system

Abstract

We provide the system. [Solution] A device that collects personal biometric information, Means for transmitting the aforementioned biometric information to a remote server, The aforementioned server uses artificial intelligence to analyze the biometric information, A means for generating personalized health advice based on the aforementioned analysis, A means for translating the aforementioned health advice into multiple languages, A means for notifying the user of the translated health advice, A system that includes this.

Description

Technical Field

[0004]

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern society, it is very important for an individual to always grasp and appropriately manage their own health condition. However, with conventional methods, it is difficult to receive health advice suitable for individual conditions, and furthermore, in many cases, differences in language and culture become barriers and appropriate information does not reach. Therefore, there is a need to develop a system that can provide individualized health advice while accommodating multiple languages and cultures.

Means for Solving the Problems

[0005] This invention provides a means for collecting personal biometric information and transmitting it to a remote server. This server has the function of analyzing the biometric information using artificial intelligence and generating personalized health advice based on the analysis. Furthermore, it translates the generated health advice into the user's language and notifies the user's terminal, thereby realizing multilingual and multicultural information provision. This enables individual users to easily obtain and appropriately manage information related to their own health.

[0006] "Personal biometric information" refers to data that indicates the physiological state of an individual's body, primarily including heart rate, basal body temperature, and sleep patterns.

[0007] The term "device" refers to equipment or machines used to collect biometric information, and wearable devices may fall into this category.

[0008] "Means of transmission" refers to the technical methods used to transfer collected information to systems or devices located in remote locations, and wireless communication technology is often used.

[0009] "Artificial intelligence" refers to computer programs that analyze large amounts of data and derive useful insights and predictions from it.

[0010] "Analyzing" refers to the process of analyzing collected biological information and extracting meaningful patterns and trends.

[0011] "Health advice" refers to documented suggestions and instructions for the user's health management, based on analyzed biometric information.

[0012] "Multilingual translation" refers to the process of converting generated health advice into different languages, which is necessary to deliver information to users who speak different languages.

[0013] "Means of notification" refers to technical means of conveying generated information or advice to users, primarily through electronic devices.

[0014] A "system" refers to a collection of interconnected and cooperating elements or components designed to achieve a specific function or purpose. [Brief explanation of the drawing]

[0015] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying out the Invention

[0016] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0017] First, the language used in the following description will be explained.

[0018] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0019] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0020] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0021] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0023] [First Embodiment]

[0024] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0025] As shown in Figure 1, the 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.

[0026] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0028] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and 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.

[0029] 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 perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0031] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 according to the specific processing program 56 executed on the RAM 30.

[0033] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0034] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0035] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0036] This invention provides a system that supports health management using an individual's biometric information. This system consists of a wearable device worn by the user, a mobile device used by the user to receive data from the wearable device and transmit it to a server, and a server that analyzes the data and provides feedback.

[0037] First, the device collects biometric information such as the user's heart rate, basal body temperature, and sleep patterns through a wearable device. This data is continuously collected at regular intervals and transmitted to the mobile device. The mobile device has communication capabilities to send the collected data to a server.

[0038] Next, the server analyzes the received biometric information. This analysis utilizes artificial intelligence technology to understand data trends and identify unusual patterns, thereby evaluating the user's health status. In this process, the server generates personalized health advice based on the analysis results.

[0039] The generated health advice is translated into the user's chosen language via a multilingual translation function on the server. This makes it possible to provide information to users in an easily understandable format, overcoming language barriers.

[0040] Finally, the user's mobile device receives and is notified of health advice provided in multiple languages. For example, the user can receive feedback on recent heart rate variability to help manage their health. Furthermore, the user can provide feedback on the advice, which is then used in subsequent analyses to provide more appropriate advice.

[0041] This system allows users to monitor their health in real time and manage their own health without the need for expert advice. For example, if an abnormal sleep pattern persists, the server can detect that pattern and provide advice such as, "Your health can be improved by ensuring regular sleep schedules."

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The device collects biometric information such as the user's heart rate, basal body temperature, and sleep patterns in real time via a wearable device. This data collection is performed via Bluetooth or Wi-Fi and transmitted as a digital signal from the device to the terminal.

[0045] Step 2:

[0046] The device formats the collected biometric information and sends it to the server via the internet. The data is transferred as JSON packets using the SSL / TLS protocol to ensure secure communication.

[0047] Step 3:

[0048] The server receives data sent from the terminal via the API endpoint. The received data is validated to confirm the consistency of the data format before being stored in the database.

[0049] Step 4:

[0050] The server analyzes the stored data using an artificial intelligence engine. It compares it with past data to extract trends and abnormal patterns in health conditions. This analysis uses machine learning algorithms for pattern recognition.

[0051] Step 5:

[0052] Based on the analysis results, the server generates personalized health advice for the user. The generated advice is presented in text format and provides specific information necessary for improving the user's lifestyle.

[0053] Step 6:

[0054] The server translates advice into multiple languages ​​based on user settings. By utilizing natural language processing technology and incorporating multilingual capabilities, it caters to a global user base.

[0055] Step 7:

[0056] The device receives translation advice from the server and notifies the user. Specifically, push notifications are used to provide advice in real time, even when the application is not running on the device.

[0057] Step 8:

[0058] Users can review the advice provided on their devices and reassess their own health status. Based on the advice, users can take steps to improve their individual health management and behaviors. They can also submit feedback on the advice provided, contributing to system improvements.

[0059] (Example 1)

[0060] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0061] In modern society, it is crucial for individuals to effectively and in real time manage their own health. However, it is not easy for ordinary users without specialized knowledge to accurately interpret their own health data and manage their health appropriately. Therefore, there is a need for a system that can provide appropriate health advice quickly in multiple languages ​​and incorporate user feedback.

[0062] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0063] In this invention, the server includes means for using a learning model to analyze an individual's biometric information, means for generating personalized health guidelines based on the analysis, and means for notifying the user of the translated health guidelines. This enables the user to receive appropriate health advice in real time. Furthermore, by incorporating the user's feedback into the next analysis, more accurate health management can be achieved.

[0064] "Device" refers to a device used to collect an individual's biometric information.

[0065] An "information processing device" refers to a computer system used to receive and analyze data transmitted from a remote location.

[0066] A "learning model" refers to artificial intelligence technology that learns specific patterns from large amounts of data and extracts useful information through data analysis.

[0067] "Health guidelines" refer to specific advice for maintaining and improving health, generated based on an individual's health condition.

[0068] "Means of multilingual translation" refers to technology that translates generated health guidelines into the user's chosen language so that the information can be effectively conveyed even in different linguistic environments.

[0069] A "user" refers to a person who uses the system to manage their health based on their own biometric information.

[0070] This invention is a system for managing an individual's health status in real time and providing personalized health advice to the user. The system consists of a device worn by the user, a user terminal for receiving data and transmitting it to a server, and a server that analyzes the data and provides feedback.

[0071] The device, worn by the user, collects biometric information through a wearable device equipped with heart rate and temperature sensors. This collected biometric information includes heart rate, basal body temperature, and sleep patterns, and the device periodically updates this data. The collected information is transferred to the user's device via Bluetooth or Wi-Fi.

[0072] The server receives biometric information transmitted from the terminal using an information processing device. This server analyzes the data using a generative AI model to identify abnormal patterns and assess health status. Based on the analyzed data, the server generates personalized health guidelines and translates them into the user's chosen language. This system makes it possible to easily provide information to users, overcoming language barriers.

[0073] The user's device receives translated health guidelines from the server and provides the information to the user using a notification function. For example, if the user shows an abnormal heart rate pattern, the server can detect this and provide advice such as, "Take regular breaks to reduce stress."

[0074] Examples of prompt messages include the following:

[0075] "We analyze recent biometric data to assess the user's health status."

[0076] "Generate health guidelines based on heart rate data."

[0077] This invention makes it possible for individuals to manage their own health and take appropriate action without requiring specialized knowledge.

[0078] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0079] Step 1:

[0080] The device collects biometric information such as the user's heart rate, basal body temperature, and sleep patterns using a wearable device. Sensors measure this data from the user's body and update it every second. The collected data is the input and is temporarily stored in the device.

[0081] Step 2:

[0082] The device transmits collected biometric information to the user's mobile device via Bluetooth or Wi-Fi. Data transfer is performed securely using communication protocols, and the received data becomes the output on the mobile device. Battery consumption is minimized during this process.

[0083] Step 3:

[0084] Mobile devices transmit received biometric information to a server using security protocols such as SSL. The data is converted to a format suitable for transmission to the server, and the server's reception becomes the output. This allows for data transfer to the server while maintaining data confidentiality.

[0085] Step 4:

[0086] The server stores the received biometric information in a database and prepares it for analysis. This data is the input, and the organized data before analysis becomes the output.

[0087] Step 5:

[0088] The server analyzes biometric information using a generative AI model to identify data trends and anomalies. Data analysis is performed here, and data calculations such as anomaly pattern detection are carried out. The analysis results are output, and the health status is evaluated.

[0089] Step 6:

[0090] The server generates individual health guidelines based on the analyzed data. These generated health guidelines are the output and are then passed to a multilingual translation system.

[0091] Step 7:

[0092] The server translates the generated health guidelines into the user's chosen language using its multilingual translation function. The input is the generated health guidelines, and the output is the translated guidelines. This ensures that information is provided in a format easily understood by any user.

[0093] Step 8:

[0094] The user's mobile device receives translated health guidelines from the server and provides them to the user using its notification function. The translated health guidelines are the input, and the notifications displayed to the user are the output. Specifically, the system displays advice on the screen along with a notification sound and vibration.

[0095] This processing flow allows users to obtain health information in real time and improve their daily lives based on specific advice.

[0096] (Application Example 1)

[0097] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0098] While conventional systems could collect users' biometric information and provide health advice to support individual health management, this advice rarely reflected daily behaviors directly related to maintaining health, particularly dietary choices. This made it difficult for users to choose foods appropriate to their health condition, resulting in missed opportunities for health improvement.

[0099] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0100] In this invention, the server includes means for analyzing the user's biometric information and generating personalized health advice, means for translating the advice into multiple languages, and means for recommending appropriate meal menus based on the advice. This enables users to make choices that take their health condition into consideration when making daily meal choices, and actual health improvements can be expected.

[0101] "Personal biometric information" refers to important biological data for understanding a user's health status, such as their heart rate, basal body temperature, and sleep patterns.

[0102] A "remote server" refers to a group of data processing devices, either physical or cloud-based, that are accessible via a communication network such as the internet.

[0103] "Analytical artificial intelligence" refers to a group of programs that have the ability to analyze biometric data and automatically determine changes and trends in health status.

[0104] "Personalized health advice" refers to specific recommendations and suggestions optimized for an individual's health condition, based on the user's biometric information.

[0105] "Means of multilingual translation" refers to language processing programs or algorithms for converting health advice into any language specified by the user.

[0106] "Means of notifying users" refers to communication functions that provide users with advice and information in real time via smartphones or other devices.

[0107] "Means of recommending appropriate meal menus" refers to programs or algorithms that provide menus that take into account the nutritional value and ingredients tailored to the user's health condition, based on the generated health advice.

[0108] This invention is a system for managing an individual's health status in real time, utilizing a wearable device, a smartphone, and a remote server. Specifically, the user collects biometric information such as heart rate and basal body temperature using a wearable device, such as a health monitoring device. The wearable device transmits the data to the smartphone via Bluetooth communication.

[0109] The smartphone temporarily stores the collected data and then transfers it to a cloud-based server via Wi-Fi or mobile data communication. The server analyzes the received biometric information using artificial intelligence, such as TENSORFLOW®. This analysis generates a meal plan optimized for the individual's health condition and provides it to the user as health advice.

[0110] The server also has a multilingual translation function and translates the analysis results into the user's specified language. The translated advice and recommended meal menus are sent to the smartphone app using push notification technology. This app provides users with real-time alerts and health information to support them in making healthy eating choices.

[0111] For example, if a user is experiencing stress due to an elevated heart rate, the server will suggest a relaxing meal. This suggestion is made using a prompt message sent to the user's terminal that reads, "We have detected that the user's heart rate is high, so please suggest a relaxing meal. Please also consider relevant nutrients."

[0112] This system allows users to easily make daily dietary choices tailored to their health condition, thereby enabling them to maintain and improve their health.

[0113] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0114] Step 1:

[0115] The device collects biometric information such as the user's heart rate, basal body temperature, and sleep patterns. The input is biometric data from the wearable device, and the output is ready to transmit this data to a smartphone. Bluetooth communication is used, and the data is collected in real time.

[0116] Step 2:

[0117] The device transmits the collected biometric information to the smartphone. The input is data from the wearable device, and the output is biometric information stored locally. The smartphone temporarily stores this data and verifies its integrity and completeness.

[0118] Step 3:

[0119] The user's smartphone uses an internet connection to transfer aggregated biometric information to a cloud server. The input is biometric information stored on the smartphone, and the output is data securely stored in a database on the cloud.

[0120] Step 4:

[0121] The server analyzes received biometric data using an artificial intelligence model. The input is biometric data stored in the cloud, and the output is a health status assessment result. This analysis utilizes learning models such as TensorFlow.

[0122] Step 5:

[0123] The server generates health advice and meal menus tailored to the user's health condition based on the analysis results. The input is the health condition assessment results, and the output is personalized health advice and recommended menus. Generation and personalization are performed by an AI module.

[0124] Step 6:

[0125] The generated health advice and menus are translated into the user's chosen language using the server's multilingual translation function. The input is the generated advice and menus, and the output is the translated information.

[0126] Step 7:

[0127] The server notifies the user's smartphone with translated health advice and recommended meal menus. The input is the translated advice and menus, and the output is the information displayed on the smartphone app. The notification function enables push notifications.

[0128] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0129] This invention realizes a system that provides personalized health management based on a user's biometric information and emotional state. This system includes a wearable information collection device, an emotion engine that recognizes the user's emotions, a server that performs analysis, and a terminal that provides an interface with the user.

[0130] First, the device collects the user's biometric information, such as heart rate, basal body temperature, and sleep patterns, through a wearable device. This information is periodically transmitted to a server. In addition, the device uses an emotion engine to obtain emotional data from the user's voice and facial expressions. This emotional data is used to determine whether the user is stressed, relaxed, or in any other emotional state.

[0131] The server comprehensively analyzes the received biometric and emotional data. Applying artificial intelligence technology, it understands how individual data points interact and assesses the user's overall health status. Specifically, if an elevated heart rate is detected along with stressful emotions, it can provide stress management advice.

[0132] Based on the analysis results, the server generates personalized health advice. This advice outlines actions the user should take to achieve better health and takes into account the user's emotional state. The generated advice is translated into the user's chosen language via the server's multilingual translation function.

[0133] The device receives translated health advice and notifies the user. Notifications are typically delivered via push notifications and are designed to easily grab the user's attention. For example, if the emotion engine determines that the user has been busy and stressed recently, it will notify the user with advice such as, "It is recommended that you take regular breaks and engage in relaxation activities."

[0134] In this way, the system combines the user's biometric and emotional information to enable more precise and personalized health management. The system supports users in maintaining their health by allowing them to understand their health status in real time and take appropriate actions continuously.

[0135] The following describes the processing flow.

[0136] Step 1:

[0137] The device periodically collects biometric information from the wearable device, such as the user's heart rate, basal body temperature, and sleep patterns. This data is transmitted to the device in real time via Bluetooth or Wi-Fi.

[0138] Step 2:

[0139] The device uses a built-in emotion engine to recognize the user's voice and facial expressions and determine the user's emotional state. It utilizes voice tone analysis and facial recognition technology to generate emotional data such as stress levels and relaxation levels.

[0140] Step 3:

[0141] The device combines biometric information and emotional data and transmits it to a server via a secure internet connection. The data is encrypted and transmitted in JSON format.

[0142] Step 4:

[0143] The server stores the received data for analysis and uses an artificial intelligence model to analyze the correlation between biometric information and emotional data. For example, if a high heart rate and stressful emotions are observed simultaneously, a detailed causal analysis is performed.

[0144] Step 5:

[0145] Based on the data analysis results, the server generates personalized health advice for the user. This advice includes specific suggestions for maintaining health and managing stress.

[0146] Step 6:

[0147] The server translates the generated advice into the user's chosen language. A multilingual translation system is used to accurately translate the advice to the user's selected language.

[0148] Step 7:

[0149] The device receives translated health advice from the server and notifies the user in real time using push notifications. The notifications are displayed on the user's device as symbolic icons or banners.

[0150] Step 8:

[0151] Users can check notifications on their devices and adjust their health management and daily activities based on the advice they receive. Furthermore, they can provide feedback on the advice given, which can then be used for future analyses.

[0152] (Example 2)

[0153] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0154] In modern society, the information individuals need to manage their own health is fragmented, making comprehensive, real-time health management difficult. In particular, the lack of systems that integrate and analyze biometric data and emotional states to provide personalized health advice makes it difficult for individuals to effectively manage stress and maintain their health.

[0155] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0156] In this invention, the server includes means for integrating and analyzing an individual's biometric information and emotional data, means for generating personalized health advice based on the analysis results, and means for translating the generated health advice into multiple languages ​​and providing it to the user. This enables individuals to understand their own health status in real time and receive accurate advice tailored to their specific emotional state.

[0157] "Biometric information" refers to data that indicates an individual's physical condition, including heart rate, body temperature, and sleep cycle.

[0158] "Data collection means" refers to devices and programs for acquiring an individual's biometric information and emotional data.

[0159] An "information processing device" refers to a computer system or server used to receive and analyze collected biometric and emotional data.

[0160] "Emotional data" refers to information about a user's emotional state obtained by analyzing their voice, facial expressions, and other data.

[0161] "Artificial intelligence technology" refers to technologies that gain insights from data through machine learning and data analysis.

[0162] "Health advice" refers to specific action guidelines and suggestions for promoting health, provided to users based on analyzed data.

[0163] "Translation methods" refer to systems or programs that can translate health advice into multiple languages.

[0164] "Push notifications" refer to a function that automatically sends information to a user's device and is used as a means to attract the user's attention.

[0165] This invention relates to a personalized health management system that utilizes biometric and emotional data. This system primarily combines a wearable device, a data processing server, and a terminal serving as a user interface.

[0166] The device collects biometric information from the wearable device worn by the user. This device incorporates a heart rate sensor, a temperature sensor, and an accelerometer, and has the ability to measure heart rate, body temperature, and sleep patterns in real time. The device also captures the user's voice and facial expressions and extracts emotional data using an emotion engine. This emotional data is used to identify the user's emotional state.

[0167] The server receives biometric and emotional data transmitted from the terminal and analyzes it using a generative AI model. This analysis process detects specific patterns and trends from the collected data to gain insights into the user's health status. For example, it may analyze the correlation between stress levels and high heart rate.

[0168] Based on the analysis results, the server automatically generates health advice tailored to the user's current state. This advice includes specific suggestions for maintaining health and managing stress. The generated advice is then translated into the user's preferred language using the server's built-in multilingual translation function.

[0169] The device receives translated health advice from the server and delivers it to the user as a push notification. This notification is designed to be sent at times when the user is likely to be interested in their health. For example, during a break after a long day of work, a notification might say, "Take a short walk to refresh yourself."

[0170] As a concrete example, a possible prompt for a user using this system to manage their stress level might be, "Please describe the health advice to be provided when the system determines that the user is in a relaxed state." Based on this prompt, the system provides advice optimized for the user.

[0171] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0172] Step 1:

[0173] The device collects the user's biometric information via a wearable device. This information includes heart rate, body temperature, and sleep patterns. The input is continuous biosensor data provided by the wearable device, and the output is organized biometric data. This data is periodically sent to a server for later analysis. Specifically, the device averages and records heart rate at regular intervals, records daily body temperature fluctuations, and analyzes sleep movements to evaluate sleep depth.

[0174] Step 2:

[0175] The device uses an emotion engine to analyze the user's voice and facial expression data and acquire emotional data. Input is voice and video data obtained from the microphone and camera, and output includes identified emotional states (e.g., stress, joy, relaxation). In this process, the emotion engine performs voice tone analysis and facial feature point analysis. Specifically, the device extracts emotional features from the voice data and uses video data to identify emotions from the movement of facial muscles.

[0176] Step 3:

[0177] The server receives biometric and emotional data transmitted from the terminal, integrates and analyzes the data. The input is organized biometric and emotional state data, and the output is an analysis result indicating the user's current health status. The server uses a generative AI model to analyze the correlations between this data and assess health risks and stress levels. Specifically, it uses artificial intelligence to perform correlation analysis to determine whether stress levels are elevated when heart rate is high.

[0178] Step 4:

[0179] The server generates personalized health advice based on the analysis results. The input is the analysis results from step 3, and the output is specific advice for improving the user's health. A multilingual translation function is used to generate this advice, and it is translated according to the user's set language. For example, if the stress level is high, advice such as "Make slow walks a part of your daily routine" will be generated.

[0180] Step 5:

[0181] The device provides the user with translated health advice received from the server as a push notification. The input is the translated advice from the server, and the output is a practical notification to the user. Specifically, the device displays the notification in a pop-up format, showing the details of the advice for easy access by the user. The timing of the notifications is also considered, and they are sent to avoid busy times.

[0182] (Application Example 2)

[0183] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0184] Traditionally, customer service and product recommendations in stores have often relied on the experience and intuition of store employees, making it extremely difficult to provide personalized recommendations that take into account each customer's individual health and emotional state. As a result, the effectiveness of improving customer satisfaction and increasing sales has been limited. The present invention aims to solve these problems and provide a system that enables more appropriate and satisfying recommendations for each individual customer.

[0185] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0186] In this invention, the server includes means for collecting personal biometric information, means for suggesting products and services according to the user's emotional state, and means for notifying the user of translated health advice. This enables detailed, personalized suggestions that take into account not only the customer's biometric information but also their emotional state.

[0187] A "device that collects personal biometric information" is a device that acquires data indicating the user's health status, such as heart rate, basal body temperature, and sleep patterns.

[0188] "Means of sending to a remote server" refers to the function of transferring data collected from wearable devices, etc., to a remotely located server via the internet or other means.

[0189] "Methods using artificial intelligence" refer to technologies that use machine learning and data analysis techniques to analyze collected data and generate personalized information.

[0190] "Means for generating health advice" refers to the process of creating specific suggestions and instructions to support users' health management based on analysis results.

[0191] The "means of translating into multiple languages" refer to a function that converts the generated health advice into the user's preferred language, making it available to users who speak a variety of languages.

[0192] "Means of notifying users" refers to methods of delivering information to users through their devices, and typically includes push notifications.

[0193] "Methods for detecting emotional states" refer to technologies that analyze the tone of a user's voice and changes in their facial expressions to recognize their emotions at that moment.

[0194] "Means of suggesting products and services" refers to a process that recommends appropriate products and services in real time based on the detected emotions and health status of the user.

[0195] The system that realizes this invention mainly consists of a wearable device, a smart terminal, a remote server, and an emotion analysis module.

[0196] The device collects the user's biometric information through wearable devices. Examples of such devices include smartwatches and fitness trackers, which can acquire data such as heart rate, basal body temperature, and sleep patterns. This biometric data is transmitted to the device via Bluetooth or Wi-Fi.

[0197] The device is equipped with an emotion recognition engine that uses the camera and microphone to analyze changes in the user's facial expressions and voice in real time to detect their emotional state.

[0198] Next, the device sends the collected biometric and emotional data to a remote server. The server uses machine learning models such as Google Cloud AI to analyze the received data and evaluate the user's health and emotional state. Based on the analysis results, the server generates personalized health advice, which is then translated into the user's chosen language through a multilingual translation function.

[0199] Furthermore, the server suggests products and services tailored to the visitor's emotional state based on the analysis results. For example, if a visitor exhibits a specific emotion or health condition, information is generated to recommend relevant products.

[0200] The generated advice and suggestions are notified to the device and delivered to the user via smart glasses or smartphone push notifications. This allows users to receive real-time suggestions for appropriate actions and products tailored to their health status and emotions.

[0201] For example, if a customer is using smart glasses in a specific store within a shopping mall, and an elevated heart rate and high stress index are detected, a message such as "Click here for our selection of products with relaxing effects" will appear in their field of vision.

[0202] Examples of input prompts for a generative AI model:

[0203] "The user's heart rate is 90 bpm, and their stress level is high based on facial expression analysis. Please recommend products and services that are best suited for maintaining their health."

[0204] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0205] Step 1:

[0206] The device collects the user's biometric information from wearable devices. Specifically, it acquires data such as heart rate, basal body temperature, and sleep patterns via Bluetooth. This input data forms the basis for subsequent analysis.

[0207] Step 2:

[0208] The device analyzes the user's emotional state using its built-in emotion recognition engine. It captures the user's facial expressions and voice using the camera and microphone, and converts this into data to determine their emotional state. In this step, changes in facial expressions and voice are processed by an algorithm, and emotional information is output.

[0209] Step 3:

[0210] The device integrates the collected biometric and emotional information and transmits it to a remote server via the internet. At this point, the input includes both biometric and emotional information. The server prepares this data for analysis.

[0211] Step 4:

[0212] The server uses machine learning models such as Google Cloud AI to analyze biometric and emotional information. Here, the collected data is used to assess the user's health and emotional state. This analysis understands the interactions between each data point and outputs an overall health status.

[0213] Step 5:

[0214] The server generates personalized health advice based on the analysis results and translates it into the user's chosen language using its multilingual translation function. The generated advice includes specific suggestions for maintaining good health, and this is the information provided to the user.

[0215] Step 6:

[0216] The server generates data to suggest products and services that are appropriate for the user's emotional and health state. Based on the analysis results, it uses a generative AI model to create necessary prompt statements and output appropriate options.

[0217] Step 7:

[0218] The device pushes translated health advice and product / service suggestions received from the server to the user. Users can receive this information in real time via smart glasses or smartphones. This step involves receiving information and creating an incentive for the user to take direct action.

[0219] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0220] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0221] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0222] [Second Embodiment]

[0223] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0224] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0225] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0226] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0227] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0228] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0229] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0230] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0231] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0232] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0233] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0234] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0235] This invention provides a system that supports health management using an individual's biometric information. This system consists of a wearable device worn by the user, a mobile device used by the user to receive data from the wearable device and transmit it to a server, and a server that analyzes the data and provides feedback.

[0236] First, the device collects biometric information such as the user's heart rate, basal body temperature, and sleep patterns through a wearable device. This data is continuously collected at regular intervals and transmitted to the mobile device. The mobile device has communication capabilities to send the collected data to a server.

[0237] Next, the server analyzes the received biometric information. This analysis utilizes artificial intelligence technology to understand data trends and identify unusual patterns, thereby evaluating the user's health status. In this process, the server generates personalized health advice based on the analysis results.

[0238] The generated health advice is translated into the user's chosen language via a multilingual translation function on the server. This makes it possible to provide information to users in an easily understandable format, overcoming language barriers.

[0239] Finally, the user's mobile device receives and is notified of health advice provided in multiple languages. For example, the user can receive feedback on recent heart rate variability to help manage their health. Furthermore, the user can provide feedback on the advice, which is then used in subsequent analyses to provide more appropriate advice.

[0240] This system allows users to monitor their health in real time and manage their own health without the need for expert advice. For example, if an abnormal sleep pattern persists, the server can detect that pattern and provide advice such as, "Your health can be improved by ensuring regular sleep schedules."

[0241] The following describes the processing flow.

[0242] Step 1:

[0243] The device collects biometric information such as the user's heart rate, basal body temperature, and sleep patterns in real time via a wearable device. This data collection is performed via Bluetooth or Wi-Fi and transmitted as a digital signal from the device to the terminal.

[0244] Step 2:

[0245] The device formats the collected biometric information and sends it to the server via the internet. The data is transferred as JSON packets using the SSL / TLS protocol to ensure secure communication.

[0246] Step 3:

[0247] The server receives data sent from the terminal via the API endpoint. The received data is validated to confirm the consistency of the data format before being stored in the database.

[0248] Step 4:

[0249] The server analyzes the stored data using an artificial intelligence engine. It compares it with past data to extract trends and abnormal patterns in health conditions. This analysis uses machine learning algorithms for pattern recognition.

[0250] Step 5:

[0251] Based on the analysis results, the server generates personalized health advice for the user. The generated advice is presented in text format and provides specific information necessary for improving the user's lifestyle.

[0252] Step 6:

[0253] The server translates advice into multiple languages ​​based on user settings. By utilizing natural language processing technology and incorporating multilingual capabilities, it caters to a global user base.

[0254] Step 7:

[0255] The device receives translation advice from the server and notifies the user. Specifically, push notifications are used to provide advice in real time, even when the application is not running on the device.

[0256] Step 8:

[0257] Users can review the advice provided on their devices and reassess their own health status. Based on the advice, users can take steps to improve their individual health management and behaviors. They can also submit feedback on the advice provided, contributing to system improvements.

[0258] (Example 1)

[0259] Next, we will describe Example 1. 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."

[0260] In modern society, it is crucial for individuals to effectively and in real time manage their own health. However, it is not easy for ordinary users without specialized knowledge to accurately interpret their own health data and manage their health appropriately. Therefore, there is a need for a system that can provide appropriate health advice quickly in multiple languages ​​and incorporate user feedback.

[0261] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0262] In this invention, the server includes means for using a learning model to analyze an individual's biometric information, means for generating personalized health guidelines based on the analysis, and means for notifying the user of the translated health guidelines. This enables the user to receive appropriate health advice in real time. Furthermore, by incorporating the user's feedback into the next analysis, more accurate health management can be achieved.

[0263] "Device" refers to a device used to collect an individual's biometric information.

[0264] An "information processing device" refers to a computer system used to receive and analyze data transmitted from a remote location.

[0265] A "learning model" refers to artificial intelligence technology that learns specific patterns from large amounts of data and extracts useful information through data analysis.

[0266] "Health guidelines" refer to specific advice for maintaining and improving health, generated based on an individual's health condition.

[0267] "Means of multilingual translation" refers to technology that translates generated health guidelines into the user's chosen language so that the information can be effectively conveyed even in different linguistic environments.

[0268] A "user" refers to a person who uses the system to manage their health based on their own biometric information.

[0269] This invention is a system for managing an individual's health status in real time and providing personalized health advice to the user. The system consists of a device worn by the user, a user terminal for receiving data and transmitting it to a server, and a server that analyzes the data and provides feedback.

[0270] The device, worn by the user, collects biometric information through a wearable device equipped with heart rate and temperature sensors. This collected biometric information includes heart rate, basal body temperature, and sleep patterns, and the device periodically updates this data. The collected information is transferred to the user's device via Bluetooth or Wi-Fi.

[0271] The server receives biometric information transmitted from the terminal using an information processing device. This server analyzes the data using a generative AI model to identify abnormal patterns and assess health status. Based on the analyzed data, the server generates personalized health guidelines and translates them into the user's chosen language. This system makes it possible to easily provide information to users, overcoming language barriers.

[0272] The user's device receives translated health guidelines from the server and provides the information to the user using a notification function. For example, if the user shows an abnormal heart rate pattern, the server can detect this and provide advice such as, "Take regular breaks to reduce stress."

[0273] Examples of prompt messages include the following:

[0274] "We analyze recent biometric data to assess the user's health status."

[0275] "Generate health guidelines based on heart rate data."

[0276] This invention makes it possible for individuals to manage their own health and take appropriate action without requiring specialized knowledge.

[0277] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0278] Step 1:

[0279] The device collects biometric information such as the user's heart rate, basal body temperature, and sleep patterns using a wearable device. Sensors measure this data from the user's body and update it every second. The collected data is the input and is temporarily stored in the device.

[0280] Step 2:

[0281] The device transmits collected biometric information to the user's mobile device via Bluetooth or Wi-Fi. Data transfer is performed securely using communication protocols, and the received data becomes the output on the mobile device. Battery consumption is minimized during this process.

[0282] Step 3:

[0283] The mobile terminal transmits the received biometric information to the server using a security protocol such as SSL. Data format conversion for transmission to the server is performed, and the server's reception becomes the output. This enables transfer to the server while maintaining data confidentiality.

[0284] Step 4:

[0285] The server stores the received biometric information in a database and prepares for analysis. This data is the input, and the data in an organized format before analysis becomes the output.

[0286] Step 5:

[0287] The server analyzes the biometric information using a generated AI model and identifies data trends and anomalies. Here, data analysis is performed, and data operations such as detecting abnormal patterns are carried out. The analysis result is output, and the health status is evaluated.

[0288] Step 6:

[0289] The server generates individual health guidelines based on the analyzed data. The generated health guidelines are the output and are then passed to a multi - language translation means.

[0290] Step 7:

[0291] The server translates the generated health guidelines into the language set by the user using a multi - language translation function. The input is the generated health guidelines, and the output is the translated guidelines. This provides information in an easily understandable form for any user.

[0292] Step 8:

[0293] The user's mobile terminal receives the translated health guidelines from the server and provides them to the user using the notification function. The translated health guidelines are the input, and the notification displayed to the user is the output. As a specific operation, advice is displayed on the screen along with a notification sound and vibration.

[0294] This processing flow allows users to obtain health information in real time and improve their daily lives based on specific advice.

[0295] (Application Example 1)

[0296] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0297] While conventional systems could collect users' biometric information and provide health advice to support individual health management, this advice rarely reflected daily behaviors directly related to maintaining health, particularly dietary choices. This made it difficult for users to choose foods appropriate to their health condition, resulting in missed opportunities for health improvement.

[0298] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0299] In this invention, the server includes means for analyzing the user's biometric information and generating personalized health advice, means for translating the advice into multiple languages, and means for recommending appropriate meal menus based on the advice. This enables users to make choices that take their health condition into consideration when making daily meal choices, and actual health improvements can be expected.

[0300] "Personal biometric information" refers to important biological data for understanding a user's health status, such as their heart rate, basal body temperature, and sleep patterns.

[0301] A "remote server" refers to a group of data processing devices, either physical or cloud-based, that are accessible via a communication network such as the internet.

[0302] The "artificial intelligence for analysis" refers to a group of programs that analyze biometric data and automatically determine changes and trends in the health status.

[0303] The "personalized health advice" refers to specific recommendations and advice optimized for the individual's health status based on the user's biometric information.

[0304] The "means for translating in multiple languages" refers to language processing programs and algorithms for converting health advice into any language specified by the user.

[0305] The "means for notifying the user" refers to a communication function for providing advice and information to the user in real time via a smartphone or other device.

[0306] The "means for recommending an appropriate diet menu" refers to programs and algorithms for providing a menu that takes into account nutritional value and ingredients suitable for the user's health status based on the generated health advice.

[0307] This invention is a system for managing an individual's health status in real time, utilizing a wearable terminal, a smartphone, and a remote server. As a specific implementation method, the user collects biometric information such as heart rate and basal body temperature using a wearable terminal, such as a health monitoring device. The wearable terminal transmits the data to the smartphone via Bluetooth communication.

[0308] The smartphone temporarily stores the collected data and then transfers the data to a cloud-based server via Wi-Fi or mobile data communication. The server analyzes the received biometric information using artificial intelligence, such as TensorFlow. Through this analysis, a diet menu optimal for each individual's health status is generated and provided to the user as health advice.

[0309] The server also has a multilingual translation function and translates the analysis results into the user's specified language. The translated advice and recommended meal menus are sent to the smartphone app using push notification technology. This app provides users with real-time alerts and health information to support them in making healthy eating choices.

[0310] For example, if a user is experiencing stress due to an elevated heart rate, the server will suggest a relaxing meal. This suggestion is made using a prompt message sent to the user's terminal that reads, "We have detected that the user's heart rate is high, so please suggest a relaxing meal. Please also consider relevant nutrients."

[0311] This system allows users to easily make daily dietary choices tailored to their health condition, thereby enabling them to maintain and improve their health.

[0312] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0313] Step 1:

[0314] The device collects biometric information such as the user's heart rate, basal body temperature, and sleep patterns. The input is biometric data from the wearable device, and the output is ready to transmit this data to a smartphone. Bluetooth communication is used, and the data is collected in real time.

[0315] Step 2:

[0316] The device transmits the collected biometric information to the smartphone. The input is data from the wearable device, and the output is biometric information stored locally. The smartphone temporarily stores this data and verifies its integrity and completeness.

[0317] Step 3:

[0318] The user's smartphone uses an internet connection to transfer aggregated biometric information to a cloud server. The input is biometric information stored on the smartphone, and the output is data securely stored in a database on the cloud.

[0319] Step 4:

[0320] The server analyzes received biometric data using an artificial intelligence model. The input is biometric data stored in the cloud, and the output is a health status assessment result. This analysis utilizes learning models such as TensorFlow.

[0321] Step 5:

[0322] The server generates health advice and meal menus tailored to the user's health condition based on the analysis results. The input is the health condition assessment results, and the output is personalized health advice and recommended menus. Generation and personalization are performed by an AI module.

[0323] Step 6:

[0324] The generated health advice and menus are translated into the user's chosen language using the server's multilingual translation function. The input is the generated advice and menus, and the output is the translated information.

[0325] Step 7:

[0326] The server notifies the user's smartphone with translated health advice and recommended meal menus. The input is the translated advice and menus, and the output is the information displayed on the smartphone app. The notification function enables push notifications.

[0327] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0328] This invention realizes a system that provides personalized health management based on a user's biometric information and emotional state. This system includes a wearable information collection device, an emotion engine that recognizes the user's emotions, a server that performs analysis, and a terminal that provides an interface with the user.

[0329] First, the device collects the user's biometric information, such as heart rate, basal body temperature, and sleep patterns, through a wearable device. This information is periodically transmitted to a server. In addition, the device uses an emotion engine to obtain emotional data from the user's voice and facial expressions. This emotional data is used to determine whether the user is stressed, relaxed, or in any other emotional state.

[0330] The server comprehensively analyzes the received biometric and emotional data. Applying artificial intelligence technology, it understands how individual data points interact and assesses the user's overall health status. Specifically, if an elevated heart rate is detected along with stressful emotions, it can provide stress management advice.

[0331] Based on the analysis results, the server generates personalized health advice. This advice outlines actions the user should take to achieve better health and takes into account the user's emotional state. The generated advice is translated into the user's chosen language via the server's multilingual translation function.

[0332] The device receives translated health advice and notifies the user. Notifications are typically delivered via push notifications and are designed to easily grab the user's attention. For example, if the emotion engine determines that the user has been busy and stressed recently, it will notify the user with advice such as, "It is recommended that you take regular breaks and engage in relaxation activities."

[0333] In this way, the system combines the user's biometric and emotional information to enable more precise and personalized health management. The system supports users in maintaining their health by allowing them to understand their health status in real time and take appropriate actions continuously.

[0334] The following describes the processing flow.

[0335] Step 1:

[0336] The device periodically collects biometric information from the wearable device, such as the user's heart rate, basal body temperature, and sleep patterns. This data is transmitted to the device in real time via Bluetooth or Wi-Fi.

[0337] Step 2:

[0338] The device uses a built-in emotion engine to recognize the user's voice and facial expressions and determine the user's emotional state. It utilizes voice tone analysis and facial recognition technology to generate emotional data such as stress levels and relaxation levels.

[0339] Step 3:

[0340] The device combines biometric information and emotional data and transmits it to a server via a secure internet connection. The data is encrypted and transmitted in JSON format.

[0341] Step 4:

[0342] The server stores the received data for analysis and uses an artificial intelligence model to analyze the correlation between biometric information and emotional data. For example, if a high heart rate and stressful emotions are observed simultaneously, a detailed causal analysis is performed.

[0343] Step 5:

[0344] Based on the data analysis results, the server generates personalized health advice for the user. This advice includes specific suggestions for maintaining health and managing stress.

[0345] Step 6:

[0346] The server translates the generated advice into the user's chosen language. A multilingual translation system is used to accurately translate the advice to the user's selected language.

[0347] Step 7:

[0348] The device receives translated health advice from the server and notifies the user in real time using push notifications. The notifications are displayed on the user's device as symbolic icons or banners.

[0349] Step 8:

[0350] Users can check notifications on their devices and adjust their health management and daily activities based on the advice they receive. Furthermore, they can provide feedback on the advice given, which can then be used for future analyses.

[0351] (Example 2)

[0352] Next, we will describe Example 2. 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".

[0353] In modern society, the information individuals need to manage their own health is fragmented, making comprehensive, real-time health management difficult. In particular, the lack of systems that integrate and analyze biometric data and emotional states to provide personalized health advice makes it difficult for individuals to effectively manage stress and maintain their health.

[0354] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0355] In this invention, the server includes means for integrating and analyzing an individual's biometric information and emotional data, means for generating personalized health advice based on the analysis results, and means for translating the generated health advice into multiple languages ​​and providing it to the user. This enables individuals to understand their own health status in real time and receive accurate advice tailored to their specific emotional state.

[0356] "Biometric information" refers to data that indicates an individual's physical condition, including heart rate, body temperature, and sleep cycle.

[0357] "Data collection means" refers to devices and programs for acquiring an individual's biometric information and emotional data.

[0358] An "information processing device" refers to a computer system or server used to receive and analyze collected biometric and emotional data.

[0359] "Emotional data" refers to information about a user's emotional state obtained by analyzing their voice, facial expressions, and other data.

[0360] "Artificial intelligence technology" refers to technologies that gain insights from data through machine learning and data analysis.

[0361] "Health advice" refers to specific action guidelines and suggestions for promoting health, provided to users based on analyzed data.

[0362] "Translation methods" refer to systems or programs that can translate health advice into multiple languages.

[0363] "Push notifications" refer to a function that automatically sends information to a user's device and is used as a means to attract the user's attention.

[0364] This invention relates to a personalized health management system that utilizes biometric and emotional data. This system primarily combines a wearable device, a data processing server, and a terminal serving as a user interface.

[0365] The device collects biometric information from the wearable device worn by the user. This device incorporates a heart rate sensor, a temperature sensor, and an accelerometer, and has the ability to measure heart rate, body temperature, and sleep patterns in real time. The device also captures the user's voice and facial expressions and extracts emotional data using an emotion engine. This emotional data is used to identify the user's emotional state.

[0366] The server receives biometric and emotional data transmitted from the terminal and analyzes it using a generative AI model. This analysis process detects specific patterns and trends from the collected data to gain insights into the user's health status. For example, it may analyze the correlation between stress levels and high heart rate.

[0367] Based on the analysis results, the server automatically generates health advice tailored to the user's current state. This advice includes specific suggestions for maintaining health and managing stress. The generated advice is then translated into the user's preferred language using the server's built-in multilingual translation function.

[0368] The device receives translated health advice from the server and delivers it to the user as a push notification. This notification is designed to be sent at times when the user is likely to be interested in their health. For example, during a break after a long day of work, a notification might say, "Take a short walk to refresh yourself."

[0369] As a concrete example, a possible prompt for a user using this system to manage their stress level might be, "Please describe the health advice to be provided when the system determines that the user is in a relaxed state." Based on this prompt, the system provides advice optimized for the user.

[0370] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0371] Step 1:

[0372] The device collects the user's biometric information via a wearable device. This information includes heart rate, body temperature, and sleep patterns. The input is continuous biosensor data provided by the wearable device, and the output is organized biometric data. This data is periodically sent to a server for later analysis. Specifically, the device averages and records heart rate at regular intervals, records daily body temperature fluctuations, and analyzes sleep movements to evaluate sleep depth.

[0373] Step 2:

[0374] The device uses an emotion engine to analyze the user's voice and facial expression data and acquire emotional data. Input is voice and video data obtained from the microphone and camera, and output includes identified emotional states (e.g., stress, joy, relaxation). In this process, the emotion engine performs voice tone analysis and facial feature point analysis. Specifically, the device extracts emotional features from the voice data and uses video data to identify emotions from the movement of facial muscles.

[0375] Step 3:

[0376] The server receives biometric and emotional data transmitted from the terminal, integrates and analyzes the data. The input is organized biometric and emotional state data, and the output is an analysis result indicating the user's current health status. The server uses a generative AI model to analyze the correlations between this data and assess health risks and stress levels. Specifically, it uses artificial intelligence to perform correlation analysis to determine whether stress levels are elevated when heart rate is high.

[0377] Step 4:

[0378] The server generates personalized health advice based on the analysis results. The input is the analysis results from step 3, and the output is specific advice for improving the user's health. A multilingual translation function is used to generate this advice, and it is translated according to the user's set language. For example, if the stress level is high, advice such as "Make slow walks a part of your daily routine" will be generated.

[0379] Step 5:

[0380] The device provides the user with translated health advice received from the server as a push notification. The input is the translated advice from the server, and the output is a practical notification to the user. Specifically, the device displays the notification in a pop-up format, showing the details of the advice for easy access by the user. The timing of the notifications is also considered, and they are sent to avoid busy times.

[0381] (Application Example 2)

[0382] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0383] Traditionally, customer service and product recommendations in stores have often relied on the experience and intuition of store employees, making it extremely difficult to provide personalized recommendations that take into account each customer's individual health and emotional state. As a result, the effectiveness of improving customer satisfaction and increasing sales has been limited. The present invention aims to solve these problems and provide a system that enables more appropriate and satisfying recommendations for each individual customer.

[0384] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0385] In this invention, the server includes means for collecting personal biometric information, means for suggesting products and services according to the user's emotional state, and means for notifying the user of translated health advice. This enables detailed, personalized suggestions that take into account not only the customer's biometric information but also their emotional state.

[0386] A "device that collects personal biometric information" is a device that acquires data indicating the user's health status, such as heart rate, basal body temperature, and sleep patterns.

[0387] "Means of sending to a remote server" refers to the function of transferring data collected from wearable devices, etc., to a remotely located server via the internet or other means.

[0388] "Methods using artificial intelligence" refer to technologies that use machine learning and data analysis techniques to analyze collected data and generate personalized information.

[0389] "Means for generating health advice" refers to the process of creating specific suggestions and instructions to support users' health management based on analysis results.

[0390] The "means of translating into multiple languages" refer to a function that converts the generated health advice into the user's preferred language, making it available to users who speak a variety of languages.

[0391] "Means of notifying users" refers to methods of delivering information to users through their devices, and typically includes push notifications.

[0392] "Methods for detecting emotional states" refer to technologies that analyze the tone of a user's voice and changes in their facial expressions to recognize their emotions at that moment.

[0393] "Means of suggesting products and services" refers to a process that recommends appropriate products and services in real time based on the detected emotions and health status of the user.

[0394] The system that realizes this invention mainly consists of a wearable device, a smart terminal, a remote server, and an emotion analysis module.

[0395] The device collects the user's biometric information through wearable devices. Examples of such devices include smartwatches and fitness trackers, which can acquire data such as heart rate, basal body temperature, and sleep patterns. This biometric data is transmitted to the device via Bluetooth or Wi-Fi.

[0396] The device is equipped with an emotion recognition engine that uses the camera and microphone to analyze changes in the user's facial expressions and voice in real time to detect their emotional state.

[0397] Next, the device sends the collected biometric and emotional data to a remote server. The server uses machine learning models such as Google Cloud AI to analyze the received data and evaluate the user's health and emotional state. Based on the analysis results, the server generates personalized health advice, which is then translated into the user's chosen language through a multilingual translation function.

[0398] Furthermore, the server suggests products and services tailored to the visitor's emotional state based on the analysis results. For example, if a visitor exhibits a specific emotion or health condition, information is generated to recommend relevant products.

[0399] The generated advice and suggestions are notified to the device and delivered to the user via smart glasses or smartphone push notifications. This allows users to receive real-time suggestions for appropriate actions and products tailored to their health status and emotions.

[0400] For example, if a customer is using smart glasses in a specific store within a shopping mall, and an elevated heart rate and high stress index are detected, a message such as "Click here for our selection of products with relaxing effects" will appear in their field of vision.

[0401] Examples of input prompts for a generative AI model:

[0402] "The user's heart rate is 90 bpm, and their stress level is high based on facial expression analysis. Please recommend products and services that are best suited for maintaining their health."

[0403] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0404] Step 1:

[0405] The device collects the user's biometric information from wearable devices. Specifically, it acquires data such as heart rate, basal body temperature, and sleep patterns via Bluetooth. This input data forms the basis for subsequent analysis.

[0406] Step 2:

[0407] The device analyzes the user's emotional state using its built-in emotion recognition engine. It captures the user's facial expressions and voice using the camera and microphone, and converts this into data to determine their emotional state. In this step, changes in facial expressions and voice are processed by an algorithm, and emotional information is output.

[0408] Step 3:

[0409] The device integrates the collected biometric and emotional information and transmits it to a remote server via the internet. At this point, the input includes both biometric and emotional information. The server prepares this data for analysis.

[0410] Step 4:

[0411] The server uses machine learning models such as Google Cloud AI to analyze biometric and emotional information. Here, the collected data is used to assess the user's health and emotional state. This analysis understands the interactions between each data point and outputs an overall health status.

[0412] Step 5:

[0413] The server generates personalized health advice based on the analysis results and translates it into the user's chosen language using its multilingual translation function. The generated advice includes specific suggestions for maintaining good health, and this is the information provided to the user.

[0414] Step 6:

[0415] The server generates data to suggest products and services that are appropriate for the user's emotional and health state. Based on the analysis results, it uses a generative AI model to create necessary prompt statements and output appropriate options.

[0416] Step 7:

[0417] The device pushes translated health advice and product / service suggestions received from the server to the user. Users can receive this information in real time via smart glasses or smartphones. This step involves receiving information and creating an incentive for the user to take direct action.

[0418] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0419] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0420] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0421] [Third Embodiment]

[0422] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0423] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0424] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0425] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0426] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0427] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0428] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0429] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0430] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0431] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0432] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0433] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0434] This invention provides a system that supports health management using an individual's biometric information. This system consists of a wearable device worn by the user, a mobile device used by the user to receive data from the wearable device and transmit it to a server, and a server that analyzes the data and provides feedback.

[0435] First, the device collects biometric information such as the user's heart rate, basal body temperature, and sleep patterns through a wearable device. This data is continuously collected at regular intervals and transmitted to the mobile device. The mobile device has communication capabilities to send the collected data to a server.

[0436] Next, the server analyzes the received biometric information. This analysis utilizes artificial intelligence technology to understand data trends and identify unusual patterns, thereby evaluating the user's health status. In this process, the server generates personalized health advice based on the analysis results.

[0437] The generated health advice is translated into the user's chosen language via a multilingual translation function on the server. This makes it possible to provide information to users in an easily understandable format, overcoming language barriers.

[0438] Finally, the user's mobile device receives and is notified of health advice provided in multiple languages. For example, the user can receive feedback on recent heart rate variability to help manage their health. Furthermore, the user can provide feedback on the advice, which is then used in subsequent analyses to provide more appropriate advice.

[0439] This system allows users to monitor their health in real time and manage their own health without the need for expert advice. For example, if an abnormal sleep pattern persists, the server can detect that pattern and provide advice such as, "Your health can be improved by ensuring regular sleep schedules."

[0440] The following describes the processing flow.

[0441] Step 1:

[0442] The device collects biometric information such as the user's heart rate, basal body temperature, and sleep patterns in real time via a wearable device. This data collection is performed via Bluetooth or Wi-Fi and transmitted as a digital signal from the device to the terminal.

[0443] Step 2:

[0444] The device formats the collected biometric information and sends it to the server via the internet. The data is transferred as JSON packets using the SSL / TLS protocol to ensure secure communication.

[0445] Step 3:

[0446] The server receives data sent from the terminal via the API endpoint. The received data is validated to confirm the consistency of the data format before being stored in the database.

[0447] Step 4:

[0448] The server analyzes the stored data using an artificial intelligence engine. It compares it with past data to extract trends and abnormal patterns in health conditions. This analysis uses machine learning algorithms for pattern recognition.

[0449] Step 5:

[0450] Based on the analysis results, the server generates personalized health advice for the user. The generated advice is presented in text format and provides specific information necessary for improving the user's lifestyle.

[0451] Step 6:

[0452] The server translates advice into multiple languages ​​based on user settings. By utilizing natural language processing technology and incorporating multilingual capabilities, it caters to a global user base.

[0453] Step 7:

[0454] The device receives translation advice from the server and notifies the user. Specifically, push notifications are used to provide advice in real time, even when the application is not running on the device.

[0455] Step 8:

[0456] Users can review the advice provided on their devices and reassess their own health status. Based on the advice, users can take steps to improve their individual health management and behaviors. They can also submit feedback on the advice provided, contributing to system improvements.

[0457] (Example 1)

[0458] Next, we will describe Example 1. 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."

[0459] In modern society, it is crucial for individuals to effectively and in real time manage their own health. However, it is not easy for ordinary users without specialized knowledge to accurately interpret their own health data and manage their health appropriately. Therefore, there is a need for a system that can provide appropriate health advice quickly in multiple languages ​​and incorporate user feedback.

[0460] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0461] In this invention, the server includes means for using a learning model to analyze an individual's biometric information, means for generating personalized health guidelines based on the analysis, and means for notifying the user of the translated health guidelines. This enables the user to receive appropriate health advice in real time. Furthermore, by incorporating the user's feedback into the next analysis, more accurate health management can be achieved.

[0462] "Device" refers to a device used to collect an individual's biometric information.

[0463] An "information processing device" refers to a computer system used to receive and analyze data transmitted from a remote location.

[0464] A "learning model" refers to artificial intelligence technology that learns specific patterns from large amounts of data and extracts useful information through data analysis.

[0465] "Health guidelines" refer to specific advice for maintaining and improving health, generated based on an individual's health condition.

[0466] "Means of multilingual translation" refers to technology that translates generated health guidelines into the user's chosen language so that the information can be effectively conveyed even in different linguistic environments.

[0467] A "user" refers to a person who uses the system to manage their health based on their own biometric information.

[0468] This invention is a system for managing an individual's health status in real time and providing personalized health advice to the user. The system consists of a device worn by the user, a user terminal for receiving data and transmitting it to a server, and a server that analyzes the data and provides feedback.

[0469] The device, worn by the user, collects biometric information through a wearable device equipped with heart rate and temperature sensors. This collected biometric information includes heart rate, basal body temperature, and sleep patterns, and the device periodically updates this data. The collected information is transferred to the user's device via Bluetooth or Wi-Fi.

[0470] The server receives biometric information transmitted from the terminal using an information processing device. This server analyzes the data using a generative AI model to identify abnormal patterns and assess health status. Based on the analyzed data, the server generates personalized health guidelines and translates them into the user's chosen language. This system makes it possible to easily provide information to users, overcoming language barriers.

[0471] The user's device receives translated health guidelines from the server and provides the information to the user using a notification function. For example, if the user shows an abnormal heart rate pattern, the server can detect this and provide advice such as, "Take regular breaks to reduce stress."

[0472] Examples of prompt messages include the following:

[0473] "We analyze recent biometric data to assess the user's health status."

[0474] "Generate health guidelines based on heart rate data."

[0475] This invention makes it possible for individuals to manage their own health and take appropriate action without requiring specialized knowledge.

[0476] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0477] Step 1:

[0478] The device collects biometric information such as the user's heart rate, basal body temperature, and sleep patterns using a wearable device. Sensors measure this data from the user's body and update it every second. The collected data is the input and is temporarily stored in the device.

[0479] Step 2:

[0480] The device transmits collected biometric information to the user's mobile device via Bluetooth or Wi-Fi. Data transfer is performed securely using communication protocols, and the received data becomes the output on the mobile device. Battery consumption is minimized during this process.

[0481] Step 3:

[0482] Mobile devices transmit received biometric information to a server using security protocols such as SSL. The data is converted to a format suitable for transmission to the server, and the server's reception becomes the output. This allows for data transfer to the server while maintaining data confidentiality.

[0483] Step 4:

[0484] The server stores the received biometric information in a database and prepares it for analysis. This data is the input, and the organized data before analysis becomes the output.

[0485] Step 5:

[0486] The server analyzes biometric information using a generative AI model to identify data trends and anomalies. Data analysis is performed here, and data calculations such as anomaly pattern detection are carried out. The analysis results are output, and the health status is evaluated.

[0487] Step 6:

[0488] The server generates individual health guidelines based on the analyzed data. These generated health guidelines are the output and are then passed to a multilingual translation system.

[0489] Step 7:

[0490] The server translates the generated health guidelines into the user's chosen language using its multilingual translation function. The input is the generated health guidelines, and the output is the translated guidelines. This ensures that information is provided in a format easily understood by any user.

[0491] Step 8:

[0492] The user's mobile device receives translated health guidelines from the server and provides them to the user using its notification function. The translated health guidelines are the input, and the notifications displayed to the user are the output. Specifically, the system displays advice on the screen along with a notification sound and vibration.

[0493] This processing flow allows users to obtain health information in real time and improve their daily lives based on specific advice.

[0494] (Application Example 1)

[0495] Next, we will explain Application Example 1. In the following explanation, 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."

[0496] While conventional systems could collect users' biometric information and provide health advice to support individual health management, this advice rarely reflected daily behaviors directly related to maintaining health, particularly dietary choices. This made it difficult for users to choose foods appropriate to their health condition, resulting in missed opportunities for health improvement.

[0497] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0498] In this invention, the server includes means for analyzing the user's biometric information and generating personalized health advice, means for translating the advice into multiple languages, and means for recommending appropriate meal menus based on the advice. This enables users to make choices that take their health condition into consideration when making daily meal choices, and actual health improvements can be expected.

[0499] "Personal biometric information" refers to important biological data for understanding a user's health status, such as their heart rate, basal body temperature, and sleep patterns.

[0500] A "remote server" refers to a group of data processing devices, either physical or cloud-based, that are accessible via a communication network such as the internet.

[0501] "Analytical artificial intelligence" refers to a group of programs that have the ability to analyze biometric data and automatically determine changes and trends in health status.

[0502] "Personalized health advice" refers to specific recommendations and suggestions optimized for an individual's health condition, based on the user's biometric information.

[0503] "Means of multilingual translation" refers to language processing programs or algorithms for converting health advice into any language specified by the user.

[0504] "Means of notifying users" refers to communication functions that provide users with advice and information in real time via smartphones or other devices.

[0505] "Means of recommending appropriate meal menus" refers to programs or algorithms that provide menus that take into account the nutritional value and ingredients tailored to the user's health condition, based on the generated health advice.

[0506] This invention is a system for managing an individual's health status in real time, utilizing a wearable device, a smartphone, and a remote server. Specifically, the user collects biometric information such as heart rate and basal body temperature using a wearable device, such as a health monitoring device. The wearable device transmits the data to the smartphone via Bluetooth communication.

[0507] The smartphone temporarily stores the collected data, then transfers it to a cloud-based server via Wi-Fi or mobile data communication. The server analyzes the received biometric information using artificial intelligence, such as TensorFlow. This analysis generates a meal plan optimized for the individual's health condition and provides it to the user as health advice.

[0508] The server also has a multilingual translation function and translates the analysis results into the user's specified language. The translated advice and recommended meal menus are sent to the smartphone app using push notification technology. This app provides users with real-time alerts and health information to support them in making healthy eating choices.

[0509] For example, if a user is experiencing stress due to an elevated heart rate, the server will suggest a relaxing meal. This suggestion is made using a prompt message sent to the user's terminal that reads, "We have detected that the user's heart rate is high, so please suggest a relaxing meal. Please also consider relevant nutrients."

[0510] This system allows users to easily make daily dietary choices tailored to their health condition, thereby enabling them to maintain and improve their health.

[0511] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0512] Step 1:

[0513] The device collects biometric information such as the user's heart rate, basal body temperature, and sleep patterns. The input is biometric data from the wearable device, and the output is ready to transmit this data to a smartphone. Bluetooth communication is used, and the data is collected in real time.

[0514] Step 2:

[0515] The device transmits the collected biometric information to the smartphone. The input is data from the wearable device, and the output is biometric information stored locally. The smartphone temporarily stores this data and verifies its integrity and completeness.

[0516] Step 3:

[0517] The user's smartphone uses an internet connection to transfer aggregated biometric information to a cloud server. The input is biometric information stored on the smartphone, and the output is data securely stored in a database on the cloud.

[0518] Step 4:

[0519] The server analyzes received biometric data using an artificial intelligence model. The input is biometric data stored in the cloud, and the output is a health status assessment result. This analysis utilizes learning models such as TensorFlow.

[0520] Step 5:

[0521] The server generates health advice and meal menus tailored to the user's health condition based on the analysis results. The input is the health condition assessment results, and the output is personalized health advice and recommended menus. Generation and personalization are performed by an AI module.

[0522] Step 6:

[0523] The generated health advice and menus are translated into the user's chosen language using the server's multilingual translation function. The input is the generated advice and menus, and the output is the translated information.

[0524] Step 7:

[0525] The server notifies the user's smartphone with translated health advice and recommended meal menus. The input is the translated advice and menus, and the output is the information displayed on the smartphone app. The notification function enables push notifications.

[0526] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0527] This invention realizes a system that provides personalized health management based on a user's biometric information and emotional state. This system includes a wearable information collection device, an emotion engine that recognizes the user's emotions, a server that performs analysis, and a terminal that provides an interface with the user.

[0528] First, the device collects the user's biometric information, such as heart rate, basal body temperature, and sleep patterns, through a wearable device. This information is periodically transmitted to a server. In addition, the device uses an emotion engine to obtain emotional data from the user's voice and facial expressions. This emotional data is used to determine whether the user is stressed, relaxed, or in any other emotional state.

[0529] The server comprehensively analyzes the received biometric and emotional data. Applying artificial intelligence technology, it understands how individual data points interact and assesses the user's overall health status. Specifically, if an elevated heart rate is detected along with stressful emotions, it can provide stress management advice.

[0530] Based on the analysis results, the server generates personalized health advice. This advice outlines actions the user should take to achieve better health and takes into account the user's emotional state. The generated advice is translated into the user's chosen language via the server's multilingual translation function.

[0531] The device receives translated health advice and notifies the user. Notifications are typically delivered via push notifications and are designed to easily grab the user's attention. For example, if the emotion engine determines that the user has been busy and stressed recently, it will notify the user with advice such as, "It is recommended that you take regular breaks and engage in relaxation activities."

[0532] In this way, the system combines the user's biometric and emotional information to enable more precise and personalized health management. The system supports users in maintaining their health by allowing them to understand their health status in real time and take appropriate actions continuously.

[0533] The following describes the processing flow.

[0534] Step 1:

[0535] The device periodically collects biometric information from the wearable device, such as the user's heart rate, basal body temperature, and sleep patterns. This data is transmitted to the device in real time via Bluetooth or Wi-Fi.

[0536] Step 2:

[0537] The device uses a built-in emotion engine to recognize the user's voice and facial expressions and determine the user's emotional state. It utilizes voice tone analysis and facial recognition technology to generate emotional data such as stress levels and relaxation levels.

[0538] Step 3:

[0539] The device combines biometric information and emotional data and transmits it to a server via a secure internet connection. The data is encrypted and transmitted in JSON format.

[0540] Step 4:

[0541] The server stores the received data for analysis and uses an artificial intelligence model to analyze the correlation between biometric information and emotional data. For example, if a high heart rate and stressful emotions are observed simultaneously, a detailed causal analysis is performed.

[0542] Step 5:

[0543] Based on the data analysis results, the server generates personalized health advice for the user. This advice includes specific suggestions for maintaining health and managing stress.

[0544] Step 6:

[0545] The server translates the generated advice into the user's chosen language. A multilingual translation system is used to accurately translate the advice to the user's selected language.

[0546] Step 7:

[0547] The device receives translated health advice from the server and notifies the user in real time using push notifications. The notifications are displayed on the user's device as symbolic icons or banners.

[0548] Step 8:

[0549] Users can check notifications on their devices and adjust their health management and daily activities based on the advice they receive. Furthermore, they can provide feedback on the advice given, which can then be used for future analyses.

[0550] (Example 2)

[0551] Next, we will describe Example 2. 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."

[0552] In modern society, the information individuals need to manage their own health is fragmented, making comprehensive, real-time health management difficult. In particular, the lack of systems that integrate and analyze biometric data and emotional states to provide personalized health advice makes it difficult for individuals to effectively manage stress and maintain their health.

[0553] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0554] In this invention, the server includes means for integrating and analyzing an individual's biometric information and emotional data, means for generating personalized health advice based on the analysis results, and means for translating the generated health advice into multiple languages ​​and providing it to the user. This enables individuals to understand their own health status in real time and receive accurate advice tailored to their specific emotional state.

[0555] "Biometric information" refers to data that indicates an individual's physical condition, including heart rate, body temperature, and sleep cycle.

[0556] "Data collection means" refers to devices and programs for acquiring an individual's biometric information and emotional data.

[0557] An "information processing device" refers to a computer system or server used to receive and analyze collected biometric and emotional data.

[0558] "Emotional data" refers to information about a user's emotional state obtained by analyzing their voice, facial expressions, and other data.

[0559] "Artificial intelligence technology" refers to technologies that gain insights from data through machine learning and data analysis.

[0560] "Health advice" refers to specific action guidelines and suggestions for promoting health, provided to users based on analyzed data.

[0561] "Translation methods" refer to systems or programs that can translate health advice into multiple languages.

[0562] "Push notifications" refer to a function that automatically sends information to a user's device and is used as a means to attract the user's attention.

[0563] This invention relates to a personalized health management system that utilizes biometric and emotional data. This system primarily combines a wearable device, a data processing server, and a terminal serving as a user interface.

[0564] The device collects biometric information from the wearable device worn by the user. This device incorporates a heart rate sensor, a temperature sensor, and an accelerometer, and has the ability to measure heart rate, body temperature, and sleep patterns in real time. The device also captures the user's voice and facial expressions and extracts emotional data using an emotion engine. This emotional data is used to identify the user's emotional state.

[0565] The server receives biometric and emotional data transmitted from the terminal and analyzes it using a generative AI model. This analysis process detects specific patterns and trends from the collected data to gain insights into the user's health status. For example, it may analyze the correlation between stress levels and high heart rate.

[0566] Based on the analysis results, the server automatically generates health advice tailored to the user's current state. This advice includes specific suggestions for maintaining health and managing stress. The generated advice is then translated into the user's preferred language using the server's built-in multilingual translation function.

[0567] The device receives translated health advice from the server and delivers it to the user as a push notification. This notification is designed to be sent at times when the user is likely to be interested in their health. For example, during a break after a long day of work, a notification might say, "Take a short walk to refresh yourself."

[0568] As a concrete example, a possible prompt for a user using this system to manage their stress level might be, "Please describe the health advice to be provided when the system determines that the user is in a relaxed state." Based on this prompt, the system provides advice optimized for the user.

[0569] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0570] Step 1:

[0571] The device collects the user's biometric information via a wearable device. This information includes heart rate, body temperature, and sleep patterns. The input is continuous biosensor data provided by the wearable device, and the output is organized biometric data. This data is periodically sent to a server for later analysis. Specifically, the device averages and records heart rate at regular intervals, records daily body temperature fluctuations, and analyzes sleep movements to evaluate sleep depth.

[0572] Step 2:

[0573] The device uses an emotion engine to analyze the user's voice and facial expression data and acquire emotional data. Input is voice and video data obtained from the microphone and camera, and output includes identified emotional states (e.g., stress, joy, relaxation). In this process, the emotion engine performs voice tone analysis and facial feature point analysis. Specifically, the device extracts emotional features from the voice data and uses video data to identify emotions from the movement of facial muscles.

[0574] Step 3:

[0575] The server receives biometric and emotional data transmitted from the terminal, integrates and analyzes the data. The input is organized biometric and emotional state data, and the output is an analysis result indicating the user's current health status. The server uses a generative AI model to analyze the correlations between this data and assess health risks and stress levels. Specifically, it uses artificial intelligence to perform correlation analysis to determine whether stress levels are elevated when heart rate is high.

[0576] Step 4:

[0577] The server generates personalized health advice based on the analysis results. The input is the analysis results from step 3, and the output is specific advice for improving the user's health. A multilingual translation function is used to generate this advice, and it is translated according to the user's set language. For example, if the stress level is high, advice such as "Make slow walks a part of your daily routine" will be generated.

[0578] Step 5:

[0579] The device provides the user with translated health advice received from the server as a push notification. The input is the translated advice from the server, and the output is a practical notification to the user. Specifically, the device displays the notification in a pop-up format, showing the details of the advice for easy access by the user. The timing of the notifications is also considered, and they are sent to avoid busy times.

[0580] (Application Example 2)

[0581] Next, we will explain application example 2. In the following explanation, 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."

[0582] Traditionally, customer service and product recommendations in stores have often relied on the experience and intuition of store employees, making it extremely difficult to provide personalized recommendations that take into account each customer's individual health and emotional state. As a result, the effectiveness of improving customer satisfaction and increasing sales has been limited. The present invention aims to solve these problems and provide a system that enables more appropriate and satisfying recommendations for each individual customer.

[0583] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0584] In this invention, the server includes means for collecting personal biometric information, means for suggesting products and services according to the user's emotional state, and means for notifying the user of translated health advice. This enables detailed, personalized suggestions that take into account not only the customer's biometric information but also their emotional state.

[0585] A "device that collects personal biometric information" is a device that acquires data indicating the user's health status, such as heart rate, basal body temperature, and sleep patterns.

[0586] "Means of sending to a remote server" refers to the function of transferring data collected from wearable devices, etc., to a remotely located server via the internet or other means.

[0587] "Methods using artificial intelligence" refer to technologies that use machine learning and data analysis techniques to analyze collected data and generate personalized information.

[0588] "Means for generating health advice" refers to the process of creating specific suggestions and instructions to support users' health management based on analysis results.

[0589] The "means of translating into multiple languages" refer to a function that converts the generated health advice into the user's preferred language, making it available to users who speak a variety of languages.

[0590] "Means of notifying users" refers to methods of delivering information to users through their devices, and typically includes push notifications.

[0591] "Methods for detecting emotional states" refer to technologies that analyze the tone of a user's voice and changes in their facial expressions to recognize their emotions at that moment.

[0592] "Means of suggesting products and services" refers to a process that recommends appropriate products and services in real time based on the detected emotions and health status of the user.

[0593] The system that realizes this invention mainly consists of a wearable device, a smart terminal, a remote server, and an emotion analysis module.

[0594] The device collects the user's biometric information through wearable devices. Examples of such devices include smartwatches and fitness trackers, which can acquire data such as heart rate, basal body temperature, and sleep patterns. This biometric data is transmitted to the device via Bluetooth or Wi-Fi.

[0595] The device is equipped with an emotion recognition engine that uses the camera and microphone to analyze changes in the user's facial expressions and voice in real time to detect their emotional state.

[0596] Next, the device sends the collected biometric and emotional data to a remote server. The server uses machine learning models such as Google Cloud AI to analyze the received data and evaluate the user's health and emotional state. Based on the analysis results, the server generates personalized health advice, which is then translated into the user's chosen language through a multilingual translation function.

[0597] Furthermore, the server suggests products and services tailored to the visitor's emotional state based on the analysis results. For example, if a visitor exhibits a specific emotion or health condition, information is generated to recommend relevant products.

[0598] The generated advice and suggestions are notified to the device and delivered to the user via smart glasses or smartphone push notifications. This allows users to receive real-time suggestions for appropriate actions and products tailored to their health status and emotions.

[0599] For example, if a customer is using smart glasses in a specific store within a shopping mall, and an elevated heart rate and high stress index are detected, a message such as "Click here for our selection of products with relaxing effects" will appear in their field of vision.

[0600] Examples of input prompts for a generative AI model:

[0601] "The user's heart rate is 90 bpm, and their stress level is high based on facial expression analysis. Please recommend products and services that are best suited for maintaining their health."

[0602] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0603] Step 1:

[0604] The device collects the user's biometric information from wearable devices. Specifically, it acquires data such as heart rate, basal body temperature, and sleep patterns via Bluetooth. This input data forms the basis for subsequent analysis.

[0605] Step 2:

[0606] The device analyzes the user's emotional state using its built-in emotion recognition engine. It captures the user's facial expressions and voice using the camera and microphone, and converts this into data to determine their emotional state. In this step, changes in facial expressions and voice are processed by an algorithm, and emotional information is output.

[0607] Step 3:

[0608] The device integrates the collected biometric and emotional information and transmits it to a remote server via the internet. At this point, the input includes both biometric and emotional information. The server prepares this data for analysis.

[0609] Step 4:

[0610] The server uses machine learning models such as Google Cloud AI to analyze biometric and emotional information. Here, the collected data is used to assess the user's health and emotional state. This analysis understands the interactions between each data point and outputs an overall health status.

[0611] Step 5:

[0612] The server generates personalized health advice based on the analysis results and translates it into the user's chosen language using its multilingual translation function. The generated advice includes specific suggestions for maintaining good health, and this is the information provided to the user.

[0613] Step 6:

[0614] The server generates data to suggest products and services that are appropriate for the user's emotional and health state. Based on the analysis results, it uses a generative AI model to create necessary prompt statements and output appropriate options.

[0615] Step 7:

[0616] The device pushes translated health advice and product / service suggestions received from the server to the user. Users can receive this information in real time via smart glasses or smartphones. This step involves receiving information and creating an incentive for the user to take direct action.

[0617] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0618] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0619] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0620] [Fourth Embodiment]

[0621] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0622] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0623] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0624] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0625] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0626] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0627] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0628] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0629] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0630] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0631] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0632] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0633] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0634] This invention provides a system that supports health management using an individual's biometric information. This system consists of a wearable device worn by the user, a mobile device used by the user to receive data from the wearable device and transmit it to a server, and a server that analyzes the data and provides feedback.

[0635] First, the device collects biometric information such as the user's heart rate, basal body temperature, and sleep patterns through a wearable device. This data is continuously collected at regular intervals and transmitted to the mobile device. The mobile device has communication capabilities to send the collected data to a server.

[0636] Next, the server analyzes the received biometric information. This analysis utilizes artificial intelligence technology to understand data trends and identify unusual patterns, thereby evaluating the user's health status. In this process, the server generates personalized health advice based on the analysis results.

[0637] The generated health advice is translated into the user's chosen language via a multilingual translation function on the server. This makes it possible to provide information to users in an easily understandable format, overcoming language barriers.

[0638] Finally, the user's mobile device receives and is notified of health advice provided in multiple languages. For example, the user can receive feedback on recent heart rate variability to help manage their health. Furthermore, the user can provide feedback on the advice, which is then used in subsequent analyses to provide more appropriate advice.

[0639] This system allows users to monitor their health in real time and manage their own health without the need for expert advice. For example, if an abnormal sleep pattern persists, the server can detect that pattern and provide advice such as, "Your health can be improved by ensuring regular sleep schedules."

[0640] The following describes the processing flow.

[0641] Step 1:

[0642] The device collects biometric information such as the user's heart rate, basal body temperature, and sleep patterns in real time via a wearable device. This data collection is performed via Bluetooth or Wi-Fi and transmitted as a digital signal from the device to the terminal.

[0643] Step 2:

[0644] The device formats the collected biometric information and sends it to the server via the internet. The data is transferred as JSON packets using the SSL / TLS protocol to ensure secure communication.

[0645] Step 3:

[0646] The server receives data sent from the terminal via the API endpoint. The received data is validated to confirm the consistency of the data format before being stored in the database.

[0647] Step 4:

[0648] The server analyzes the stored data using an artificial intelligence engine. It compares it with past data to extract trends and abnormal patterns in health conditions. This analysis uses machine learning algorithms for pattern recognition.

[0649] Step 5:

[0650] Based on the analysis results, the server generates personalized health advice for the user. The generated advice is presented in text format and provides specific information necessary for improving the user's lifestyle.

[0651] Step 6:

[0652] The server translates advice into multiple languages ​​based on user settings. By utilizing natural language processing technology and incorporating multilingual capabilities, it caters to a global user base.

[0653] Step 7:

[0654] The device receives translation advice from the server and notifies the user. Specifically, push notifications are used to provide advice in real time, even when the application is not running on the device.

[0655] Step 8:

[0656] Users can review the advice provided on their devices and reassess their own health status. Based on the advice, users can take steps to improve their individual health management and behaviors. They can also submit feedback on the advice provided, contributing to system improvements.

[0657] (Example 1)

[0658] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0659] In modern society, it is crucial for individuals to effectively and in real time manage their own health. However, it is not easy for ordinary users without specialized knowledge to accurately interpret their own health data and manage their health appropriately. Therefore, there is a need for a system that can provide appropriate health advice quickly in multiple languages ​​and incorporate user feedback.

[0660] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0661] In this invention, the server includes means for using a learning model to analyze an individual's biometric information, means for generating personalized health guidelines based on the analysis, and means for notifying the user of the translated health guidelines. This enables the user to receive appropriate health advice in real time. Furthermore, by incorporating the user's feedback into the next analysis, more accurate health management can be achieved.

[0662] "Device" refers to a device used to collect an individual's biometric information.

[0663] An "information processing device" refers to a computer system used to receive and analyze data transmitted from a remote location.

[0664] A "learning model" refers to artificial intelligence technology that learns specific patterns from large amounts of data and extracts useful information through data analysis.

[0665] "Health guidelines" refer to specific advice for maintaining and improving health, generated based on an individual's health condition.

[0666] "Means of multilingual translation" refers to technology that translates generated health guidelines into the user's chosen language so that the information can be effectively conveyed even in different linguistic environments.

[0667] A "user" refers to a person who uses the system to manage their health based on their own biometric information.

[0668] This invention is a system for managing an individual's health status in real time and providing personalized health advice to the user. The system consists of a device worn by the user, a user terminal for receiving data and transmitting it to a server, and a server that analyzes the data and provides feedback.

[0669] The device, worn by the user, collects biometric information through a wearable device equipped with heart rate and temperature sensors. This collected biometric information includes heart rate, basal body temperature, and sleep patterns, and the device periodically updates this data. The collected information is transferred to the user's device via Bluetooth or Wi-Fi.

[0670] The server receives biometric information transmitted from the terminal using an information processing device. This server analyzes the data using a generative AI model to identify abnormal patterns and assess health status. Based on the analyzed data, the server generates personalized health guidelines and translates them into the user's chosen language. This system makes it possible to easily provide information to users, overcoming language barriers.

[0671] The user's device receives translated health guidelines from the server and provides the information to the user using a notification function. For example, if the user shows an abnormal heart rate pattern, the server can detect this and provide advice such as, "Take regular breaks to reduce stress."

[0672] Examples of prompt messages include the following:

[0673] "We analyze recent biometric data to assess the user's health status."

[0674] "Generate health guidelines based on heart rate data."

[0675] This invention makes it possible for individuals to manage their own health and take appropriate action without requiring specialized knowledge.

[0676] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0677] Step 1:

[0678] The device collects biometric information such as the user's heart rate, basal body temperature, and sleep patterns using a wearable device. Sensors measure this data from the user's body and update it every second. The collected data is the input and is temporarily stored in the device.

[0679] Step 2:

[0680] The device transmits collected biometric information to the user's mobile device via Bluetooth or Wi-Fi. Data transfer is performed securely using communication protocols, and the received data becomes the output on the mobile device. Battery consumption is minimized during this process.

[0681] Step 3:

[0682] Mobile devices transmit received biometric information to a server using security protocols such as SSL. The data is converted to a format suitable for transmission to the server, and the server's reception becomes the output. This allows for data transfer to the server while maintaining data confidentiality.

[0683] Step 4:

[0684] The server stores the received biometric information in a database and prepares it for analysis. This data is the input, and the organized data before analysis becomes the output.

[0685] Step 5:

[0686] The server analyzes biometric information using a generative AI model to identify data trends and anomalies. Data analysis is performed here, and data calculations such as anomaly pattern detection are carried out. The analysis results are output, and the health status is evaluated.

[0687] Step 6:

[0688] The server generates individual health guidelines based on the analyzed data. These generated health guidelines are the output and are then passed to a multilingual translation system.

[0689] Step 7:

[0690] The server translates the generated health guidelines into the user's chosen language using its multilingual translation function. The input is the generated health guidelines, and the output is the translated guidelines. This ensures that information is provided in a format easily understood by any user.

[0691] Step 8:

[0692] The user's mobile device receives translated health guidelines from the server and provides them to the user using its notification function. The translated health guidelines are the input, and the notifications displayed to the user are the output. Specifically, the system displays advice on the screen along with a notification sound and vibration.

[0693] This processing flow allows users to obtain health information in real time and improve their daily lives based on specific advice.

[0694] (Application Example 1)

[0695] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0696] While conventional systems could collect users' biometric information and provide health advice to support individual health management, this advice rarely reflected daily behaviors directly related to maintaining health, particularly dietary choices. This made it difficult for users to choose foods appropriate to their health condition, resulting in missed opportunities for health improvement.

[0697] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0698] In this invention, the server includes means for analyzing the user's biometric information and generating personalized health advice, means for translating the advice into multiple languages, and means for recommending appropriate meal menus based on the advice. This enables users to make choices that take their health condition into consideration when making daily meal choices, and actual health improvements can be expected.

[0699] "Personal biometric information" refers to important biological data for understanding a user's health status, such as their heart rate, basal body temperature, and sleep patterns.

[0700] A "remote server" refers to a group of data processing devices, either physical or cloud-based, that are accessible via a communication network such as the internet.

[0701] "Analytical artificial intelligence" refers to a group of programs that have the ability to analyze biometric data and automatically determine changes and trends in health status.

[0702] "Personalized health advice" refers to specific recommendations and suggestions optimized for an individual's health condition, based on the user's biometric information.

[0703] "Means of multilingual translation" refers to language processing programs or algorithms for converting health advice into any language specified by the user.

[0704] "Means of notifying users" refers to communication functions that provide users with advice and information in real time via smartphones or other devices.

[0705] "Means of recommending appropriate meal menus" refers to programs or algorithms that provide menus that take into account the nutritional value and ingredients tailored to the user's health condition, based on the generated health advice.

[0706] This invention is a system for managing an individual's health status in real time, utilizing a wearable device, a smartphone, and a remote server. Specifically, the user collects biometric information such as heart rate and basal body temperature using a wearable device, such as a health monitoring device. The wearable device transmits the data to the smartphone via Bluetooth communication.

[0707] The smartphone temporarily stores the collected data, then transfers it to a cloud-based server via Wi-Fi or mobile data communication. The server analyzes the received biometric information using artificial intelligence, such as TensorFlow. This analysis generates a meal plan optimized for the individual's health condition and provides it to the user as health advice.

[0708] The server also has a multilingual translation function and translates the analysis results into the user's specified language. The translated advice and recommended meal menus are sent to the smartphone app using push notification technology. This app provides users with real-time alerts and health information to support them in making healthy eating choices.

[0709] For example, if a user is experiencing stress due to an elevated heart rate, the server will suggest a relaxing meal. This suggestion is made using a prompt message sent to the user's terminal that reads, "We have detected that the user's heart rate is high, so please suggest a relaxing meal. Please also consider relevant nutrients."

[0710] This system allows users to easily make daily dietary choices tailored to their health condition, thereby enabling them to maintain and improve their health.

[0711] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0712] Step 1:

[0713] The device collects biometric information such as the user's heart rate, basal body temperature, and sleep patterns. The input is biometric data from the wearable device, and the output is ready to transmit this data to a smartphone. Bluetooth communication is used, and the data is collected in real time.

[0714] Step 2:

[0715] The device transmits the collected biometric information to the smartphone. The input is data from the wearable device, and the output is biometric information stored locally. The smartphone temporarily stores this data and verifies its integrity and completeness.

[0716] Step 3:

[0717] The user's smartphone uses an internet connection to transfer aggregated biometric information to a cloud server. The input is biometric information stored on the smartphone, and the output is data securely stored in a database on the cloud.

[0718] Step 4:

[0719] The server analyzes received biometric data using an artificial intelligence model. The input is biometric data stored in the cloud, and the output is a health status assessment result. This analysis utilizes learning models such as TensorFlow.

[0720] Step 5:

[0721] The server generates health advice and meal menus tailored to the user's health condition based on the analysis results. The input is the health condition assessment results, and the output is personalized health advice and recommended menus. Generation and personalization are performed by an AI module.

[0722] Step 6:

[0723] The generated health advice and menus are translated into the user's chosen language using the server's multilingual translation function. The input is the generated advice and menus, and the output is the translated information.

[0724] Step 7:

[0725] The server notifies the user's smartphone with translated health advice and recommended meal menus. The input is the translated advice and menus, and the output is the information displayed on the smartphone app. The notification function enables push notifications.

[0726] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0727] This invention realizes a system that provides personalized health management based on a user's biometric information and emotional state. This system includes a wearable information collection device, an emotion engine that recognizes the user's emotions, a server that performs analysis, and a terminal that provides an interface with the user.

[0728] First, the device collects the user's biometric information, such as heart rate, basal body temperature, and sleep patterns, through a wearable device. This information is periodically transmitted to a server. In addition, the device uses an emotion engine to obtain emotional data from the user's voice and facial expressions. This emotional data is used to determine whether the user is stressed, relaxed, or in any other emotional state.

[0729] The server comprehensively analyzes the received biometric and emotional data. Applying artificial intelligence technology, it understands how individual data points interact and assesses the user's overall health status. Specifically, if an elevated heart rate is detected along with stressful emotions, it can provide stress management advice.

[0730] Based on the analysis results, the server generates personalized health advice. This advice outlines actions the user should take to achieve better health and takes into account the user's emotional state. The generated advice is translated into the user's chosen language via the server's multilingual translation function.

[0731] The device receives translated health advice and notifies the user. Notifications are typically delivered via push notifications and are designed to easily grab the user's attention. For example, if the emotion engine determines that the user has been busy and stressed recently, it will notify the user with advice such as, "It is recommended that you take regular breaks and engage in relaxation activities."

[0732] In this way, the system combines the user's biometric and emotional information to enable more precise and personalized health management. The system supports users in maintaining their health by allowing them to understand their health status in real time and take appropriate actions continuously.

[0733] The following describes the processing flow.

[0734] Step 1:

[0735] The device periodically collects biometric information from the wearable device, such as the user's heart rate, basal body temperature, and sleep patterns. This data is transmitted to the device in real time via Bluetooth or Wi-Fi.

[0736] Step 2:

[0737] The device uses a built-in emotion engine to recognize the user's voice and facial expressions and determine the user's emotional state. It utilizes voice tone analysis and facial recognition technology to generate emotional data such as stress levels and relaxation levels.

[0738] Step 3:

[0739] The device combines biometric information and emotional data and transmits it to a server via a secure internet connection. The data is encrypted and transmitted in JSON format.

[0740] Step 4:

[0741] The server stores the received data for analysis and uses an artificial intelligence model to analyze the correlation between biometric information and emotional data. For example, if a high heart rate and stressful emotions are observed simultaneously, a detailed causal analysis is performed.

[0742] Step 5:

[0743] Based on the data analysis results, the server generates personalized health advice for the user. This advice includes specific suggestions for maintaining health and managing stress.

[0744] Step 6:

[0745] The server translates the generated advice into the user's chosen language. A multilingual translation system is used to accurately translate the advice to the user's selected language.

[0746] Step 7:

[0747] The device receives translated health advice from the server and notifies the user in real time using push notifications. The notifications are displayed on the user's device as symbolic icons or banners.

[0748] Step 8:

[0749] Users can check notifications on their devices and adjust their health management and daily activities based on the advice they receive. Furthermore, they can provide feedback on the advice given, which can then be used for future analyses.

[0750] (Example 2)

[0751] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0752] In modern society, the information individuals need to manage their own health is fragmented, making comprehensive, real-time health management difficult. In particular, the lack of systems that integrate and analyze biometric data and emotional states to provide personalized health advice makes it difficult for individuals to effectively manage stress and maintain their health.

[0753] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0754] In this invention, the server includes means for integrating and analyzing an individual's biometric information and emotional data, means for generating personalized health advice based on the analysis results, and means for translating the generated health advice into multiple languages ​​and providing it to the user. This enables individuals to understand their own health status in real time and receive accurate advice tailored to their specific emotional state.

[0755] "Biometric information" refers to data that indicates an individual's physical condition, including heart rate, body temperature, and sleep cycle.

[0756] "Data collection means" refers to devices and programs for acquiring an individual's biometric information and emotional data.

[0757] An "information processing device" refers to a computer system or server used to receive and analyze collected biometric and emotional data.

[0758] "Emotional data" refers to information about a user's emotional state obtained by analyzing their voice, facial expressions, and other data.

[0759] "Artificial intelligence technology" refers to technologies that gain insights from data through machine learning and data analysis.

[0760] "Health advice" refers to specific action guidelines and suggestions for promoting health, provided to users based on analyzed data.

[0761] "Translation methods" refer to systems or programs that can translate health advice into multiple languages.

[0762] "Push notifications" refer to a function that automatically sends information to a user's device and is used as a means to attract the user's attention.

[0763] This invention relates to a personalized health management system that utilizes biometric and emotional data. This system primarily combines a wearable device, a data processing server, and a terminal serving as a user interface.

[0764] The device collects biometric information from the wearable device worn by the user. This device incorporates a heart rate sensor, a temperature sensor, and an accelerometer, and has the ability to measure heart rate, body temperature, and sleep patterns in real time. The device also captures the user's voice and facial expressions and extracts emotional data using an emotion engine. This emotional data is used to identify the user's emotional state.

[0765] The server receives biometric and emotional data transmitted from the terminal and analyzes it using a generative AI model. This analysis process detects specific patterns and trends from the collected data to gain insights into the user's health status. For example, it may analyze the correlation between stress levels and high heart rate.

[0766] Based on the analysis results, the server automatically generates health advice tailored to the user's current state. This advice includes specific suggestions for maintaining health and managing stress. The generated advice is then translated into the user's preferred language using the server's built-in multilingual translation function.

[0767] The device receives translated health advice from the server and delivers it to the user as a push notification. This notification is designed to be sent at times when the user is likely to be interested in their health. For example, during a break after a long day of work, a notification might say, "Take a short walk to refresh yourself."

[0768] As a concrete example, a possible prompt for a user using this system to manage their stress level might be, "Please describe the health advice to be provided when the system determines that the user is in a relaxed state." Based on this prompt, the system provides advice optimized for the user.

[0769] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0770] Step 1:

[0771] The device collects the user's biometric information via a wearable device. This information includes heart rate, body temperature, and sleep patterns. The input is continuous biosensor data provided by the wearable device, and the output is organized biometric data. This data is periodically sent to a server for later analysis. Specifically, the device averages and records heart rate at regular intervals, records daily body temperature fluctuations, and analyzes sleep movements to evaluate sleep depth.

[0772] Step 2:

[0773] The device uses an emotion engine to analyze the user's voice and facial expression data and acquire emotional data. Input is voice and video data obtained from the microphone and camera, and output includes identified emotional states (e.g., stress, joy, relaxation). In this process, the emotion engine performs voice tone analysis and facial feature point analysis. Specifically, the device extracts emotional features from the voice data and uses video data to identify emotions from the movement of facial muscles.

[0774] Step 3:

[0775] The server receives biometric and emotional data transmitted from the terminal, integrates and analyzes the data. The input is organized biometric and emotional state data, and the output is an analysis result indicating the user's current health status. The server uses a generative AI model to analyze the correlations between this data and assess health risks and stress levels. Specifically, it uses artificial intelligence to perform correlation analysis to determine whether stress levels are elevated when heart rate is high.

[0776] Step 4:

[0777] The server generates personalized health advice based on the analysis results. The input is the analysis results from step 3, and the output is specific advice for improving the user's health. A multilingual translation function is used to generate this advice, and it is translated according to the user's set language. For example, if the stress level is high, advice such as "Make slow walks a part of your daily routine" will be generated.

[0778] Step 5:

[0779] The device provides the user with translated health advice received from the server as a push notification. The input is the translated advice from the server, and the output is a practical notification to the user. Specifically, the device displays the notification in a pop-up format, showing the details of the advice for easy access by the user. The timing of the notifications is also considered, and they are sent to avoid busy times.

[0780] (Application Example 2)

[0781] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0782] Traditionally, customer service and product recommendations in stores have often relied on the experience and intuition of store employees, making it extremely difficult to provide personalized recommendations that take into account each customer's individual health and emotional state. As a result, the effectiveness of improving customer satisfaction and increasing sales has been limited. The present invention aims to solve these problems and provide a system that enables more appropriate and satisfying recommendations for each individual customer.

[0783] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0784] In this invention, the server includes means for collecting personal biometric information, means for suggesting products and services according to the user's emotional state, and means for notifying the user of translated health advice. This enables detailed, personalized suggestions that take into account not only the customer's biometric information but also their emotional state.

[0785] A "device that collects personal biometric information" is a device that acquires data indicating the user's health status, such as heart rate, basal body temperature, and sleep patterns.

[0786] "Means of sending to a remote server" refers to the function of transferring data collected from wearable devices, etc., to a remotely located server via the internet or other means.

[0787] "Methods using artificial intelligence" refer to technologies that use machine learning and data analysis techniques to analyze collected data and generate personalized information.

[0788] "Means for generating health advice" refers to the process of creating specific suggestions and instructions to support users' health management based on analysis results.

[0789] The "means of translating into multiple languages" refer to a function that converts the generated health advice into the user's preferred language, making it available to users who speak a variety of languages.

[0790] "Means of notifying users" refers to methods of delivering information to users through their devices, and typically includes push notifications.

[0791] "Methods for detecting emotional states" refer to technologies that analyze the tone of a user's voice and changes in their facial expressions to recognize their emotions at that moment.

[0792] "Means of suggesting products and services" refers to a process that recommends appropriate products and services in real time based on the detected emotions and health status of the user.

[0793] The system that realizes this invention mainly consists of a wearable device, a smart terminal, a remote server, and an emotion analysis module.

[0794] The device collects the user's biometric information through wearable devices. Examples of such devices include smartwatches and fitness trackers, which can acquire data such as heart rate, basal body temperature, and sleep patterns. This biometric data is transmitted to the device via Bluetooth or Wi-Fi.

[0795] The device is equipped with an emotion recognition engine that uses the camera and microphone to analyze changes in the user's facial expressions and voice in real time to detect their emotional state.

[0796] Next, the device sends the collected biometric and emotional data to a remote server. The server uses machine learning models such as Google Cloud AI to analyze the received data and evaluate the user's health and emotional state. Based on the analysis results, the server generates personalized health advice, which is then translated into the user's chosen language through a multilingual translation function.

[0797] Furthermore, the server suggests products and services tailored to the visitor's emotional state based on the analysis results. For example, if a visitor exhibits a specific emotion or health condition, information is generated to recommend relevant products.

[0798] The generated advice and suggestions are notified to the device and delivered to the user via smart glasses or smartphone push notifications. This allows users to receive real-time suggestions for appropriate actions and products tailored to their health status and emotions.

[0799] For example, if a customer is using smart glasses in a specific store within a shopping mall, and an elevated heart rate and high stress index are detected, a message such as "Click here for our selection of products with relaxing effects" will appear in their field of vision.

[0800] Examples of input prompts for a generative AI model:

[0801] "The user's heart rate is 90 bpm, and their stress level is high based on facial expression analysis. Please recommend products and services that are best suited for maintaining their health."

[0802] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0803] Step 1:

[0804] The device collects the user's biometric information from wearable devices. Specifically, it acquires data such as heart rate, basal body temperature, and sleep patterns via Bluetooth. This input data forms the basis for subsequent analysis.

[0805] Step 2:

[0806] The device analyzes the user's emotional state using its built-in emotion recognition engine. It captures the user's facial expressions and voice using the camera and microphone, and converts this into data to determine their emotional state. In this step, changes in facial expressions and voice are processed by an algorithm, and emotional information is output.

[0807] Step 3:

[0808] The device integrates the collected biometric and emotional information and transmits it to a remote server via the internet. At this point, the input includes both biometric and emotional information. The server prepares this data for analysis.

[0809] Step 4:

[0810] The server uses machine learning models such as Google Cloud AI to analyze biometric and emotional information. Here, the collected data is used to assess the user's health and emotional state. This analysis understands the interactions between each data point and outputs an overall health status.

[0811] Step 5:

[0812] The server generates personalized health advice based on the analysis results and translates it into the user's chosen language using its multilingual translation function. The generated advice includes specific suggestions for maintaining good health, and this is the information provided to the user.

[0813] Step 6:

[0814] The server generates data to suggest products and services that are appropriate for the user's emotional and health state. Based on the analysis results, it uses a generative AI model to create necessary prompt statements and output appropriate options.

[0815] Step 7:

[0816] The device pushes translated health advice and product / service suggestions received from the server to the user. Users can receive this information in real time via smart glasses or smartphones. This step involves receiving information and creating an incentive for the user to take direct action.

[0817] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0818] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0819] 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 this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0820] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0821] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0822] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0823] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0824] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0825] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0826] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0827] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0828] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0829] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0830] 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.

[0831] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0832] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0833] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0834] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0835] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0836] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0837] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0838] The following is further disclosed regarding the embodiments described above.

[0839] (Claim 1)

[0840] A device that collects personal biometric information,

[0841] Means for transmitting the aforementioned biometric information to a remote server,

[0842] The aforementioned server uses artificial intelligence to analyze the biometric information,

[0843] A means for generating personalized health advice based on the aforementioned analysis,

[0844] A means for translating the aforementioned health advice into multiple languages,

[0845] A means for notifying the user of the translated health advice,

[0846] A system that includes this.

[0847] (Claim 2)

[0848] The system according to claim 1, wherein the biometric information includes heart rate, basal body temperature, and sleep patterns.

[0849] (Claim 3)

[0850] The system according to claim 1, further comprising means for sending the aforementioned health advice to a user terminal using push notifications.

[0851] "Example 1"

[0852] (Claim 1)

[0853] A device that collects personal biometric information,

[0854] Means for transmitting the aforementioned biological information to a remote information processing device,

[0855] The means of using a learning model to analyze the biological information in the aforementioned information processing device,

[0856] A means for generating personalized health guidelines based on the aforementioned analysis,

[0857] A means for translating the aforementioned health guidelines into multiple languages,

[0858] A means of notifying users of the translated health guidelines,

[0859] A means by which users can provide feedback based on the aforementioned notified health guidelines and reflect it in the next analysis,

[0860] A system that includes this.

[0861] (Claim 2)

[0862] The system according to claim 1, wherein the biometric information includes heart rate, basal body temperature, and sleep patterns.

[0863] (Claim 3)

[0864] The system according to claim 1, further comprising means for transmitting the aforementioned health guidelines to a user device using push notifications.

[0865] "Application Example 1"

[0866] (Claim 1)

[0867] A device that collects personal biometric information,

[0868] Means for transmitting the aforementioned biometric information to a remote server,

[0869] The aforementioned server uses artificial intelligence to analyze the biometric information,

[0870] A means for generating personalized health advice based on the aforementioned analysis,

[0871] A means for translating the aforementioned health advice into multiple languages,

[0872] A means for notifying the user of the translated health advice,

[0873] A means of recommending appropriate meal menus based on the aforementioned health advice,

[0874] A system that includes this.

[0875] (Claim 2)

[0876] The system according to claim 1, wherein the aforementioned biological information generates a meal menu based on heart rate, basal body temperature, and sleep patterns.

[0877] (Claim 3)

[0878] The system according to claim 1, further comprising means for sending the aforementioned meal menu to the user's terminal via push notification.

[0879] "Example 2 of combining an emotion engine"

[0880] (Claim 1)

[0881] Data collection methods for collecting personal biometric information,

[0882] Means for transmitting the aforementioned biometric information and emotional data to a remote information processing device,

[0883] The aforementioned information processing device includes means using artificial intelligence technology to analyze the biometric information and emotional data,

[0884] A means for generating personalized health advice that takes into account the user's emotional state based on the aforementioned analysis,

[0885] A translation means for translating the aforementioned health advice into multiple languages,

[0886] A means of providing the translated health advice to the user as a push notification,

[0887] A system that includes this.

[0888] (Claim 2)

[0889] The system according to claim 1, wherein the biometric information includes heart rate, body temperature, and sleep cycle, and emotional data is acquired through voice and facial expression analysis.

[0890] (Claim 3)

[0891] The system according to claim 1, wherein the push notification is made at an appropriate time based on the user's work status.

[0892] "Application example 2 when combining with an emotional engine"

[0893] (Claim 1)

[0894] A device that collects personal biometric information,

[0895] Means for transmitting the aforementioned biometric information to a remote server,

[0896] The aforementioned server uses artificial intelligence to analyze the biometric information,

[0897] A means for generating personalized health advice based on the aforementioned analysis,

[0898] A means for translating the aforementioned health advice into multiple languages,

[0899] A means for notifying the user of the translated health advice,

[0900] A means of detecting the emotional state of visitors and suggesting products and services that correspond to that emotional state,

[0901] A system that includes this.

[0902] (Claim 2)

[0903] The system according to claim 1, wherein the biometric information includes heart rate, basal body temperature, and sleep patterns.

[0904] (Claim 3)

[0905] The system according to claim 1, further comprising means for sending the aforementioned health advice to a user terminal using push notifications. [Explanation of Symbols]

[0906] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A device that collects personal biometric information, Means for transmitting the aforementioned biometric information to a remote server, The aforementioned server uses artificial intelligence to analyze the biometric information, A means for generating personalized health advice based on the aforementioned analysis, A means for translating the aforementioned health advice into multiple languages, A means for notifying the user of the translated health advice, A system that includes this.

2. The system according to claim 1, wherein the biological information includes heart rate, basal body temperature, and sleep pattern.

3. The system according to claim 1, further comprising means for sending the aforementioned health advice to a user terminal using push notifications.

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