system

Abstract

Provide a system. [Solution] a means for recording weight, walking distance and dietary habits for collecting personal health data; A means for collecting external weather information and infectious disease information; a means for integrating and analyzing the personal health data and external data to evaluate the user's health condition; means for generating dietary and health management advice for the user based on said evaluation; means for notifying a user terminal of the advice; A means for collecting feedback information from the user and utilizing it for generating next advice. 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] In recent years, the risk of lifestyle-related diseases and infectious diseases has increased, while the importance of individual health management has also increased. However, conventional health management methods have been problematic in that they do not collect enough personal data, making it difficult to provide appropriate advice in conjunction with external environmental data. Furthermore, health management is generally deeply rooted in people's lifestyles and should be continuously improved, which requires personalized advice. However, conventional systems are limited to providing advice based on uniform standards, making it difficult to provide specific and useful advice tailored to each individual's condition. Another issue is the lack of a feedback loop that utilizes individual feedback to improve future advice. [Means for solving the problem]

[0005] This invention provides a system that accurately assesses a user's health status and provides optimal dietary and health management advice to individuals by collecting personal health data and integrating and analyzing it with external weather and infectious disease information. Specifically, the system includes a means for recording weight, walking distance, and dietary habits, and a means for collecting external weather and infectious disease information. The system then evaluates the user's health status by integrating and analyzing this data. Based on the evaluation results, the system generates dietary and health management advice for the user and notifies the user's device. Furthermore, the system collects user feedback information and uses it to generate advice for future sessions, thereby continuously supporting the user's health management. This allows the user to receive personalized health management advice and effectively improve and maintain their health.

[0006] "Personal health data" refers to data relating to the user's individual health condition and lifestyle, such as weight, walking distance, heart rate, and diet.

[0007] "Weather information" is data related to the weather on that day, such as outside temperature, weather, and air pressure.

[0008] "Infectious disease information" refers to data on the spread of infectious diseases, such as the latest outbreak status, epidemic areas, and number of infected people.

[0009] "Collection means" refers to devices and systems for obtaining personal health data, weather information, and infectious disease information, such as smartphone apps, wearable devices, and API connections.

[0010] "Integrated analysis" is a process of combining and analyzing information obtained from multiple data sources to comprehensively evaluate the user's health condition.

[0011] "Health assessment" is the process of determining a user's health risks and status based on collected data.

[0012] "Generating advice" is a process for presenting recommended actions and dietary details to the user based on the results of the health status evaluation.

[0013] A "user terminal" is a device used by a user, such as a smartphone, tablet, or PC.

[0014] "Feedback information" is data on the results and effects of the user's actual actions, and is used to provide advice from the next time onwards.

[0015] A "smartphone app" is application software that runs on a smartphone and allows users to input and collect data, display advice, and more.

[0016] A "wearable device" is a device worn by a user that collects health data, and includes smartwatches and fitness trackers.

[0017] An "API" is an interface for connecting with other systems and services to obtain and send data. [Brief explanation of the drawings]

[0018] [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

[0019] 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.

[0020] First, the terms used in the following description will be explained.

[0021] 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).

[0022] 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.

[0023] 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.

[0024] 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.

[0025] 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."

[0026] [First embodiment]

[0027] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0028] 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.

[0029] 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).

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] 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.

[0036] 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.

[0037] 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.

[0038] 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."

[0039] Data collection and integration

[0040] The system according to the present invention provides a function for collecting personal health data and external data from multiple data sources, and integrating and analyzing them. A specific embodiment of the system and its operation will be described below.

[0041] Collection of personal health data

[0042] Users use a smartphone app every day to input data such as their weight, walking distance, diet, etc. If they use a wearable device, this data is automatically synchronized to the device.

[0043] example:

[0044] The user steps on the scale in the morning and enters the weight reading into a smartphone app.

[0045] The smartwatch measures the distance walked during the day and automatically transmits the data to a smartphone via Bluetooth.

[0046] External Data Collection

[0047] The server periodically accesses external APIs to obtain weather and infectious disease information, which is used to personalize health advice for users.

[0048] example:

[0049] The server accesses the weather information API at a fixed time each day to obtain the temperature, humidity, and weather for that day.

[0050] The latest infectious disease spread status and preventive measures are obtained weekly from the infectious disease information API.

[0051] Data integration and analysis

[0052] The personal health data collected by the device is sent in real time to a server, which then combines this data with external data to analyze the user's health condition.

[0053] example:

[0054] The device transmits weight data, walking distance data, and dietary data to the server.

[0055] The data is analyzed in conjunction with temperature data and infectious disease information obtained by the server.

[0056] Generating and Serving Advice

[0057] The server generates health advice for the user based on the analysis results. This advice includes dietary recommendations and exercise suggestions. This advice is then sent to the user via their device.

[0058] example:

[0059] The server analyzes the data and determines that "taking vitamin C is an effective way to combat recent infectious diseases," and recommends that the user consume citrus fruits.

[0060] The server generates advice such as "Today's temperature is low, so let's do some indoor exercises" and sends it to the device.

[0061] The device will notify the user of the advice it has received and display details within the app.

[0062] Feedback gathering and learning

[0063] The user follows the advice and inputs the results into a smartphone app, or the wearable device automatically collects the feedback. This feedback information is then sent back to the server and used to generate the next advice.

[0064] example:

[0065] The user enters "I walked for 30 minutes" in the app.

[0066] The smartwatch automatically collects the user's exercise records and sends them to the device.

[0067] The device sends this feedback information to the server, which then reflects it in the next analysis.

[0068] In this way, the system of the present invention effectively integrates and analyzes the user's daily health data and external environmental data, and provides personalized health advice, thereby supporting the user in sustainable health management.

[0069] The processing flow will be explained below.

[0070] Step 1: Data entry and acquisition

[0071] 1.1 The user opens the smartphone app and manually enters their weight, as well as their dietary and calorie information.

[0072] 1.2 The device connects to a wearable device such as a smartwatch and automatically collects data such as walking distance and heart rate.

[0073] 1.3 The device temporarily stores the acquired personal health data in local storage.

[0074] Step 2: Send data

[0075] 2.1 The device periodically sends this personal health data to the server. The data is encrypted and transmitted securely.

[0076] 2.2 The server stores the personal health data transmitted to the database.

[0077] Step 3: Gather external data

[0078] 3.1 The server sends a request to the weather information API to obtain weather data such as the current temperature and weather.

[0079] 3.2 The server obtains the latest infectious disease outbreak information from the infectious disease information API.

[0080] Step 4: Integrate the data

[0081] 4.1 The server integrates the user's personal health data with the acquired weather data and infectious disease data.

[0082] 4.2 The server centralizes the data and creates a dataset for analysis.

[0083] Step 5: Analyze your health status

[0084] 5.1 The server's analytics engine assesses the user's health status based on the integrated dataset, using machine learning algorithms and rule-based systems.

[0085] 5.2 The server performs risk assessment based on the evaluation results and generates appropriate health advice for the user.

[0086] Step 6: Generating and notifying advice

[0087] 6.1 The server compiles the generated advice and formats it into a recommendation for the user.

[0088] 6.2 The server sends the advice to the terminal and sends it as a notification message.

[0089] 6.3 The device notifies the user of the advice and displays detailed advice content.

[0090] Step 7: Gather feedback

[0091] 7.1 The user acts on the advice and inputs the results into the app, for example, recording the duration of a walk or what they ate.

[0092] 7.2 The terminal automatically collects feedback information from the wearable device and sends it to the server.

[0093] Step 8: Learn and improve your advice

[0094] 8.1 The server updates the analytical model based on the collected feedback information and uses it to generate advice from the next time onwards.

[0095] 8.2 The server takes in new data and re-learns to improve the accuracy of advice given to users.

[0096] These steps enable the system to assist users in managing their individual health and provide personalized advice for continually improving their health.

[0097] Example 1

[0098] 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."

[0099] To provide efficient and personalized health management, it is necessary to integrate personal health data with external data and provide users with appropriate advice based on the analysis results. However, current systems lack real-time performance and individualized accuracy in the collection, integration, and analysis of data and the provision of advice. Therefore, there is a need for a system that can solve these issues and enable more effective health management.

[0100] 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.

[0101] In this invention, the server includes means for collecting personal health data in real time and transmitting it to the server, means for integrating and analyzing the personal health data and external data to evaluate the user's health condition, and means for transmitting advice generated based on the analysis results to the user terminal. This makes it possible to integrate and analyze the user's health data and external data in real time and provide personalized health management advice.

[0102] "Personal health data" refers to information about the user's health status, such as weight, walking distance, and diet.

[0103] "External data" refers to information about the external environment that affects the user's health management, such as weather information and infectious disease information.

[0104] "Collection methods" refer to the methods and technologies used to acquire and store data.

[0105] "Integrated analysis" means processing multiple data sets together and conducting a comprehensive analysis.

[0106] "Health status assessment" refers to diagnosing the user's health status based on the integrated data and evaluating it according to certain criteria.

[0107] "Advice generator" refers to a method or technology that generates recommendations or instructions to provide to a user based on the evaluation results.

[0108] "Notification means" refers to a method or technique for notifying the user of the generated advice.

[0109] "Feedback information" refers to information relating to the results of a user's actions in accordance with advice.

[0110] "Server" means a central processing unit for processing collected data and storing and transmitting analysis results.

[0111] "Terminal" refers to a device used by a user to input data and receive notifications from a server.

[0112] A "mobile device app" is an application that runs on a mobile electronic device such as a smartphone or tablet.

[0113] A "wearable device" is a data collection device that is worn by a user.

[0114] "Communication network" refers to a network for sending and receiving data, such as the Internet.

[0115] "Software interface" refers to the programs and protocols used to obtain external data.

[0116] MODE FOR CARRYING OUT THE INVENTION

[0117] The present invention relates to a system for collecting personal health data of a user, evaluating the user's health condition by integrating and analyzing the data with external data, and providing appropriate health advice. Specific embodiments of the present invention will be described in detail below.

[0118] Collection of personal health data

[0119] Users use a mobile device app to input daily health data such as weight, walking distance, dietary habits, etc. Wearable devices (e.g., smartwatches) automatically measure data such as walking distance and heart rate, and synchronize the data with a smartphone using communication technologies such as Bluetooth.

[0120] example:

[0121] The user steps on the scale every morning and enters their weight into a smartphone app.

[0122] The smartwatch measures the user's walking distance during the day and automatically sends the data to the device.

[0123] External Data Collection

[0124] The server periodically obtains external data, such as weather information and infectious disease information, using a software interface (API) provided via the Internet, thereby collecting external environmental data that may affect the user's health condition.

[0125] example:

[0126] The server obtains the day's temperature, humidity, and weather from the weather information API at a fixed time each day.

[0127] The server obtains the latest infectious disease spread status and preventive measures from the infectious disease information API every week.

[0128] Data integration and analysis

[0129] The personal health data collected by the device is sent in real time to a server, where it is integrated with external data and analyzed to assess the user's health status.

[0130] example:

[0131] The terminal transmits the user's weight data, walking distance data, and dietary habit data to the server.

[0132] The server integrates temperature data and infectious disease information to analyze the user's health risk.

[0133] Generating and Serving Advice

[0134] The server generates health advice for the user based on the analysis results, and the advice is sent to the user's smartphone or other device. The advice includes dietary recommendations and exercise suggestions.

[0135] example:

[0136] The server analyzes the data and determines that "taking vitamin C is effective in preventing infectious diseases," and recommends that the user consume citrus fruits.

[0137] The system generates advice such as "Today's temperature is low, so let's do some indoor exercises," and notifies the user's device.

[0138] Feedback gathering and learning

[0139] The user follows the advice and inputs the results into a smartphone app, or the wearable device automatically collects exercise records. This feedback information is then sent back to the server and used to generate advice for the next time.

[0140] example:

[0141] The user enters "I walked for 30 minutes" in the app.

[0142] The smartwatch automatically collects the user's exercise records and sends them to the device.

[0143] The device sends this feedback information to the server, which then reflects it in the next analysis.

[0144] This system effectively integrates users' daily health data with external environmental data and provides personalized health advice, helping users manage their health sustainably.

[0145] Example prompts for generative AI models

[0146] "A user weighs themselves using a scale in the morning and enters that data into a smartphone app. The server periodically uses an external API to obtain the temperature, humidity, and weather for that day. Based on that data, it generates health advice tailored to the user and sends it to their smartphone. Please explain each processing step of this system in detail."

[0147] This prompt allows the generative AI model to generate detailed explanations of each processing step in the system.

[0148] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0149] Step 1: User enters personal health data

[0150] Users manually enter personal health data such as weight, walking distance, and dietary habits into a smartphone app. The entered data is stored in the smartphone's internal database. For example, a user can step on a scale every morning and enter their measured weight into the app. This input data becomes the basis for subsequent analysis.

[0151] Input: Personal health data such as weight, walking distance, dietary habits, etc.

[0152] Output: Personal health data stored in the smartphone's internal database

[0153] Step 2: Automated data collection with wearable devices

[0154] The device automatically collects data from wearable devices such as smartwatches, which constantly measure the user's walking distance, heart rate, sleep patterns, and more, and transmits the data via Bluetooth to a smartphone, where it is integrated into the smartphone's internal database.

[0155] Input: Walking distance, heart rate, sleep data from smartwatch

[0156] Output: Wearable device data integrated into smartphone internal database

[0157] Step 3: Gather external data

[0158] The server periodically obtains external data, such as weather information and infectious disease information, via the internet using an external API. For example, the server accesses the weather information API at 7:00 a.m. every day to obtain the temperature, humidity, and weather for that day. This data is then stored in the server's database.

[0159] Input: Temperature, humidity, and weather data obtained from the weather information API

[0160] Output: Weather information stored in the server database

[0161] Step 4: Send your personal health data

[0162] The device sends the collected personal health data to the server in real time, allowing the server to always have the latest user information. For example, a smartphone periodically sends weight data, walking distance data, and dietary habit data to the server.

[0163] Input: Personal health data stored in the smartphone's internal database

[0164] Output: Personal health data sent to the server

[0165] Step 5: Data integration and analysis

[0166] The server integrates the received personal health data with external data to assess the user's health status. For example, the server combines weight data, walking distance data, and dietary data with temperature data and infectious disease information to perform statistical analysis. The results of this analysis serve as the basis for health advice for the user.

[0167] Input: Personal health data and external data sent to the server

[0168] Output: Analysis results (evaluation of the user's health status)

[0169] Step 6: Generate health advice

[0170] The server generates health advice for the user based on the analysis results. For example, if the server determines that "taking vitamin C is an effective measure against recent infectious diseases," it generates advice recommending the user to consume citrus fruits. This advice is stored in the server's memory in text format.

[0171] Input: User's health assessment

[0172] Output: Generated health advice

[0173] Step 7: Advice Notification

[0174] The server sends the generated advice to the device and notifies the user. For example, advice such as "Today's temperature is low, so try doing some indoor exercises" is sent in text format to the device, and the device notifies the user via a push notification.

[0175] Input: Generated health advice

[0176] Output: Advice notification sent to user terminal

[0177] Step 8: Gather user feedback

[0178] The user inputs the results of their actions based on the advice into a smartphone app, or the wearable device automatically collects feedback data. For example, the user can input "I walked for 30 minutes" into the app, or the smartwatch can send exercise data to the device. This feedback information is then sent back to the server.

[0179] Input: User-entered feedback data or data from a wearable device

[0180] Output: Feedback information sent to the server

[0181] Step 9: Analyze the feedback and incorporate it into your next advice

[0182] The server analyzes the received feedback information and uses it to generate the next health advice. For example, the server analyzes the feedback information to understand the user's exercise habits and dietary patterns and reflect them in the next advice. This process enables more accurate and personalized health management.

[0183] Input: Feedback information sent to the server

[0184] Output: Next health advice reflecting the feedback information

[0185] (Application example 1)

[0186] 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."

[0187] Personal health management is an important issue today, and there is a need to effectively collect and analyze health data, particularly daily weight, walking distance, and dietary habits, and provide appropriate advice. However, it is difficult to collect and analyze this data using a single device or platform, and it is also difficult to provide comprehensive health advice in real time that takes into account external weather and infectious disease information. Additionally, there are few opportunities to receive instant, personalized health advice in physical stores, and there is insufficient integration between in-store product information and health advice.

[0188] 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.

[0189] In this invention, the server includes: means for recording weight, walking distance, and dietary habits to collect personal health data; means for collecting external weather information and infectious disease information; means for integrating and analyzing the personal health data and external data to evaluate the user's health status; means for generating dietary and health management advice for the user based on the evaluation; means for notifying the advice to the user terminal and to an information providing terminal in the physical store; means for the information providing terminal to instantly provide personalized health advice based on the user's health data and external data; means for providing in-store guidance on products related to the advice; and means for collecting feedback information from the user and using it to generate next advice. This makes it possible to effectively integrate and analyze personal health data and external environmental data, instantly provide personalized health advice in the physical store, and further guide users to related products.

[0190] "Personal health data" refers to data that indicates the individual health condition of a user, such as weight, walking distance, and dietary habits.

[0191] "Weather information" is data that indicates the state of the external environment, such as the temperature, humidity, and weather of the area.

[0192] "Infectious disease information" refers to data related to infectious diseases, such as the prevalence of infectious diseases in the area and preventive measures.

[0193] "Integrated analysis" is the process of combining and analyzing multiple data to find overall patterns and meanings.

[0194] A "user terminal" is a device used by a user, such as a smartphone or tablet.

[0195] An "information providing terminal" is a device that is installed in a physical store and provides information to users.

[0196] "Evaluating health status" means analyzing the user's health status based on collected personal health data and external data.

[0197] "Dietary and health management advice" is information that suggests dietary habits, exercise, supplements, etc. to improve the user's health.

[0198] "Notifying" refers to the act of sending the generated advice to the user or other terminals.

[0199] "Feedback information" is information about the actions that the user actually takes and the results of those actions.

[0200] "Personalized health advice" refers to personalized health management suggestions based on an individual user's health data and external data.

[0201] "In-store guidance" refers to the act of providing the location and detailed information of related products within a physical store.

[0202] This invention is a system that integrates personal health data with external environmental data to provide users with personalized health advice. This system is composed of a personal health data collection means, a weather information and infectious disease information collection means, a data integration and analysis means, an advice generation means, an information provision means, and a feedback collection means.

[0203] 1. Collection of personal health data

[0204] The server collects daily health data such as weight, walking distance, and dietary habits from users using smartphone apps and wearable devices. This data is usually automatically synchronized with the smartphone via wireless communication such as Bluetooth and sent to the server.

[0205] For example, after a user steps on a scale in the morning, the weight is automatically recorded in a smartphone app. The smartwatch also measures the distance walked during the day and sends the data to the smartphone, which then sends it to a server.

[0206] 2. External Data Collection

[0207] The server periodically obtains weather and infectious disease information from an external API provided via the Internet, and this data is used to evaluate the user's health status.

[0208] Specifically, the server accesses the weather information API at a fixed time each day to obtain the temperature, humidity, and weather for that day, and also accesses the infectious disease information API weekly to obtain the latest information on the spread of infectious diseases and preventive measures.

[0209] 3. Data integration and analysis

[0210] The server integrates the collected personal health data with external data to analyze the user's health status in real time, and generates specific health advice for the user based on the analysis results.

[0211] For example, the server analyzes the user's walking distance data, weight data, and dietary data in combination with external data such as temperature and humidity. Based on the analysis, specific advice such as "Today's temperature is low, so let's do some indoor exercises" is generated.

[0212] 4. Providing advice

[0213] The generated advice is notified to the user terminal and an information providing terminal in the physical store. The information providing terminal is a device that provides health advice to the user in real time within the store.

[0214] Specifically, advice generated by the server, such as "Today's temperature is low, so we recommend you drink a hot drink," is displayed on the robot's display in the physical store, and the user is guided to related products in the store.

[0215] 5. Feedback Collection

[0216] The user can input the results of their actions based on the advice into a smartphone app, or the wearable device can automatically collect the feedback. This feedback information is then sent back to the server and used to generate the next advice.

[0217] For example, if a user enters "I walked for 30 minutes" in the app, that information is sent to the server, and the smartwatch automatically collects and sends walking data to the server in the same way.

[0218] Prompt Sentence Examples

[0219] "Due to the low temperatures today, we recommend that you eat some warm soup. We also recommend taking vitamin C supplements to prevent infection. We have related products available in the XX section of our store, so please feel free to use them."

[0220] This system effectively integrates and analyzes personal health data and external environmental data, making it possible to provide instant, personalized health advice in physical stores and even introduce related products.

[0221] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0222] Step 1: Collect personal health data

[0223] Users use a smartphone app and a wearable device to collect daily health data such as weight, walking distance, and diet. The smartphone app records this data on the device and automatically synchronizes it with the wearable device using Bluetooth. The device then sends the collected data to a server. Health data such as weight, walking distance, and dietary details are used as input. The output is personal health data sent to the server. Specifically, the user steps on the scale every morning, and the weight is automatically recorded in the smartphone app. The smartphone then retrieves walking distance data from the wearable device and sends all data to the server.

[0224] Step 2: Gather external data

[0225] The server periodically obtains weather information and infectious disease information from an external API provided via the internet. It obtains data such as temperature, humidity, and weather from the weather information API, and data on infectious disease epidemic status and preventive measures from the infectious disease information API. The input is weather information and infectious disease information from the external API, and the output is integrated external data. Specifically, the server sends a request to the weather information API at a fixed time each day to obtain the temperature, humidity, and weather for that day, and accesses the infectious disease information API weekly to obtain the latest infectious disease spread status.

[0226] Step 3: Data synthesis and analysis

[0227] The server integrates the collected personal health data with external data for analysis. This analysis uses an algorithm to evaluate the user's health status by combining the personal health data and external data. The input is the personal health data and external data, and the output is the evaluation result of the user's health status. Specifically, the server combines data such as the individual's weight, walking distance, and diet with weather information such as temperature and humidity, and analyzes the user's health status using a health evaluation algorithm.

[0228] Step 4: Generating Advice

[0229] The server generates health advice for the user based on the analysis results. This advice includes dietary recommendations, exercise plans, and infection prevention measures. The input is the health status assessment result, and the output is advice to be provided to the user. Specifically, the server generates advice such as "Today's temperature is low, so we recommend drinking a hot drink."

[0230] Step 5: Providing advice

[0231] The generated advice is notified to the user terminal and an information providing terminal in the physical store. The information providing terminal immediately provides health advice to the user in the store and also introduces related products. The input is the generated advice, and the output is a notification to the user and the information providing terminal. In specific operations, the server notifies the user's smartphone of the advice, and a robot in the physical store displays the advice on a display.

[0232] Step 6: Gather feedback

[0233] The user inputs the results of their actions based on the advice into the smartphone app, or the wearable device automatically collects the data. The server uses this feedback information to generate the next piece of advice. The input is feedback information about the user's actions, and the output is data that will be used to generate the next piece of advice. Specifically, the user inputs "I walked for 30 minutes" into the app, and this information is sent to the server. Similarly, the smartwatch automatically collects and sends the walking data to the server.

[0234] Through these processing steps, the system supports personal healthcare and provides added value within physical stores.

[0235] 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.

[0236] Data collection and integration

[0237] The system according to the present invention provides a function for collecting personal health data and emotion data, and integrating and analyzing them. A specific embodiment of the system and its operation will be described below.

[0238] Collection of personal health data

[0239] Users use a smartphone app every day to input data such as their weight, walking distance, diet, etc. If they use a wearable device, this data is automatically synchronized to the device.

[0240] example:

[0241] The user steps on the scale in the morning and enters the weight reading into a smartphone app.

[0242] The smartwatch measures the distance walked during the day and automatically transmits the data to a smartphone via Bluetooth.

[0243] External Data Collection

[0244] The server periodically accesses external APIs to obtain weather and infectious disease information, which is used to personalize health advice for users.

[0245] example:

[0246] The server accesses the weather information API at a fixed time each day to obtain the temperature, humidity, and weather for that day.

[0247] The latest infectious disease spread status and preventive measures are obtained weekly from the infectious disease information API.

[0248] Collecting Emotional Data

[0249] The device uses an emotion engine to collect user emotion data, which is analyzed based on the user's facial expressions, voice, and input data.

[0250] example:

[0251] The smartphone camera captures the user's facial expressions, and the emotion engine analyzes them.

[0252] The user's voice is recorded through a microphone and analyzed.

[0253] Data is collected from text input, etc., to infer the user's emotions.

[0254] Data integration and analysis

[0255] The device sends the collected personal health and emotional data to a server, which then integrates this data with external data to perform a multifaceted analysis of the user's health condition.

[0256] example:

[0257] The terminal transmits weight data, walking distance data, dietary habit data, and emotion data to the server.

[0258] The data is analyzed in conjunction with weather data and infectious disease information obtained by the server.

[0259] Generating and Serving Advice

[0260] The server generates health advice for the user based on the analysis results. This advice includes dietary recommendations and exercise suggestions. It also takes into account emotional data. This advice is then sent to the user via their device.

[0261] example:

[0262] The server analyzes the data and determines that "taking vitamin C is an effective way to combat recent infectious diseases," and recommends that the user consume citrus fruits.

[0263] The server generates advice such as "Today's temperature is low, so let's do some indoor exercises" and sends it to the device.

[0264] The server determines from emotional data that the user is feeling stressed and recommends activities that are effective for relaxation and stress relief.

[0265] The terminal notifies the user of the received advice and displays detailed advice content.

[0266] Feedback gathering and learning

[0267] The user follows the advice and inputs the results into a smartphone app, or the wearable device automatically collects the feedback. This feedback information is then sent back to the server and used to generate the next advice.

[0268] example:

[0269] The user enters "I walked for 30 minutes" in the app.

[0270] The smartwatch automatically collects the user's exercise records and sends them to the device.

[0271] The device sends this feedback information to the server, which then reflects it in the next analysis.

[0272] These steps enable the system to assist users in managing their individual health and provide personalized advice to help improve their health, including their emotional state, over time.

[0273] The processing flow will be explained below.

[0274] Step 1: Data entry and acquisition

[0275] 1.1 The user opens the smartphone app and manually enters their weight, as well as their dietary and calorie information.

[0276] 1.2 The device connects to a wearable device such as a smartwatch and automatically collects data such as walking distance and heart rate.

[0277] 1.3 The device uses a camera and microphone to capture the user's facial expressions and voice to collect emotional data.

[0278] Step 2: Send data

[0279] 2.1 The device sends personal health and emotion data to the server. The data is encrypted and securely transmitted.

[0280] 2.2 The server stores the personal health data and emotion data sent to the database.

[0281] Step 3: Gather external data

[0282] 3.1 The server sends a request to the weather information API to obtain weather data such as the current temperature and weather.

[0283] 3.2 The server obtains the latest infectious disease outbreak information from the infectious disease information API.

[0284] 3.3 The server stores these weather and infectious disease data in a database.

[0285] Step 4: Integrate the data

[0286] 4.1 The server integrates the user's personal health data, emotion data, weather data, and infectious disease data.

[0287] 4.2 The server creates the integrated dataset and feeds it into the analysis engine.

[0288] Step 5: Analyze your health status

[0289] 5.1 The server's analytics engine assesses the user's health and emotional state based on the integrated dataset, using machine learning algorithms and rule-based systems.

[0290] 5.2 The server performs risk assessment based on the evaluation results and generates health advice and emotion-based advice appropriate for the user.

[0291] Step 6: Generating and notifying advice

[0292] 6.1 The server compiles and formats the generated advice into a recommendation for the user, including dietary recommendations, exercise suggestions, and emotional care advice.

[0293] 6.2 The server sends the advice to the terminal and sends it as a notification message.

[0294] 6.3 The device notifies the user of the advice and displays detailed advice content.

[0295] Step 7: Gather feedback

[0296] 7.1 The user acts on the advice and inputs the results into the app, for example, recording the duration of a walk or what they ate.

[0297] 7.2 The terminal automatically collects feedback information from the wearable device and sends it to the server.

[0298] Step 8: Learn and improve your advice

[0299] 8.1 The server updates the analytical model based on the collected feedback information and uses it to generate advice from the next time onwards.

[0300] 8.2 The server takes in new data and re-learns to improve the accuracy of advice given to users.

[0301] These steps enable the system to assist users in managing their individual health and emotions, providing personalized advice that can help improve their health and emotional state over time.

[0302] Example 2

[0303] 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."

[0304] In modern society, personal health management is becoming increasingly important, necessitating the development of health management systems that are more detailed and flexible. However, while existing health management systems provide basic health management based on personal health data (weight, walking distance, dietary habits) and external data (weather information, infectious disease information), they are unable to provide personalized health management advice that takes emotional data into account. Furthermore, they lack the functionality to quickly and effectively incorporate user feedback information and utilize it in generating next advice, making it difficult to translate this into practical health improvements.

[0305] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0306] In this invention, the server includes means for recording weight, walking distance, and dietary habits to collect personal health data, means for collecting external weather information and infectious disease information, means for analyzing the user's facial expressions, voice, and input data to collect user emotion data, means for integrating and analyzing the personal health data, external data, and emotion data to evaluate the user's health condition from various angles, means for generating health management advice for the user based on the evaluation and taking into account diet, exercise, and emotion state, means for notifying the user terminal of the advice, and means for collecting feedback information from the user and using it to generate next advice. This makes it possible to provide personalized health management advice that integrates the personal health data, external data, and emotion data, and to quickly reflect user feedback.

[0307] "Personal health data" refers to data that indicates the user's personal health condition, such as weight, walking distance, and dietary habits.

[0308] "External data" refers to external information obtained via the Internet, such as weather information and infectious disease information.

[0309] "Emotion data" is data that indicates the user's emotional state, obtained by analyzing the user's facial expressions, voice, and input data.

[0310] "Integrated analysis" is a process of integrating and analyzing personal health data, external data, and emotional data to evaluate the user's health condition from multiple angles.

[0311] "Health Management Advice" refers to instructions or recommendations given to the user based on the analysis results, taking into account diet, exercise, and emotional state.

[0312] "Feedback information" is data collected as a result of a user acting on advice.

[0313] A "server" is a computer system that collects, analyzes, and generates advice from data.

[0314] "Terminal" means a device (e.g., smartphone, wearable device) that a user uses to input and receive data.

[0315] "API" is an abbreviation for Application Program Interface, which provides a mechanism for exchanging data between different software programs.

[0316] An "emotion engine" is software that analyzes a user's facial expressions, voice, and input data to infer their emotional state.

[0317] MODE FOR CARRYING OUT THE INVENTION

[0318] The present invention is a system that integrates multiple data collection and analysis means, evaluates a user's health condition from multiple angles, and provides personalized health management advice. Specific embodiments and operations of the system are described below.

[0319] System Configuration Overview

[0320] 1. The system mainly consists of three elements: server, terminal, and user.

[0321] 2. The server collects, analyzes, and generates advice on data.

[0322] 3. A terminal is a device (e.g., a smartphone or wearable device) through which a user inputs and receives data.

[0323] 4. Users provide personal health data and feedback information.

[0324] Hardware and software used

[0325] Server: Uses cloud-based computing resources to run the database and generative AI models.

[0326] Devices: Smartphones and wearable devices (e.g., smartwatches).

[0327] Emotion Engine: Includes facial expression analysis software and a voice analysis engine.

[0328] Data collection

[0329] Users use a smartphone app every day to enter personal health data such as weight, walking distance, diet, etc. If they use a wearable device, this data is automatically synced to the device.

[0330] example:

[0331] The user steps on the scale in the morning and enters the weight reading into a smartphone app.

[0332] The smartwatch measures the distance walked during the day and automatically transmits the data to a smartphone via Bluetooth.

[0333] The server periodically uses external APIs to obtain weather and infectious disease information, which is used to personalize health advice for users.

[0334] example:

[0335] The server accesses the weather information API at a fixed time each day to obtain the temperature, humidity, and weather for that day.

[0336] The latest infectious disease spread status and preventive measures are obtained weekly from the infectious disease information API.

[0337] The device uses an emotion engine to collect user emotion data, which is analyzed based on the user's facial expressions, voice, and input data.

[0338] example:

[0339] The smartphone camera captures the user's facial expressions, and the emotion engine analyzes them.

[0340] The user's voice is recorded through a microphone and analyzed.

[0341] Data is collected from text input, etc., to infer the user's emotions.

[0342] Data transmission and analysis

[0343] The device sends the collected personal health and emotional data to a server, which then integrates this data with external data and uses a generative AI model to perform a multifaceted analysis of the user's health condition. Based on the results of this analysis, the server generates personalized health management advice.

[0344] Providing advice and feedback

[0345] The generated advice is sent to the user via the device, and includes dietary recommendations, exercise suggestions, and advice that takes into account emotional state.

[0346] Example prompt statement:

[0347] "Tell me how you're feeling today."

[0348] Enter your morning weight

[0349] "Tell me about your recent eating habits."

[0350] "Check out the advice based on infectious disease information."

[0351] The results of the user's actions based on the advice are entered into a smartphone app or automatically collected by a wearable device. This feedback information is then sent back to the server and used to generate the next advice.

[0352] In this way, this system supports users in managing their health by integrating personal health data, external data, and emotional data. Detailed collection and analysis of various data makes it possible to provide users with personalized health advice.

[0353] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0354] Step 1:

[0355] User input of personal health data

[0356] Users use a smartphone app to input personal health data such as weight, walking distance, and dietary habits. The input data is temporarily stored within the app. Specifically, the user opens the smartphone app and inputs their weight for the day. The app then stores this data within the device.

[0357] Input: weight, walking distance, dietary data

[0358] Output: Personal health data stored on the device

[0359] Step 2:

[0360] Terminal syncs data from wearable devices

[0361] A wearable device (e.g., a smartwatch) automatically transmits data collected, such as walking distance, to a smartphone via Bluetooth. The device then integrates this data into an app. Specifically, the smartwatch measures the distance walked during the day and transmits the data to the smartphone via Bluetooth.

[0362] Input: Walking distance data from a wearable device

[0363] Output: Walking distance data integrated into a smartphone app

[0364] Step 3:

[0365] Server-based external data collection

[0366] The server periodically accesses an external API to obtain weather and infectious disease information. This data is organized and stored on the server side. Specifically, the server accesses the weather information API at a fixed time each day to obtain the temperature, humidity, and weather for that day.

[0367] Input: Weather information and infectious disease information obtained from external API

[0368] Output: External data stored on the server side

[0369] Step 4:

[0370] Collecting emotion data from devices

[0371] The device's emotion engine analyzes the user's facial expressions, voice, and input data to generate emotion data. Specifically, the smartphone camera captures the user's facial expressions, and the emotion engine analyzes them.

[0372] Input: facial expressions, voice, input data

[0373] Output: Parsed emotion data

[0374] Step 5:

[0375] Sending data from the device to the server

[0376] The device periodically transmits the collected personal health data and emotion data to a server, which receives the data and stores it in a database. Specifically, the device periodically uploads the data to the server in the background.

[0377] Input: Personal health data and emotional data collected on the device

[0378] Output: Data stored on the server

[0379] Step 6:

[0380] Data integration and analysis by the server

[0381] The server integrates the received personal health data, emotion data, and external data, and analyzes them using a generative AI model. Specifically, the server integrates each data set into a single dataset and then performs analysis using the generative AI model.

[0382] Input: personal health data, emotion data, external data

[0383] Output: Analysis results

[0384] Step 7:

[0385] Server-generated advice

[0386] The server generates health management advice for the user based on the analysis results. This advice takes into account diet, exercise, and emotional state. Specifically, the server generates advice text based on the generated data and prepares to send it to the device in the next step.

[0387] Input: Analysis results

[0388] Output: Health care advice

[0389] Step 8:

[0390] Sending advice from the server to the device

[0391] The server generates health management advice and sends it to the device via a dedicated API, which then notifies the user. Specifically, the server sends the advice to the device, which then displays it to the user using its notification function.

[0392] Input: Health Care Advice

[0393] Output: Advice displayed on terminal

[0394] Step 9:

[0395] Gathering user feedback

[0396] The user can follow the advice and enter the results into a smartphone app, or the wearable device can automatically collect the data. Specifically, the user can enter "I walked for 30 minutes" in the app, or the smartwatch can automatically record the exercise results and send them to the device.

[0397] Input: User action result

[0398] Output: Feedback information

[0399] Step 10:

[0400] Sending feedback information to the server by the terminal

[0401] The device sends the collected feedback information to the server, which then uses it to generate the next advice. Specifically, the device automatically sends the feedback data to the server, and the server reflects the received data in the next analysis.

[0402] Input: Feedback information

[0403] Output: Data that will be reflected in the next advice generation

[0404] (Application example 2)

[0405] 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."

[0406] Conventional health management systems have difficulty managing individual health data and emotional data in a unified manner and providing optimal health advice to consumers. Furthermore, they were unable to provide advice that took into account the real-time health and emotional state of consumers visiting the store, which meant that the effectiveness of health management could not be maximized.

[0407] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0408] In this invention, the server includes: means for recording weight, walking distance, and dietary habits to collect personal health data; means for analyzing facial expressions, voice, and input data to collect emotion data; means for collecting external weather information and infectious disease information; means for integrating and analyzing the personal health data, emotion data, and external data to evaluate the user's health status; means for generating dietary and health management advice for the user based on the evaluation; means for notifying the advice on an in-store display or a user terminal; and means for collecting user feedback information and using it to generate next advice. This makes it possible to provide individually optimized health advice based on the customer's real-time health and emotion data.

[0409] "Personal health data" refers to information about an individual's health status, such as weight, walking distance, and diet.

[0410] "Emotional data" refers to information about an individual's emotional state analyzed based on facial expressions, voice, and input data.

[0411] "Facial expression analysis" is a technology that uses an input device such as a camera to detect an individual's facial expression and analyze their emotional state.

[0412] "Voice analysis" is a technology that analyzes voice data collected through a microphone or other device to determine the emotions and state of the voice.

[0413] "Input data analysis" is a technology that infers and analyzes emotions and states from text data entered by the user.

[0414] "External data" refers to data such as weather information and infectious disease information that is collected mainly via the Internet, separate from personal health data.

[0415] "Weather information" refers to information about the weather conditions of the day, such as temperature, humidity, and weather.

[0416] "Infectious disease information" refers to information on the spread of infectious diseases and preventive measures.

[0417] "Integrated analysis" refers to the centralized handling of multiple different data sets and comprehensive analysis.

[0418] "Evaluation" refers to objectively determining the user's health condition based on collected data.

[0419] "Advice" refers to suggestions about diet and health management provided to the user based on the evaluation results.

[0420] "Notification" is the act of notifying the user of the generated advice.

[0421] "Feedback information" is information relating to the results of actions taken by the user in accordance with the advice.

[0422] "Collecting feedback information" refers to collecting information entered by the user or automatically collected by the wearable device.

[0423] An "IoT device" is a physical device that connects to the internet, including things like smart mirrors and bathroom scales.

[0424] A "display" is a device that displays images or data.

[0425] "Integrated data" refers to comprehensive data generated by integrating individual data.

[0426] The present invention is a system that collects personal health data and emotional data, and integrates and analyzes them. Specific embodiments for carrying out the invention are described below.

[0427] System Configuration

[0428] This system consists of a smartphone app, a wearable device, an IoT device, a server, and a user terminal.

[0429] 1. Data Collection Methods

[0430] Smartphone app: Collects personal health data entered by the user, such as weight, walking distance, dietary habits, etc. The smartphone is equipped with a camera that captures facial expressions and a microphone that records voice, which are used to obtain emotional data as well.

[0431] Wearable devices: Smartwatches and fitness trackers automatically measure a user's weight and walking distance and sync with a smartphone via Bluetooth.

[0432] IoT devices: Smart mirrors, scales, sensors connected to exercise machines, etc. are used to collect health and emotional data from visitors.

[0433] 2. External Data Collection Methods

[0434] Server: Uses APIs provided via the internet to collect weather and infectious disease information. Temperature, humidity, and weather are collected from the weather information API, and the latest infectious disease spread status and preventive measures are obtained from the infectious disease information API.

[0435] 3. Integrated analysis method

[0436] Server: Collects and analyzes collected personal health data, emotional data, and external data in a unified manner. For example, it integrates weight data, walking distance data, dietary data, and emotional data to evaluate the user's health status.

[0437] 4. Advice Generation Methods

[0438] Server: Generates health advice for the user based on the assessment results, including dietary recommendations, exercise suggestions, and emotion-based relaxation and stress-relief activities.

[0439] 5. Means of notification

[0440] In-store display or user device: Advice is sent to a smart display or user's smartphone, for example, a message such as "You need vitamin C, so it's recommended that you eat citrus fruits."

[0441] 6. Feedback Collection Methods

[0442] Smartphone app and wearable device: Users enter the results of their activities into the app or the wearable device automatically records them. This information is sent to the server and used to generate advice for the next time.

[0443] This will enable the provision of real-time, individually optimized health advice based on collected personal health data, emotional data, and external data.

[0444] Specific examples

[0445] For example, a user stands in front of a smart mirror in a store, their facial expressions are captured, and then their weight data is entered into their smartphone. Meanwhile, the server retrieves weather and infectious disease information from the API and analyzes all the data. As a result, it is determined that the user's emotional state indicates stress, and specific relaxation exercises are recommended. This information is then displayed on the store's smart display and notified to the user.

[0446] Prompt Sentence Examples

[0447] "Describe the steps required to collect weight and emotion data and analyze an individual's health status. Also, describe the process for retrieving weather and infectious disease information from external APIs and integrating that data to generate recommendations."

[0448] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0449] Step 1:

[0450] Users can input data such as weight, walking distance, and dietary habits into a smartphone app, or data can be automatically synchronized from a wearable device. Numerical values ​​such as weight and walking distance are obtained as input data, and personal health data is collected based on these values. Specifically, users can step on a scale in the morning and enter the values ​​into a smartphone app, or a smartwatch can measure the distance walked during the day and send the data to a smartphone via Bluetooth.

[0451] Step 2:

[0452] The device collects emotion data. This includes capturing the user's facial expressions with the smartphone camera, collecting audio with the voice recording function, and analyzing text input. The input data includes facial expression images, audio files, and text, which are then analyzed using an emotion analysis engine to extract emotion data. Specifically, the smartphone camera captures the user's smile, and the image analysis engine classifies it as "happy."

[0453] Step 3:

[0454] The server obtains external weather and infectious disease information from APIs. The input data is the response from the external API, with temperature, humidity, and weather data obtained from the weather API, and infectious disease spread status and preventive measures obtained from the infectious disease information API. This data is used to personalize health advice for users.

[0455] Step 4:

[0456] The device sends the collected personal health data and emotional data to a server. The input data is personal health data and emotional data, which are stored in an integrated manner by the server. The server then integrates this data and performs multifaceted analysis. For example, it may integrate weight data, walking distance data, dietary data, and emotional data to evaluate the user's health condition.

[0457] Step 5:

[0458] The server performs an integrated analysis of personal data and external data to assess the user's health condition. The input data includes personal health data, emotional data, weather data, and infectious disease information, which are processed by the integrated analysis engine to assess the user's health condition. Statistical analysis and machine learning algorithms are used for data calculations. For example, statistical analysis may determine that "the recent drop in temperature has led to a decrease in physical activity."

[0459] Step 6:

[0460] The server generates health advice based on the evaluation results. The input data is the evaluation results, and appropriate advice is generated based on them. For example, advice such as "Since you need to take in vitamin C, it is recommended that you eat a lot of citrus fruits" is generated.

[0461] Step 7:

[0462] The server notifies the generated advice to the in-store display or the user's device. The input data is the generated advice, which is displayed on the store's smart display or the user's smartphone. Specifically, the message "It's cold today, so let's do some indoor exercises" is displayed on the in-store display.

[0463] Step 8:

[0464] The user can input the results of their actions based on the advice into a smartphone app, or data from a wearable device is automatically collected. The input data is feedback information that will be reflected in the generation of the next piece of advice. For example, if a user reports in the app that they have walked for 30 minutes, that information will be sent to the server and used to generate the next health advice.

[0465] 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.

[0466] 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.

[0467] 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.

[0468] [Second embodiment]

[0469] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0470] 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.

[0471] 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).

[0472] 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.

[0473] 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.

[0474] 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).

[0475] 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.

[0476] 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.

[0477] 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.

[0478] 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.

[0479] 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.

[0480] 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."

[0481] Data collection and integration

[0482] The system according to the present invention provides a function for collecting personal health data and external data from multiple data sources, and integrating and analyzing them. A specific embodiment of the system and its operation will be described below.

[0483] Collection of personal health data

[0484] Users use a smartphone app every day to input data such as their weight, walking distance, diet, etc. If they use a wearable device, this data is automatically synchronized to the device.

[0485] example:

[0486] The user steps on the scale in the morning and enters the weight reading into a smartphone app.

[0487] The smartwatch measures the distance walked during the day and automatically transmits the data to a smartphone via Bluetooth.

[0488] External Data Collection

[0489] The server periodically accesses external APIs to obtain weather and infectious disease information, which is used to personalize health advice for users.

[0490] example:

[0491] The server accesses the weather information API at a fixed time each day to obtain the temperature, humidity, and weather for that day.

[0492] The latest infectious disease spread status and preventive measures are obtained weekly from the infectious disease information API.

[0493] Data integration and analysis

[0494] The personal health data collected by the device is sent in real time to a server, which then combines this data with external data to analyze the user's health condition.

[0495] example:

[0496] The device transmits weight data, walking distance data, and dietary data to the server.

[0497] The data is analyzed in conjunction with temperature data and infectious disease information obtained by the server.

[0498] Generating and Serving Advice

[0499] The server generates health advice for the user based on the analysis results. This advice includes dietary recommendations and exercise suggestions. This advice is then sent to the user via their device.

[0500] example:

[0501] The server analyzes the data and determines that "taking vitamin C is an effective way to combat recent infectious diseases," and recommends that the user consume citrus fruits.

[0502] The server generates advice such as "Today's temperature is low, so let's do some indoor exercises" and sends it to the device.

[0503] The device will notify the user of the advice it has received and display details within the app.

[0504] Feedback gathering and learning

[0505] The user follows the advice and inputs the results into a smartphone app, or the wearable device automatically collects the feedback. This feedback information is then sent back to the server and used to generate the next advice.

[0506] example:

[0507] The user enters "I walked for 30 minutes" in the app.

[0508] The smartwatch automatically collects the user's exercise records and sends them to the device.

[0509] The device sends this feedback information to the server, which then reflects it in the next analysis.

[0510] In this way, the system of the present invention effectively integrates and analyzes the user's daily health data and external environmental data, and provides personalized health advice, thereby supporting the user in sustainable health management.

[0511] The processing flow will be explained below.

[0512] Step 1: Data entry and acquisition

[0513] 1.1 The user opens the smartphone app and manually enters their weight, as well as their dietary and calorie information.

[0514] 1.2 The device connects to a wearable device such as a smartwatch and automatically collects data such as walking distance and heart rate.

[0515] 1.3 The device temporarily stores the acquired personal health data in local storage.

[0516] Step 2: Send data

[0517] 2.1 The device periodically sends this personal health data to the server. The data is encrypted and transmitted securely.

[0518] 2.2 The server stores the personal health data transmitted to the database.

[0519] Step 3: Gather external data

[0520] 3.1 The server sends a request to the weather information API to obtain weather data such as the current temperature and weather.

[0521] 3.2 The server obtains the latest infectious disease outbreak information from the infectious disease information API.

[0522] Step 4: Integrate the data

[0523] 4.1 The server integrates the user's personal health data with the acquired weather data and infectious disease data.

[0524] 4.2 The server centralizes the data and creates a dataset for analysis.

[0525] Step 5: Analyze your health status

[0526] 5.1 The server's analytics engine assesses the user's health status based on the integrated dataset, using machine learning algorithms and rule-based systems.

[0527] 5.2 The server performs risk assessment based on the evaluation results and generates appropriate health advice for the user.

[0528] Step 6: Generating and notifying advice

[0529] 6.1 The server compiles the generated advice and formats it into a recommendation for the user.

[0530] 6.2 The server sends the advice to the terminal and sends it as a notification message.

[0531] 6.3 The device notifies the user of the advice and displays detailed advice content.

[0532] Step 7: Gather feedback

[0533] 7.1 The user acts on the advice and inputs the results into the app, for example, recording the duration of a walk or what they ate.

[0534] 7.2 The terminal automatically collects feedback information from the wearable device and sends it to the server.

[0535] Step 8: Learn and improve your advice

[0536] 8.1 The server updates the analytical model based on the collected feedback information and uses it to generate advice from the next time onwards.

[0537] 8.2 The server takes in new data and re-learns to improve the accuracy of advice given to users.

[0538] These steps enable the system to assist users in managing their individual health and provide personalized advice for continually improving their health.

[0539] Example 1

[0540] 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."

[0541] To provide efficient and personalized health management, it is necessary to integrate personal health data with external data and provide users with appropriate advice based on the analysis results. However, current systems lack real-time performance and individualized accuracy in the collection, integration, and analysis of data and the provision of advice. Therefore, there is a need for a system that can solve these issues and enable more effective health management.

[0542] 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.

[0543] In this invention, the server includes means for collecting personal health data in real time and transmitting it to the server, means for integrating and analyzing the personal health data and external data to evaluate the user's health condition, and means for transmitting advice generated based on the analysis results to the user terminal. This makes it possible to integrate and analyze the user's health data and external data in real time and provide personalized health management advice.

[0544] "Personal health data" refers to information about the user's health status, such as weight, walking distance, and diet.

[0545] "External data" refers to information about the external environment that affects the user's health management, such as weather information and infectious disease information.

[0546] "Collection methods" refer to the methods and technologies used to acquire and store data.

[0547] "Integrated analysis" means processing multiple data sets together and conducting a comprehensive analysis.

[0548] "Health status assessment" refers to diagnosing the user's health status based on the integrated data and evaluating it according to certain criteria.

[0549] "Advice generator" refers to a method or technology that generates recommendations or instructions to provide to a user based on the evaluation results.

[0550] "Notification means" refers to a method or technique for notifying the user of the generated advice.

[0551] "Feedback information" refers to information relating to the results of a user's actions in accordance with advice.

[0552] "Server" means a central processing unit for processing collected data and storing and transmitting analysis results.

[0553] "Terminal" refers to a device used by a user to input data and receive notifications from a server.

[0554] A "mobile device app" is an application that runs on a mobile electronic device such as a smartphone or tablet.

[0555] A "wearable device" is a data collection device that is worn by a user.

[0556] "Communication network" refers to a network for sending and receiving data, such as the Internet.

[0557] "Software interface" refers to the programs and protocols used to obtain external data.

[0558] MODE FOR CARRYING OUT THE INVENTION

[0559] The present invention relates to a system for collecting personal health data of a user, evaluating the user's health condition by integrating and analyzing the data with external data, and providing appropriate health advice. Specific embodiments of the present invention will be described in detail below.

[0560] Collection of personal health data

[0561] Users use a mobile device app to input daily health data such as weight, walking distance, dietary habits, etc. Wearable devices (e.g., smartwatches) automatically measure data such as walking distance and heart rate, and synchronize the data with a smartphone using communication technologies such as Bluetooth.

[0562] example:

[0563] The user steps on the scale every morning and enters their weight into a smartphone app.

[0564] The smartwatch measures the user's walking distance during the day and automatically sends the data to the device.

[0565] External Data Collection

[0566] The server periodically obtains external data, such as weather information and infectious disease information, using a software interface (API) provided via the Internet, thereby collecting external environmental data that may affect the user's health condition.

[0567] example:

[0568] The server obtains the day's temperature, humidity, and weather from the weather information API at a fixed time each day.

[0569] The server obtains the latest infectious disease spread status and preventive measures from the infectious disease information API every week.

[0570] Data integration and analysis

[0571] The personal health data collected by the device is sent in real time to a server, where it is integrated with external data and analyzed to assess the user's health status.

[0572] example:

[0573] The terminal transmits the user's weight data, walking distance data, and dietary habit data to the server.

[0574] The server integrates temperature data and infectious disease information to analyze the user's health risk.

[0575] Generating and Serving Advice

[0576] The server generates health advice for the user based on the analysis results, and the advice is sent to the user's smartphone or other device. The advice includes dietary recommendations and exercise suggestions.

[0577] example:

[0578] The server analyzes the data and determines that "taking vitamin C is effective in preventing infectious diseases," and recommends that the user consume citrus fruits.

[0579] The system generates advice such as "Today's temperature is low, so let's do some indoor exercises," and notifies the user's device.

[0580] Feedback gathering and learning

[0581] The user follows the advice and inputs the results into a smartphone app, or the wearable device automatically collects exercise records. This feedback information is then sent back to the server and used to generate advice for the next time.

[0582] example:

[0583] The user enters "I walked for 30 minutes" in the app.

[0584] The smartwatch automatically collects the user's exercise records and sends them to the device.

[0585] The device sends this feedback information to the server, which then reflects it in the next analysis.

[0586] This system effectively integrates users' daily health data with external environmental data and provides personalized health advice, helping users manage their health sustainably.

[0587] Example prompts for generative AI models

[0588] "A user weighs themselves using a scale in the morning and enters that data into a smartphone app. The server periodically uses an external API to obtain the temperature, humidity, and weather for that day. Based on that data, it generates health advice tailored to the user and sends it to their smartphone. Please explain each processing step of this system in detail."

[0589] This prompt allows the generative AI model to generate detailed explanations of each processing step in the system.

[0590] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0591] Step 1: User enters personal health data

[0592] Users manually enter personal health data such as weight, walking distance, and dietary habits into a smartphone app. The entered data is stored in the smartphone's internal database. For example, a user can step on a scale every morning and enter their measured weight into the app. This input data becomes the basis for subsequent analysis.

[0593] Input: Personal health data such as weight, walking distance, dietary habits, etc.

[0594] Output: Personal health data stored in the smartphone's internal database

[0595] Step 2: Automated data collection with wearable devices

[0596] The device automatically collects data from wearable devices such as smartwatches, which constantly measure the user's walking distance, heart rate, sleep patterns, and more, and transmits the data via Bluetooth to a smartphone, where it is integrated into the smartphone's internal database.

[0597] Input: Walking distance, heart rate, sleep data from smartwatch

[0598] Output: Wearable device data integrated into smartphone internal database

[0599] Step 3: Gather external data

[0600] The server periodically obtains external data, such as weather information and infectious disease information, via the internet using an external API. For example, the server accesses the weather information API at 7:00 a.m. every day to obtain the temperature, humidity, and weather for that day. This data is then stored in the server's database.

[0601] Input: Temperature, humidity, and weather data obtained from the weather information API

[0602] Output: Weather information stored in the server database

[0603] Step 4: Send your personal health data

[0604] The device sends the collected personal health data to the server in real time, allowing the server to always have the latest user information. For example, a smartphone periodically sends weight data, walking distance data, and dietary habit data to the server.

[0605] Input: Personal health data stored in the smartphone's internal database

[0606] Output: Personal health data sent to the server

[0607] Step 5: Data integration and analysis

[0608] The server integrates the received personal health data with external data to assess the user's health status. For example, the server combines weight data, walking distance data, and dietary data with temperature data and infectious disease information to perform statistical analysis. The results of this analysis serve as the basis for health advice for the user.

[0609] Input: Personal health data and external data sent to the server

[0610] Output: Analysis results (evaluation of the user's health status)

[0611] Step 6: Generate health advice

[0612] The server generates health advice for the user based on the analysis results. For example, if the server determines that "taking vitamin C is an effective measure against recent infectious diseases," it generates advice recommending the user to consume citrus fruits. This advice is stored in the server's memory in text format.

[0613] Input: User's health assessment

[0614] Output: Generated health advice

[0615] Step 7: Advice Notification

[0616] The server sends the generated advice to the device and notifies the user. For example, advice such as "Today's temperature is low, so try doing some indoor exercises" is sent in text format to the device, and the device notifies the user via a push notification.

[0617] Input: Generated health advice

[0618] Output: Advice notification sent to user terminal

[0619] Step 8: Gather user feedback

[0620] The user inputs the results of their actions based on the advice into a smartphone app, or the wearable device automatically collects feedback data. For example, the user can input "I walked for 30 minutes" into the app, or the smartwatch can send exercise data to the device. This feedback information is then sent back to the server.

[0621] Input: User-entered feedback data or data from a wearable device

[0622] Output: Feedback information sent to the server

[0623] Step 9: Analyze the feedback and incorporate it into your next advice

[0624] The server analyzes the received feedback information and uses it to generate the next health advice. For example, the server analyzes the feedback information to understand the user's exercise habits and dietary patterns and reflect them in the next advice. This process enables more accurate and personalized health management.

[0625] Input: Feedback information sent to the server

[0626] Output: Next health advice reflecting the feedback information

[0627] (Application example 1)

[0628] 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."

[0629] Personal health management is an important issue today, and there is a need to effectively collect and analyze health data, particularly daily weight, walking distance, and dietary habits, and provide appropriate advice. However, it is difficult to collect and analyze this data using a single device or platform, and it is also difficult to provide comprehensive health advice in real time that takes into account external weather and infectious disease information. Additionally, there are few opportunities to receive instant, personalized health advice in physical stores, and there is insufficient integration between in-store product information and health advice.

[0630] 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.

[0631] In this invention, the server includes: means for recording weight, walking distance, and dietary habits to collect personal health data; means for collecting external weather information and infectious disease information; means for integrating and analyzing the personal health data and external data to evaluate the user's health status; means for generating dietary and health management advice for the user based on the evaluation; means for notifying the advice to the user terminal and to an information providing terminal in the physical store; means for the information providing terminal to instantly provide personalized health advice based on the user's health data and external data; means for providing in-store guidance on products related to the advice; and means for collecting feedback information from the user and using it to generate next advice. This makes it possible to effectively integrate and analyze personal health data and external environmental data, instantly provide personalized health advice in the physical store, and further guide users to related products.

[0632] "Personal health data" refers to data that indicates the individual health condition of a user, such as weight, walking distance, and dietary habits.

[0633] "Weather information" is data that indicates the state of the external environment, such as the temperature, humidity, and weather of the area.

[0634] "Infectious disease information" refers to data related to infectious diseases, such as the prevalence of infectious diseases in the area and preventive measures.

[0635] "Integrated analysis" is the process of combining and analyzing multiple data to find overall patterns and meanings.

[0636] A "user terminal" is a device used by a user, such as a smartphone or tablet.

[0637] An "information providing terminal" is a device that is installed in a physical store and provides information to users.

[0638] "Evaluating health status" means analyzing the user's health status based on collected personal health data and external data.

[0639] "Dietary and health management advice" is information that suggests dietary habits, exercise, supplements, etc. to improve the user's health.

[0640] "Notifying" refers to the act of sending the generated advice to the user or other terminals.

[0641] "Feedback information" is information about the actions that the user actually takes and the results of those actions.

[0642] "Personalized health advice" refers to personalized health management suggestions based on an individual user's health data and external data.

[0643] "In-store guidance" refers to the act of providing the location and detailed information of related products within a physical store.

[0644] This invention is a system that integrates personal health data with external environmental data to provide users with personalized health advice. This system is composed of a personal health data collection means, a weather information and infectious disease information collection means, a data integration and analysis means, an advice generation means, an information provision means, and a feedback collection means.

[0645] 1. Collection of personal health data

[0646] The server collects daily health data such as weight, walking distance, and dietary habits from users using smartphone apps and wearable devices. This data is usually automatically synchronized with the smartphone via wireless communication such as Bluetooth and sent to the server.

[0647] For example, after a user steps on a scale in the morning, the weight is automatically recorded in a smartphone app. The smartwatch also measures the distance walked during the day and sends the data to the smartphone, which then sends it to a server.

[0648] 2. External Data Collection

[0649] The server periodically obtains weather and infectious disease information from an external API provided via the Internet, and this data is used to evaluate the user's health status.

[0650] Specifically, the server accesses the weather information API at a fixed time each day to obtain the temperature, humidity, and weather for that day, and also accesses the infectious disease information API weekly to obtain the latest information on the spread of infectious diseases and preventive measures.

[0651] 3. Data integration and analysis

[0652] The server integrates the collected personal health data with external data to analyze the user's health status in real time, and generates specific health advice for the user based on the analysis results.

[0653] For example, the server analyzes the user's walking distance data, weight data, and dietary data in combination with external data such as temperature and humidity. Based on the analysis, specific advice such as "Today's temperature is low, so let's do some indoor exercises" is generated.

[0654] 4. Providing advice

[0655] The generated advice is notified to the user terminal and an information providing terminal in the physical store. The information providing terminal is a device that provides health advice to the user in real time within the store.

[0656] Specifically, advice generated by the server, such as "Today's temperature is low, so we recommend you drink a hot drink," is displayed on the robot's display in the physical store, and the user is guided to related products in the store.

[0657] 5. Feedback Collection

[0658] The user can input the results of their actions based on the advice into a smartphone app, or the wearable device can automatically collect the feedback. This feedback information is then sent back to the server and used to generate the next advice.

[0659] For example, if a user enters "I walked for 30 minutes" in the app, that information is sent to the server, and the smartwatch automatically collects and sends walking data to the server in the same way.

[0660] Prompt Sentence Examples

[0661] "Due to the low temperatures today, we recommend that you eat some warm soup. We also recommend taking vitamin C supplements to prevent infection. We have related products available in the XX section of our store, so please feel free to use them."

[0662] This system effectively integrates and analyzes personal health data and external environmental data, making it possible to provide instant, personalized health advice in physical stores and even introduce related products.

[0663] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0664] Step 1: Collect personal health data

[0665] Users use a smartphone app and a wearable device to collect daily health data such as weight, walking distance, and diet. The smartphone app records this data on the device and automatically synchronizes it with the wearable device using Bluetooth. The device then sends the collected data to a server. Health data such as weight, walking distance, and dietary details are used as input. The output is personal health data sent to the server. Specifically, the user steps on the scale every morning, and the weight is automatically recorded in the smartphone app. The smartphone then retrieves walking distance data from the wearable device and sends all data to the server.

[0666] Step 2: Gather external data

[0667] The server periodically obtains weather information and infectious disease information from an external API provided via the internet. It obtains data such as temperature, humidity, and weather from the weather information API, and data on infectious disease epidemic status and preventive measures from the infectious disease information API. The input is weather information and infectious disease information from the external API, and the output is integrated external data. Specifically, the server sends a request to the weather information API at a fixed time each day to obtain the temperature, humidity, and weather for that day, and accesses the infectious disease information API weekly to obtain the latest infectious disease spread status.

[0668] Step 3: Data synthesis and analysis

[0669] The server integrates the collected personal health data with external data for analysis. This analysis uses an algorithm to evaluate the user's health status by combining the personal health data and external data. The input is the personal health data and external data, and the output is the evaluation result of the user's health status. Specifically, the server combines data such as the individual's weight, walking distance, and diet with weather information such as temperature and humidity, and analyzes the user's health status using a health evaluation algorithm.

[0670] Step 4: Generating Advice

[0671] The server generates health advice for the user based on the analysis results. This advice includes dietary recommendations, exercise plans, and infection prevention measures. The input is the health status assessment result, and the output is advice to be provided to the user. Specifically, the server generates advice such as "Today's temperature is low, so we recommend drinking a hot drink."

[0672] Step 5: Providing advice

[0673] The generated advice is notified to the user terminal and an information providing terminal in the physical store. The information providing terminal immediately provides health advice to the user in the store and also introduces related products. The input is the generated advice, and the output is a notification to the user and the information providing terminal. In specific operations, the server notifies the user's smartphone of the advice, and a robot in the physical store displays the advice on a display.

[0674] Step 6: Gather feedback

[0675] The user inputs the results of their actions based on the advice into the smartphone app, or the wearable device automatically collects the data. The server uses this feedback information to generate the next piece of advice. The input is feedback information about the user's actions, and the output is data that will be used to generate the next piece of advice. Specifically, the user inputs "I walked for 30 minutes" into the app, and this information is sent to the server. Similarly, the smartwatch automatically collects and sends the walking data to the server.

[0676] Through these processing steps, the system supports personal healthcare and provides added value within physical stores.

[0677] 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.

[0678] Data collection and integration

[0679] The system according to the present invention provides a function for collecting personal health data and emotion data, and integrating and analyzing them. A specific embodiment of the system and its operation will be described below.

[0680] Collection of personal health data

[0681] Users use a smartphone app every day to input data such as their weight, walking distance, diet, etc. If they use a wearable device, this data is automatically synchronized to the device.

[0682] example:

[0683] The user steps on the scale in the morning and enters the weight reading into a smartphone app.

[0684] The smartwatch measures the distance walked during the day and automatically transmits the data to a smartphone via Bluetooth.

[0685] External Data Collection

[0686] The server periodically accesses external APIs to obtain weather and infectious disease information, which is used to personalize health advice for users.

[0687] example:

[0688] The server accesses the weather information API at a fixed time each day to obtain the temperature, humidity, and weather for that day.

[0689] The latest infectious disease spread status and preventive measures are obtained weekly from the infectious disease information API.

[0690] Collecting Emotional Data

[0691] The device uses an emotion engine to collect user emotion data, which is analyzed based on the user's facial expressions, voice, and input data.

[0692] example:

[0693] The smartphone camera captures the user's facial expressions, and the emotion engine analyzes them.

[0694] The user's voice is recorded through a microphone and analyzed.

[0695] Data is collected from text input, etc., to infer the user's emotions.

[0696] Data integration and analysis

[0697] The device sends the collected personal health and emotional data to a server, which then integrates this data with external data to perform a multifaceted analysis of the user's health condition.

[0698] example:

[0699] The terminal transmits weight data, walking distance data, dietary habit data, and emotion data to the server.

[0700] The data is analyzed in conjunction with weather data and infectious disease information obtained by the server.

[0701] Generating and Serving Advice

[0702] The server generates health advice for the user based on the analysis results. This advice includes dietary recommendations and exercise suggestions. It also takes into account emotional data. This advice is then sent to the user via their device.

[0703] example:

[0704] The server analyzes the data and determines that "taking vitamin C is an effective way to combat recent infectious diseases," and recommends that the user consume citrus fruits.

[0705] The server generates advice such as "Today's temperature is low, so let's do some indoor exercises" and sends it to the device.

[0706] The server determines from emotional data that the user is feeling stressed and recommends activities that are effective for relaxation and stress relief.

[0707] The terminal notifies the user of the received advice and displays detailed advice content.

[0708] Feedback gathering and learning

[0709] The user follows the advice and inputs the results into a smartphone app, or the wearable device automatically collects the feedback. This feedback information is then sent back to the server and used to generate the next advice.

[0710] example:

[0711] The user enters "I walked for 30 minutes" in the app.

[0712] The smartwatch automatically collects the user's exercise records and sends them to the device.

[0713] The device sends this feedback information to the server, which then reflects it in the next analysis.

[0714] These steps enable the system to assist users in managing their individual health and provide personalized advice to help improve their health, including their emotional state, over time.

[0715] The processing flow will be explained below.

[0716] Step 1: Data entry and acquisition

[0717] 1.1 The user opens the smartphone app and manually enters their weight, as well as their dietary and calorie information.

[0718] 1.2 The device connects to a wearable device such as a smartwatch and automatically collects data such as walking distance and heart rate.

[0719] 1.3 The device uses a camera and microphone to capture the user's facial expressions and voice to collect emotional data.

[0720] Step 2: Send data

[0721] 2.1 The device sends personal health and emotion data to the server. The data is encrypted and securely transmitted.

[0722] 2.2 The server stores the personal health data and emotion data sent to the database.

[0723] Step 3: Gather external data

[0724] 3.1 The server sends a request to the weather information API to obtain weather data such as the current temperature and weather.

[0725] 3.2 The server obtains the latest infectious disease outbreak information from the infectious disease information API.

[0726] 3.3 The server stores these weather and infectious disease data in a database.

[0727] Step 4: Integrate the data

[0728] 4.1 The server integrates the user's personal health data, emotion data, weather data, and infectious disease data.

[0729] 4.2 The server creates the integrated dataset and feeds it into the analysis engine.

[0730] Step 5: Analyze your health status

[0731] 5.1 The server's analytics engine assesses the user's health and emotional state based on the integrated dataset, using machine learning algorithms and rule-based systems.

[0732] 5.2 The server performs risk assessment based on the evaluation results and generates health advice and emotion-based advice appropriate for the user.

[0733] Step 6: Generating and notifying advice

[0734] 6.1 The server compiles and formats the generated advice into a recommendation for the user, including dietary recommendations, exercise suggestions, and emotional care advice.

[0735] 6.2 The server sends the advice to the terminal and sends it as a notification message.

[0736] 6.3 The device notifies the user of the advice and displays detailed advice content.

[0737] Step 7: Gather feedback

[0738] 7.1 The user acts on the advice and inputs the results into the app, for example, recording the duration of a walk or what they ate.

[0739] 7.2 The terminal automatically collects feedback information from the wearable device and sends it to the server.

[0740] Step 8: Learn and improve your advice

[0741] 8.1 The server updates the analytical model based on the collected feedback information and uses it to generate advice from the next time onwards.

[0742] 8.2 The server takes in new data and re-learns to improve the accuracy of advice given to users.

[0743] These steps enable the system to assist users in managing their individual health and emotions, providing personalized advice that can help improve their health and emotional state over time.

[0744] Example 2

[0745] 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."

[0746] In modern society, personal health management is becoming increasingly important, necessitating the development of health management systems that are more detailed and flexible. However, while existing health management systems provide basic health management based on personal health data (weight, walking distance, dietary habits) and external data (weather information, infectious disease information), they are unable to provide personalized health management advice that takes emotional data into account. Furthermore, they lack the functionality to quickly and effectively incorporate user feedback information and utilize it in generating next advice, making it difficult to translate this into practical health improvements.

[0747] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0748] In this invention, the server includes means for recording weight, walking distance, and dietary habits to collect personal health data, means for collecting external weather information and infectious disease information, means for analyzing the user's facial expressions, voice, and input data to collect user emotion data, means for integrating and analyzing the personal health data, external data, and emotion data to evaluate the user's health condition from various angles, means for generating health management advice for the user based on the evaluation and taking into account diet, exercise, and emotion state, means for notifying the user terminal of the advice, and means for collecting feedback information from the user and using it to generate next advice. This makes it possible to provide personalized health management advice that integrates the personal health data, external data, and emotion data, and to quickly reflect user feedback.

[0749] "Personal health data" refers to data that indicates the user's personal health condition, such as weight, walking distance, and dietary habits.

[0750] "External data" refers to external information obtained via the Internet, such as weather information and infectious disease information.

[0751] "Emotion data" is data that indicates the user's emotional state, obtained by analyzing the user's facial expressions, voice, and input data.

[0752] "Integrated analysis" is a process of integrating and analyzing personal health data, external data, and emotional data to evaluate the user's health condition from multiple angles.

[0753] "Health Management Advice" refers to instructions or recommendations given to the user based on the analysis results, taking into account diet, exercise, and emotional state.

[0754] "Feedback information" is data collected as a result of a user acting on advice.

[0755] A "server" is a computer system that collects, analyzes, and generates advice from data.

[0756] "Terminal" means a device (e.g., smartphone, wearable device) that a user uses to input and receive data.

[0757] "API" is an abbreviation for Application Program Interface, which provides a mechanism for exchanging data between different software programs.

[0758] An "emotion engine" is software that analyzes a user's facial expressions, voice, and input data to infer their emotional state.

[0759] MODE FOR CARRYING OUT THE INVENTION

[0760] The present invention is a system that integrates multiple data collection and analysis means, evaluates a user's health condition from multiple angles, and provides personalized health management advice. Specific embodiments and operations of the system are described below.

[0761] System Configuration Overview

[0762] 1. The system mainly consists of three elements: server, terminal, and user.

[0763] 2. The server collects, analyzes, and generates advice on data.

[0764] 3. A terminal is a device (e.g., a smartphone or wearable device) through which a user inputs and receives data.

[0765] 4. Users provide personal health data and feedback information.

[0766] Hardware and software used

[0767] Server: Uses cloud-based computing resources to run the database and generative AI models.

[0768] Devices: Smartphones and wearable devices (e.g., smartwatches).

[0769] Emotion Engine: Includes facial expression analysis software and a voice analysis engine.

[0770] Data collection

[0771] Users use a smartphone app every day to enter personal health data such as weight, walking distance, diet, etc. If they use a wearable device, this data is automatically synced to the device.

[0772] example:

[0773] The user steps on the scale in the morning and enters the weight reading into a smartphone app.

[0774] The smartwatch measures the distance walked during the day and automatically transmits the data to a smartphone via Bluetooth.

[0775] The server periodically uses external APIs to obtain weather and infectious disease information, which is used to personalize health advice for users.

[0776] example:

[0777] The server accesses the weather information API at a fixed time each day to obtain the temperature, humidity, and weather for that day.

[0778] The latest infectious disease spread status and preventive measures are obtained weekly from the infectious disease information API.

[0779] The device uses an emotion engine to collect user emotion data, which is analyzed based on the user's facial expressions, voice, and input data.

[0780] example:

[0781] The smartphone camera captures the user's facial expressions, and the emotion engine analyzes them.

[0782] The user's voice is recorded through a microphone and analyzed.

[0783] Data is collected from text input, etc., to infer the user's emotions.

[0784] Data transmission and analysis

[0785] The device sends the collected personal health and emotional data to a server, which then integrates this data with external data and uses a generative AI model to perform a multifaceted analysis of the user's health condition. Based on the results of this analysis, the server generates personalized health management advice.

[0786] Providing advice and feedback

[0787] The generated advice is sent to the user via the device, and includes dietary recommendations, exercise suggestions, and advice that takes into account emotional state.

[0788] Example prompt statement:

[0789] "Tell me how you're feeling today."

[0790] Enter your morning weight

[0791] "Tell me about your recent eating habits."

[0792] "Check out the advice based on infectious disease information."

[0793] The results of the user's actions based on the advice are entered into a smartphone app or automatically collected by a wearable device. This feedback information is then sent back to the server and used to generate the next advice.

[0794] In this way, this system supports users in managing their health by integrating personal health data, external data, and emotional data. Detailed collection and analysis of various data makes it possible to provide users with personalized health advice.

[0795] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0796] Step 1:

[0797] User input of personal health data

[0798] Users use a smartphone app to input personal health data such as weight, walking distance, and dietary habits. The input data is temporarily stored within the app. Specifically, the user opens the smartphone app and inputs their weight for the day. The app then stores this data within the device.

[0799] Input: weight, walking distance, dietary data

[0800] Output: Personal health data stored on the device

[0801] Step 2:

[0802] Terminal syncs data from wearable devices

[0803] A wearable device (e.g., a smartwatch) automatically transmits data collected, such as walking distance, to a smartphone via Bluetooth. The device then integrates this data into an app. Specifically, the smartwatch measures the distance walked during the day and transmits the data to the smartphone via Bluetooth.

[0804] Input: Walking distance data from a wearable device

[0805] Output: Walking distance data integrated into a smartphone app

[0806] Step 3:

[0807] Server-based external data collection

[0808] The server periodically accesses an external API to obtain weather and infectious disease information. This data is organized and stored on the server side. Specifically, the server accesses the weather information API at a fixed time each day to obtain the temperature, humidity, and weather for that day.

[0809] Input: Weather information and infectious disease information obtained from external API

[0810] Output: External data stored on the server side

[0811] Step 4:

[0812] Collecting emotion data from devices

[0813] The device's emotion engine analyzes the user's facial expressions, voice, and input data to generate emotion data. Specifically, the smartphone camera captures the user's facial expressions, and the emotion engine analyzes them.

[0814] Input: facial expressions, voice, input data

[0815] Output: Parsed emotion data

[0816] Step 5:

[0817] Sending data from the device to the server

[0818] The device periodically transmits the collected personal health data and emotion data to a server, which receives the data and stores it in a database. Specifically, the device periodically uploads the data to the server in the background.

[0819] Input: Personal health data and emotional data collected on the device

[0820] Output: Data stored on the server

[0821] Step 6:

[0822] Data integration and analysis by the server

[0823] The server integrates the received personal health data, emotion data, and external data, and analyzes them using a generative AI model. Specifically, the server integrates each data set into a single dataset and then performs analysis using the generative AI model.

[0824] Input: personal health data, emotion data, external data

[0825] Output: Analysis results

[0826] Step 7:

[0827] Server-generated advice

[0828] The server generates health management advice for the user based on the analysis results. This advice takes into account diet, exercise, and emotional state. Specifically, the server generates advice text based on the generated data and prepares to send it to the device in the next step.

[0829] Input: Analysis results

[0830] Output: Health care advice

[0831] Step 8:

[0832] Sending advice from the server to the device

[0833] The server generates health management advice and sends it to the device via a dedicated API, which then notifies the user. Specifically, the server sends the advice to the device, which then displays it to the user using its notification function.

[0834] Input: Health Care Advice

[0835] Output: Advice displayed on terminal

[0836] Step 9:

[0837] Gathering user feedback

[0838] The user can follow the advice and enter the results into a smartphone app, or the wearable device can automatically collect the data. Specifically, the user can enter "I walked for 30 minutes" in the app, or the smartwatch can automatically record the exercise results and send them to the device.

[0839] Input: User action result

[0840] Output: Feedback information

[0841] Step 10:

[0842] Sending feedback information to the server by the terminal

[0843] The device sends the collected feedback information to the server, which then uses it to generate the next advice. Specifically, the device automatically sends the feedback data to the server, and the server reflects the received data in the next analysis.

[0844] Input: Feedback information

[0845] Output: Data that will be reflected in the next advice generation

[0846] (Application example 2)

[0847] 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."

[0848] Conventional health management systems have difficulty managing individual health data and emotional data in a unified manner and providing optimal health advice to consumers. Furthermore, they were unable to provide advice that took into account the real-time health and emotional state of consumers visiting the store, which meant that the effectiveness of health management could not be maximized.

[0849] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0850] In this invention, the server includes: means for recording weight, walking distance, and dietary habits to collect personal health data; means for analyzing facial expressions, voice, and input data to collect emotion data; means for collecting external weather information and infectious disease information; means for integrating and analyzing the personal health data, emotion data, and external data to evaluate the user's health status; means for generating dietary and health management advice for the user based on the evaluation; means for notifying the advice on an in-store display or a user terminal; and means for collecting user feedback information and using it to generate next advice. This makes it possible to provide individually optimized health advice based on the customer's real-time health and emotion data.

[0851] "Personal health data" refers to information about an individual's health status, such as weight, walking distance, and diet.

[0852] "Emotional data" refers to information about an individual's emotional state analyzed based on facial expressions, voice, and input data.

[0853] "Facial expression analysis" is a technology that uses an input device such as a camera to detect an individual's facial expression and analyze their emotional state.

[0854] "Voice analysis" is a technology that analyzes voice data collected through a microphone or other device to determine the emotions and state of the voice.

[0855] "Input data analysis" is a technology that infers and analyzes emotions and states from text data entered by the user.

[0856] "External data" refers to data such as weather information and infectious disease information that is collected mainly via the Internet, separate from personal health data.

[0857] "Weather information" refers to information about the weather conditions of the day, such as temperature, humidity, and weather.

[0858] "Infectious disease information" refers to information on the spread of infectious diseases and preventive measures.

[0859] "Integrated analysis" refers to the centralized handling of multiple different data sets and comprehensive analysis.

[0860] "Evaluation" refers to objectively determining the user's health condition based on collected data.

[0861] "Advice" refers to suggestions about diet and health management provided to the user based on the evaluation results.

[0862] "Notification" is the act of notifying the user of the generated advice.

[0863] "Feedback information" is information relating to the results of actions taken by the user in accordance with the advice.

[0864] "Collecting feedback information" refers to collecting information entered by the user or automatically collected by the wearable device.

[0865] An "IoT device" is a physical device that connects to the internet, including things like smart mirrors and bathroom scales.

[0866] A "display" is a device that displays images or data.

[0867] "Integrated data" refers to comprehensive data generated by integrating individual data.

[0868] The present invention is a system that collects personal health data and emotional data, and integrates and analyzes them. Specific embodiments for carrying out the invention are described below.

[0869] System Configuration

[0870] This system consists of a smartphone app, a wearable device, an IoT device, a server, and a user terminal.

[0871] 1. Data Collection Methods

[0872] Smartphone app: Collects personal health data entered by the user, such as weight, walking distance, dietary habits, etc. The smartphone is equipped with a camera that captures facial expressions and a microphone that records voice, which are used to obtain emotional data as well.

[0873] Wearable devices: Smartwatches and fitness trackers automatically measure a user's weight and walking distance and sync with a smartphone via Bluetooth.

[0874] IoT devices: Smart mirrors, scales, sensors connected to exercise machines, etc. are used to collect health and emotional data from visitors.

[0875] 2. External Data Collection Methods

[0876] Server: Uses APIs provided via the internet to collect weather and infectious disease information. Temperature, humidity, and weather are collected from the weather information API, and the latest infectious disease spread status and preventive measures are obtained from the infectious disease information API.

[0877] 3. Integrated analysis method

[0878] Server: Collects and analyzes collected personal health data, emotional data, and external data in a unified manner. For example, it integrates weight data, walking distance data, dietary data, and emotional data to evaluate the user's health status.

[0879] 4. Advice Generation Methods

[0880] Server: Generates health advice for the user based on the assessment results, including dietary recommendations, exercise suggestions, and emotion-based relaxation and stress-relief activities.

[0881] 5. Means of notification

[0882] In-store display or user device: Advice is sent to a smart display or user's smartphone, for example, a message such as "You need vitamin C, so it's recommended that you eat citrus fruits."

[0883] 6. Feedback Collection Methods

[0884] Smartphone app and wearable device: Users enter the results of their activities into the app or the wearable device automatically records them. This information is sent to the server and used to generate advice for the next time.

[0885] This will enable the provision of real-time, individually optimized health advice based on collected personal health data, emotional data, and external data.

[0886] Specific examples

[0887] For example, a user stands in front of a smart mirror in a store, their facial expressions are captured, and then their weight data is entered into their smartphone. Meanwhile, the server retrieves weather and infectious disease information from the API and analyzes all the data. As a result, it is determined that the user's emotional state indicates stress, and specific relaxation exercises are recommended. This information is then displayed on the store's smart display and notified to the user.

[0888] Prompt Sentence Examples

[0889] "Describe the steps required to collect weight and emotion data and analyze an individual's health status. Also, describe the process for retrieving weather and infectious disease information from external APIs and integrating that data to generate recommendations."

[0890] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0891] Step 1:

[0892] Users can input data such as weight, walking distance, and dietary habits into a smartphone app, or data can be automatically synchronized from a wearable device. Numerical values ​​such as weight and walking distance are obtained as input data, and personal health data is collected based on these values. Specifically, users can step on a scale in the morning and enter the values ​​into a smartphone app, or a smartwatch can measure the distance walked during the day and send the data to a smartphone via Bluetooth.

[0893] Step 2:

[0894] The device collects emotion data. This includes capturing the user's facial expressions with the smartphone camera, collecting audio with the voice recording function, and analyzing text input. The input data includes facial expression images, audio files, and text, which are then analyzed using an emotion analysis engine to extract emotion data. Specifically, the smartphone camera captures the user's smile, and the image analysis engine classifies it as "happy."

[0895] Step 3:

[0896] The server obtains external weather and infectious disease information from APIs. The input data is the response from the external API, with temperature, humidity, and weather data obtained from the weather API, and infectious disease spread status and preventive measures obtained from the infectious disease information API. This data is used to personalize health advice for users.

[0897] Step 4:

[0898] The device sends the collected personal health data and emotional data to a server. The input data is personal health data and emotional data, which are stored in an integrated manner by the server. The server then integrates this data and performs multifaceted analysis. For example, it may integrate weight data, walking distance data, dietary data, and emotional data to evaluate the user's health condition.

[0899] Step 5:

[0900] The server performs an integrated analysis of personal data and external data to assess the user's health condition. The input data includes personal health data, emotional data, weather data, and infectious disease information, which are processed by the integrated analysis engine to assess the user's health condition. Statistical analysis and machine learning algorithms are used for data calculations. For example, statistical analysis may determine that "the recent drop in temperature has led to a decrease in physical activity."

[0901] Step 6:

[0902] The server generates health advice based on the evaluation results. The input data is the evaluation results, and appropriate advice is generated based on them. For example, advice such as "Since you need to take in vitamin C, it is recommended that you eat a lot of citrus fruits" is generated.

[0903] Step 7:

[0904] The server notifies the generated advice to the in-store display or the user's device. The input data is the generated advice, which is displayed on the store's smart display or the user's smartphone. Specifically, the message "It's cold today, so let's do some indoor exercises" is displayed on the in-store display.

[0905] Step 8:

[0906] The user can input the results of their actions based on the advice into a smartphone app, or data from a wearable device is automatically collected. The input data is feedback information that will be reflected in the generation of the next piece of advice. For example, if a user reports in the app that they have walked for 30 minutes, that information will be sent to the server and used to generate the next health advice.

[0907] 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.

[0908] 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.

[0909] 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.

[0910] [Third embodiment]

[0911] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0912] 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.

[0913] 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).

[0914] 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.

[0915] 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.

[0916] 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).

[0917] 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.

[0918] 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.

[0919] 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.

[0920] 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.

[0921] 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.

[0922] 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."

[0923] Data collection and integration

[0924] The system according to the present invention provides a function for collecting personal health data and external data from multiple data sources, and integrating and analyzing them. A specific embodiment of the system and its operation will be described below.

[0925] Collection of personal health data

[0926] Users use a smartphone app every day to input data such as their weight, walking distance, diet, etc. If they use a wearable device, this data is automatically synchronized to the device.

[0927] example:

[0928] The user steps on the scale in the morning and enters the weight reading into a smartphone app.

[0929] The smartwatch measures the distance walked during the day and automatically transmits the data to a smartphone via Bluetooth.

[0930] External Data Collection

[0931] The server periodically accesses external APIs to obtain weather and infectious disease information, which is used to personalize health advice for users.

[0932] example:

[0933] The server accesses the weather information API at a fixed time each day to obtain the temperature, humidity, and weather for that day.

[0934] The latest infectious disease spread status and preventive measures are obtained weekly from the infectious disease information API.

[0935] Data integration and analysis

[0936] The personal health data collected by the device is sent in real time to a server, which then combines this data with external data to analyze the user's health condition.

[0937] example:

[0938] The device transmits weight data, walking distance data, and dietary data to the server.

[0939] The data is analyzed in conjunction with temperature data and infectious disease information obtained by the server.

[0940] Generating and Serving Advice

[0941] The server generates health advice for the user based on the analysis results. This advice includes dietary recommendations and exercise suggestions. This advice is then sent to the user via their device.

[0942] example:

[0943] The server analyzes the data and determines that "taking vitamin C is an effective way to combat recent infectious diseases," and recommends that the user consume citrus fruits.

[0944] The server generates advice such as "Today's temperature is low, so let's do some indoor exercises" and sends it to the device.

[0945] The device will notify the user of the advice it has received and display details within the app.

[0946] Feedback gathering and learning

[0947] The user follows the advice and inputs the results into a smartphone app, or the wearable device automatically collects the feedback. This feedback information is then sent back to the server and used to generate the next advice.

[0948] example:

[0949] The user enters "I walked for 30 minutes" in the app.

[0950] The smartwatch automatically collects the user's exercise records and sends them to the device.

[0951] The device sends this feedback information to the server, which then reflects it in the next analysis.

[0952] In this way, the system of the present invention effectively integrates and analyzes the user's daily health data and external environmental data, and provides personalized health advice, thereby supporting the user in sustainable health management.

[0953] The processing flow will be explained below.

[0954] Step 1: Data entry and acquisition

[0955] 1.1 The user opens the smartphone app and manually enters their weight, as well as their dietary and calorie information.

[0956] 1.2 The device connects to a wearable device such as a smartwatch and automatically collects data such as walking distance and heart rate.

[0957] 1.3 The device temporarily stores the acquired personal health data in local storage.

[0958] Step 2: Send data

[0959] 2.1 The device periodically sends this personal health data to the server. The data is encrypted and transmitted securely.

[0960] 2.2 The server stores the personal health data transmitted to the database.

[0961] Step 3: Gather external data

[0962] 3.1 The server sends a request to the weather information API to obtain weather data such as the current temperature and weather.

[0963] 3.2 The server obtains the latest infectious disease outbreak information from the infectious disease information API.

[0964] Step 4: Integrate the data

[0965] 4.1 The server integrates the user's personal health data with the acquired weather data and infectious disease data.

[0966] 4.2 The server centralizes the data and creates a dataset for analysis.

[0967] Step 5: Analyze your health status

[0968] 5.1 The server's analytics engine assesses the user's health status based on the integrated dataset, using machine learning algorithms and rule-based systems.

[0969] 5.2 The server performs risk assessment based on the evaluation results and generates appropriate health advice for the user.

[0970] Step 6: Generating and notifying advice

[0971] 6.1 The server compiles the generated advice and formats it into a recommendation for the user.

[0972] 6.2 The server sends the advice to the terminal and sends it as a notification message.

[0973] 6.3 The device notifies the user of the advice and displays detailed advice content.

[0974] Step 7: Gather feedback

[0975] 7.1 The user acts on the advice and inputs the results into the app, for example, recording the duration of a walk or what they ate.

[0976] 7.2 The terminal automatically collects feedback information from the wearable device and sends it to the server.

[0977] Step 8: Learn and improve your advice

[0978] 8.1 The server updates the analytical model based on the collected feedback information and uses it to generate advice from the next time onwards.

[0979] 8.2 The server takes in new data and re-learns to improve the accuracy of advice given to users.

[0980] These steps enable the system to assist users in managing their individual health and provide personalized advice for continually improving their health.

[0981] Example 1

[0982] 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."

[0983] To provide efficient and personalized health management, it is necessary to integrate personal health data with external data and provide users with appropriate advice based on the analysis results. However, current systems lack real-time performance and individualized accuracy in the collection, integration, and analysis of data and the provision of advice. Therefore, there is a need for a system that can solve these issues and enable more effective health management.

[0984] 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.

[0985] In this invention, the server includes means for collecting personal health data in real time and transmitting it to the server, means for integrating and analyzing the personal health data and external data to evaluate the user's health condition, and means for transmitting advice generated based on the analysis results to the user terminal. This makes it possible to integrate and analyze the user's health data and external data in real time and provide personalized health management advice.

[0986] "Personal health data" refers to information about the user's health status, such as weight, walking distance, and diet.

[0987] "External data" refers to information about the external environment that affects the user's health management, such as weather information and infectious disease information.

[0988] "Collection methods" refer to the methods and technologies used to acquire and store data.

[0989] "Integrated analysis" means processing multiple data sets together and conducting a comprehensive analysis.

[0990] "Health status assessment" refers to diagnosing the user's health status based on the integrated data and evaluating it according to certain criteria.

[0991] "Advice generator" refers to a method or technology that generates recommendations or instructions to provide to a user based on the evaluation results.

[0992] "Notification means" refers to a method or technique for notifying the user of the generated advice.

[0993] "Feedback information" refers to information relating to the results of a user's actions in accordance with advice.

[0994] "Server" means a central processing unit for processing collected data and storing and transmitting analysis results.

[0995] "Terminal" refers to a device used by a user to input data and receive notifications from a server.

[0996] A "mobile device app" is an application that runs on a mobile electronic device such as a smartphone or tablet.

[0997] A "wearable device" is a data collection device that is worn by a user.

[0998] "Communication network" refers to a network for sending and receiving data, such as the Internet.

[0999] "Software interface" refers to the programs and protocols used to obtain external data.

[1000] MODE FOR CARRYING OUT THE INVENTION

[1001] The present invention relates to a system for collecting personal health data of a user, evaluating the user's health condition by integrating and analyzing the data with external data, and providing appropriate health advice. Specific embodiments of the present invention will be described in detail below.

[1002] Collection of personal health data

[1003] Users use a mobile device app to input daily health data such as weight, walking distance, dietary habits, etc. Wearable devices (e.g., smartwatches) automatically measure data such as walking distance and heart rate, and synchronize the data with a smartphone using communication technologies such as Bluetooth.

[1004] example:

[1005] The user steps on the scale every morning and enters their weight into a smartphone app.

[1006] The smartwatch measures the user's walking distance during the day and automatically sends the data to the device.

[1007] External Data Collection

[1008] The server periodically obtains external data, such as weather information and infectious disease information, using a software interface (API) provided via the Internet, thereby collecting external environmental data that may affect the user's health condition.

[1009] example:

[1010] The server obtains the day's temperature, humidity, and weather from the weather information API at a fixed time each day.

[1011] The server obtains the latest infectious disease spread status and preventive measures from the infectious disease information API every week.

[1012] Data integration and analysis

[1013] The personal health data collected by the device is sent in real time to a server, where it is integrated with external data and analyzed to assess the user's health status.

[1014] example:

[1015] The terminal transmits the user's weight data, walking distance data, and dietary habit data to the server.

[1016] The server integrates temperature data and infectious disease information to analyze the user's health risk.

[1017] Generating and Serving Advice

[1018] The server generates health advice for the user based on the analysis results, and the advice is sent to the user's smartphone or other device. The advice includes dietary recommendations and exercise suggestions.

[1019] example:

[1020] The server analyzes the data and determines that "taking vitamin C is effective in preventing infectious diseases," and recommends that the user consume citrus fruits.

[1021] The system generates advice such as "Today's temperature is low, so let's do some indoor exercises," and notifies the user's device.

[1022] Feedback gathering and learning

[1023] The user follows the advice and inputs the results into a smartphone app, or the wearable device automatically collects exercise records. This feedback information is then sent back to the server and used to generate advice for the next time.

[1024] example:

[1025] The user enters "I walked for 30 minutes" in the app.

[1026] The smartwatch automatically collects the user's exercise records and sends them to the device.

[1027] The device sends this feedback information to the server, which then reflects it in the next analysis.

[1028] This system effectively integrates users' daily health data with external environmental data and provides personalized health advice, helping users manage their health sustainably.

[1029] Example prompts for generative AI models

[1030] "A user weighs themselves using a scale in the morning and enters that data into a smartphone app. The server periodically uses an external API to obtain the temperature, humidity, and weather for that day. Based on that data, it generates health advice tailored to the user and sends it to their smartphone. Please explain each processing step of this system in detail."

[1031] This prompt allows the generative AI model to generate detailed explanations of each processing step in the system.

[1032] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1033] Step 1: User enters personal health data

[1034] Users manually enter personal health data such as weight, walking distance, and dietary habits into a smartphone app. The entered data is stored in the smartphone's internal database. For example, a user can step on a scale every morning and enter their measured weight into the app. This input data becomes the basis for subsequent analysis.

[1035] Input: Personal health data such as weight, walking distance, dietary habits, etc.

[1036] Output: Personal health data stored in the smartphone's internal database

[1037] Step 2: Automated data collection with wearable devices

[1038] The device automatically collects data from wearable devices such as smartwatches, which constantly measure the user's walking distance, heart rate, sleep patterns, and more, and transmits the data via Bluetooth to a smartphone, where it is integrated into the smartphone's internal database.

[1039] Input: Walking distance, heart rate, sleep data from smartwatch

[1040] Output: Wearable device data integrated into smartphone internal database

[1041] Step 3: Gather external data

[1042] The server periodically obtains external data, such as weather information and infectious disease information, via the internet using an external API. For example, the server accesses the weather information API at 7:00 a.m. every day to obtain the temperature, humidity, and weather for that day. This data is then stored in the server's database.

[1043] Input: Temperature, humidity, and weather data obtained from the weather information API

[1044] Output: Weather information stored in the server database

[1045] Step 4: Send your personal health data

[1046] The device sends the collected personal health data to the server in real time, allowing the server to always have the latest user information. For example, a smartphone periodically sends weight data, walking distance data, and dietary habit data to the server.

[1047] Input: Personal health data stored in the smartphone's internal database

[1048] Output: Personal health data sent to the server

[1049] Step 5: Data integration and analysis

[1050] The server integrates the received personal health data with external data to assess the user's health status. For example, the server combines weight data, walking distance data, and dietary data with temperature data and infectious disease information to perform statistical analysis. The results of this analysis serve as the basis for health advice for the user.

[1051] Input: Personal health data and external data sent to the server

[1052] Output: Analysis results (evaluation of the user's health status)

[1053] Step 6: Generate health advice

[1054] The server generates health advice for the user based on the analysis results. For example, if the server determines that "taking vitamin C is an effective measure against recent infectious diseases," it generates advice recommending the user to consume citrus fruits. This advice is stored in the server's memory in text format.

[1055] Input: User's health assessment

[1056] Output: Generated health advice

[1057] Step 7: Advice Notification

[1058] The server sends the generated advice to the device and notifies the user. For example, advice such as "Today's temperature is low, so try doing some indoor exercises" is sent in text format to the device, and the device notifies the user via a push notification.

[1059] Input: Generated health advice

[1060] Output: Advice notification sent to user terminal

[1061] Step 8: Gather user feedback

[1062] The user inputs the results of their actions based on the advice into a smartphone app, or the wearable device automatically collects feedback data. For example, the user can input "I walked for 30 minutes" into the app, or the smartwatch can send exercise data to the device. This feedback information is then sent back to the server.

[1063] Input: User-entered feedback data or data from a wearable device

[1064] Output: Feedback information sent to the server

[1065] Step 9: Analyze the feedback and incorporate it into your next advice

[1066] The server analyzes the received feedback information and uses it to generate the next health advice. For example, the server analyzes the feedback information to understand the user's exercise habits and dietary patterns and reflect them in the next advice. This process enables more accurate and personalized health management.

[1067] Input: Feedback information sent to the server

[1068] Output: Next health advice reflecting the feedback information

[1069] (Application example 1)

[1070] 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."

[1071] Personal health management is an important issue today, and there is a need to effectively collect and analyze health data, particularly daily weight, walking distance, and dietary habits, and provide appropriate advice. However, it is difficult to collect and analyze this data using a single device or platform, and it is also difficult to provide comprehensive health advice in real time that takes into account external weather and infectious disease information. Additionally, there are few opportunities to receive instant, personalized health advice in physical stores, and there is insufficient integration between in-store product information and health advice.

[1072] 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.

[1073] In this invention, the server includes: means for recording weight, walking distance, and dietary habits to collect personal health data; means for collecting external weather information and infectious disease information; means for integrating and analyzing the personal health data and external data to evaluate the user's health status; means for generating dietary and health management advice for the user based on the evaluation; means for notifying the advice to the user terminal and to an information providing terminal in the physical store; means for the information providing terminal to instantly provide personalized health advice based on the user's health data and external data; means for providing in-store guidance on products related to the advice; and means for collecting feedback information from the user and using it to generate next advice. This makes it possible to effectively integrate and analyze personal health data and external environmental data, instantly provide personalized health advice in the physical store, and further guide users to related products.

[1074] "Personal health data" refers to data that indicates the individual health condition of a user, such as weight, walking distance, and dietary habits.

[1075] "Weather information" is data that indicates the state of the external environment, such as the temperature, humidity, and weather of the area.

[1076] "Infectious disease information" refers to data related to infectious diseases, such as the prevalence of infectious diseases in the area and preventive measures.

[1077] "Integrated analysis" is the process of combining and analyzing multiple data to find overall patterns and meanings.

[1078] A "user terminal" is a device used by a user, such as a smartphone or tablet.

[1079] An "information providing terminal" is a device that is installed in a physical store and provides information to users.

[1080] "Evaluating health status" means analyzing the user's health status based on collected personal health data and external data.

[1081] "Dietary and health management advice" is information that suggests dietary habits, exercise, supplements, etc. to improve the user's health.

[1082] "Notifying" refers to the act of sending the generated advice to the user or other terminals.

[1083] "Feedback information" is information about the actions that the user actually takes and the results of those actions.

[1084] "Personalized health advice" refers to personalized health management suggestions based on an individual user's health data and external data.

[1085] "In-store guidance" refers to the act of providing the location and detailed information of related products within a physical store.

[1086] This invention is a system that integrates personal health data with external environmental data to provide users with personalized health advice. This system is composed of a personal health data collection means, a weather information and infectious disease information collection means, a data integration and analysis means, an advice generation means, an information provision means, and a feedback collection means.

[1087] 1. Collection of personal health data

[1088] The server collects daily health data such as weight, walking distance, and dietary habits from users using smartphone apps and wearable devices. This data is usually automatically synchronized with the smartphone via wireless communication such as Bluetooth and sent to the server.

[1089] For example, after a user steps on a scale in the morning, the weight is automatically recorded in a smartphone app. The smartwatch also measures the distance walked during the day and sends the data to the smartphone, which then sends it to a server.

[1090] 2. External Data Collection

[1091] The server periodically obtains weather and infectious disease information from an external API provided via the Internet, and this data is used to evaluate the user's health status.

[1092] Specifically, the server accesses the weather information API at a fixed time each day to obtain the temperature, humidity, and weather for that day, and also accesses the infectious disease information API weekly to obtain the latest information on the spread of infectious diseases and preventive measures.

[1093] 3. Data integration and analysis

[1094] The server integrates the collected personal health data with external data to analyze the user's health status in real time, and generates specific health advice for the user based on the analysis results.

[1095] For example, the server analyzes the user's walking distance data, weight data, and dietary data in combination with external data such as temperature and humidity. Based on the analysis, specific advice such as "Today's temperature is low, so let's do some indoor exercises" is generated.

[1096] 4. Providing advice

[1097] The generated advice is notified to the user terminal and an information providing terminal in the physical store. The information providing terminal is a device that provides health advice to the user in real time within the store.

[1098] Specifically, advice generated by the server, such as "Today's temperature is low, so we recommend you drink a hot drink," is displayed on the robot's display in the physical store, and the user is guided to related products in the store.

[1099] 5. Feedback Collection

[1100] The user can input the results of their actions based on the advice into a smartphone app, or the wearable device can automatically collect the feedback. This feedback information is then sent back to the server and used to generate the next advice.

[1101] For example, if a user enters "I walked for 30 minutes" in the app, that information is sent to the server, and the smartwatch automatically collects and sends walking data to the server in the same way.

[1102] Prompt Sentence Examples

[1103] "Due to the low temperatures today, we recommend that you eat some warm soup. We also recommend taking vitamin C supplements to prevent infection. We have related products available in the XX section of our store, so please feel free to use them."

[1104] This system effectively integrates and analyzes personal health data and external environmental data, making it possible to provide instant, personalized health advice in physical stores and even introduce related products.

[1105] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1106] Step 1: Collect personal health data

[1107] Users use a smartphone app and a wearable device to collect daily health data such as weight, walking distance, and diet. The smartphone app records this data on the device and automatically synchronizes it with the wearable device using Bluetooth. The device then sends the collected data to a server. Health data such as weight, walking distance, and dietary details are used as input. The output is personal health data sent to the server. Specifically, the user steps on the scale every morning, and the weight is automatically recorded in the smartphone app. The smartphone then retrieves walking distance data from the wearable device and sends all data to the server.

[1108] Step 2: Gather external data

[1109] The server periodically obtains weather information and infectious disease information from an external API provided via the internet. It obtains data such as temperature, humidity, and weather from the weather information API, and data on infectious disease epidemic status and preventive measures from the infectious disease information API. The input is weather information and infectious disease information from the external API, and the output is integrated external data. Specifically, the server sends a request to the weather information API at a fixed time each day to obtain the temperature, humidity, and weather for that day, and accesses the infectious disease information API weekly to obtain the latest infectious disease spread status.

[1110] Step 3: Data synthesis and analysis

[1111] The server integrates the collected personal health data with external data for analysis. This analysis uses an algorithm to evaluate the user's health status by combining the personal health data and external data. The input is the personal health data and external data, and the output is the evaluation result of the user's health status. Specifically, the server combines data such as the individual's weight, walking distance, and diet with weather information such as temperature and humidity, and analyzes the user's health status using a health evaluation algorithm.

[1112] Step 4: Generating Advice

[1113] The server generates health advice for the user based on the analysis results. This advice includes dietary recommendations, exercise plans, and infection prevention measures. The input is the health status assessment result, and the output is advice to be provided to the user. Specifically, the server generates advice such as "Today's temperature is low, so we recommend drinking a hot drink."

[1114] Step 5: Providing advice

[1115] The generated advice is notified to the user terminal and an information providing terminal in the physical store. The information providing terminal immediately provides health advice to the user in the store and also introduces related products. The input is the generated advice, and the output is a notification to the user and the information providing terminal. In specific operations, the server notifies the user's smartphone of the advice, and a robot in the physical store displays the advice on a display.

[1116] Step 6: Gather feedback

[1117] The user inputs the results of their actions based on the advice into the smartphone app, or the wearable device automatically collects the data. The server uses this feedback information to generate the next piece of advice. The input is feedback information about the user's actions, and the output is data that will be used to generate the next piece of advice. Specifically, the user inputs "I walked for 30 minutes" into the app, and this information is sent to the server. Similarly, the smartwatch automatically collects and sends the walking data to the server.

[1118] Through these processing steps, the system supports personal healthcare and provides added value within physical stores.

[1119] 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.

[1120] Data collection and integration

[1121] The system according to the present invention provides a function for collecting personal health data and emotion data, and integrating and analyzing them. A specific embodiment of the system and its operation will be described below.

[1122] Collection of personal health data

[1123] Users use a smartphone app every day to input data such as their weight, walking distance, diet, etc. If they use a wearable device, this data is automatically synchronized to the device.

[1124] example:

[1125] The user steps on the scale in the morning and enters the weight reading into a smartphone app.

[1126] The smartwatch measures the distance walked during the day and automatically transmits the data to a smartphone via Bluetooth.

[1127] External Data Collection

[1128] The server periodically accesses external APIs to obtain weather and infectious disease information, which is used to personalize health advice for users.

[1129] example:

[1130] The server accesses the weather information API at a fixed time each day to obtain the temperature, humidity, and weather for that day.

[1131] The latest infectious disease spread status and preventive measures are obtained weekly from the infectious disease information API.

[1132] Collecting Emotional Data

[1133] The device uses an emotion engine to collect user emotion data, which is analyzed based on the user's facial expressions, voice, and input data.

[1134] example:

[1135] The smartphone camera captures the user's facial expressions, and the emotion engine analyzes them.

[1136] The user's voice is recorded through a microphone and analyzed.

[1137] Data is collected from text input, etc., to infer the user's emotions.

[1138] Data integration and analysis

[1139] The device sends the collected personal health and emotional data to a server, which then integrates this data with external data to perform a multifaceted analysis of the user's health condition.

[1140] example:

[1141] The terminal transmits weight data, walking distance data, dietary habit data, and emotion data to the server.

[1142] The data is analyzed in conjunction with weather data and infectious disease information obtained by the server.

[1143] Generating and Serving Advice

[1144] The server generates health advice for the user based on the analysis results. This advice includes dietary recommendations and exercise suggestions. It also takes into account emotional data. This advice is then sent to the user via their device.

[1145] example:

[1146] The server analyzes the data and determines that "taking vitamin C is an effective way to combat recent infectious diseases," and recommends that the user consume citrus fruits.

[1147] The server generates advice such as "Today's temperature is low, so let's do some indoor exercises" and sends it to the device.

[1148] The server determines from emotional data that the user is feeling stressed and recommends activities that are effective for relaxation and stress relief.

[1149] The terminal notifies the user of the received advice and displays detailed advice content.

[1150] Feedback gathering and learning

[1151] The user follows the advice and inputs the results into a smartphone app, or the wearable device automatically collects the feedback. This feedback information is then sent back to the server and used to generate the next advice.

[1152] example:

[1153] The user enters "I walked for 30 minutes" in the app.

[1154] The smartwatch automatically collects the user's exercise records and sends them to the device.

[1155] The device sends this feedback information to the server, which then reflects it in the next analysis.

[1156] These steps enable the system to assist users in managing their individual health and provide personalized advice to help improve their health, including their emotional state, over time.

[1157] The processing flow will be explained below.

[1158] Step 1: Data entry and acquisition

[1159] 1.1 The user opens the smartphone app and manually enters their weight, as well as their dietary and calorie information.

[1160] 1.2 The device connects to a wearable device such as a smartwatch and automatically collects data such as walking distance and heart rate.

[1161] 1.3 The device uses a camera and microphone to capture the user's facial expressions and voice to collect emotional data.

[1162] Step 2: Send data

[1163] 2.1 The device sends personal health and emotion data to the server. The data is encrypted and securely transmitted.

[1164] 2.2 The server stores the personal health data and emotion data sent to the database.

[1165] Step 3: Gather external data

[1166] 3.1 The server sends a request to the weather information API to obtain weather data such as the current temperature and weather.

[1167] 3.2 The server obtains the latest infectious disease outbreak information from the infectious disease information API.

[1168] 3.3 The server stores these weather and infectious disease data in a database.

[1169] Step 4: Integrate the data

[1170] 4.1 The server integrates the user's personal health data, emotion data, weather data, and infectious disease data.

[1171] 4.2 The server creates the integrated dataset and feeds it into the analysis engine.

[1172] Step 5: Analyze your health status

[1173] 5.1 The server's analytics engine assesses the user's health and emotional state based on the integrated dataset, using machine learning algorithms and rule-based systems.

[1174] 5.2 The server performs risk assessment based on the evaluation results and generates health advice and emotion-based advice appropriate for the user.

[1175] Step 6: Generating and notifying advice

[1176] 6.1 The server compiles and formats the generated advice into a recommendation for the user, including dietary recommendations, exercise suggestions, and emotional care advice.

[1177] 6.2 The server sends the advice to the terminal and sends it as a notification message.

[1178] 6.3 The device notifies the user of the advice and displays detailed advice content.

[1179] Step 7: Gather feedback

[1180] 7.1 The user acts on the advice and inputs the results into the app, for example, recording the duration of a walk or what they ate.

[1181] 7.2 The terminal automatically collects feedback information from the wearable device and sends it to the server.

[1182] Step 8: Learn and improve your advice

[1183] 8.1 The server updates the analytical model based on the collected feedback information and uses it to generate advice from the next time onwards.

[1184] 8.2 The server takes in new data and re-learns to improve the accuracy of advice given to users.

[1185] These steps enable the system to assist users in managing their individual health and emotions, providing personalized advice that can help improve their health and emotional state over time.

[1186] Example 2

[1187] 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."

[1188] In modern society, personal health management is becoming increasingly important, necessitating the development of health management systems that are more detailed and flexible. However, while existing health management systems provide basic health management based on personal health data (weight, walking distance, dietary habits) and external data (weather information, infectious disease information), they are unable to provide personalized health management advice that takes emotional data into account. Furthermore, they lack the functionality to quickly and effectively incorporate user feedback information and utilize it in generating next advice, making it difficult to translate this into practical health improvements.

[1189] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1190] In this invention, the server includes means for recording weight, walking distance, and dietary habits to collect personal health data, means for collecting external weather information and infectious disease information, means for analyzing the user's facial expressions, voice, and input data to collect user emotion data, means for integrating and analyzing the personal health data, external data, and emotion data to evaluate the user's health condition from various angles, means for generating health management advice for the user based on the evaluation and taking into account diet, exercise, and emotion state, means for notifying the user terminal of the advice, and means for collecting feedback information from the user and using it to generate next advice. This makes it possible to provide personalized health management advice that integrates the personal health data, external data, and emotion data, and to quickly reflect user feedback.

[1191] "Personal health data" refers to data that indicates the user's personal health condition, such as weight, walking distance, and dietary habits.

[1192] "External data" refers to external information obtained via the Internet, such as weather information and infectious disease information.

[1193] "Emotion data" is data that indicates the user's emotional state, obtained by analyzing the user's facial expressions, voice, and input data.

[1194] "Integrated analysis" is a process of integrating and analyzing personal health data, external data, and emotional data to evaluate the user's health condition from multiple angles.

[1195] "Health Management Advice" refers to instructions or recommendations given to the user based on the analysis results, taking into account diet, exercise, and emotional state.

[1196] "Feedback information" is data collected as a result of a user acting on advice.

[1197] A "server" is a computer system that collects, analyzes, and generates advice from data.

[1198] "Terminal" means a device (e.g., smartphone, wearable device) that a user uses to input and receive data.

[1199] "API" is an abbreviation for Application Program Interface, which provides a mechanism for exchanging data between different software programs.

[1200] An "emotion engine" is software that analyzes a user's facial expressions, voice, and input data to infer their emotional state.

[1201] MODE FOR CARRYING OUT THE INVENTION

[1202] The present invention is a system that integrates multiple data collection and analysis means, evaluates a user's health condition from multiple angles, and provides personalized health management advice. Specific embodiments and operations of the system are described below.

[1203] System Configuration Overview

[1204] 1. The system mainly consists of three elements: server, terminal, and user.

[1205] 2. The server collects, analyzes, and generates advice on data.

[1206] 3. A terminal is a device (e.g., a smartphone or wearable device) through which a user inputs and receives data.

[1207] 4. Users provide personal health data and feedback information.

[1208] Hardware and software used

[1209] Server: Uses cloud-based computing resources to run the database and generative AI models.

[1210] Devices: Smartphones and wearable devices (e.g., smartwatches).

[1211] Emotion Engine: Includes facial expression analysis software and a voice analysis engine.

[1212] Data collection

[1213] Users use a smartphone app every day to enter personal health data such as weight, walking distance, diet, etc. If they use a wearable device, this data is automatically synced to the device.

[1214] example:

[1215] The user steps on the scale in the morning and enters the weight reading into a smartphone app.

[1216] The smartwatch measures the distance walked during the day and automatically transmits the data to a smartphone via Bluetooth.

[1217] The server periodically uses external APIs to obtain weather and infectious disease information, which is used to personalize health advice for users.

[1218] example:

[1219] The server accesses the weather information API at a fixed time each day to obtain the temperature, humidity, and weather for that day.

[1220] The latest infectious disease spread status and preventive measures are obtained weekly from the infectious disease information API.

[1221] The device uses an emotion engine to collect user emotion data, which is analyzed based on the user's facial expressions, voice, and input data.

[1222] example:

[1223] The smartphone camera captures the user's facial expressions, and the emotion engine analyzes them.

[1224] The user's voice is recorded through a microphone and analyzed.

[1225] Data is collected from text input, etc., to infer the user's emotions.

[1226] Data transmission and analysis

[1227] The device sends the collected personal health and emotional data to a server, which then integrates this data with external data and uses a generative AI model to perform a multifaceted analysis of the user's health condition. Based on the results of this analysis, the server generates personalized health management advice.

[1228] Providing advice and feedback

[1229] The generated advice is sent to the user via the device, and includes dietary recommendations, exercise suggestions, and advice that takes into account emotional state.

[1230] Example prompt statement:

[1231] "Tell me how you're feeling today."

[1232] Enter your morning weight

[1233] "Tell me about your recent eating habits."

[1234] "Check out the advice based on infectious disease information."

[1235] The results of the user's actions based on the advice are entered into a smartphone app or automatically collected by a wearable device. This feedback information is then sent back to the server and used to generate the next advice.

[1236] In this way, this system supports users in managing their health by integrating personal health data, external data, and emotional data. Detailed collection and analysis of various data makes it possible to provide users with personalized health advice.

[1237] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1238] Step 1:

[1239] User input of personal health data

[1240] Users use a smartphone app to input personal health data such as weight, walking distance, and dietary habits. The input data is temporarily stored within the app. Specifically, the user opens the smartphone app and inputs their weight for the day. The app then stores this data within the device.

[1241] Input: weight, walking distance, dietary data

[1242] Output: Personal health data stored on the device

[1243] Step 2:

[1244] Terminal syncs data from wearable devices

[1245] A wearable device (e.g., a smartwatch) automatically transmits data collected, such as walking distance, to a smartphone via Bluetooth. The device then integrates this data into an app. Specifically, the smartwatch measures the distance walked during the day and transmits the data to the smartphone via Bluetooth.

[1246] Input: Walking distance data from a wearable device

[1247] Output: Walking distance data integrated into a smartphone app

[1248] Step 3:

[1249] Server-based external data collection

[1250] The server periodically accesses an external API to obtain weather and infectious disease information. This data is organized and stored on the server side. Specifically, the server accesses the weather information API at a fixed time each day to obtain the temperature, humidity, and weather for that day.

[1251] Input: Weather information and infectious disease information obtained from external API

[1252] Output: External data stored on the server side

[1253] Step 4:

[1254] Collecting emotion data from devices

[1255] The device's emotion engine analyzes the user's facial expressions, voice, and input data to generate emotion data. Specifically, the smartphone camera captures the user's facial expressions, and the emotion engine analyzes them.

[1256] Input: facial expressions, voice, input data

[1257] Output: Parsed emotion data

[1258] Step 5:

[1259] Sending data from the device to the server

[1260] The device periodically transmits the collected personal health data and emotion data to a server, which receives the data and stores it in a database. Specifically, the device periodically uploads the data to the server in the background.

[1261] Input: Personal health data and emotional data collected on the device

[1262] Output: Data stored on the server

[1263] Step 6:

[1264] Data integration and analysis by the server

[1265] The server integrates the received personal health data, emotion data, and external data, and analyzes them using a generative AI model. Specifically, the server integrates each data set into a single dataset and then performs analysis using the generative AI model.

[1266] Input: personal health data, emotion data, external data

[1267] Output: Analysis results

[1268] Step 7:

[1269] Server-generated advice

[1270] The server generates health management advice for the user based on the analysis results. This advice takes into account diet, exercise, and emotional state. Specifically, the server generates advice text based on the generated data and prepares to send it to the device in the next step.

[1271] Input: Analysis results

[1272] Output: Health care advice

[1273] Step 8:

[1274] Sending advice from the server to the device

[1275] The server generates health management advice and sends it to the device via a dedicated API, which then notifies the user. Specifically, the server sends the advice to the device, which then displays it to the user using its notification function.

[1276] Input: Health Care Advice

[1277] Output: Advice displayed on terminal

[1278] Step 9:

[1279] Gathering user feedback

[1280] The user can follow the advice and enter the results into a smartphone app, or the wearable device can automatically collect the data. Specifically, the user can enter "I walked for 30 minutes" in the app, or the smartwatch can automatically record the exercise results and send them to the device.

[1281] Input: User action result

[1282] Output: Feedback information

[1283] Step 10:

[1284] Sending feedback information to the server by the terminal

[1285] The device sends the collected feedback information to the server, which then uses it to generate the next advice. Specifically, the device automatically sends the feedback data to the server, and the server reflects the received data in the next analysis.

[1286] Input: Feedback information

[1287] Output: Data that will be reflected in the next advice generation

[1288] (Application example 2)

[1289] 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."

[1290] Conventional health management systems have difficulty managing individual health data and emotional data in a unified manner and providing optimal health advice to consumers. Furthermore, they were unable to provide advice that took into account the real-time health and emotional state of consumers visiting the store, which meant that the effectiveness of health management could not be maximized.

[1291] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1292] In this invention, the server includes: means for recording weight, walking distance, and dietary habits to collect personal health data; means for analyzing facial expressions, voice, and input data to collect emotion data; means for collecting external weather information and infectious disease information; means for integrating and analyzing the personal health data, emotion data, and external data to evaluate the user's health status; means for generating dietary and health management advice for the user based on the evaluation; means for notifying the advice on an in-store display or a user terminal; and means for collecting user feedback information and using it to generate next advice. This makes it possible to provide individually optimized health advice based on the customer's real-time health and emotion data.

[1293] "Personal health data" refers to information about an individual's health status, such as weight, walking distance, and diet.

[1294] "Emotional data" refers to information about an individual's emotional state analyzed based on facial expressions, voice, and input data.

[1295] "Facial expression analysis" is a technology that uses an input device such as a camera to detect an individual's facial expression and analyze their emotional state.

[1296] "Voice analysis" is a technology that analyzes voice data collected through a microphone or other device to determine the emotions and state of the voice.

[1297] "Input data analysis" is a technology that infers and analyzes emotions and states from text data entered by the user.

[1298] "External data" refers to data such as weather information and infectious disease information that is collected mainly via the Internet, separate from personal health data.

[1299] "Weather information" refers to information about the weather conditions of the day, such as temperature, humidity, and weather.

[1300] "Infectious disease information" refers to information on the spread of infectious diseases and preventive measures.

[1301] "Integrated analysis" refers to the centralized handling of multiple different data sets and comprehensive analysis.

[1302] "Evaluation" refers to objectively determining the user's health condition based on collected data.

[1303] "Advice" refers to suggestions about diet and health management provided to the user based on the evaluation results.

[1304] "Notification" is the act of notifying the user of the generated advice.

[1305] "Feedback information" is information relating to the results of actions taken by the user in accordance with the advice.

[1306] "Collecting feedback information" refers to collecting information entered by the user or automatically collected by the wearable device.

[1307] An "IoT device" is a physical device that connects to the internet, including things like smart mirrors and bathroom scales.

[1308] A "display" is a device that displays images or data.

[1309] "Integrated data" refers to comprehensive data generated by integrating individual data.

[1310] The present invention is a system that collects personal health data and emotional data, and integrates and analyzes them. Specific embodiments for carrying out the invention are described below.

[1311] System Configuration

[1312] This system consists of a smartphone app, a wearable device, an IoT device, a server, and a user terminal.

[1313] 1. Data Collection Methods

[1314] Smartphone app: Collects personal health data entered by the user, such as weight, walking distance, dietary habits, etc. The smartphone is equipped with a camera that captures facial expressions and a microphone that records voice, which are used to obtain emotional data as well.

[1315] Wearable devices: Smartwatches and fitness trackers automatically measure a user's weight and walking distance and sync with a smartphone via Bluetooth.

[1316] IoT devices: Smart mirrors, scales, sensors connected to exercise machines, etc. are used to collect health and emotional data from visitors.

[1317] 2. External Data Collection Methods

[1318] Server: Uses APIs provided via the internet to collect weather and infectious disease information. Temperature, humidity, and weather are collected from the weather information API, and the latest infectious disease spread status and preventive measures are obtained from the infectious disease information API.

[1319] 3. Integrated analysis method

[1320] Server: Collects and analyzes collected personal health data, emotional data, and external data in a unified manner. For example, it integrates weight data, walking distance data, dietary data, and emotional data to evaluate the user's health status.

[1321] 4. Advice Generation Methods

[1322] Server: Generates health advice for the user based on the assessment results, including dietary recommendations, exercise suggestions, and emotion-based relaxation and stress-relief activities.

[1323] 5. Means of notification

[1324] In-store display or user device: Advice is sent to a smart display or user's smartphone, for example, a message such as "You need vitamin C, so it's recommended that you eat citrus fruits."

[1325] 6. Feedback Collection Methods

[1326] Smartphone app and wearable device: Users enter the results of their activities into the app or the wearable device automatically records them. This information is sent to the server and used to generate advice for the next time.

[1327] This will enable the provision of real-time, individually optimized health advice based on collected personal health data, emotional data, and external data.

[1328] Specific examples

[1329] For example, a user stands in front of a smart mirror in a store, their facial expressions are captured, and then their weight data is entered into their smartphone. Meanwhile, the server retrieves weather and infectious disease information from the API and analyzes all the data. As a result, it is determined that the user's emotional state indicates stress, and specific relaxation exercises are recommended. This information is then displayed on the store's smart display and notified to the user.

[1330] Prompt Sentence Examples

[1331] "Describe the steps required to collect weight and emotion data and analyze an individual's health status. Also, describe the process for retrieving weather and infectious disease information from external APIs and integrating that data to generate recommendations."

[1332] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1333] Step 1:

[1334] Users can input data such as weight, walking distance, and dietary habits into a smartphone app, or data can be automatically synchronized from a wearable device. Numerical values ​​such as weight and walking distance are obtained as input data, and personal health data is collected based on these values. Specifically, users can step on a scale in the morning and enter the values ​​into a smartphone app, or a smartwatch can measure the distance walked during the day and send the data to a smartphone via Bluetooth.

[1335] Step 2:

[1336] The device collects emotion data. This includes capturing the user's facial expressions with the smartphone camera, collecting audio with the voice recording function, and analyzing text input. The input data includes facial expression images, audio files, and text, which are then analyzed using an emotion analysis engine to extract emotion data. Specifically, the smartphone camera captures the user's smile, and the image analysis engine classifies it as "happy."

[1337] Step 3:

[1338] The server obtains external weather and infectious disease information from APIs. The input data is the response from the external API, with temperature, humidity, and weather data obtained from the weather API, and infectious disease spread status and preventive measures obtained from the infectious disease information API. This data is used to personalize health advice for users.

[1339] Step 4:

[1340] The device sends the collected personal health data and emotional data to a server. The input data is personal health data and emotional data, which are stored in an integrated manner by the server. The server then integrates this data and performs multifaceted analysis. For example, it may integrate weight data, walking distance data, dietary data, and emotional data to evaluate the user's health condition.

[1341] Step 5:

[1342] The server performs an integrated analysis of personal data and external data to assess the user's health condition. The input data includes personal health data, emotional data, weather data, and infectious disease information, which are processed by the integrated analysis engine to assess the user's health condition. Statistical analysis and machine learning algorithms are used for data calculations. For example, statistical analysis may determine that "the recent drop in temperature has led to a decrease in physical activity."

[1343] Step 6:

[1344] The server generates health advice based on the evaluation results. The input data is the evaluation results, and appropriate advice is generated based on them. For example, advice such as "Since you need to take in vitamin C, it is recommended that you eat a lot of citrus fruits" is generated.

[1345] Step 7:

[1346] The server notifies the generated advice to the in-store display or the user's device. The input data is the generated advice, which is displayed on the store's smart display or the user's smartphone. Specifically, the message "It's cold today, so let's do some indoor exercises" is displayed on the in-store display.

[1347] Step 8:

[1348] The user can input the results of their actions based on the advice into a smartphone app, or data from a wearable device is automatically collected. The input data is feedback information that will be reflected in the generation of the next piece of advice. For example, if a user reports in the app that they have walked for 30 minutes, that information will be sent to the server and used to generate the next health advice.

[1349] 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.

[1350] 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.

[1351] 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.

[1352] [Fourth embodiment]

[1353] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1354] 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.

[1355] 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).

[1356] 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.

[1357] 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.

[1358] 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).

[1359] 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.

[1360] 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.

[1361] 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.

[1362] 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.

[1363] 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.

[1364] 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.

[1365] 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."

[1366] Data collection and integration

[1367] The system according to the present invention provides a function for collecting personal health data and external data from multiple data sources, and integrating and analyzing them. A specific embodiment of the system and its operation will be described below.

[1368] Collection of personal health data

[1369] Users use a smartphone app every day to input data such as their weight, walking distance, diet, etc. If they use a wearable device, this data is automatically synchronized to the device.

[1370] example:

[1371] The user steps on the scale in the morning and enters the weight reading into a smartphone app.

[1372] The smartwatch measures the distance walked during the day and automatically transmits the data to a smartphone via Bluetooth.

[1373] External Data Collection

[1374] The server periodically accesses external APIs to obtain weather and infectious disease information, which is used to personalize health advice for users.

[1375] example:

[1376] The server accesses the weather information API at a fixed time each day to obtain the temperature, humidity, and weather for that day.

[1377] The latest infectious disease spread status and preventive measures are obtained weekly from the infectious disease information API.

[1378] Data integration and analysis

[1379] The personal health data collected by the device is sent in real time to a server, which then combines this data with external data to analyze the user's health condition.

[1380] example:

[1381] The device transmits weight data, walking distance data, and dietary data to the server.

[1382] The data is analyzed in conjunction with temperature data and infectious disease information obtained by the server.

[1383] Generating and Serving Advice

[1384] The server generates health advice for the user based on the analysis results. This advice includes dietary recommendations and exercise suggestions. This advice is then sent to the user via their device.

[1385] example:

[1386] The server analyzes the data and determines that "taking vitamin C is an effective way to combat recent infectious diseases," and recommends that the user consume citrus fruits.

[1387] The server generates advice such as "Today's temperature is low, so let's do some indoor exercises" and sends it to the device.

[1388] The device will notify the user of the advice it has received and display details within the app.

[1389] Feedback gathering and learning

[1390] The user follows the advice and inputs the results into a smartphone app, or the wearable device automatically collects the feedback. This feedback information is then sent back to the server and used to generate the next advice.

[1391] example:

[1392] The user enters "I walked for 30 minutes" in the app.

[1393] The smartwatch automatically collects the user's exercise records and sends them to the device.

[1394] The device sends this feedback information to the server, which then reflects it in the next analysis.

[1395] In this way, the system of the present invention effectively integrates and analyzes the user's daily health data and external environmental data, and provides personalized health advice, thereby supporting the user in sustainable health management.

[1396] The processing flow will be explained below.

[1397] Step 1: Data entry and acquisition

[1398] 1.1 The user opens the smartphone app and manually enters their weight, as well as their dietary and calorie information.

[1399] 1.2 The device connects to a wearable device such as a smartwatch and automatically collects data such as walking distance and heart rate.

[1400] 1.3 The device temporarily stores the acquired personal health data in local storage.

[1401] Step 2: Send data

[1402] 2.1 The device periodically sends this personal health data to the server. The data is encrypted and transmitted securely.

[1403] 2.2 The server stores the personal health data transmitted to the database.

[1404] Step 3: Gather external data

[1405] 3.1 The server sends a request to the weather information API to obtain weather data such as the current temperature and weather.

[1406] 3.2 The server obtains the latest infectious disease outbreak information from the infectious disease information API.

[1407] Step 4: Integrate the data

[1408] 4.1 The server integrates the user's personal health data with the acquired weather data and infectious disease data.

[1409] 4.2 The server centralizes the data and creates a dataset for analysis.

[1410] Step 5: Analyze your health status

[1411] 5.1 The server's analytics engine assesses the user's health status based on the integrated dataset, using machine learning algorithms and rule-based systems.

[1412] 5.2 The server performs risk assessment based on the evaluation results and generates appropriate health advice for the user.

[1413] Step 6: Generating and notifying advice

[1414] 6.1 The server compiles the generated advice and formats it into a recommendation for the user.

[1415] 6.2 The server sends the advice to the terminal and sends it as a notification message.

[1416] 6.3 The device notifies the user of the advice and displays detailed advice content.

[1417] Step 7: Gather feedback

[1418] 7.1 The user acts on the advice and inputs the results into the app, for example, recording the duration of a walk or what they ate.

[1419] 7.2 The terminal automatically collects feedback information from the wearable device and sends it to the server.

[1420] Step 8: Learn and improve your advice

[1421] 8.1 The server updates the analytical model based on the collected feedback information and uses it to generate advice from the next time onwards.

[1422] 8.2 The server takes in new data and re-learns to improve the accuracy of advice given to users.

[1423] These steps enable the system to assist users in managing their individual health and provide personalized advice for continually improving their health.

[1424] Example 1

[1425] 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."

[1426] To provide efficient and personalized health management, it is necessary to integrate personal health data with external data and provide users with appropriate advice based on the analysis results. However, current systems lack real-time performance and individualized accuracy in the collection, integration, and analysis of data and the provision of advice. Therefore, there is a need for a system that can solve these issues and enable more effective health management.

[1427] 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.

[1428] In this invention, the server includes means for collecting personal health data in real time and transmitting it to the server, means for integrating and analyzing the personal health data and external data to evaluate the user's health condition, and means for transmitting advice generated based on the analysis results to the user terminal. This makes it possible to integrate and analyze the user's health data and external data in real time and provide personalized health management advice.

[1429] "Personal health data" refers to information about the user's health status, such as weight, walking distance, and diet.

[1430] "External data" refers to information about the external environment that affects the user's health management, such as weather information and infectious disease information.

[1431] "Collection methods" refer to the methods and technologies used to acquire and store data.

[1432] "Integrated analysis" means processing multiple data sets together and conducting a comprehensive analysis.

[1433] "Health status assessment" refers to diagnosing the user's health status based on the integrated data and evaluating it according to certain criteria.

[1434] "Advice generator" refers to a method or technology that generates recommendations or instructions to provide to a user based on the evaluation results.

[1435] "Notification means" refers to a method or technique for notifying the user of the generated advice.

[1436] "Feedback information" refers to information relating to the results of a user's actions in accordance with advice.

[1437] "Server" means a central processing unit for processing collected data and storing and transmitting analysis results.

[1438] "Terminal" refers to a device used by a user to input data and receive notifications from a server.

[1439] A "mobile device app" is an application that runs on a mobile electronic device such as a smartphone or tablet.

[1440] A "wearable device" is a data collection device that is worn by a user.

[1441] "Communication network" refers to a network for sending and receiving data, such as the Internet.

[1442] "Software interface" refers to the programs and protocols used to obtain external data.

[1443] MODE FOR CARRYING OUT THE INVENTION

[1444] The present invention relates to a system for collecting personal health data of a user, evaluating the user's health condition by integrating and analyzing the data with external data, and providing appropriate health advice. Specific embodiments of the present invention will be described in detail below.

[1445] Collection of personal health data

[1446] Users use a mobile device app to input daily health data such as weight, walking distance, dietary habits, etc. Wearable devices (e.g., smartwatches) automatically measure data such as walking distance and heart rate, and synchronize the data with a smartphone using communication technologies such as Bluetooth.

[1447] example:

[1448] The user steps on the scale every morning and enters their weight into a smartphone app.

[1449] The smartwatch measures the user's walking distance during the day and automatically sends the data to the device.

[1450] External Data Collection

[1451] The server periodically obtains external data, such as weather information and infectious disease information, using a software interface (API) provided via the Internet, thereby collecting external environmental data that may affect the user's health condition.

[1452] example:

[1453] The server obtains the day's temperature, humidity, and weather from the weather information API at a fixed time each day.

[1454] The server obtains the latest infectious disease spread status and preventive measures from the infectious disease information API every week.

[1455] Data integration and analysis

[1456] The personal health data collected by the device is sent in real time to a server, where it is integrated with external data and analyzed to assess the user's health status.

[1457] example:

[1458] The terminal transmits the user's weight data, walking distance data, and dietary habit data to the server.

[1459] The server integrates temperature data and infectious disease information to analyze the user's health risk.

[1460] Generating and Serving Advice

[1461] The server generates health advice for the user based on the analysis results, and the advice is sent to the user's smartphone or other device. The advice includes dietary recommendations and exercise suggestions.

[1462] example:

[1463] The server analyzes the data and determines that "taking vitamin C is effective in preventing infectious diseases," and recommends that the user consume citrus fruits.

[1464] The system generates advice such as "Today's temperature is low, so let's do some indoor exercises," and notifies the user's device.

[1465] Feedback gathering and learning

[1466] The user follows the advice and inputs the results into a smartphone app, or the wearable device automatically collects exercise records. This feedback information is then sent back to the server and used to generate advice for the next time.

[1467] example:

[1468] The user enters "I walked for 30 minutes" in the app.

[1469] The smartwatch automatically collects the user's exercise records and sends them to the device.

[1470] The device sends this feedback information to the server, which then reflects it in the next analysis.

[1471] This system effectively integrates users' daily health data with external environmental data and provides personalized health advice, helping users manage their health sustainably.

[1472] Example prompts for generative AI models

[1473] "A user weighs themselves using a scale in the morning and enters that data into a smartphone app. The server periodically uses an external API to obtain the temperature, humidity, and weather for that day. Based on that data, it generates health advice tailored to the user and sends it to their smartphone. Please explain each processing step of this system in detail."

[1474] This prompt allows the generative AI model to generate detailed explanations of each processing step in the system.

[1475] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1476] Step 1: User enters personal health data

[1477] Users manually enter personal health data such as weight, walking distance, and dietary habits into a smartphone app. The entered data is stored in the smartphone's internal database. For example, a user can step on a scale every morning and enter their measured weight into the app. This input data becomes the basis for subsequent analysis.

[1478] Input: Personal health data such as weight, walking distance, dietary habits, etc.

[1479] Output: Personal health data stored in the smartphone's internal database

[1480] Step 2: Automated data collection with wearable devices

[1481] The device automatically collects data from wearable devices such as smartwatches, which constantly measure the user's walking distance, heart rate, sleep patterns, and more, and transmits the data via Bluetooth to a smartphone, where it is integrated into the smartphone's internal database.

[1482] Input: Walking distance, heart rate, sleep data from smartwatch

[1483] Output: Wearable device data integrated into smartphone internal database

[1484] Step 3: Gather external data

[1485] The server periodically obtains external data, such as weather information and infectious disease information, via the internet using an external API. For example, the server accesses the weather information API at 7:00 a.m. every day to obtain the temperature, humidity, and weather for that day. This data is then stored in the server's database.

[1486] Input: Temperature, humidity, and weather data obtained from the weather information API

[1487] Output: Weather information stored in the server database

[1488] Step 4: Send your personal health data

[1489] The device sends the collected personal health data to the server in real time, allowing the server to always have the latest user information. For example, a smartphone periodically sends weight data, walking distance data, and dietary habit data to the server.

[1490] Input: Personal health data stored in the smartphone's internal database

[1491] Output: Personal health data sent to the server

[1492] Step 5: Data integration and analysis

[1493] The server integrates the received personal health data with external data to assess the user's health status. For example, the server combines weight data, walking distance data, and dietary data with temperature data and infectious disease information to perform statistical analysis. The results of this analysis serve as the basis for health advice for the user.

[1494] Input: Personal health data and external data sent to the server

[1495] Output: Analysis results (evaluation of the user's health status)

[1496] Step 6: Generate health advice

[1497] The server generates health advice for the user based on the analysis results. For example, if the server determines that "taking vitamin C is an effective measure against recent infectious diseases," it generates advice recommending the user to consume citrus fruits. This advice is stored in the server's memory in text format.

[1498] Input: User's health assessment

[1499] Output: Generated health advice

[1500] Step 7: Advice Notification

[1501] The server sends the generated advice to the device and notifies the user. For example, advice such as "Today's temperature is low, so try doing some indoor exercises" is sent in text format to the device, and the device notifies the user via a push notification.

[1502] Input: Generated health advice

[1503] Output: Advice notification sent to user terminal

[1504] Step 8: Gather user feedback

[1505] The user inputs the results of their actions based on the advice into a smartphone app, or the wearable device automatically collects feedback data. For example, the user can input "I walked for 30 minutes" into the app, or the smartwatch can send exercise data to the device. This feedback information is then sent back to the server.

[1506] Input: User-entered feedback data or data from a wearable device

[1507] Output: Feedback information sent to the server

[1508] Step 9: Analyze the feedback and incorporate it into your next advice

[1509] The server analyzes the received feedback information and uses it to generate the next health advice. For example, the server analyzes the feedback information to understand the user's exercise habits and dietary patterns and reflect them in the next advice. This process enables more accurate and personalized health management.

[1510] Input: Feedback information sent to the server

[1511] Output: Next health advice reflecting the feedback information

[1512] (Application example 1)

[1513] 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."

[1514] Personal health management is an important issue today, and there is a need to effectively collect and analyze health data, particularly daily weight, walking distance, and dietary habits, and provide appropriate advice. However, it is difficult to collect and analyze this data using a single device or platform, and it is also difficult to provide comprehensive health advice in real time that takes into account external weather and infectious disease information. Additionally, there are few opportunities to receive instant, personalized health advice in physical stores, and there is insufficient integration between in-store product information and health advice.

[1515] 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.

[1516] In this invention, the server includes: means for recording weight, walking distance, and dietary habits to collect personal health data; means for collecting external weather information and infectious disease information; means for integrating and analyzing the personal health data and external data to evaluate the user's health status; means for generating dietary and health management advice for the user based on the evaluation; means for notifying the advice to the user terminal and to an information providing terminal in the physical store; means for the information providing terminal to instantly provide personalized health advice based on the user's health data and external data; means for providing in-store guidance on products related to the advice; and means for collecting feedback information from the user and using it to generate next advice. This makes it possible to effectively integrate and analyze personal health data and external environmental data, instantly provide personalized health advice in the physical store, and further guide users to related products.

[1517] "Personal health data" refers to data that indicates the individual health condition of a user, such as weight, walking distance, and dietary habits.

[1518] "Weather information" is data that indicates the state of the external environment, such as the temperature, humidity, and weather of the area.

[1519] "Infectious disease information" refers to data related to infectious diseases, such as the prevalence of infectious diseases in the area and preventive measures.

[1520] "Integrated analysis" is the process of combining and analyzing multiple data to find overall patterns and meanings.

[1521] A "user terminal" is a device used by a user, such as a smartphone or tablet.

[1522] An "information providing terminal" is a device that is installed in a physical store and provides information to users.

[1523] "Evaluating health status" means analyzing the user's health status based on collected personal health data and external data.

[1524] "Dietary and health management advice" is information that suggests dietary habits, exercise, supplements, etc. to improve the user's health.

[1525] "Notifying" refers to the act of sending the generated advice to the user or other terminals.

[1526] "Feedback information" is information about the actions that the user actually takes and the results of those actions.

[1527] "Personalized health advice" refers to personalized health management suggestions based on an individual user's health data and external data.

[1528] "In-store guidance" refers to the act of providing the location and detailed information of related products within a physical store.

[1529] This invention is a system that integrates personal health data with external environmental data to provide users with personalized health advice. This system is composed of a personal health data collection means, a weather information and infectious disease information collection means, a data integration and analysis means, an advice generation means, an information provision means, and a feedback collection means.

[1530] 1. Collection of personal health data

[1531] The server collects daily health data such as weight, walking distance, and dietary habits from users using smartphone apps and wearable devices. This data is usually automatically synchronized with the smartphone via wireless communication such as Bluetooth and sent to the server.

[1532] For example, after a user steps on a scale in the morning, the weight is automatically recorded in a smartphone app. The smartwatch also measures the distance walked during the day and sends the data to the smartphone, which then sends it to a server.

[1533] 2. External Data Collection

[1534] The server periodically obtains weather and infectious disease information from an external API provided via the Internet, and this data is used to evaluate the user's health status.

[1535] Specifically, the server accesses the weather information API at a fixed time each day to obtain the temperature, humidity, and weather for that day, and also accesses the infectious disease information API weekly to obtain the latest information on the spread of infectious diseases and preventive measures.

[1536] 3. Data integration and analysis

[1537] The server integrates the collected personal health data with external data to analyze the user's health status in real time, and generates specific health advice for the user based on the analysis results.

[1538] For example, the server analyzes the user's walking distance data, weight data, and dietary data in combination with external data such as temperature and humidity. Based on the analysis, specific advice such as "Today's temperature is low, so let's do some indoor exercises" is generated.

[1539] 4. Providing advice

[1540] The generated advice is notified to the user terminal and an information providing terminal in the physical store. The information providing terminal is a device that provides health advice to the user in real time within the store.

[1541] Specifically, advice generated by the server, such as "Today's temperature is low, so we recommend you drink a hot drink," is displayed on the robot's display in the physical store, and the user is guided to related products in the store.

[1542] 5. Feedback Collection

[1543] The user can input the results of their actions based on the advice into a smartphone app, or the wearable device can automatically collect the feedback. This feedback information is then sent back to the server and used to generate the next advice.

[1544] For example, if a user enters "I walked for 30 minutes" in the app, that information is sent to the server, and the smartwatch automatically collects and sends walking data to the server in the same way.

[1545] Prompt Sentence Examples

[1546] "Due to the low temperatures today, we recommend that you eat some warm soup. We also recommend taking vitamin C supplements to prevent infection. We have related products available in the XX section of our store, so please feel free to use them."

[1547] This system effectively integrates and analyzes personal health data and external environmental data, making it possible to provide instant, personalized health advice in physical stores and even introduce related products.

[1548] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1549] Step 1: Collect personal health data

[1550] Users use a smartphone app and a wearable device to collect daily health data such as weight, walking distance, and diet. The smartphone app records this data on the device and automatically synchronizes it with the wearable device using Bluetooth. The device then sends the collected data to a server. Health data such as weight, walking distance, and dietary details are used as input. The output is personal health data sent to the server. Specifically, the user steps on the scale every morning, and the weight is automatically recorded in the smartphone app. The smartphone then retrieves walking distance data from the wearable device and sends all data to the server.

[1551] Step 2: Gather external data

[1552] The server periodically obtains weather information and infectious disease information from an external API provided via the internet. It obtains data such as temperature, humidity, and weather from the weather information API, and data on infectious disease epidemic status and preventive measures from the infectious disease information API. The input is weather information and infectious disease information from the external API, and the output is integrated external data. Specifically, the server sends a request to the weather information API at a fixed time each day to obtain the temperature, humidity, and weather for that day, and accesses the infectious disease information API weekly to obtain the latest infectious disease spread status.

[1553] Step 3: Data synthesis and analysis

[1554] The server integrates the collected personal health data with external data for analysis. This analysis uses an algorithm to evaluate the user's health status by combining the personal health data and external data. The input is the personal health data and external data, and the output is the evaluation result of the user's health status. Specifically, the server combines data such as the individual's weight, walking distance, and diet with weather information such as temperature and humidity, and analyzes the user's health status using a health evaluation algorithm.

[1555] Step 4: Generating Advice

[1556] The server generates health advice for the user based on the analysis results. This advice includes dietary recommendations, exercise plans, and infection prevention measures. The input is the health status assessment result, and the output is advice to be provided to the user. Specifically, the server generates advice such as "Today's temperature is low, so we recommend drinking a hot drink."

[1557] Step 5: Providing advice

[1558] The generated advice is notified to the user terminal and an information providing terminal in the physical store. The information providing terminal immediately provides health advice to the user in the store and also introduces related products. The input is the generated advice, and the output is a notification to the user and the information providing terminal. In specific operations, the server notifies the user's smartphone of the advice, and a robot in the physical store displays the advice on a display.

[1559] Step 6: Gather feedback

[1560] The user inputs the results of their actions based on the advice into the smartphone app, or the wearable device automatically collects the data. The server uses this feedback information to generate the next piece of advice. The input is feedback information about the user's actions, and the output is data that will be used to generate the next piece of advice. Specifically, the user inputs "I walked for 30 minutes" into the app, and this information is sent to the server. Similarly, the smartwatch automatically collects and sends the walking data to the server.

[1561] Through these processing steps, the system supports personal healthcare and provides added value within physical stores.

[1562] 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.

[1563] Data collection and integration

[1564] The system according to the present invention provides a function for collecting personal health data and emotion data, and integrating and analyzing them. A specific embodiment of the system and its operation will be described below.

[1565] Collection of personal health data

[1566] Users use a smartphone app every day to input data such as their weight, walking distance, diet, etc. If they use a wearable device, this data is automatically synchronized to the device.

[1567] example:

[1568] The user steps on the scale in the morning and enters the weight reading into a smartphone app.

[1569] The smartwatch measures the distance walked during the day and automatically transmits the data to a smartphone via Bluetooth.

[1570] External Data Collection

[1571] The server periodically accesses external APIs to obtain weather and infectious disease information, which is used to personalize health advice for users.

[1572] example:

[1573] The server accesses the weather information API at a fixed time each day to obtain the temperature, humidity, and weather for that day.

[1574] The latest infectious disease spread status and preventive measures are obtained weekly from the infectious disease information API.

[1575] Collecting Emotional Data

[1576] The device uses an emotion engine to collect user emotion data, which is analyzed based on the user's facial expressions, voice, and input data.

[1577] example:

[1578] The smartphone camera captures the user's facial expressions, and the emotion engine analyzes them.

[1579] The user's voice is recorded through a microphone and analyzed.

[1580] Data is collected from text input, etc., to infer the user's emotions.

[1581] Data integration and analysis

[1582] The device sends the collected personal health and emotional data to a server, which then integrates this data with external data to perform a multifaceted analysis of the user's health condition.

[1583] example:

[1584] The terminal transmits weight data, walking distance data, dietary habit data, and emotion data to the server.

[1585] The data is analyzed in conjunction with weather data and infectious disease information obtained by the server.

[1586] Generating and Serving Advice

[1587] The server generates health advice for the user based on the analysis results. This advice includes dietary recommendations and exercise suggestions. It also takes into account emotional data. This advice is then sent to the user via their device.

[1588] example:

[1589] The server analyzes the data and determines that "taking vitamin C is an effective way to combat recent infectious diseases," and recommends that the user consume citrus fruits.

[1590] The server generates advice such as "Today's temperature is low, so let's do some indoor exercises" and sends it to the device.

[1591] The server determines from emotional data that the user is feeling stressed and recommends activities that are effective for relaxation and stress relief.

[1592] The terminal notifies the user of the received advice and displays detailed advice content.

[1593] Feedback gathering and learning

[1594] The user follows the advice and inputs the results into a smartphone app, or the wearable device automatically collects the feedback. This feedback information is then sent back to the server and used to generate the next advice.

[1595] example:

[1596] The user enters "I walked for 30 minutes" in the app.

[1597] The smartwatch automatically collects the user's exercise records and sends them to the device.

[1598] The device sends this feedback information to the server, which then reflects it in the next analysis.

[1599] These steps enable the system to assist users in managing their individual health and provide personalized advice to help improve their health, including their emotional state, over time.

[1600] The processing flow will be explained below.

[1601] Step 1: Data entry and acquisition

[1602] 1.1 The user opens the smartphone app and manually enters their weight, as well as their dietary and calorie information.

[1603] 1.2 The device connects to a wearable device such as a smartwatch and automatically collects data such as walking distance and heart rate.

[1604] 1.3 The device uses a camera and microphone to capture the user's facial expressions and voice to collect emotional data.

[1605] Step 2: Send data

[1606] 2.1 The device sends personal health and emotion data to the server. The data is encrypted and securely transmitted.

[1607] 2.2 The server stores the personal health data and emotion data sent to the database.

[1608] Step 3: Gather external data

[1609] 3.1 The server sends a request to the weather information API to obtain weather data such as the current temperature and weather.

[1610] 3.2 The server obtains the latest infectious disease outbreak information from the infectious disease information API.

[1611] 3.3 The server stores these weather and infectious disease data in a database.

[1612] Step 4: Integrate the data

[1613] 4.1 The server integrates the user's personal health data, emotion data, weather data, and infectious disease data.

[1614] 4.2 The server creates the integrated dataset and feeds it into the analysis engine.

[1615] Step 5: Analyze your health status

[1616] 5.1 The server's analytics engine assesses the user's health and emotional state based on the integrated dataset, using machine learning algorithms and rule-based systems.

[1617] 5.2 The server performs risk assessment based on the evaluation results and generates health advice and emotion-based advice appropriate for the user.

[1618] Step 6: Generating and notifying advice

[1619] 6.1 The server compiles and formats the generated advice into a recommendation for the user, including dietary recommendations, exercise suggestions, and emotional care advice.

[1620] 6.2 The server sends the advice to the terminal and sends it as a notification message.

[1621] 6.3 The device notifies the user of the advice and displays detailed advice content.

[1622] Step 7: Gather feedback

[1623] 7.1 The user acts on the advice and inputs the results into the app, for example, recording the duration of a walk or what they ate.

[1624] 7.2 The terminal automatically collects feedback information from the wearable device and sends it to the server.

[1625] Step 8: Learn and improve your advice

[1626] 8.1 The server updates the analytical model based on the collected feedback information and uses it to generate advice from the next time onwards.

[1627] 8.2 The server takes in new data and re-learns to improve the accuracy of advice given to users.

[1628] These steps enable the system to assist users in managing their individual health and emotions, providing personalized advice that can help improve their health and emotional state over time.

[1629] Example 2

[1630] 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."

[1631] In modern society, personal health management is becoming increasingly important, necessitating the development of health management systems that are more detailed and flexible. However, while existing health management systems provide basic health management based on personal health data (weight, walking distance, dietary habits) and external data (weather information, infectious disease information), they are unable to provide personalized health management advice that takes emotional data into account. Furthermore, they lack the functionality to quickly and effectively incorporate user feedback information and utilize it in generating next advice, making it difficult to translate this into practical health improvements.

[1632] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1633] In this invention, the server includes means for recording weight, walking distance, and dietary habits to collect personal health data, means for collecting external weather information and infectious disease information, means for analyzing the user's facial expressions, voice, and input data to collect user emotion data, means for integrating and analyzing the personal health data, external data, and emotion data to evaluate the user's health condition from various angles, means for generating health management advice for the user based on the evaluation and taking into account diet, exercise, and emotion state, means for notifying the user terminal of the advice, and means for collecting feedback information from the user and using it to generate next advice. This makes it possible to provide personalized health management advice that integrates the personal health data, external data, and emotion data, and to quickly reflect user feedback.

[1634] "Personal health data" refers to data that indicates the user's personal health condition, such as weight, walking distance, and dietary habits.

[1635] "External data" refers to external information obtained via the Internet, such as weather information and infectious disease information.

[1636] "Emotion data" is data that indicates the user's emotional state, obtained by analyzing the user's facial expressions, voice, and input data.

[1637] "Integrated analysis" is a process of integrating and analyzing personal health data, external data, and emotional data to evaluate the user's health condition from multiple angles.

[1638] "Health Management Advice" refers to instructions or recommendations given to the user based on the analysis results, taking into account diet, exercise, and emotional state.

[1639] "Feedback information" is data collected as a result of a user acting on advice.

[1640] A "server" is a computer system that collects, analyzes, and generates advice from data.

[1641] "Terminal" means a device (e.g., smartphone, wearable device) that a user uses to input and receive data.

[1642] "API" is an abbreviation for Application Program Interface, which provides a mechanism for exchanging data between different software programs.

[1643] An "emotion engine" is software that analyzes a user's facial expressions, voice, and input data to infer their emotional state.

[1644] MODE FOR CARRYING OUT THE INVENTION

[1645] The present invention is a system that integrates multiple data collection and analysis means, evaluates a user's health condition from multiple angles, and provides personalized health management advice. Specific embodiments and operations of the system are described below.

[1646] System Configuration Overview

[1647] 1. The system mainly consists of three elements: server, terminal, and user.

[1648] 2. The server collects, analyzes, and generates advice on data.

[1649] 3. A terminal is a device (e.g., a smartphone or wearable device) through which a user inputs and receives data.

[1650] 4. Users provide personal health data and feedback information.

[1651] Hardware and software used

[1652] Server: Uses cloud-based computing resources to run the database and generative AI models.

[1653] Devices: Smartphones and wearable devices (e.g., smartwatches).

[1654] Emotion Engine: Includes facial expression analysis software and a voice analysis engine.

[1655] Data collection

[1656] Users use a smartphone app every day to enter personal health data such as weight, walking distance, diet, etc. If they use a wearable device, this data is automatically synced to the device.

[1657] example:

[1658] The user steps on the scale in the morning and enters the weight reading into a smartphone app.

[1659] The smartwatch measures the distance walked during the day and automatically transmits the data to a smartphone via Bluetooth.

[1660] The server periodically uses external APIs to obtain weather and infectious disease information, which is used to personalize health advice for users.

[1661] example:

[1662] The server accesses the weather information API at a fixed time each day to obtain the temperature, humidity, and weather for that day.

[1663] The latest infectious disease spread status and preventive measures are obtained weekly from the infectious disease information API.

[1664] The device uses an emotion engine to collect user emotion data, which is analyzed based on the user's facial expressions, voice, and input data.

[1665] example:

[1666] The smartphone camera captures the user's facial expressions, and the emotion engine analyzes them.

[1667] The user's voice is recorded through a microphone and analyzed.

[1668] Data is collected from text input, etc., to infer the user's emotions.

[1669] Data transmission and analysis

[1670] The device sends the collected personal health and emotional data to a server, which then integrates this data with external data and uses a generative AI model to perform a multifaceted analysis of the user's health condition. Based on the results of this analysis, the server generates personalized health management advice.

[1671] Providing advice and feedback

[1672] The generated advice is sent to the user via the device, and includes dietary recommendations, exercise suggestions, and advice that takes into account emotional state.

[1673] Example prompt statement:

[1674] "Tell me how you're feeling today."

[1675] Enter your morning weight

[1676] "Tell me about your recent eating habits."

[1677] "Check out the advice based on infectious disease information."

[1678] The results of the user's actions based on the advice are entered into a smartphone app or automatically collected by a wearable device. This feedback information is then sent back to the server and used to generate the next advice.

[1679] In this way, this system supports users in managing their health by integrating personal health data, external data, and emotional data. Detailed collection and analysis of various data makes it possible to provide users with personalized health advice.

[1680] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1681] Step 1:

[1682] User input of personal health data

[1683] Users use a smartphone app to input personal health data such as weight, walking distance, and dietary habits. The input data is temporarily stored within the app. Specifically, the user opens the smartphone app and inputs their weight for the day. The app then stores this data within the device.

[1684] Input: weight, walking distance, dietary data

[1685] Output: Personal health data stored on the device

[1686] Step 2:

[1687] Terminal syncs data from wearable devices

[1688] A wearable device (e.g., a smartwatch) automatically transmits data collected, such as walking distance, to a smartphone via Bluetooth. The device then integrates this data into an app. Specifically, the smartwatch measures the distance walked during the day and transmits the data to the smartphone via Bluetooth.

[1689] Input: Walking distance data from a wearable device

[1690] Output: Walking distance data integrated into a smartphone app

[1691] Step 3:

[1692] Server-based external data collection

[1693] The server periodically accesses an external API to obtain weather and infectious disease information. This data is organized and stored on the server side. Specifically, the server accesses the weather information API at a fixed time each day to obtain the temperature, humidity, and weather for that day.

[1694] Input: Weather information and infectious disease information obtained from external API

[1695] Output: External data stored on the server side

[1696] Step 4:

[1697] Collecting emotion data from devices

[1698] The device's emotion engine analyzes the user's facial expressions, voice, and input data to generate emotion data. Specifically, the smartphone camera captures the user's facial expressions, and the emotion engine analyzes them.

[1699] Input: facial expressions, voice, input data

[1700] Output: Parsed emotion data

[1701] Step 5:

[1702] Sending data from the device to the server

[1703] The device periodically transmits the collected personal health data and emotion data to a server, which receives the data and stores it in a database. Specifically, the device periodically uploads the data to the server in the background.

[1704] Input: Personal health data and emotional data collected on the device

[1705] Output: Data stored on the server

[1706] Step 6:

[1707] Data integration and analysis by the server

[1708] The server integrates the received personal health data, emotion data, and external data, and analyzes them using a generative AI model. Specifically, the server integrates each data set into a single dataset and then performs analysis using the generative AI model.

[1709] Input: personal health data, emotion data, external data

[1710] Output: Analysis results

[1711] Step 7:

[1712] Server-generated advice

[1713] The server generates health management advice for the user based on the analysis results. This advice takes into account diet, exercise, and emotional state. Specifically, the server generates advice text based on the generated data and prepares to send it to the device in the next step.

[1714] Input: Analysis results

[1715] Output: Health care advice

[1716] Step 8:

[1717] Sending advice from the server to the device

[1718] The server generates health management advice and sends it to the device via a dedicated API, which then notifies the user. Specifically, the server sends the advice to the device, which then displays it to the user using its notification function.

[1719] Input: Health Care Advice

[1720] Output: Advice displayed on terminal

[1721] Step 9:

[1722] Gathering user feedback

[1723] The user can follow the advice and enter the results into a smartphone app, or the wearable device can automatically collect the data. Specifically, the user can enter "I walked for 30 minutes" in the app, or the smartwatch can automatically record the exercise results and send them to the device.

[1724] Input: User action result

[1725] Output: Feedback information

[1726] Step 10:

[1727] Sending feedback information to the server by the terminal

[1728] The device sends the collected feedback information to the server, which then uses it to generate the next advice. Specifically, the device automatically sends the feedback data to the server, and the server reflects the received data in the next analysis.

[1729] Input: Feedback information

[1730] Output: Data that will be reflected in the next advice generation

[1731] (Application example 2)

[1732] 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."

[1733] Conventional health management systems have difficulty managing individual health data and emotional data in a unified manner and providing optimal health advice to consumers. Furthermore, they were unable to provide advice that took into account the real-time health and emotional state of consumers visiting the store, which meant that the effectiveness of health management could not be maximized.

[1734] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1735] In this invention, the server includes: means for recording weight, walking distance, and dietary habits to collect personal health data; means for analyzing facial expressions, voice, and input data to collect emotion data; means for collecting external weather information and infectious disease information; means for integrating and analyzing the personal health data, emotion data, and external data to evaluate the user's health status; means for generating dietary and health management advice for the user based on the evaluation; means for notifying the advice on an in-store display or a user terminal; and means for collecting user feedback information and using it to generate next advice. This makes it possible to provide individually optimized health advice based on the customer's real-time health and emotion data.

[1736] "Personal health data" refers to information about an individual's health status, such as weight, walking distance, and diet.

[1737] "Emotional data" refers to information about an individual's emotional state analyzed based on facial expressions, voice, and input data.

[1738] "Facial expression analysis" is a technology that uses an input device such as a camera to detect an individual's facial expression and analyze their emotional state.

[1739] "Voice analysis" is a technology that analyzes voice data collected through a microphone or other device to determine the emotions and state of the voice.

[1740] "Input data analysis" is a technology that infers and analyzes emotions and states from text data entered by the user.

[1741] "External data" refers to data such as weather information and infectious disease information that is collected mainly via the Internet, separate from personal health data.

[1742] "Weather information" refers to information about the weather conditions of the day, such as temperature, humidity, and weather.

[1743] "Infectious disease information" refers to information on the spread of infectious diseases and preventive measures.

[1744] "Integrated analysis" refers to the centralized handling of multiple different data sets and comprehensive analysis.

[1745] "Evaluation" refers to objectively determining the user's health condition based on collected data.

[1746] "Advice" refers to suggestions about diet and health management provided to the user based on the evaluation results.

[1747] "Notification" is the act of notifying the user of the generated advice.

[1748] "Feedback information" is information relating to the results of actions taken by the user in accordance with the advice.

[1749] "Collecting feedback information" refers to collecting information entered by the user or automatically collected by the wearable device.

[1750] An "IoT device" is a physical device that connects to the internet, including things like smart mirrors and bathroom scales.

[1751] A "display" is a device that displays images or data.

[1752] "Integrated data" refers to comprehensive data generated by integrating individual data.

[1753] The present invention is a system that collects personal health data and emotional data, and integrates and analyzes them. Specific embodiments for carrying out the invention are described below.

[1754] System Configuration

[1755] This system consists of a smartphone app, a wearable device, an IoT device, a server, and a user terminal.

[1756] 1. Data Collection Methods

[1757] Smartphone app: Collects personal health data entered by the user, such as weight, walking distance, dietary habits, etc. The smartphone is equipped with a camera that captures facial expressions and a microphone that records voice, which are used to obtain emotional data as well.

[1758] Wearable devices: Smartwatches and fitness trackers automatically measure a user's weight and walking distance and sync with a smartphone via Bluetooth.

[1759] IoT devices: Smart mirrors, scales, sensors connected to exercise machines, etc. are used to collect health and emotional data from visitors.

[1760] 2. External Data Collection Methods

[1761] Server: Uses APIs provided via the internet to collect weather and infectious disease information. Temperature, humidity, and weather are collected from the weather information API, and the latest infectious disease spread status and preventive measures are obtained from the infectious disease information API.

[1762] 3. Integrated analysis method

[1763] Server: Collects and analyzes collected personal health data, emotional data, and external data in a unified manner. For example, it integrates weight data, walking distance data, dietary data, and emotional data to evaluate the user's health status.

[1764] 4. Advice Generation Methods

[1765] Server: Generates health advice for the user based on the assessment results, including dietary recommendations, exercise suggestions, and emotion-based relaxation and stress-relief activities.

[1766] 5. Means of notification

[1767] In-store display or user device: Advice is sent to a smart display or user's smartphone, for example, a message such as "You need vitamin C, so it's recommended that you eat citrus fruits."

[1768] 6. Feedback Collection Methods

[1769] Smartphone app and wearable device: Users enter the results of their activities into the app or the wearable device automatically records them. This information is sent to the server and used to generate advice for the next time.

[1770] This will enable the provision of real-time, individually optimized health advice based on collected personal health data, emotional data, and external data.

[1771] Specific examples

[1772] For example, a user stands in front of a smart mirror in a store, their facial expressions are captured, and then their weight data is entered into their smartphone. Meanwhile, the server retrieves weather and infectious disease information from the API and analyzes all the data. As a result, it is determined that the user's emotional state indicates stress, and specific relaxation exercises are recommended. This information is then displayed on the store's smart display and notified to the user.

[1773] Prompt Sentence Examples

[1774] "Describe the steps required to collect weight and emotion data and analyze an individual's health status. Also, describe the process for retrieving weather and infectious disease information from external APIs and integrating that data to generate recommendations."

[1775] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1776] Step 1:

[1777] Users can input data such as weight, walking distance, and dietary habits into a smartphone app, or data can be automatically synchronized from a wearable device. Numerical values ​​such as weight and walking distance are obtained as input data, and personal health data is collected based on these values. Specifically, users can step on a scale in the morning and enter the values ​​into a smartphone app, or a smartwatch can measure the distance walked during the day and send the data to a smartphone via Bluetooth.

[1778] Step 2:

[1779] The device collects emotion data. This includes capturing the user's facial expressions with the smartphone camera, collecting audio with the voice recording function, and analyzing text input. The input data includes facial expression images, audio files, and text, which are then analyzed using an emotion analysis engine to extract emotion data. Specifically, the smartphone camera captures the user's smile, and the image analysis engine classifies it as "happy."

[1780] Step 3:

[1781] The server obtains external weather and infectious disease information from APIs. The input data is the response from the external API, with temperature, humidity, and weather data obtained from the weather API, and infectious disease spread status and preventive measures obtained from the infectious disease information API. This data is used to personalize health advice for users.

[1782] Step 4:

[1783] The device sends the collected personal health data and emotional data to a server. The input data is personal health data and emotional data, which are stored in an integrated manner by the server. The server then integrates this data and performs multifaceted analysis. For example, it may integrate weight data, walking distance data, dietary data, and emotional data to evaluate the user's health condition.

[1784] Step 5:

[1785] The server performs an integrated analysis of personal data and external data to assess the user's health condition. The input data includes personal health data, emotional data, weather data, and infectious disease information, which are processed by the integrated analysis engine to assess the user's health condition. Statistical analysis and machine learning algorithms are used for data calculations. For example, statistical analysis may determine that "the recent drop in temperature has led to a decrease in physical activity."

[1786] Step 6:

[1787] The server generates health advice based on the evaluation results. The input data is the evaluation results, and appropriate advice is generated based on them. For example, advice such as "Since you need to take in vitamin C, it is recommended that you eat a lot of citrus fruits" is generated.

[1788] Step 7:

[1789] The server notifies the generated advice to the in-store display or the user's device. The input data is the generated advice, which is displayed on the store's smart display or the user's smartphone. Specifically, the message "It's cold today, so let's do some indoor exercises" is displayed on the in-store display.

[1790] Step 8:

[1791] The user can input the results of their actions based on the advice into a smartphone app, or data from a wearable device is automatically collected. The input data is feedback information that will be reflected in the generation of the next piece of advice. For example, if a user reports in the app that they have walked for 30 minutes, that information will be sent to the server and used to generate the next health advice.

[1792] 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.

[1793] 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.

[1794] 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.

[1795] 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.

[1796] 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.

[1797] 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.

[1798] 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).

[1799] 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.

[1800] 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."

[1801] 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.

[1802] 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).

[1803] 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.

[1804] 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.

[1805] 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.

[1806] 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.

[1807] 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.

[1808] 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.

[1809] 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.

[1810] 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.

[1811] 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.

[1812] 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.

[1813] The following is further disclosed regarding the above embodiment.

[1814] (Claim 1)

[1815] a means for recording weight, walking distance and dietary habits for collecting personal health data;

[1816] A means for collecting external weather information and infectious disease information;

[1817] a means for integrating and analyzing the personal health data and external data to evaluate the user's health condition;

[1818] means for generating dietary and health management advice for the user based on said evaluation;

[1819] means for notifying a user terminal of the advice;

[1820] A means for collecting feedback information from the user and utilizing it for generating next advice.

[1821] A system including:

[1822] (Claim 2)

[1823] 2. The system of claim 1, wherein the means for collecting personal health data uses a smartphone app and a wearable device.

[1824] (Claim 3)

[1825] 2. The system according to claim 1, wherein the means for collecting external data acquires data from an API provided via the Internet.

[1826] "Example 1"

[1827] (Claim 1)

[1828] a means for recording weight, walking distance and dietary habits for collecting personal health data;

[1829] A means for collecting external weather information and infectious disease information;

[1830] a means for integrating and analyzing the personal health data and external data to evaluate the user's health condition;

[1831] means for generating dietary and health management advice for the user based on said evaluation;

[1832] means for notifying a user terminal of the advice;

[1833] a means for collecting feedback information from the user and utilizing the information to generate next advice;

[1834] means for collecting and transmitting user health data in real time to a server;

[1835] A means for transmitting and notifying advice generated by the server based on the analysis results to a user terminal.

[1836] A system including:

[1837] (Claim 2)

[1838] 2. The system of claim 1, wherein the means for collecting personal health data uses a mobile terminal app and a wearable device.

[1839] (Claim 3)

[1840] 2. The system according to claim 1, wherein the external data collection means acquires data from a software interface provided via a communication network.

[1841] "Application Example 1"

[1842] (Claim 1)

[1843] a means for recording weight, walking distance and dietary habits for collecting personal health data;

[1844] A means for collecting external weather information and infectious disease information;

[1845] a means for integrating and analyzing the personal health data and external data to evaluate the user's health condition;

[1846] means for generating dietary and health management advice for the user based on said evaluation;

[1847] a means for notifying the advice to a user terminal and a means for notifying the advice to an information providing terminal in a physical store;

[1848] a means for the information providing terminal to instantly provide personalized health advice based on the user's health data and external data;

[1849] a means for providing in-store guidance on products related to the advice;

[1850] A means for collecting feedback information from the user and utilizing it for generating next advice.

[1851] A system including:

[1852] (Claim 2)

[1853] 2. The system of claim 1, wherein the means for collecting personal health data uses a smartphone app and a wearable device.

[1854] (Claim 3)

[1855] 2. The system according to claim 1, wherein the means for collecting external data acquires data from an API provided via the Internet.

[1856] "Example 2: Combining Emotion Engines"

[1857] (Claim 1)

[1858] a means for recording weight, walking distance and dietary habits for collecting personal health data;

[1859] A means for collecting external weather information and infectious disease information;

[1860] means for analyzing a user's facial expression, voice and input data to collect user's emotion data;

[1861] a means for integrating and analyzing the personal health data, external data, and emotional data to evaluate the user's health condition from multiple angles;

[1862] means for generating health care advice for the user based on said evaluation, the advice taking into account diet, exercise and emotional state;

[1863] means for notifying a user terminal of the advice;

[1864] A means for collecting feedback information from the user and utilizing it for generating next advice.

[1865] A system including:

[1866] (Claim 2)

[1867] 2. The system of claim 1, wherein the means for collecting personal health data uses a smartphone app and a wearable device.

[1868] (Claim 3)

[1869] 2. The system according to claim 1, wherein the means for collecting external data acquires data from an API provided via the Internet.

[1870] "Application example 2 when combining emotion engines"

[1871] (Claim 1)

[1872] a means for recording weight, walking distance and dietary habits for collecting personal health data;

[1873] means for performing facial expression analysis, voice analysis, and input data analysis to collect emotion data;

[1874] A means for collecting external weather information and infectious disease information;

[1875] a means for integrating and analyzing the personal health data, emotion data, and external data to evaluate the user's health condition;

[1876] means for generating dietary and health management advice for the user based on said evaluation;

[1877] a means for notifying the advice on a display in the store or on a user terminal;

[1878] A means for collecting feedback information from the user and utilizing it for generating next advice.

[1879] A system including:

[1880] (Claim 2)

[1881] The system of claim 1, wherein the means for collecting the personal health data and emotional data uses a smartphone app, a wearable device, and an IoT device.

[1882] (Claim 3)

[1883] 2. The system according to claim 1, wherein the means for collecting external data acquires data from an API provided via the Internet. [Explanation of symbols]

[1884] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for recording weight, walking distance and dietary habits for collecting personal health data; A means for collecting external weather information and infectious disease information; a means for integrating and analyzing the personal health data and external data to evaluate the user's health condition; means for generating dietary and health management advice for the user based on said evaluation; means for notifying a user terminal of the advice; A means for collecting feedback information from the user and utilizing it for generating next advice. A system including:

2. The system of claim 1 , wherein the means for collecting personal health data uses a smartphone app and a wearable device.

3. 2. The system according to claim 1, wherein the means for collecting external data acquires data from an API provided via the Internet.

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