 85, steps 7000, systolic blood pressure 130, diastolic blood pressure 85, weight 70 kg, body fat percentage 22%) and emotional data (stress, fatigue, anxiety) obtained from the user."

[0963] Using the above methods, the system can grasp the user's health condition from multiple angles and support an appropriate healthy lifestyle.

[0964] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0965] Step 1: Obtaining health data

[0966] Input: Data from smartwatches, blood pressure monitors, and scales (e.g., heart rate, steps, blood pressure, weight, body fat percentage)

[0967] Output: A set of acquired health data

[0968] Specific operation:

[0969] The server sends an HTTP GET request to the smartwatch's API endpoint to retrieve heart rate and step count data, for example, to the URL GET https: / / api.smartwatch.com / userdata?metrics=heart_rate,steps.

[0970] The server sends an HTTP GET request to the API endpoint of the blood pressure monitor to retrieve blood pressure data, for example, to the URL GET https: / / api.bloodpressuremonitor.com / data?metrics=systolic,diastolic.

[0971] The server sends an HTTP GET request to the scale's API endpoint to retrieve weight and body fat percentage data, for example, to the URL GET https: / / api.weighingscale.com / data?metrics=weight,body_fat.

[0972] Step 2: Integrate and transform data

[0973] Input: Individually acquired health data (heart rate, steps, blood pressure, weight, body fat percentage)

[0974] Output: Unified dataset converted to a unified format

[0975] Specific operation:

[0976] The server converts the individually acquired data into a unified format, for example, {"heart_rate": 85, "steps": 7000, "blood_pressure": {"systolic": 130, "diastolic": 85}, "weight": 70, "body_fat": 22}.

[0977] Step 3: Obtaining emotion data

[0978] Input: User's voice, facial expressions, and text data

[0979] Output: A set of retrieved emotion data

[0980] Specific operation:

[0981] The device records the user's voice and uses a voice analysis engine to extract emotional data. For example, if the user says, "I'm a little tired today," the device can extract the emotional data "fatigue."

[0982] The device captures the user's face with a camera and uses a facial expression analysis engine to obtain emotional data. For example, it can extract emotional data indicating that the user is feeling "stressed" from the user's facial image.

[0983] The device analyzes the text entered by the user and extracts emotional data. For example, the device extracts the emotional data "anxiety" from the text "I'm anxious because things aren't going well at work."

[0984] Step 4: Data analysis

[0985] Input: Integrated health dataset, emotion data

[0986] Output: Analysis results and health advice

[0987] Specific operation:

[0988] The server sends the combined health dataset and emotion data to the analyzer, for example, by sending the dataset via API request POST https: / / api.analysisengine.com / analyze.

[0989] The analyzer analyzes the transmitted data and uses a generation algorithm to generate personalized health advice, such as heart rate 85, steps 7000, blood pressure 130 / 85, weight 70kg, body fat percentage 22%, and emotional data such as stress, fatigue, and anxiety.

[0990] Step 5: Providing health advice

[0991] Input: Generated health advice

[0992] Output: Advice displayed on the user's terminal

[0993] Specific operation:

[0994] The server sends the health advice returned from the analyzer to the user's device, for example, by sending an API request POST https: / / api.userterminal.com / advice { "advice": "Your heart rate has been a little high recently..."}.

[0995] The device displays the received advice to the user. For example, the device may display the following text on the user's smartphone: "Your heart rate has been a little high recently, and your blood pressure is also a little above normal..."

[0996] (Application example 2)

[0997] 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."

[0998] There is a need to improve work efficiency and safety by monitoring the health and psychological states of employees in factories in real time and providing appropriate health advice. However, existing systems lack the means to effectively integrate data collected from multiple health monitoring devices and provide specific health advice that takes into account employees' emotional states.

[0999] The specific processing by the specific 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 acquiring data from multiple different health monitoring devices of the user, means for integrating the acquired data and emotional data, and means for transmitting the integrated data to the analysis device. This makes it possible to integrate the data and emotional data collected from the health monitoring devices and provide health advice generated using the analysis device in real time.

[1000] A "health monitoring device" is a device for measuring and recording a user's health data, such as a smart watch, blood pressure monitor, or weight scale.

[1001] "Emotional data" is data that indicates a user's emotional state, obtained using voice input, facial recognition using a camera, or text analysis.

[1002] "Server" is a computing system for collecting and integrating data from health monitoring devices and emotion engines and transmitting it to an analysis device.

[1003] "Integration" is the process of bringing together data from multiple health monitoring devices and emotion engines and converting it into an analyzable format.

[1004] An "analysis device" is an electronic device or software used to analyze the integrated health and emotional data and generate health advice.

[1005] "Health advice" is specific behavioral suggestions or advice generated by the analysis device based on the user's health and emotional state.

[1006] The "chat generation algorithm" is a computer program that uses natural language processing technology to generate health advice to provide to users.

[1007] "Employee" refers to an individual worker working at a production site such as a factory.

[1008] This invention is a system that monitors the health and emotional states of factory workers in real time and provides appropriate health advice. This system aims to improve employee work efficiency and safety by integrating factory robots, employee health monitoring devices, and an emotion analysis engine.

[1009] Hardware used

[1010] 1. Factory robots: Collecting employee health and emotional data.

[1011] 2. Employee smartwatches: measure heart rate, steps, etc.

[1012] 3. Employee blood pressure monitor: Captures blood pressure data.

[1013] 4. Employee scale: Measures weight and body fat percentage.

[1014] 5. Camera: Acquires emotion data through facial expression recognition.

[1015] 6. Microphone: Acquires emotion data through voice analysis.

[1016] Software used

[1017] 1. Robot control software: Control software that allows the robot to operate in conjunction with other hardware.

[1018] 2. Health Data Collection API: An interface for acquiring data from smartwatches, blood pressure monitors, and weight scales.

[1019] 3. Emotion analysis engine: Software that uses cameras and microphones to analyze employees' emotional states.

[1020] 4. Data analysis device: A device that runs on the server and analyzes the integrated data to generate health advice.

[1021] Program processing explanation

[1022] The server first collects health data from employee smartwatches, blood pressure monitors, and weight scales by sending HTTP GET requests to the API endpoints of each device, converting the collected data into a unified format, and then integrating it into a single dataset.

[1023] The device uses an emotion analysis engine to capture employee emotional data, analyzing their emotional state using voice input, facial recognition via a camera, or text analysis, and also sends this data to the server.

[1024] The server sends the integrated health data and emotional data to a data analysis device, which analyzes the data and generates personalized health advice based on the employee's health and emotional state. The generated health advice is displayed on the employee's device via the server.

[1025] This system allows employees to use health monitoring devices to collect emotional data, allowing them to understand their health status in real time and receive appropriate advice.

[1026] Specific examples

[1027] Health data examples

[1028] 1. Smartwatch: Heart rate 90, steps 5000

[1029] 2. Sphygmomanometer: systolic blood pressure 140, diastolic blood pressure 90

[1030] 3. Scale: Weight 68 kg, body fat percentage 25%

[1031] Emotion data example

[1032] Facial expression recognition result: Feeling tired

[1033] Voice analysis results: Feeling stressed

[1034] Text analysis results: Impatience

[1035] Prompt Sentence Examples

[1036] "Based on the latest employee health and sentiment data, determine what health advice is appropriate for the employee in their current work performance and generate specific advice."

[1037] In this way, the present invention makes it possible to improve work efficiency and safety in factories by monitoring the health and emotional state of employees in real time and providing individualized health advice.

[1038] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1039] Step 1:

[1040] The server collects health data from employees' smartwatches, blood pressure monitors, and scales. Specifically, it sends an HTTP GET request to the API endpoint of each device and receives data on heart rate, steps, blood pressure, weight, and body fat percentage in response. The input is raw data from each device, and the output is health data converted into a unified format.

[1041] Step 2:

[1042] The device uses an emotion analysis engine to obtain employee emotional data. This can be done using voice input, camera-based facial recognition, or text analysis. The input is the employee's facial expression, voice, and text data, and the output is analyzed data indicating their emotional state.

[1043] Step 3:

[1044] The server integrates the data acquired in steps 1 and 2. Specifically, it compiles the heart rate, step count, blood pressure, weight, body fat percentage data, and emotional data into a unified format based on the same timestamp. The input is health data and emotional data, and the output is an integrated dataset.

[1045] Step 4:

[1046] The server sends the integrated data to a data analyzer, which processes the integrated data and generates personalized health advice based on the employee's health and emotional state. The input is the integrated dataset, and the output is the health advice generated by the generative AI model.

[1047] Step 5:

[1048] The server sends the generated health advice to the employee's device and displays it. Specifically, the advice is provided to the employee using the device's display and notification function. The input is the health advice data, and the output is the specific advice displayed on the employee's device.

[1049] Step 6:

[1050] Employees receive health advice displayed on the device and modify or improve their behavior based on it. For example, specific actions are recommended, such as taking regular breaks or practicing deep breathing to reduce stress. The input is the health advice displayed on the device, and the output is the change in employee behavior.

[1051] 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.

[1052] 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.

[1053] 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.

[1054] [Fourth embodiment]

[1055] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1056] 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.

[1057] 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).

[1058] 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.

[1059] 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.

[1060] 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).

[1061] 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.

[1062] 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.

[1063] 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.

[1064] 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.

[1065] 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.

[1066] 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.

[1067] 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."

[1068] System Overview

[1069] This invention is a system that acquires, integrates, and analyzes data from different health monitoring devices such as smartwatches, blood pressure monitors, and weight scales to provide health advice to users. Specific embodiments of this system are described below.

[1070] Inter-device communication and data integration

[1071] The server collects data from multiple health monitoring devices, such as a user's smartwatch, blood pressure monitor, and weight scale. Each device sends data through its own API endpoint. The server converts the data from the devices into a unified format and combines it into a single dataset.

[1072] Data analysis

[1073] The server sends the integrated data set to an analysis device equipped with a chat generation algorithm. The analysis device analyzes the data (heart rate, blood pressure, weight, body fat percentage, etc.) acquired from each device. Based on the analysis results, it generates individualized, specific health advice and sends it back to the server.

[1074] Providing health advice

[1075] The server provides the health advice returned from the analyzer to the user. For example, the advice is displayed in text format to the user via a device such as a smartphone or PC.

[1076] Specific examples

[1077] Health data examples

[1078] 1. Smartwatch: Heart rate 85, steps 7000

[1079] 2. Sphygmomanometer: systolic blood pressure 130, diastolic blood pressure 85

[1080] 3. Scale: Weight 70kg, body fat percentage 22%

[1081] Advice from chat generation algorithms

[1082] When a user steps on the scale, uses a smartwatch, or measures their blood pressure with a blood pressure monitor, this data is automatically sent to the server, which then converts the acquired data into a unified format and sends it to the analyzer, which analyzes the data and generates health advice such as:

[1083] "Your heart rate has been a little high recently, and your blood pressure is also a little higher than normal. These may be due to stress or lack of exercise. I recommend incorporating about 30 minutes of aerobic exercise every day and making time to relax. Also, although your weight is within the appropriate range, it is important to manage your body fat percentage, so make sure to eat a balanced diet."

[1084] This health advice is displayed on the user's terminal via the server, and the user can use it as a reference to improve their daily health management.

[1085] In this way, this invention centrally manages data obtained from different health monitoring devices and provides individual, specific health advice based on that data, thereby providing multifaceted support for users' health management.

[1086] The processing flow will be explained below.

[1087] Step 1:

[1088] The server sends an HTTP GET request to the API endpoint of the smartwatch to obtain heart rate and step count data from the user's smartwatch. The server receives the obtained data in JSON format, analyzes it, and extracts the necessary data.

[1089] Step 2:

[1090] The server sends an HTTP GET request to the API endpoint of the blood pressure monitor to retrieve blood pressure data from the user's blood pressure monitor. The server receives the retrieved data in JSON format and parses it to extract systolic and diastolic blood pressure data.

[1091] Step 3:

[1092] The server sends an HTTP GET request to the scale's API endpoint to retrieve weight and body fat percentage data from the user's scale, receives the retrieved data in JSON format, and parses it to extract the appropriate data.

[1093] Step 4:

[1094] The server integrates the data acquired in steps 1 to 3 into a single dataset. It converts the data acquired from each health monitoring device into a unified format and combines them into a single dataset.

[1095] Step 5:

[1096] The server sends an HTTP POST request to the API endpoint of the analysis device to send the integrated dataset to the analysis device equipped with the chat generation algorithm. The dataset is sent in JSON format.

[1097] Step 6:

[1098] The chat generation algorithm analyzes the data set received from the server, including heart rate, blood pressure, weight, and body fat percentage, to generate personalized health advice for each user.

[1099] Step 7:

[1100] The analyzer sends the generated health advice back to the server in JSON format, which then analyzes the data and extracts the advice.

[1101] Step 8:

[1102] The server sends the extracted health advice to the user's terminal for display, and the terminal presents the received advice to the user in text format.

[1103] Through these steps, users can receive personalized and specific health advice based on data collected from multiple health monitoring devices, helping them manage their daily health more effectively.

[1104] Example 1

[1105] 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."

[1106] Effective health management is required to combat the increasing number of lifestyle-related diseases and health risks in modern society. However, the process of collecting, integrating, and analyzing data from multiple different health monitoring devices is complex and time-consuming. In addition, there is a lack of appropriate analytical methods to provide useful health advice from the collected data. To solve this problem, a system is needed that can comprehensively and efficiently manage health data and provide accurate health advice.

[1107] 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.

[1108] In this invention, the server includes means for acquiring data from multiple different health monitoring devices of a user, means for converting the acquired data into an integrated format and integrating them into a single data set, means for transmitting the integrated data set to an analysis device, and means for providing health advice generated by the analysis device, thereby enabling efficient integration and analysis of data from multiple devices and providing useful health advice to the user in a timely manner.

[1109] A "health monitoring device" is a device that measures a user's biometric information and collects that data.

[1110] A "integrated format" is a data structure or format for converting data captured in multiple different formats into a consistent format.

[1111] A "dataset" is a collection of data obtained from health monitoring devices and converted into a unified format.

[1112] An "analyzer" is a computer and software for analyzing a data set and generating health advice.

[1113] A "generative AI model" is an artificial intelligence model that generates output in natural language format from input data.

[1114] "Health advice" refers to specific instructions or recommendations for improving lifestyle habits or managing health for a user, which are generated by the analysis device based on a data set.

[1115] MODE FOR CARRYING OUT THE INVENTION

[1116] This invention is a system that acquires data from multiple health monitoring devices of a user, integrates it, and provides health advice through analysis. Next, the program processing is explained in natural language, and the details of the hardware and software used, as well as data processing and data calculation, are clearly stated.

[1117] System configuration and operation

[1118] The server collects data from users' health monitoring devices, such as smartwatches, blood pressure monitors, and weight scales, and receives data from these devices via communication methods such as Bluetooth and Wi-Fi.

[1119] Data Integration

[1120] The server converts the data acquired from each device into a unified format (JSON format) and integrates it into a single data set, allowing data acquired from different devices to be handled consistently.

[1121] Data analysis

[1122] The server sends the combined data set to an analysis device, which incorporates a generative AI model (e.g., GPT-3) to analyze the data and generate health advice based on biometric data such as heart rate, blood pressure, weight, and body fat percentage.

[1123] Generating health advice

[1124] The generative AI model generates health advice in natural language based on the input prompt. Here are some examples of specific prompts:

[1125] "Generate health advice based on the following data: heart rate 85, systolic blood pressure 130, diastolic blood pressure 85, weight 70kg, body fat percentage 22%."

[1126] Providing advice to users

[1127] The server then sends the health advice received from the analysis device to the user's device. The user receives the advice via a device such as a smartphone or computer. This allows the user to understand their own health condition and take appropriate measures to improve their lifestyle habits.

[1128] Specific examples

[1129] For example, if a user provides the following health monitoring data:

[1130] Smartwatch: Heart rate 85, steps 7000

[1131] Sphygmomanometer: systolic blood pressure 130, diastolic blood pressure 85

[1132] Scale: Weight 70kg, body fat percentage 22%

[1133] The server aggregates this data and sends it to an analysis device, which generates health advice such as:

[1134] "Your heart rate has been a little high recently, and your blood pressure is also a little higher than normal. These may be due to stress or lack of exercise. I recommend incorporating about 30 minutes of aerobic exercise every day and making time to relax. Also, although your weight is within the appropriate range, it is important to manage your body fat percentage, so make sure to eat a balanced diet."

[1135] This advice is displayed on the user's device via the server, allowing the user to refer to it and improve their daily health management.By using this system, it is possible to efficiently integrate and analyze health data obtained from multiple devices and provide users with accurate and timely health advice.

[1136] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1137] Step 1:

[1138] Collecting data from devices

[1139] The server collects data from different health monitoring devices such as users' smartwatches, blood pressure monitors, and weight scales, which transmit data via communication methods such as Bluetooth and Wi-Fi.

[1140] Input: Biometric data sent from smartwatches, blood pressure monitors, and weight scales

[1141] Output: Biometric data stored on the server

[1142] Specific operation: The smartwatch sends heart rate and step count data to the server, the blood pressure monitor sends systolic and diastolic blood pressure data, and the scale sends weight and body fat percentage data to the server.

[1143] Step 2:

[1144] Data integration and format conversion

[1145] The server converts the data received from each device into a unified format (e.g., JSON format) and integrates it into a single data set.

[1146] Input: Unintegrated biometric data captured from each device

[1147] Output: Dataset converted to a unified format (JSON format)

[1148] Specific operation: The server converts the data from the smartwatch, blood pressure monitor, and weight scale into JSON format and combines them into one.

[1149] Step 3:

[1150] Data analysis

[1151] The server sends the combined dataset to an analyzer that incorporates a chat generation algorithm, which incorporates a generative AI model (e.g., GPT-3).

[1152] Input: Dataset converted to unified format (JSON format)

[1153] Output: Health advice based on the analysis results

[1154] How it works: The server sends the integrated data set to the analysis device, which then uses the generative AI model to analyze the data. For example, if your heart rate is high, it will generate relevant advice.

[1155] Step 4:

[1156] Generating health advice

[1157] The analysis device generates health advice in natural language format based on the results of the analysis of the data.

[1158] Input: Data analysis results by the analysis device

[1159] Output: Health advice summarized in natural language format

[1160] Specific operation: The analysis device uses the generative AI model to input the following prompt: "Please generate health advice based on the following data: heart rate 85, systolic blood pressure 130, diastolic blood pressure 85, weight 70 kg, and body fat percentage 22%." Based on this, the generative AI model creates advice in natural language.

[1161] Step 5:

[1162] Providing advice to users

[1163] The server sends the health advice returned by the analysis device to the user's device, where the user can check the advice via a smartphone, computer, or other device and use it to help manage their health.

[1164] Input: Health advice in natural language form returned by the analyzer

[1165] Output: Health advice displayed on the user's device

[1166] Specific operation: The server sends the generated health advice to the user's smartphone or computer, where the user can review it and use it to help manage their daily health.

[1167] (Application example 1)

[1168] 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."

[1169] Conventional health monitoring systems are primarily limited to monitoring the health status of individuals, and lack the means to comprehensively monitor the health status of equipment used in industrial settings such as factories and manage optimal maintenance. In addition, the technology to comprehensively analyze different types of health indicators and provide effective advice is underdeveloped.

[1170] 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.

[1171] In this invention, the server includes means for acquiring data from a plurality of different health monitoring devices of a user, means for integrating the acquired data, means for transmitting the integrated data to an analysis device, means for providing health advice generated by the analysis device, means for collecting health indicators from factory automation equipment, and means for presenting an optimal maintenance schedule based on the collected health indicators, thereby enabling both personal health management and optimal maintenance management of factory equipment.

[1172] "Health monitoring device" is a general term for devices used to collect a user's health data, including smartwatches, blood pressure monitors, and weighing scales.

[1173] "Data integration" is the process of converting data obtained from multiple different health monitoring devices into a single unified format and managing it centrally.

[1174] An "analysis device" is a device or system that performs analysis based on integrated data obtained through data integration and generates output such as health advice.

[1175] "Health Advice" means a health care recommendation or advice generated by an analytical device and provided to a user.

[1176] "Factory automation equipment" is a general term for automated machinery and systems used in factories and manufacturing sites, including robotic arms, assembly lines, and sensor devices.

[1177] "Health indicators" is a general term for data measured to indicate the health status of equipment or the human body, and includes operating time, wear level, internal temperature, vibration level, etc.

[1178] An "optimal maintenance schedule" is a plan that indicates the optimal timing and procedures for performing equipment maintenance based on collected health indicator data.

[1179] This invention is a system that collects data from multiple different health monitoring devices of a user, integrates and analyzes it, provides individualized health advice, collects health indicators of factory automation equipment, and presents optimal maintenance schedules.

[1180] System Overview

[1181] First, the server collects data from different health monitoring devices such as a user's smartwatch, blood pressure monitor, weight scale, etc. Each device sends data through its own API endpoint, and the server converts this data into a unified format and combines it into a single dataset.

[1182] The server then sends the combined data set to an analyzer, which uses algorithms called generative AI models to analyze the data and generate personalized health advice, which is then sent back to the server.

[1183] In addition, the server collects health indicator data from factory automation equipment (e.g., operating hours, wear level, internal temperature, vibration level, etc.) This data is also sent to the server and converted into a unified format.

[1184] Providing health advice and maintenance schedules

[1185] The server provides the user with the health advice returned from the analyzer. For example, the advice is displayed in text format on a smartphone, PC, or other device. It also provides optimal maintenance schedules and operational precautions to factory managers based on the health index data of factory automation equipment.

[1186] Hardware and software used

[1187] The hardware used includes automated devices equipped with various sensors, such as smartwatches, blood pressure monitors, weight scales, and factory robots. The software uses Python 3.8 or higher and the requests library. The server manages the entire process, including data collection, integration, transmission, and return of analysis results.

[1188] A specific example is the process of collecting, analyzing, and providing advice on health data.

[1189] For example, if the following data is sent to the server from a smartwatch: heart rate 85, step count 7000, systolic blood pressure 130, diastolic blood pressure 85, weight 70 kg, and body fat percentage 22% from a scale, the generative AI model will generate the following advice:

[1190] "Your heart rate has been a little high recently, and your blood pressure is also a little higher than normal. These may be due to stress or lack of exercise. I recommend incorporating about 30 minutes of aerobic exercise every day and making time to relax. Also, although your weight is within the appropriate range, it is important to manage your body fat percentage, so make sure to eat a balanced diet."

[1191] For example, for factory automation equipment, data such as 5,000 hours of operation, 15% wear level, 45°C internal temperature, and low vibration levels can be sent to a server, and the following maintenance advice can be provided:

[1192] "The robot has been operating for a long time and its wear level is low. The internal temperature is normal, but caution is needed for continued operation in the future. Regular maintenance is recommended."

[1193] Example prompt sentence:

[1194] "What is the health advice for a factory robot that has 5000 operating hours, is currently at 15% wear, has an internal temperature of 45 degrees, and has low vibration levels?"

[1195] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1196] Step 1:

[1197] The server collects data from multiple different health monitoring devices of the user. Specifically, it collects data such as heart rate and step count from a smartwatch, blood pressure from a blood pressure monitor, and weight and body fat percentage from a weighing scale through API endpoints. The input is raw data obtained from each device's API, and the output is pre-processed data for integration.

[1198] Step 2:

[1199] The server converts the acquired data into a unified format and integrates it into a single dataset. Data processing includes converting data from different formats into a common format and adding timestamps. The input is the preprocessed raw data, and the output is an integrated dataset converted into a unified format.

[1200] Step 3:

[1201] The server sends the integrated dataset to the analysis device. Specifically, it generates an analysis request and sends the data to the analysis device using an HTTP request. The input is the integrated dataset, and the output is an HTTP response to the analysis request.

[1202] Step 4:

[1203] The analysis device performs data analysis based on the integrated data sent. Using a generative AI model, it analyzes various health data and generates individual, specific health advice. The input is the integrated data set sent from the server, and the output is text data containing health advice.

[1204] Step 5:

[1205] The analysis device returns the generated health advice to the server. The input is text data containing the health advice, and the output is an HTTP response to the server.

[1206] Step 6:

[1207] The server provides the returned health advice to the user. Specifically, the advice is displayed in text format on a device such as a smartphone or PC. The input is the received health advice, and the output is the text advice displayed on the user's device.

[1208] Step 7:

[1209] The server collects health indicators from factory automation equipment, specifically, data such as operating hours, wear level, internal temperature, and vibration level obtained through sensors. The input is raw data from the sensors, and the output is preprocessed health indicator data.

[1210] Step 8:

[1211] The server presents an optimal maintenance schedule based on the collected health indicators. Data analysis is performed through an analyzer to generate optimal maintenance timing and operational precautions. The input is preprocessed health indicator data, and the output is text data including the maintenance schedule and advice.

[1212] Step 9:

[1213] The server provides the presented maintenance schedule to the factory manager. Specifically, it displays the schedule and advice on the manager's terminal. The input is text data including the maintenance schedule and advice, and the output is the content displayed on the manager's terminal.

[1214] 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.

[1215] System Overview

[1216] This invention is a system that acquires data from different health monitoring devices such as smartwatches, blood pressure monitors, and weight scales, and combines it with an emotion engine that recognizes the user's emotions to integrate and analyze the data and provide health advice. Specific embodiments of this system are described below.

[1217] Inter-device communication and data integration

[1218] The server sends an HTTP GET request to the API endpoint of the smartwatch to obtain heart rate and step count data from the user's smartwatch. The server sends an HTTP GET request to the API endpoint of the blood pressure monitor to obtain blood pressure data from the blood pressure monitor. The server also sends an HTTP GET request to the API endpoint of the weight scale to obtain weight and body fat percentage data from the weight scale. The server converts the data obtained from each device into a unified format and integrates it into a single dataset.

[1219] Acquiring and analyzing emotion data

[1220] The device uses voice input, facial expression recognition using a camera, or text analysis to recognize the user's emotions, thereby acquiring the user's emotion data, and then transmits the acquired emotion data to a server.

[1221] Data analysis

[1222] The server sends the integrated data set obtained from the health monitoring device and the emotion data obtained from the emotion engine to the analysis device. The analysis device performs analysis taking into account the user's emotional state in addition to data such as heart rate, blood pressure, weight, and body fat percentage. This allows the analysis device to generate individualized health advice that includes stress management and psychological advice in addition to general health advice.

[1223] Providing health advice

[1224] The server sends the health advice returned from the analyzer to the user's device and displays it. For example, the advice may be presented to the user in text format via a device such as a smartphone or PC.

[1225] Specific examples

[1226] Health data examples

[1227] 1. Smartwatch: Heart rate 85, steps 7000

[1228] 2. Sphygmomanometer: systolic blood pressure 130, diastolic blood pressure 85

[1229] 3. Scale: Weight 70kg, body fat percentage 22%

[1230] Emotion data example

[1231] Facial expression recognition result: Feeling stressed

[1232] Voice analysis results: Fatigue

[1233] Text analysis results: Feeling anxious

[1234] Advice from chat generation algorithms

[1235] When a user steps on a scale, uses a smartwatch, or measures their blood pressure with a blood pressure monitor, this data is automatically sent to the server. In addition, the device captures emotional data from the user's facial expressions, voice, and text, and sends this data to the server. The server then integrates all the collected data and sends it to an analysis device. The analysis device analyzes the data and generates health advice such as:

[1236] "Your heart rate has been a little high recently, and your blood pressure is also a little higher than normal. These may be due to stress or lack of exercise. I recommend that you incorporate about 30 minutes of aerobic exercise every day and make time to relax. Also, although your weight is within the appropriate range, it is important to manage your body fat percentage, so be sure to eat a balanced diet. Regarding the stress and anxiety you are currently feeling, try taking a deep breath and relaxing. If necessary, please consider seeking psychological counseling."

[1237] This health advice is displayed on the user's terminal via the server, and the user can use it as a reference to improve their daily health management.

[1238] In this way, this invention centrally manages data and emotional data obtained from different health monitoring devices, and provides individual, specific health advice based on this data, thereby providing multifaceted support for users' health management.

[1239] The processing flow will be explained below.

[1240] Step 1:

[1241] The server sends an HTTP GET request to the API endpoint of the smartwatch to obtain heart rate and step count data from the user's smartwatch. The server then analyzes the JSON format data returned as a response and extracts the heart rate and step count data.

[1242] Step 2:

[1243] The server sends an HTTP GET request to the API endpoint of the blood pressure monitor to obtain blood pressure data from the user's blood pressure monitor. It then parses the JSON-formatted data returned as a response and extracts the systolic and diastolic blood pressure data.

[1244] Step 3:

[1245] The server sends an HTTP GET request to the scale's API endpoint to obtain the user's weight and body fat percentage data from the scale. The server then parses the JSON format data returned as a response and extracts the weight and body fat percentage data.

[1246] Step 4:

[1247] The server combines the data acquired in steps 1 to 3 into a single integrated data set, converting the data from each device into a unified format to complete the overall data set.

[1248] Step 5:

[1249] To recognize the user's emotions, the device uses sensors such as a camera and microphone to collect facial and voice data, and analyzes this emotional data in real time to determine the user's emotional state.

[1250] Step 6:

[1251] The device then sends the analyzed emotional data, including information about stress levels and emotional states, to the server.

[1252] Step 7:

[1253] To send the integrated dataset and emotion data to the analysis device, the server sends an HTTP POST request to the analysis device's API endpoint, which sends the dataset and emotion data in JSON format.

[1254] Step 8:

[1255] The analyzer analyzes the received health and emotional data, taking into account the user's heart rate, blood pressure, weight, body fat percentage, and emotional state to generate personalized health advice.

[1256] Step 9:

[1257] The analyzer sends the generated health advice back to the server in JSON format, which then analyzes the received data and extracts the advice.

[1258] Step 10:

[1259] The server then sends the extracted health advice to the user's device, which includes specific health management suggestions and psychological support suggestions.

[1260] Step 11:

[1261] The device then presents the received advice to the user in text format, allowing the user to receive comprehensive health advice based on their own health condition.

[1262] Through these steps, users can receive personalized and specific health advice based on multiple health monitoring devices and emotional data, enabling them to comprehensively manage their daily health.

[1263] Example 2

[1264] 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."

[1265] In modern society, user health management is becoming an increasingly important issue. Conventional health monitoring devices often collect data individually, making it difficult to centrally manage and analyze that data. Furthermore, while a user's emotional state is an important factor in health management, existing systems lack the means to integrate and analyze emotional state and health data, making it difficult to provide comprehensive health advice. To solve these problems, a system is needed that can perform an integrated analysis of health data and emotional data and provide individual, specific advice.

[1266] 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 acquiring data from multiple different health monitoring devices of the user, means for integrating the acquired data, means for acquiring emotional data of the user, means for transmitting the integrated data to an analytical device, and means for providing health advice generated by the analytical device. This makes it possible to centrally manage health data and emotional data and provide comprehensive and individually specific health advice to the user.

[1267] A "health monitoring device" is a device for measuring and recording a user's health status, and examples include smart watches, blood pressure monitors, and weight scales.

[1268] "Means for acquiring data" refers to the processes or technologies that have the ability to transmit and receive data collected from the health monitoring device to the server.

[1269] "Data integration measures" refers to the processes and techniques used to convert data from multiple health monitoring devices into a unified format and compile it into a single data set.

[1270] "Means for acquiring emotional data" refers to the process or technology for collecting a user's emotions using voice input, facial expression recognition, text analysis, etc., and transmitting that data to a server.

[1271] "Analysis equipment" refers to devices or software that analyze the integrated data and emotional data sent from the server and generate health advice for the user.

[1272] "Means for providing health advice" refers to the process or technology for transmitting the health advice generated by the analytical device to the user's terminal and displaying it.

[1273] "Generative algorithm" refers to a computational method or program for generating personalized, specific health advice based on integrated health and emotional data.

[1274] System Overview

[1275] This invention is a system that provides comprehensive health advice by collecting data from a user's different health monitoring devices, integrating and analyzing it together with the user's emotional data. The hardware used includes health monitoring devices such as smartwatches, blood pressure monitors, and weight scales, and the software includes API endpoint utilization, an emotional analysis engine, and a data analysis device.

[1276] Inter-device communication and data integration

[1277] The server acquires data from each health monitoring device in the following procedure.

[1278] 1. Send an HTTP GET request to the smartwatch's API endpoint to retrieve heart rate and step count data from the smartwatch.

[1279] 2. To retrieve blood pressure data from the blood pressure monitor, send an HTTP GET request to the blood pressure monitor's API endpoint.

[1280] 3. Send an HTTP GET request to the scale's API endpoint to retrieve weight and body fat percentage data from the scale.

[1281] The data acquired from each device is converted into a unified format by the server and compiled into a single integrated data set.

[1282] Acquiring and analyzing emotion data

[1283] The terminal acquires the user's emotion data in the following manner.

[1284] 1. Sentiment analysis using voice input

[1285] The device records the user's voice and analyzes the emotional data using a voice analysis engine.

[1286] 2. Camera-based facial expression recognition

[1287] The device captures the user's face with a camera and acquires emotional data using a facial expression analysis engine.

[1288] 3. Text Analysis

[1289] The device analyzes the text entered by the user and generates emotion data.

[1290] The acquired emotion data is transmitted to the server through the terminal.

[1291] Data analysis

[1292] The server performs data analysis in the following procedure.

[1293] 1. Send the combined health dataset and emotion data to the analyzer, specifically by sending the dataset as an API request.

[1294] 2. The analyzer analyzes health data such as heart rate, blood pressure, weight, and body fat percentage, as well as the user's emotional data. This analysis is then used with a generative algorithm to generate personalized health advice for the user.

[1295] Providing health advice

[1296] The server sends the health advice returned from the analyzer to the user's device, and the device displays the advice to the user. For example, if the device is a smartphone, the advice is displayed in text format on the screen.

[1297] Examples and prompts

[1298] For example, the following data is obtained:

[1299] Smartwatch: Heart rate 85, steps 7000

[1300] Sphygmomanometer: systolic blood pressure 130, diastolic blood pressure 85

[1301] Scale: Weight 70kg, body fat percentage 22%

[1302] Emotional data: stress, fatigue, anxiety

[1303] Based on this data, the following advice is given to the user:

[1304] "Your heart rate has been a little high recently, and your blood pressure is also a little higher than normal. These may be due to stress or lack of exercise. I recommend that you incorporate about 30 minutes of aerobic exercise every day and make time to relax. Also, although your weight is within the appropriate range, it is important to manage your body fat percentage, so be sure to eat a balanced diet. Regarding the stress and anxiety you are currently feeling, try taking a deep breath and relaxing. If necessary, please consider seeking psychological counseling."

[1305] Example prompt for a generative AI model:

[1306] "Please provide specific health advice based on the health data (heart rate 85, steps 7000, systolic blood pressure 130, diastolic blood pressure 85, weight 70 kg, body fat percentage 22%) and emotional data (stress, fatigue, anxiety) obtained from the user."

[1307] Using the above methods, the system can grasp the user's health condition from multiple angles and support an appropriate healthy lifestyle.

[1308] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1309] Step 1: Obtaining health data

[1310] Input: Data from smartwatches, blood pressure monitors, and scales (e.g., heart rate, steps, blood pressure, weight, body fat percentage)

[1311] Output: A set of acquired health data

[1312] Specific operation:

[1313] The server sends an HTTP GET request to the smartwatch's API endpoint to retrieve heart rate and step count data, for example, to the URL GET https: / / api.smartwatch.com / userdata?metrics=heart_rate,steps.

[1314] The server sends an HTTP GET request to the API endpoint of the blood pressure monitor to retrieve blood pressure data, for example, to the URL GET https: / / api.bloodpressuremonitor.com / data?metrics=systolic,diastolic.

[1315] The server sends an HTTP GET request to the scale's API endpoint to retrieve weight and body fat percentage data, for example, to the URL GET https: / / api.weighingscale.com / data?metrics=weight,body_fat.

[1316] Step 2: Integrate and transform data

[1317] Input: Individually acquired health data (heart rate, steps, blood pressure, weight, body fat percentage)

[1318] Output: Unified dataset converted to a unified format

[1319] Specific operation:

[1320] The server converts the individually acquired data into a unified format, for example, {"heart_rate": 85, "steps": 7000, "blood_pressure": {"systolic": 130, "diastolic": 85}, "weight": 70, "body_fat": 22}.

[1321] Step 3: Obtaining emotion data

[1322] Input: User's voice, facial expressions, and text data

[1323] Output: A set of retrieved emotion data

[1324] Specific operation:

[1325] The device records the user's voice and uses a voice analysis engine to extract emotional data. For example, if the user says, "I'm a little tired today," the device can extract the emotional data "fatigue."

[1326] The device captures the user's face with a camera and uses a facial expression analysis engine to obtain emotional data. For example, it can extract emotional data indicating that the user is feeling "stressed" from the user's facial image.

[1327] The device analyzes the text entered by the user and extracts emotional data. For example, the device extracts the emotional data "anxiety" from the text "I'm anxious because things aren't going well at work."

[1328] Step 4: Data analysis

[1329] Input: Integrated health dataset, emotion data

[1330] Output: Analysis results and health advice

[1331] Specific operation:

[1332] The server sends the combined health dataset and emotion data to the analyzer, for example, by sending the dataset via API request POST https: / / api.analysisengine.com / analyze.

[1333] The analyzer analyzes the transmitted data and uses a generation algorithm to generate personalized health advice, such as heart rate 85, steps 7000, blood pressure 130 / 85, weight 70kg, body fat percentage 22%, and emotional data such as stress, fatigue, and anxiety.

[1334] Step 5: Providing health advice

[1335] Input: Generated health advice

[1336] Output: Advice displayed on the user's terminal

[1337] Specific operation:

[1338] The server sends the health advice returned from the analyzer to the user's device, for example, by sending an API request POST https: / / api.userterminal.com / advice { "advice": "Your heart rate has been a little high recently..."}.

[1339] The device displays the received advice to the user. For example, the device may display the following text on the user's smartphone: "Your heart rate has been a little high recently, and your blood pressure is also a little above normal..."

[1340] (Application example 2)

[1341] 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."

[1342] There is a need to improve work efficiency and safety by monitoring the health and psychological states of employees in factories in real time and providing appropriate health advice. However, existing systems lack the means to effectively integrate data collected from multiple health monitoring devices and provide specific health advice that takes into account employees' emotional states.

[1343] The specific processing by the specific 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 acquiring data from multiple different health monitoring devices of the user, means for integrating the acquired data and emotional data, and means for transmitting the integrated data to the analysis device. This makes it possible to integrate the data and emotional data collected from the health monitoring devices and provide health advice generated using the analysis device in real time.

[1344] A "health monitoring device" is a device for measuring and recording a user's health data, such as a smart watch, blood pressure monitor, or weight scale.

[1345] "Emotional data" is data that indicates a user's emotional state, obtained using voice input, facial recognition using a camera, or text analysis.

[1346] "Server" is a computing system for collecting and integrating data from health monitoring devices and emotion engines and transmitting it to an analysis device.

[1347] "Integration" is the process of bringing together data from multiple health monitoring devices and emotion engines and converting it into an analyzable format.

[1348] An "analysis device" is an electronic device or software used to analyze the integrated health and emotional data and generate health advice.

[1349] "Health advice" is specific behavioral suggestions or advice generated by the analysis device based on the user's health and emotional state.

[1350] The "chat generation algorithm" is a computer program that uses natural language processing technology to generate health advice to provide to users.

[1351] "Employee" refers to an individual worker working at a production site such as a factory.

[1352] This invention is a system that monitors the health and emotional states of factory workers in real time and provides appropriate health advice. This system aims to improve employee work efficiency and safety by integrating factory robots, employee health monitoring devices, and an emotion analysis engine.

[1353] Hardware used

[1354] 1. Factory robots: Collecting employee health and emotional data.

[1355] 2. Employee smartwatches: measure heart rate, steps, etc.

[1356] 3. Employee blood pressure monitor: Captures blood pressure data.

[1357] 4. Employee scale: Measures weight and body fat percentage.

[1358] 5. Camera: Acquires emotion data through facial expression recognition.

[1359] 6. Microphone: Acquires emotion data through voice analysis.

[1360] Software used

[1361] 1. Robot control software: Control software that allows the robot to operate in conjunction with other hardware.

[1362] 2. Health Data Collection API: An interface for acquiring data from smartwatches, blood pressure monitors, and weight scales.

[1363] 3. Emotion analysis engine: Software that uses cameras and microphones to analyze employees' emotional states.

[1364] 4. Data analysis device: A device that runs on the server and analyzes the integrated data to generate health advice.

[1365] Program processing explanation

[1366] The server first collects health data from employee smartwatches, blood pressure monitors, and weight scales by sending HTTP GET requests to the API endpoints of each device, converting the collected data into a unified format, and then integrating it into a single dataset.

[1367] The device uses an emotion analysis engine to capture employee emotional data, analyzing their emotional state using voice input, facial recognition via a camera, or text analysis, and also sends this data to the server.

[1368] The server sends the integrated health data and emotional data to a data analysis device, which analyzes the data and generates personalized health advice based on the employee's health and emotional state. The generated health advice is displayed on the employee's device via the server.

[1369] This system allows employees to use health monitoring devices to collect emotional data, allowing them to understand their health status in real time and receive appropriate advice.

[1370] Specific examples

[1371] Health data examples

[1372] 1. Smartwatch: Heart rate 90, steps 5000

[1373] 2. Sphygmomanometer: systolic blood pressure 140, diastolic blood pressure 90

[1374] 3. Scale: Weight 68 kg, body fat percentage 25%

[1375] Emotion data example

[1376] Facial expression recognition result: Feeling tired

[1377] Voice analysis results: Feeling stressed

[1378] Text analysis results: Impatience

[1379] Prompt Sentence Examples

[1380] "Based on the latest employee health and sentiment data, determine what health advice is appropriate for the employee in their current work performance and generate specific advice."

[1381] In this way, the present invention makes it possible to improve work efficiency and safety in factories by monitoring the health and emotional state of employees in real time and providing individualized health advice.

[1382] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1383] Step 1:

[1384] The server collects health data from employees' smartwatches, blood pressure monitors, and scales. Specifically, it sends an HTTP GET request to the API endpoint of each device and receives data on heart rate, steps, blood pressure, weight, and body fat percentage in response. The input is raw data from each device, and the output is health data converted into a unified format.

[1385] Step 2:

[1386] The device uses an emotion analysis engine to obtain employee emotional data. This can be done using voice input, camera-based facial recognition, or text analysis. The input is the employee's facial expression, voice, and text data, and the output is analyzed data indicating their emotional state.

[1387] Step 3:

[1388] The server integrates the data acquired in steps 1 and 2. Specifically, it compiles the heart rate, step count, blood pressure, weight, body fat percentage data, and emotional data into a unified format based on the same timestamp. The input is health data and emotional data, and the output is an integrated dataset.

[1389] Step 4:

[1390] The server sends the integrated data to a data analyzer, which processes the integrated data and generates personalized health advice based on the employee's health and emotional state. The input is the integrated dataset, and the output is the health advice generated by the generative AI model.

[1391] Step 5:

[1392] The server sends the generated health advice to the employee's device and displays it. Specifically, the advice is provided to the employee using the device's display and notification function. The input is the health advice data, and the output is the specific advice displayed on the employee's device.

[1393] Step 6:

[1394] Employees receive health advice displayed on the device and modify or improve their behavior based on it. For example, specific actions are recommended, such as taking regular breaks or practicing deep breathing to reduce stress. The input is the health advice displayed on the device, and the output is the change in employee behavior.

[1395] 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.

[1396] 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.

[1397] 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.

[1398] 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.

[1399] 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.

[1400] 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.

[1401] 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).

[1402] 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.

[1403] 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."

[1404] 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.

[1405] 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).

[1406] 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.

[1407] 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.

[1408] 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.

[1409] 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.

[1410] 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.

[1411] 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.

[1412] 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.

[1413] 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.

[1414] 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.

[1415] 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.

[1416] The following is further disclosed regarding the above embodiment.

[1417] (Claim 1)

[1418] means for acquiring data from a plurality of different health monitoring devices of a user;

[1419] a means for integrating the acquired data;

[1420] means for transmitting the integrated data to an analytical device;

[1421] means for providing health advice generated by the analytical device;

[1422] A system including:

[1423] (Claim 2)

[1424] 10. The system of claim 1, wherein the health monitoring devices include a smart watch, a blood pressure monitor, and a weight scale.

[1425] (Claim 3)

[1426] 10. The system of claim 1, wherein the analysis device generates health advice using a chat generation algorithm.

[1427] "Example 1"

[1428] (Claim 1)

[1429] means for acquiring data from a plurality of different health monitoring devices of a user;

[1430] a means for converting the acquired data into a unified format and integrating them into a single data set;

[1431] means for transmitting the combined data set to an analysis device;

[1432] means for providing health advice generated by the analytical device;

[1433] A system including:

[1434] (Claim 2)

[1435] 10. The system of claim 1, wherein the health monitoring devices include a wearable device, a blood pressure monitor, and a weight monitor.

[1436] (Claim 3)

[1437] 10. The system of claim 1, wherein the analysis device generates health advice using a generative AI model.

[1438] "Application Example 1"

[1439] (Claim 1)

[1440] means for acquiring data from a plurality of different health monitoring devices of a user;

[1441] a means for integrating the acquired data;

[1442] means for transmitting the integrated data to an analytical device;

[1443] means for providing health advice generated by the analytical device;

[1444] a means for collecting health indicators from factory automation equipment;

[1445] A means for suggesting an optimal maintenance schedule based on the collected health indicators;

[1446] A system including:

[1447] (Claim 2)

[1448] 10. The system of claim 1, wherein the health monitoring devices include a smart watch, a blood pressure monitor, and a weight scale.

[1449] (Claim 3)

[1450] 10. The system of claim 1, wherein the analysis device generates health advice using a chat generation algorithm.

[1451] "Example 2: Combining Emotion Engines"

[1452] (Claim 1)

[1453] means for acquiring data from a plurality of different health monitoring devices of a user;

[1454] a means for integrating the acquired data;

[1455] A means for acquiring user emotion data;

[1456] means for transmitting the integrated data to an analytical instrument;

[1457] means for providing health advice generated by the analytical device;

[1458] A system including:

[1459] (Claim 2)

[1460] 10. The system of claim 1, wherein the health monitoring devices include a smart watch, a blood pressure monitor, and a weight scale.

[1461] (Claim 3)

[1462] 10. The system of claim 1, wherein the analytical device generates health advice using a generation algorithm.

[1463] "Application example 2 when combining emotion engines"

[1464] (Claim 1)

[1465] means for acquiring data from a plurality of different health monitoring devices of a user;

[1466] means for integrating the acquired data and emotion data;

[1467] means for transmitting the integrated data to an analytical device;

[1468] means for providing health advice generated by the analytical device;

[1469] A system including:

[1470] (Claim 2)

[1471] 10. The system of claim 1, wherein the health monitoring devices include a smart watch, a blood pressure monitor, and a weight scale.

[1472] (Claim 3)

[1473] The system according to claim 1, wherein the analysis device generates health advice using a chat generation algorithm and provides the advice to employees to improve their work efficiency and safety. [Explanation of symbols]

[1474] 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. means for acquiring data from a plurality of different health monitoring devices of a user; a means for integrating the acquired data; means for transmitting the integrated data to an analytical device; means for providing health advice generated by the analytical device; A system including:

2. The system of claim 1 , wherein the health monitoring devices include a smart watch, a blood pressure monitor, and a weight scale.

3. 10. The system of claim 1, wherein the analysis device generates health advice using a chat generation algorithm.

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