system

Abstract

We provide the system. [Solution] A means of generating personalized health information by analyzing lifestyle record data entered by users using a generative model, A means for displaying the generated health information on a communication device, A means of collaborating with external sources to obtain additional information on nutritional balance and supplement the generated health information, A system including an automated machine for home use that receives lifestyle data via voice input or a haptic display and presents generated health information.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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 difficult for an individual to appropriately manage and maintain their own health. In a busy life, there is a lack of time and knowledge resources for individual health management, resulting in a lack of nutritional balance and insufficient exercise. There is a need for a system that can solve such problems, enable anyone to easily obtain professional health information, and improve the quality of daily life.

Means for Solving the Problems

[0005] This invention provides an information processing system equipped with a generative model, and includes means for analyzing lifestyle record data entered by the user to generate personalized health information. This allows the user to receive optimal advice tailored to their health condition in real time. Furthermore, the system has the function to integrate information on nutritional balance by linking with external information sources, and can collect user feedback to improve the accuracy of personalized advice.

[0006] A "generative model" is an artificial intelligence technology that uses input data to perform pattern recognition and prediction.

[0007] "Lifestyle record data" refers to information about a user's daily activities and habits, specifically including information about diet, sleep, exercise, etc.

[0008] "Health information" refers to information used to assess a user's current health status and provide suggestions and guidelines for improvement.

[0009] A "user terminal" is a device used by a user to input or receive information, and includes smartphones and tablets.

[0010] "External information sources" refer to external databases, APIs, and other information-providing services that a system connects to and retrieves information from.

[0011] "Feedback" refers to the opinions and evaluations that users provide to a system, and it serves as the basis for information that the system uses to improve. [Brief explanation of the drawing]

[0012] [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]It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Embodiments for Carrying Out the Invention

[0013] 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.

[0014] First, the language used in the following description will be explained.

[0015] In the following embodiments, the labeled 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.

[0016] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0017] In the following embodiments, the labeled storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0018] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0019] 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."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] 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.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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".

[0033] This section describes an embodiment of this system. This system consists of a user, a terminal, and a server, and provides health information using a generative model based on individual lifestyle record data.

[0034] Users input daily lifestyle data, such as meals, exercise, and sleep, into their device. For example, they record detailed information in the application, such as "I ate 100g of oatmeal and one banana for breakfast." The entered data is then transmitted to the server via the device.

[0035] The server inputs the received lifestyle data into a generative model and performs data analysis. This generative model learns from past data patterns and has algorithms for evaluating the user's health status. Specifically, it generates personalized health information to maintain and improve the user's health, such as optimizing nutritional balance and warnings about lack of exercise. Furthermore, the server obtains the latest nutritional data from external sources and integrates this information to generate advice.

[0036] The generated health information is sent from the server to the user's device, which then presents this information to the user. Based on the presented information, the user can improve their daily life and provide feedback on their experience through the device.

[0037] This system allows users to easily manage their health in a way that suits their lifestyle. For example, if the generated health information says, "Your calorie intake today exceeded your target, so we recommend reducing the calories in your dinner," the user can adjust their dinner menu accordingly. This feedback is stored on the server and contributes to improving the accuracy of the generation model for future generations.

[0038] The following describes the processing flow.

[0039] Step 1:

[0040] Users input daily lifestyle data into a device application. This includes specific information such as meals, exercise, and sleep. For example, they might enter details like the menu and quantity of their breakfast, or the number of steps they walked in a day.

[0041] Step 2:

[0042] The terminal formats the user's entered lifestyle record data and sends it to the server in the appropriate format. The terminal also validates the data to ensure that the format is correct.

[0043] Step 3:

[0044] The server receives lifestyle record data sent from the terminal. After receiving the data, it inputs it into a generative model. This model analyzes the data based on past trends and predictive algorithms.

[0045] Step 4:

[0046] The generative model assesses the user's health status and generates personalized health information. This includes aspects such as calorie surplus or deficit, nutritional balance, and recommended exercise levels.

[0047] Step 5:

[0048] The server integrates health information generated by the generative model with external information sources to form more precise advice. For example, it uses external APIs to obtain the latest information on nutrients and incorporates it into the advice.

[0049] Step 6:

[0050] The server sends the final health information to the user's device. The information is then displayed on the device in an easy-to-understand format for the user.

[0051] Step 7:

[0052] Users can improve their daily lives based on health information presented through their devices. Furthermore, if users wish to provide feedback, they can do so through their devices to the server. This feedback is stored on the server and used to improve the generative model.

[0053] (Example 1)

[0054] 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."

[0055] In modern healthcare management, there is a lack of means to provide health information tailored to individual users. Furthermore, real-time data processing and the provision of highly accurate health information based on that data are difficult. Therefore, there is a need for a system that efficiently generates and provides personalized health information tailored to each user's lifestyle.

[0056] 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.

[0057] This invention includes a server that uses a generation algorithm to process lifestyle data entered by a user and generate customized health information, a means for displaying the generated health information on a portable electronic device, and a means for integrating with an external data source to obtain additional data on nutrient balance and enhance the generated health information. This enables users to receive specific health information tailored to their lifestyle in real time and use it to manage their health.

[0058] A "generative algorithm" is a computational method used to analyze lifestyle data obtained from users and generate customized health information.

[0059] A "user" is an individual or similar entity that uses the system to input their lifestyle data and obtain health information.

[0060] "Lifestyle data" refers to information about users' daily diet, exercise, sleep, etc., and serves as the basic data for generating health information.

[0061] "Customized health information" refers to specific and personalized health-related information generated through a generation algorithm based on the lifestyle data of each individual user.

[0062] "Portable electronic devices" refer to devices such as smartphones and tablets that are portable and used to display information.

[0063] "External data sources" refer to databases and information services located outside the system, and are used to obtain the latest data on nutrient balance.

[0064] "Nutrient balance" refers to the appropriate intake and harmony of various nutrients necessary for maintaining a healthy lifestyle, and is an indicator for enhancing health information provided to users.

[0065] This invention is based on a three-tiered structure consisting of a user, a terminal, and a server. The user inputs lifestyle data into a terminal such as a smartphone or tablet, and health information is obtained based on that data. Specifically, the user records information about their daily meals, exercise, and sleep in an application on the terminal. For example, they input detailed information such as, "I ate 100g of oatmeal and one banana for breakfast."

[0066] The terminal formats the data entered by the user and sends it to the server using a secure communication protocol (e.g., HTTPS). Upon receiving the data, the server analyzes it using a generative AI model and generates customized health information. This generative AI model incorporates an algorithm that learns from past data patterns, enabling it to generate advice tailored to the user's health condition.

[0067] Furthermore, the server accesses external data sources to obtain the latest data on nutrient balance and integrates it with the generated health information. This process allows users to receive a more accurate and detailed assessment of their health status.

[0068] The generated health information is sent from the server to the terminal. The terminal displays this information in an easy-to-understand format so that users can utilize it in their daily lives.

[0069] An example of a prompt message is, "Based on the user's lifestyle data, provide advice to optimize their nutritional balance." This allows the user to receive specific health advice tailored to their lifestyle, enabling them to adopt healthier habits.

[0070] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0071] Step 1:

[0072] Users input their daily lifestyle data into a dedicated application on their smartphone or tablet. This input data includes specific information such as details of their diet, exercise levels, and sleep quality. The entered data is stored digitally and prepared for the next processing step.

[0073] Step 2:

[0074] The terminal converts the user's entered lifestyle data into an appropriate format. It then sends the formatted data to the server via a secure communication protocol (e.g., HTTPS). The purpose of this step is to encode the input data and transmit it to the server while maintaining data integrity and confidentiality.

[0075] Step 3:

[0076] The server receives lifestyle data transmitted from the terminal. It then inputs this data into a generative AI model and begins data analysis. Based on the input data, the generative AI model uses pattern recognition algorithms to evaluate the user's health status and generate necessary health advice.

[0077] Step 4:

[0078] The server accesses external data sources based on the analysis results obtained by the generated AI model. Here, it retrieves the latest nutrition-related information and integrates additional data tailored to the user's specific health needs. This results in more precise and customized health information being output.

[0079] Step 5:

[0080] The server transmits integrated health information to the user's device. This information includes nutritional balance, exercise advice, and suggestions for improving lifestyle habits, and is provided in a format that the user can use in their daily life. The device displays the received information to the user and provides notifications to allow the user to easily access it.

[0081] Step 6:

[0082] Users adjust their lifestyle habits based on the health information presented. They input the results of these adjustments and their improvement experiences as feedback into the device. This feedback is sent to the server in digital format and used as data for generating health information in the future.

[0083] Through these steps, the system can provide personalized health advice tailored to the user's lifestyle in real time.

[0084] (Application Example 1)

[0085] 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."

[0086] Conventional health management systems have the problem that individuals find it burdensome to input and manage their health information in their daily lives. Furthermore, there is a challenge in providing users with immediate results of real-time lifestyle record analysis and appropriate health advice. Moreover, there is a need for a method that integrates daily life information and is easily usable in a home environment.

[0087] 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.

[0088] This invention includes a server that uses a generative model to analyze lifestyle data input by a user and generate personalized health information, a means for displaying the generated health information on a communication device, and an automated home machine that receives lifestyle data via voice input or a haptic display and presents the generated health information. This allows users to easily manage their daily health information, improves convenience in the home environment, and enables them to receive personalized health advice in real time.

[0089] A "generative model" is a technology that analyzes lifestyle data and generates personalized health information based on statistics and machine learning.

[0090] "User" refers to a person who provides lifestyle record data and receives health information based on that data.

[0091] "Lifestyle record data" refers to information about daily life, such as diet, exercise, and sleep, and is used to assess health status.

[0092] A "communication device" is an electronic device that displays generated health information and presents it to the user visually or audibly.

[0093] "External information sources" refer to third parties that provide the latest nutritional data and health information, and that provide information to supplement the generated health information.

[0094] "Automated machines in the home" are automated devices that receive user lifestyle data in a home environment and provide analysis results.

[0095] This invention is a system designed to support users in managing their health in their daily lives. The system mainly consists of a server, terminals, and automated machines in the home.

[0096] The server receives lifestyle data submitted by users and analyzes it using a generative AI model. The server plays a central role in data analysis, generating personalized health information based on data such as diet, exercise, and sleep. This generated health information is integrated with nutritional data obtained from external sources to provide more accurate and useful information.

[0097] The terminal receives generated health information provided by the server and presents it to the user visually or audibly. The terminal is designed to allow users to easily input lifestyle data through voice input and haptic displays.

[0098] Specifically, when a user voice-inputs something like, "I ate 150g of pasta for lunch today," an automated device in the home sends that information to a server. The server processes the data and performs analysis using a generative AI model. It then generates personalized health information, such as, "We recommend you eat more vegetables for dinner," and sends it to the device.

[0099] An example of a prompt message a user could enter would be: "My weight today is 68kg, I had 150g of pasta for lunch, and I took a 30-minute walk. Please give me some health advice."

[0100] This allows users to intuitively utilize the system in their daily lives and receive personalized health advice in real time.

[0101] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0102] Step 1:

[0103] Users input lifestyle data into the device using voice input or a haptic display. This data includes information such as meals, exercise, and sleep duration. The system is designed to be intuitive and allow for accurate recording of everyday lifestyle information.

[0104] Step 2:

[0105] The terminal digitizes the entered lifestyle record data and sends it to the server. This data processing includes converting audio data to text format and formatting the data as needed. The transmitted data is then used in subsequent analysis steps.

[0106] Step 3:

[0107] The server inputs the received lifestyle data into a generating AI model. This model learns from past data patterns and performs data calculations to optimize nutritional balance and exercise levels based on the received data. The output is personalized health information for each user.

[0108] Step 4:

[0109] The server retrieves the latest nutritional data from external sources and integrates it with the analysis results of the generated AI model. This process combines the externally acquired data with the user's individual data to establish the most beneficial health advice.

[0110] Step 5:

[0111] The generated health information is transmitted from the server to the terminal. The terminal presents this information to the user in an appropriate format, communicating its contents visually or audibly. Here, personalized health information obtained through analysis is presented, and feedback that is easy for the user to understand immediately is provided.

[0112] Step 6:

[0113] Users adjust their daily lives based on the presented health information and provide feedback to the server via their device. This feedback is used to improve the accuracy of future health information generation. The server collects this feedback and incorporates it into the generating AI model to improve the accuracy of future analyses.

[0114] 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.

[0115] As an embodiment of the present invention, a personalized health information provision system incorporating an emotion engine will be described. The system mainly consists of three elements: a user, a terminal, and a server, each playing a specific role.

[0116] Users input daily lifestyle data, including meals, exercise, and sleep, into a device application. They can also input their emotional state. This allows for a comprehensive understanding of not only the user's physical condition but also their emotional state.

[0117] The device transmits the entered lifestyle record data and emotional data to the server. During this process, the device packages the data in an appropriate format for efficient data transmission. Emotional data, in particular, is analyzed by an emotion engine and is therefore separately labeled before transmission.

[0118] The server uses a generative model to generate user health information based on the received data. The generative model compares accumulated historical data with current input data to assess health. In parallel, an emotion engine analyzes emotional data and creates health guidelines that reflect the user's emotional state. This emotion engine, for example, provides advice on relaxation and stress relief if the user is experiencing high levels of stress.

[0119] Next, the system collaborates with external sources to obtain additional information about nutritional balance, and integrates this information to form comprehensive health advice. The generated information is finally transmitted from the server to the terminal, which then presents the information to the user. For example, the server might generate specific guidance such as, "Your meal today was high in calories, and your current emotional state is somewhat stressful, so we recommend having a light dinner and trying to relax," and provide it to the user.

[0120] Finally, users can adjust their lifestyles based on the advice, and provide further feedback to improve the accuracy of future advice. This feedback data is stored on the server and used to improve the generative model. In this way, a system that incorporates an emotion engine can provide a service that takes into account both the user's health and emotions.

[0121] The following describes the processing flow.

[0122] Step 1:

[0123] Users input lifestyle data (such as meals, exercise, and sleep) and their emotional state at that time into the device. For example, they might record specific data such as "I ate salad and soup for lunch" or "I'm feeling a little anxious right now."

[0124] Step 2:

[0125] The terminal formats the user's entered lifestyle record data and emotional data and sends them to the server. The terminal verifies the data integrity and ensures that the data is in the correct format.

[0126] Step 3:

[0127] The server inputs data received from the terminal into a generative model to analyze the user's health status. The generative model generates individual health information based on the input data. In this process, past data trends and patterns are taken into consideration.

[0128] Step 4:

[0129] The server uses an emotion engine to analyze emotional data. The emotion engine quantifies the user's emotional state and generates appropriate health information and advice. For example, it might suggest relaxation techniques or deep breathing exercises to a user who is feeling stressed.

[0130] Step 5:

[0131] The server utilizes external sources to obtain the latest data on nutritional balance and supplements the generated health information. This supplementary information includes data on new nutrients and the health benefits of exercise.

[0132] Step 6:

[0133] The server sends generated health information and emotion-based advice to the terminal. The terminal displays this information in a way that is easy for the user to understand. For example, it might offer specific suggestions such as, "Try using stress-relieving ingredients in your dinner."

[0134] Step 7:

[0135] Users strive to improve their daily lives based on the health information they receive. Furthermore, they can provide subsequent feedback through their devices. This feedback is collected by the server and used to improve the accuracy of the generative model and emotion engine.

[0136] (Example 2)

[0137] 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".

[0138] Conventional health information systems are limited to providing information based on users' physiological data, making it difficult to provide comprehensive health guidelines that take emotional states into account. Furthermore, a lack of real-time data processing capabilities hinders the ability to provide prompt and effective guidance.

[0139] 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.

[0140] In this invention, the server includes means for analyzing lifestyle data and emotional data using a generative model to generate personalized health information, means for presenting health guidelines based on the generated health information and emotional state to the user's device, and means for acquiring nutritional information and supplementing the health information in cooperation with external information sources. This makes it possible to provide comprehensive and personalized health guidelines in real time, taking into account the user's emotional state and nutritional information.

[0141] A "generative model" is an algorithm that compares and analyzes past and present data to generate information tailored to the user's state.

[0142] "Lifestyle data" refers to records related to the user's daily activities, such as meals, exercise, and sleep.

[0143] "Emotional data" refers to information that indicates the user's psychological state, and is obtained through text and choices.

[0144] "Health information" refers to information about a user's health status and suggestions for lifestyle improvements, generated based on the user's lifestyle and emotional data.

[0145] "Health guidelines" are specific instructions and advice provided in consideration of the user's health information and emotional state.

[0146] "User equipment" refers to devices that users use to input or receive information.

[0147] "External information sources" refer to external databases or services accessed to obtain nutritional information or other relevant information.

[0148] As an embodiment of this system, we describe a personalized health information provision system that utilizes a generative AI model and an emotion engine. This system consists of three elements: user, terminal, and server, each playing its own unique role.

[0149] Users input lifestyle data related to their daily diet, exercise, and sleep into a device application. Simultaneously, they can also input data about their emotional state. For example, a user might input "Breakfast: toast and coffee," exercise: "30 minutes of jogging," sleep duration: "7 hours," and emotion: "no stress" through the app. This information is used to comprehensively understand the user's health and emotional state.

[0150] The terminal is responsible for converting the entered lifestyle and emotional data into an appropriate format and sending it to the server. During this process, emotional data is labeled for use in analysis by the emotion engine. The terminal uses a standard smartphone or tablet, and a dedicated health management app is used as the application.

[0151] The server receives data sent from the terminal and uses a generative AI model to generate health information optimized for the user. This generative AI model compares past lifestyle data with the input data to assess the user's health status. Furthermore, an emotion engine has the ability to analyze emotional data and create health guidelines that reflect the user's emotional state. For example, a prompt might read, "User A's meals today were high in calories, and emotional data indicates they are experiencing stress. Please provide health advice for this," and advice based on this would be generated.

[0152] This system also collaborates with external sources to acquire nutritional information and further enhance the generated health information. Finally, the information generated from the server is sent to the terminal and presented to the user. For example, it might provide advice such as, "Since today's meal was high in calories, we recommend having a light dinner. Try meditation for relaxation."

[0153] In this way, the system can provide personalized health guidance in real time based on the user's lifestyle data and emotional data.

[0154] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0155] Step 1:

[0156] Users input lifestyle data and emotional data into an application on their device. Specifically, users open the app and input details such as what they ate, exercise time, sleep time, and their emotional state. The input data is saved as "lifestyle data" and "emotional data," respectively.

[0157] Step 2:

[0158] The terminal receives input data from the user and converts it into an appropriate format. Specifically, an application within the terminal compiles the input lifestyle data into CSV format and labels the emotional data. This streamlines the transmission to the server. The output is the converted data file.

[0159] Step 3:

[0160] The device sends the formatted data to the server. The device's operation begins when the "Send" button is pressed in the app, and the data is sent to the server over the network. At this stage, the input is the converted data, and the output is the data that has arrived on the server.

[0161] Step 4:

[0162] The server analyzes the received data. Specifically, the server uses a generative AI model to generate personalized health information by comparing it with past data. An emotion engine also operates in parallel, generating guidelines based on emotional state. The input is data sent from the terminal, and the output is the generated health information and emotional guidelines.

[0163] Step 5:

[0164] The server sends the generated health information and guidelines to the terminal. The server's specific operation involves packaging the generated information into an appropriate format and sending it back to the terminal via the network. As output, the terminal receives information to be presented to the user.

[0165] Step 6:

[0166] The device presents the received information to the user. Specifically, it displays a notification on the screen and makes detailed information viewable. The user is then encouraged to adjust their lifestyle based on this information and provide feedback.

[0167] Step 7:

[0168] Users adjust their lifestyles based on the advice provided and input the results as feedback into the device. This helps improve the accuracy of future advice. The input is user feedback, and the output is information sent to the server and stored in the database.

[0169] (Application Example 2)

[0170] 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 device 14 will be referred to as the "terminal."

[0171] In modern society, there is a demand for personalized health information and comprehensive care that takes emotional states into consideration. However, conventional systems face the challenge of not being able to effectively integrate lifestyle data and emotional data, and to provide advice that considers not only the user's health but also their emotional state.

[0172] 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.

[0173] In this invention, the server includes means for analyzing user-inputted lifestyle data and emotional data using a generative model to generate personalized health and emotional information; means for presenting the generated health and emotional information to the user terminal; and means for collaborating with external information sources to obtain additional information regarding nutritional balance and to supplement the generated health and emotional information. This makes it possible to provide more accurate personalized health and emotional information that takes into account both the user's health and emotional state.

[0174] A "generative model" is a type of algorithm that generates specific information or advice based on data input by the user.

[0175] "Lifestyle data" refers to information about a user's daily activities, specifically data such as meals, exercise, and sleep.

[0176] "Emotional data" refers to information about a user's emotional state and is used to assess the user's psychological health.

[0177] "Health information" refers to personalized advice and reports regarding the user's health status.

[0178] "Emotional information" refers to advice and psychological health information that takes into account the user's emotional state.

[0179] "External information sources" refer to databases and resources that provide information other than that of the user, and are used by the system to generate more comprehensive information.

[0180] A "user terminal" is a device that a user directly operates to input and present information, and includes smartphones and tablets.

[0181] Modes for carrying out the invention

[0182] This invention is a system that analyzes a user's daily life record data and emotional data to provide personalized health and emotional information. The system mainly consists of a user terminal and a server.

[0183] Users input daily life records and emotional states through applications installed on their devices, such as smartphones and tablets. This data is transmitted to a server via the internet, and the server uses a generative AI model to analyze the received data.

[0184] On the server, a generative AI model is used to analyze the user's lifestyle data and emotional data to generate personalized health advice and emotional support. This process utilizes server applications written in Python or Flask, with the Google Cloud Natural Language API handling the emotional data analysis. Based on the analysis results, comprehensive advice is generated that considers not only health information but also emotional information.

[0185] Furthermore, the server collaborates with external sources to obtain additional information regarding nutritional balance. This reinforces the generated health and emotional information, presenting it to the user as more accurate and comprehensive advice.

[0186] Users adjust their lifestyles based on the health and emotional information provided, and then send feedback from their devices to the server to improve the accuracy of future advice.

[0187] For example, if a user inputs emotional data indicating they are "feeling stressed," the system might generate advice such as, "I recommend listening to relaxing music or taking a short walk." The following prompt can be used to generate such advice: "If the user's emotional state indicates high stress, compare this with past data and suggest the most suitable stress-relief methods."

[0188] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0189] Step 1:

[0190] Users input daily lifestyle data (such as meals, exercise, and sleep) and emotional data into an app installed on their smartphone or tablet. This data is collected on the device and converted to an appropriate data format before being sent to the server. Inputs include lifestyle data and emotional data entered by the user on the device. Outputs are formatted data.

[0191] Step 2:

[0192] The terminal sends formatted data to the server via the internet. The data processing involved is data packaging and conversion according to the transmission protocol. The input is the formatted data. The output is the data received by the server.

[0193] Step 3:

[0194] The server inputs data received from the user into a generating AI model and performs data analysis. Specifically, it uses Python to format the data and analyzes lifestyle data and emotional data. This analysis generates personalized health and emotional information. The input consists of lifestyle data and emotional data received by the server. The output is the analyzed personalized information.

[0195] Step 4:

[0196] The server utilizes the Google Cloud Natural Language API to perform detailed analysis of sentiment data and assess the user's psychological health. This data processing includes the process of extracting sentiment labels and related metrics from the sentiment data. The input is the user's sentiment data. The output is the analysis results, such as sentiment labels.

[0197] Step 5:

[0198] The server transmits the generated personalized health and emotional information to the user's terminal. It also integrates nutritional information obtained from external sources to provide the user with comprehensive health advice. Inputs include analyzed personalized information and nutritional information obtained from external sources. Output is comprehensive health advice.

[0199] Step 6:

[0200] Users adjust their lifestyles based on the health and emotional information they receive. They input the results as feedback into their device and send it to the server to improve future advice. The input is user feedback information, and the output is feedback data necessary for accuracy improvement.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] [Second Embodiment]

[0205] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0206] 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.

[0207] 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).

[0208] 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.

[0209] 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.

[0210] 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).

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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".

[0217] This section describes an embodiment of this system. This system consists of a user, a terminal, and a server, and provides health information using a generative model based on individual lifestyle record data.

[0218] Users input daily lifestyle data, such as meals, exercise, and sleep, into their device. For example, they record detailed information in the application, such as "I ate 100g of oatmeal and one banana for breakfast." The entered data is then transmitted to the server via the device.

[0219] The server inputs the received lifestyle data into a generative model and performs data analysis. This generative model learns from past data patterns and has algorithms for evaluating the user's health status. Specifically, it generates personalized health information to maintain and improve the user's health, such as optimizing nutritional balance and warnings about lack of exercise. Furthermore, the server obtains the latest nutritional data from external sources and integrates this information to generate advice.

[0220] The generated health information is sent from the server to the user's device, which then presents this information to the user. Based on the presented information, the user can improve their daily life and provide feedback on their experience through the device.

[0221] This system allows users to easily manage their health in a way that suits their lifestyle. For example, if the generated health information says, "Your calorie intake today exceeded your target, so we recommend reducing the calories in your dinner," the user can adjust their dinner menu accordingly. This feedback is stored on the server and contributes to improving the accuracy of the generation model for future generations.

[0222] The following describes the processing flow.

[0223] Step 1:

[0224] Users input daily lifestyle data into a device application. This includes specific information such as meals, exercise, and sleep. For example, they might enter details like the menu and quantity of their breakfast, or the number of steps they walked in a day.

[0225] Step 2:

[0226] The terminal formats the user's entered lifestyle record data and sends it to the server in the appropriate format. The terminal also validates the data to ensure that the format is correct.

[0227] Step 3:

[0228] The server receives lifestyle record data sent from the terminal. After receiving the data, it inputs it into a generative model. This model analyzes the data based on past trends and predictive algorithms.

[0229] Step 4:

[0230] The generative model assesses the user's health status and generates personalized health information. This includes aspects such as calorie surplus or deficit, nutritional balance, and recommended exercise levels.

[0231] Step 5:

[0232] The server integrates health information generated by the generative model with external information sources to form more precise advice. For example, it uses external APIs to obtain the latest information on nutrients and incorporates it into the advice.

[0233] Step 6:

[0234] The server sends the final health information to the user's device. The information is then displayed on the device in an easy-to-understand format for the user.

[0235] Step 7:

[0236] Users can improve their daily lives based on health information presented through their devices. Furthermore, if users wish to provide feedback, they can do so through their devices to the server. This feedback is stored on the server and used to improve the generative model.

[0237] (Example 1)

[0238] 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."

[0239] In modern healthcare management, there is a lack of means to provide health information tailored to individual users. Furthermore, real-time data processing and the provision of highly accurate health information based on that data are difficult. Therefore, there is a need for a system that efficiently generates and provides personalized health information tailored to each user's lifestyle.

[0240] 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.

[0241] This invention includes a server that uses a generation algorithm to process lifestyle data entered by a user and generate customized health information, a means for displaying the generated health information on a portable electronic device, and a means for integrating with an external data source to obtain additional data on nutrient balance and enhance the generated health information. This enables users to receive specific health information tailored to their lifestyle in real time and use it to manage their health.

[0242] A "generative algorithm" is a computational method used to analyze lifestyle data obtained from users and generate customized health information.

[0243] A "user" is an individual or similar entity that uses the system to input their lifestyle data and obtain health information.

[0244] "Lifestyle data" refers to information about users' daily diet, exercise, sleep, etc., and serves as the basic data for generating health information.

[0245] "Customized health information" refers to specific and personalized health-related information generated through a generation algorithm based on the lifestyle data of each individual user.

[0246] "Portable electronic devices" refer to devices such as smartphones and tablets that are portable and used to display information.

[0247] "External data sources" refer to databases and information services located outside the system, and are used to obtain the latest data on nutrient balance.

[0248] "Nutrient balance" refers to the appropriate intake and harmony of various nutrients necessary for maintaining a healthy lifestyle, and is an indicator for enhancing health information provided to users.

[0249] This invention is based on a three-tiered structure consisting of a user, a terminal, and a server. The user inputs lifestyle data into a terminal such as a smartphone or tablet, and health information is obtained based on that data. Specifically, the user records information about their daily meals, exercise, and sleep in an application on the terminal. For example, they input detailed information such as, "I ate 100g of oatmeal and one banana for breakfast."

[0250] The terminal formats the data entered by the user and sends it to the server using a secure communication protocol (e.g., HTTPS). Upon receiving the data, the server analyzes it using a generative AI model and generates customized health information. This generative AI model incorporates an algorithm that learns from past data patterns, enabling it to generate advice tailored to the user's health condition.

[0251] Furthermore, the server accesses external data sources to obtain the latest data on nutrient balance and integrates it with the generated health information. This process allows users to receive a more accurate and detailed assessment of their health status.

[0252] The generated health information is sent from the server to the terminal. The terminal displays this information in an easy-to-understand format so that users can utilize it in their daily lives.

[0253] An example of a prompt message is, "Based on the user's lifestyle data, provide advice to optimize their nutritional balance." This allows the user to receive specific health advice tailored to their lifestyle, enabling them to adopt healthier habits.

[0254] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0255] Step 1:

[0256] Users input their daily lifestyle data into a dedicated application on their smartphone or tablet. This input data includes specific information such as details of their diet, exercise levels, and sleep quality. The entered data is stored digitally and prepared for the next processing step.

[0257] Step 2:

[0258] The terminal converts the user's entered lifestyle data into an appropriate format. It then sends the formatted data to the server via a secure communication protocol (e.g., HTTPS). The purpose of this step is to encode the input data and transmit it to the server while maintaining data integrity and confidentiality.

[0259] Step 3:

[0260] The server receives lifestyle data transmitted from the terminal. It then inputs this data into a generative AI model and begins data analysis. Based on the input data, the generative AI model uses pattern recognition algorithms to evaluate the user's health status and generate necessary health advice.

[0261] Step 4:

[0262] The server accesses external data sources based on the analysis results obtained by the generated AI model. Here, it retrieves the latest nutrition-related information and integrates additional data tailored to the user's specific health needs. This results in more precise and customized health information being output.

[0263] Step 5:

[0264] The server transmits integrated health information to the user's device. This information includes nutritional balance, exercise advice, and suggestions for improving lifestyle habits, and is provided in a format that the user can use in their daily life. The device displays the received information to the user and provides notifications to allow the user to easily access it.

[0265] Step 6:

[0266] Users adjust their lifestyle habits based on the health information presented. They input the results of these adjustments and their improvement experiences as feedback into the device. This feedback is sent to the server in digital format and used as data for generating health information in the future.

[0267] Through these steps, the system can provide personalized health advice tailored to the user's lifestyle in real time.

[0268] (Application Example 1)

[0269] 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."

[0270] Conventional health management systems have the problem that individuals find it burdensome to input and manage their health information in their daily lives. Furthermore, there is a challenge in providing users with immediate results of real-time lifestyle record analysis and appropriate health advice. Moreover, there is a need for a method that integrates daily life information and is easily usable in a home environment.

[0271] 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.

[0272] This invention includes a server that uses a generative model to analyze lifestyle data input by a user and generate personalized health information, a means for displaying the generated health information on a communication device, and an automated home machine that receives lifestyle data via voice input or a haptic display and presents the generated health information. This allows users to easily manage their daily health information, improves convenience in the home environment, and enables them to receive personalized health advice in real time.

[0273] A "generative model" is a technology that analyzes lifestyle data and generates personalized health information based on statistics and machine learning.

[0274] "User" refers to a person who provides lifestyle record data and receives health information based on that data.

[0275] "Lifestyle record data" refers to information about daily life, such as diet, exercise, and sleep, and is used to assess health status.

[0276] A "communication device" is an electronic device that displays generated health information and presents it to the user visually or audibly.

[0277] "External information sources" refer to third parties that provide the latest nutritional data and health information, and that provide information to supplement the generated health information.

[0278] "Automated machines in the home" are automated devices that receive user lifestyle data in a home environment and provide analysis results.

[0279] This invention is a system designed to support users in managing their health in their daily lives. The system mainly consists of a server, terminals, and automated machines in the home.

[0280] The server receives the life record data sent from the user and analyzes it using a generation AI model. The server plays a central role in data analysis and generates personalized health information based on data such as diet, exercise, and sleep. The generated health information is integrated with nutritional data obtained from external information sources to provide more accurate and useful information.

[0281] The terminal receives the generated health information provided by the server and is responsible for presenting it visually or audibly to the user. The terminal is designed so that the user can easily input life record data through voice input or a tactile display.

[0282] Specifically, when the user inputs by voice "I ate 150g of pasta for lunch today", the automatic machine at home transmits this information to the server. The server processes the data and conducts analysis using the generation AI model. Then, it generates personalized health information such as "It is recommended to consume more vegetables for dinner" and transmits it to the terminal.

[0283] As an example of the prompt text, the user can input it in the form of "My weight today is 68kg, I ate 150g of pasta for lunch, and I took a 30-minute walk. Please give me some health advice."

[0284] In this way, the user can intuitively utilize the system in daily life and obtain personalized health advice in real time.

[0285] The flow of specific processing in Application Example 1 will be described using FIG. 12.

[0286] Step 1:

[0287] Users input lifestyle data into the device using voice input or a haptic display. This data includes information such as meals, exercise, and sleep duration. The system is designed to be intuitive and allow for accurate recording of everyday lifestyle information.

[0288] Step 2:

[0289] The terminal digitizes the entered lifestyle record data and sends it to the server. This data processing includes converting audio data to text format and formatting the data as needed. The transmitted data is then used in subsequent analysis steps.

[0290] Step 3:

[0291] The server inputs the received lifestyle data into a generating AI model. This model learns from past data patterns and performs data calculations to optimize nutritional balance and exercise levels based on the received data. The output is personalized health information for each user.

[0292] Step 4:

[0293] The server retrieves the latest nutritional data from external sources and integrates it with the analysis results of the generated AI model. This process combines the externally acquired data with the user's individual data to establish the most beneficial health advice.

[0294] Step 5:

[0295] The generated health information is transmitted from the server to the terminal. The terminal presents this information to the user in an appropriate format, communicating its contents visually or audibly. Here, personalized health information obtained through analysis is presented, and feedback that is easy for the user to understand immediately is provided.

[0296] Step 6:

[0297] Users adjust their daily lives based on the presented health information and provide feedback to the server via their device. This feedback is used to improve the accuracy of future health information generation. The server collects this feedback and incorporates it into the generating AI model to improve the accuracy of future analyses.

[0298] 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.

[0299] As an embodiment of the present invention, a personalized health information provision system incorporating an emotion engine will be described. The system mainly consists of three elements: a user, a terminal, and a server, each playing a specific role.

[0300] Users input daily lifestyle data, including meals, exercise, and sleep, into a device application. They can also input their emotional state. This allows for a comprehensive understanding of not only the user's physical condition but also their emotional state.

[0301] The device transmits the entered lifestyle record data and emotional data to the server. During this process, the device packages the data in an appropriate format for efficient data transmission. Emotional data, in particular, is analyzed by an emotion engine and is therefore separately labeled before transmission.

[0302] The server uses a generative model to generate user health information based on the received data. The generative model compares accumulated historical data with current input data to assess health. In parallel, an emotion engine analyzes emotional data and creates health guidelines that reflect the user's emotional state. This emotion engine, for example, provides advice on relaxation and stress relief if the user is experiencing high levels of stress.

[0303] Next, additional information regarding nutritional balance is obtained in cooperation with external information sources, and these pieces of information are integrated to form comprehensive health advice. The generated information is ultimately transmitted from the server to the terminal, and the terminal presents the information to the user. For example, the server generates specific guidelines such as "Since today's meals are high in calories and the current emotional state is somewhat stressed, it is recommended to have a lighter dinner and try relaxation" and provides them to the user.

[0304] Finally, the user can adjust their life based on the advice and further provide feedback so that the system can improve the accuracy of future advice. This feedback data is stored in the server and used to improve the generation model. In this way, the system combined with the emotion engine can provide services that take into account both the user's health and emotions.

[0305] The following describes the processing flow.

[0306] Step 1:

[0307] The user inputs life record data (such as meals, exercise, sleep, etc.) and their emotional state at that time into the terminal. For example, specific data such as "I ate salad and soup for lunch" or "Currently, I am feeling a bit anxious" is recorded.

[0308] Step 2:

[0309] The terminal formats the life record data and emotional data input by the user and transmits them to the server. The terminal checks the data integrity and ensures that the data is in the correct format.

[0310] Step 3:

[0311] The server inputs the data received from the terminal into the generation model and analyzes the user's health status. The generation model generates individual health information based on the input data. In this process, past data trends and patterns are considered.

[0312] Step 4:

[0313] The server uses an emotion engine to analyze emotional data. The emotion engine quantifies the user's emotional state and generates appropriate health information and advice. For example, it might suggest relaxation techniques or deep breathing exercises to a user who is feeling stressed.

[0314] Step 5:

[0315] The server utilizes external sources to obtain the latest data on nutritional balance and supplements the generated health information. This supplementary information includes data on new nutrients and the health benefits of exercise.

[0316] Step 6:

[0317] The server sends generated health information and emotion-based advice to the terminal. The terminal displays this information in a way that is easy for the user to understand. For example, it might offer specific suggestions such as, "Try using stress-relieving ingredients in your dinner."

[0318] Step 7:

[0319] Users strive to improve their daily lives based on the health information they receive. Furthermore, they can provide subsequent feedback through their devices. This feedback is collected by the server and used to improve the accuracy of the generative model and emotion engine.

[0320] (Example 2)

[0321] 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".

[0322] Conventional health information systems are limited to providing information based on users' physiological data, making it difficult to provide comprehensive health guidelines that take emotional states into account. Furthermore, a lack of real-time data processing capabilities hinders the ability to provide prompt and effective guidance.

[0323] 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.

[0324] In this invention, the server includes means for analyzing lifestyle data and emotional data using a generative model to generate personalized health information, means for presenting health guidelines based on the generated health information and emotional state to the user's device, and means for acquiring nutritional information and supplementing the health information in cooperation with external information sources. This makes it possible to provide comprehensive and personalized health guidelines in real time, taking into account the user's emotional state and nutritional information.

[0325] A "generative model" is an algorithm that compares and analyzes past and present data to generate information tailored to the user's state.

[0326] "Lifestyle data" refers to records related to the user's daily activities, such as meals, exercise, and sleep.

[0327] "Emotional data" refers to information that indicates the user's psychological state, and is obtained through text and choices.

[0328] "Health information" refers to information about a user's health status and suggestions for lifestyle improvements, generated based on the user's lifestyle and emotional data.

[0329] "Health guidelines" are specific instructions and advice provided in consideration of the user's health information and emotional state.

[0330] "User equipment" refers to devices that users use to input or receive information.

[0331] "External information sources" refer to external databases or services accessed to obtain nutritional information or other relevant information.

[0332] As an embodiment of this system, we describe a personalized health information provision system that utilizes a generative AI model and an emotion engine. This system consists of three elements: user, terminal, and server, each playing its own unique role.

[0333] Users input lifestyle data related to their daily diet, exercise, and sleep into a device application. Simultaneously, they can also input data about their emotional state. For example, a user might input "Breakfast: toast and coffee," exercise: "30 minutes of jogging," sleep duration: "7 hours," and emotion: "no stress" through the app. This information is used to comprehensively understand the user's health and emotional state.

[0334] The terminal is responsible for converting the entered lifestyle and emotional data into an appropriate format and sending it to the server. During this process, emotional data is labeled for use in analysis by the emotion engine. The terminal uses a standard smartphone or tablet, and a dedicated health management app is used as the application.

[0335] The server receives data sent from the terminal and uses a generative AI model to generate health information optimized for the user. This generative AI model compares past lifestyle data with the input data to assess the user's health status. Furthermore, an emotion engine has the ability to analyze emotional data and create health guidelines that reflect the user's emotional state. For example, a prompt might read, "User A's meals today were high in calories, and emotional data indicates they are experiencing stress. Please provide health advice for this," and advice based on this would be generated.

[0336] This system also collaborates with external sources to acquire nutritional information and further enhance the generated health information. Finally, the information generated from the server is sent to the terminal and presented to the user. For example, it might provide advice such as, "Since today's meal was high in calories, we recommend having a light dinner. Try meditation for relaxation."

[0337] In this way, the system can provide personalized health guidance in real time based on the user's lifestyle data and emotional data.

[0338] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0339] Step 1:

[0340] Users input lifestyle data and emotional data into an application on their device. Specifically, users open the app and input details such as their meals, exercise time, sleep time, and emotional state. The input data is saved as "lifestyle data" and "emotional data," respectively.

[0341] Step 2:

[0342] The terminal receives input data from the user and converts it into an appropriate format. Specifically, an application within the terminal compiles the input lifestyle data into CSV format and labels the emotional data. This streamlines the transmission to the server. The output is the converted data file.

[0343] Step 3:

[0344] The device sends the formatted data to the server. The device's operation begins when the "Send" button is pressed in the app, and the data is sent to the server over the network. At this stage, the input is the converted data, and the output is the data that has arrived on the server.

[0345] Step 4:

[0346] The server analyzes the received data. Specifically, the server uses a generative AI model to generate personalized health information by comparing it with past data. An emotion engine also operates in parallel, generating guidelines based on emotional state. The input is data sent from the terminal, and the output is the generated health information and emotional guidelines.

[0347] Step 5:

[0348] The server sends the generated health information and guidelines to the terminal. The server's specific operation involves packaging the generated information into an appropriate format and sending it back to the terminal via the network. As output, the terminal receives information to be presented to the user.

[0349] Step 6:

[0350] The device presents the received information to the user. Specifically, it displays a notification on the screen and makes detailed information viewable. The user is then encouraged to adjust their lifestyle based on this information and provide feedback.

[0351] Step 7:

[0352] Users adjust their lifestyles based on the advice provided and input the results as feedback into the device. This helps improve the accuracy of future advice. The input is user feedback, and the output is information sent to the server and stored in the database.

[0353] (Application Example 2)

[0354] 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."

[0355] In modern society, there is a demand for personalized health information and comprehensive care that takes emotional states into consideration. However, conventional systems face the challenge of not being able to effectively integrate lifestyle data and emotional data, and to provide advice that considers not only the user's health but also their emotional state.

[0356] 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.

[0357] In this invention, the server includes means for analyzing user-inputted lifestyle data and emotional data using a generative model to generate personalized health and emotional information; means for presenting the generated health and emotional information to the user terminal; and means for collaborating with external information sources to obtain additional information regarding nutritional balance and to supplement the generated health and emotional information. This makes it possible to provide more accurate personalized health and emotional information that takes into account both the user's health and emotional state.

[0358] A "generative model" is a type of algorithm that generates specific information or advice based on data input by the user.

[0359] "Lifestyle data" refers to information about a user's daily activities, specifically data such as meals, exercise, and sleep.

[0360] "Emotional data" refers to information about a user's emotional state and is used to assess the user's psychological health.

[0361] "Health information" refers to personalized advice and reports regarding the user's health status.

[0362] "Emotional information" refers to advice and psychological health information that takes into account the user's emotional state.

[0363] "External information sources" refer to databases and resources that provide information other than that of the user, and are used by the system to generate more comprehensive information.

[0364] A "user terminal" is a device that a user directly operates to input and present information, and includes smartphones and tablets.

[0365] Modes for carrying out the invention

[0366] This invention is a system that analyzes a user's daily life record data and emotional data to provide personalized health and emotional information. The system mainly consists of a user terminal and a server.

[0367] Users input daily life records and emotional states through applications installed on their devices, such as smartphones and tablets. This data is transmitted to a server via the internet, and the server uses a generative AI model to analyze the received data.

[0368] On the server, a generative AI model is used to analyze the user's lifestyle data and emotional data to generate personalized health advice and emotional support. This process utilizes server applications written in Python or Flask, with the Google Cloud Natural Language API handling the emotional data analysis. Based on the analysis results, comprehensive advice is generated that considers not only health information but also emotional information.

[0369] Furthermore, the server collaborates with external sources to obtain additional information regarding nutritional balance. This reinforces the generated health and emotional information, presenting it to the user as more accurate and comprehensive advice.

[0370] Users adjust their lifestyles based on the health and emotional information provided, and then send feedback from their devices to the server to improve the accuracy of future advice.

[0371] For example, if a user inputs emotional data indicating they are "feeling stressed," the system might generate advice such as, "I recommend listening to relaxing music or taking a short walk." The following prompt can be used to generate such advice: "If the user's emotional state indicates high stress, compare this with past data and suggest the most suitable stress-relief methods."

[0372] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0373] Step 1:

[0374] Users input daily lifestyle data (such as meals, exercise, and sleep) and emotional data into an app installed on their smartphone or tablet. This data is collected on the device and converted to an appropriate data format before being sent to the server. Inputs include lifestyle data and emotional data entered by the user on the device. Outputs are formatted data.

[0375] Step 2:

[0376] The terminal sends formatted data to the server via the internet. The data processing involved is data packaging and conversion according to the transmission protocol. The input is the formatted data. The output is the data received by the server.

[0377] Step 3:

[0378] The server inputs data received from the user into a generating AI model and performs data analysis. Specifically, it uses Python to format the data and analyzes lifestyle data and emotional data. This analysis generates personalized health and emotional information. The input consists of lifestyle data and emotional data received by the server. The output is the analyzed personalized information.

[0379] Step 4:

[0380] The server utilizes the Google Cloud Natural Language API to perform detailed analysis of sentiment data and assess the user's psychological health. This data processing includes the process of extracting sentiment labels and related metrics from the sentiment data. The input is the user's sentiment data. The output is the analysis results, such as sentiment labels.

[0381] Step 5:

[0382] The server transmits the generated personalized health and emotional information to the user's terminal. It also integrates nutritional information obtained from external sources to provide the user with comprehensive health advice. Inputs include analyzed personalized information and nutritional information obtained from external sources. Output is comprehensive health advice.

[0383] Step 6:

[0384] Users adjust their lifestyles based on the health and emotional information they receive. They input the results as feedback into their device and send it to the server to improve future advice. The input is user feedback information, and the output is feedback data necessary for accuracy improvement.

[0385] 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.

[0386] 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.

[0387] 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.

[0388] [Third Embodiment]

[0389] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0390] 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.

[0391] 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).

[0392] 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.

[0393] 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.

[0394] 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).

[0395] 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.

[0396] 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.

[0397] 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.

[0398] 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.

[0399] 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.

[0400] 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".

[0401] This section describes an embodiment of this system. This system consists of a user, a terminal, and a server, and provides health information using a generative model based on individual lifestyle record data.

[0402] Users input daily lifestyle data, such as meals, exercise, and sleep, into their device. For example, they record detailed information in the application, such as "I ate 100g of oatmeal and one banana for breakfast." The entered data is then transmitted to the server via the device.

[0403] The server inputs the received lifestyle data into a generative model and performs data analysis. This generative model learns from past data patterns and has algorithms for evaluating the user's health status. Specifically, it generates personalized health information to maintain and improve the user's health, such as optimizing nutritional balance and warnings about lack of exercise. Furthermore, the server obtains the latest nutritional data from external sources and integrates this information to generate advice.

[0404] The generated health information is sent from the server to the user's device, which then presents this information to the user. Based on the presented information, the user can improve their daily life and provide feedback on their experience through the device.

[0405] This system allows users to easily manage their health in a way that suits their lifestyle. For example, if the generated health information says, "Your calorie intake today exceeded your target, so we recommend reducing the calories in your dinner," the user can adjust their dinner menu accordingly. This feedback is stored on the server and contributes to improving the accuracy of the generation model for future generations.

[0406] The following describes the processing flow.

[0407] Step 1:

[0408] Users input daily lifestyle data into a device application. This includes specific information such as meals, exercise, and sleep. For example, they might enter details like the menu and quantity of their breakfast, or the number of steps they walked in a day.

[0409] Step 2:

[0410] The terminal formats the user's entered lifestyle record data and sends it to the server in the appropriate format. The terminal also validates the data to ensure that the format is correct.

[0411] Step 3:

[0412] The server receives lifestyle record data sent from the terminal. After receiving the data, it inputs it into a generative model. This model analyzes the data based on past trends and predictive algorithms.

[0413] Step 4:

[0414] The generative model assesses the user's health status and generates personalized health information. This includes aspects such as calorie surplus or deficit, nutritional balance, and recommended exercise levels.

[0415] Step 5:

[0416] The server integrates health information generated by the generative model with external information sources to form more precise advice. For example, it uses external APIs to obtain the latest information on nutrients and incorporates it into the advice.

[0417] Step 6:

[0418] The server sends the final health information to the user's device. The information is then displayed on the device in an easy-to-understand format for the user.

[0419] Step 7:

[0420] Users can improve their daily lives based on health information presented through their devices. Furthermore, if users wish to provide feedback, they can do so through their devices to the server. This feedback is stored on the server and used to improve the generative model.

[0421] (Example 1)

[0422] 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."

[0423] In modern healthcare management, there is a lack of means to provide health information tailored to individual users. Furthermore, real-time data processing and the provision of highly accurate health information based on that data are difficult. Therefore, there is a need for a system that efficiently generates and provides personalized health information tailored to each user's lifestyle.

[0424] 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.

[0425] This invention includes a server that uses a generation algorithm to process lifestyle data entered by a user and generate customized health information, a means for displaying the generated health information on a portable electronic device, and a means for integrating with an external data source to obtain additional data on nutrient balance and enhance the generated health information. This enables users to receive specific health information tailored to their lifestyle in real time and use it to manage their health.

[0426] A "generative algorithm" is a computational method used to analyze lifestyle data obtained from users and generate customized health information.

[0427] A "user" is an individual or similar entity that uses the system to input their lifestyle data and obtain health information.

[0428] "Lifestyle data" refers to information about users' daily diet, exercise, sleep, etc., and serves as the basic data for generating health information.

[0429] "Customized health information" refers to specific and personalized health-related information generated through a generation algorithm based on the lifestyle data of each individual user.

[0430] "Portable electronic devices" refer to devices such as smartphones and tablets that are portable and used to display information.

[0431] "External data sources" refer to databases and information services located outside the system, and are used to obtain the latest data on nutrient balance.

[0432] "Nutrient balance" refers to the appropriate intake and harmony of various nutrients necessary for maintaining a healthy lifestyle, and is an indicator for enhancing health information provided to users.

[0433] This invention is based on a three-tiered structure consisting of a user, a terminal, and a server. The user inputs lifestyle data into a terminal such as a smartphone or tablet, and health information is obtained based on that data. Specifically, the user records information about their daily meals, exercise, and sleep in an application on the terminal. For example, they input detailed information such as, "I ate 100g of oatmeal and one banana for breakfast."

[0434] The terminal formats the data entered by the user and sends it to the server using a secure communication protocol (e.g., HTTPS). Upon receiving the data, the server analyzes it using a generative AI model and generates customized health information. This generative AI model incorporates an algorithm that learns from past data patterns, enabling it to generate advice tailored to the user's health condition.

[0435] Furthermore, the server accesses external data sources to obtain the latest data on nutrient balance and integrates it with the generated health information. This process allows users to receive a more accurate and detailed assessment of their health status.

[0436] The generated health information is sent from the server to the terminal. The terminal displays this information in an easy-to-understand format so that users can utilize it in their daily lives.

[0437] An example of a prompt message is, "Based on the user's lifestyle data, provide advice to optimize their nutritional balance." This allows the user to receive specific health advice tailored to their lifestyle, enabling them to adopt healthier habits.

[0438] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0439] Step 1:

[0440] Users input their daily lifestyle data into a dedicated application on their smartphone or tablet. This input data includes specific information such as details of their diet, exercise levels, and sleep quality. The entered data is stored digitally and prepared for the next processing step.

[0441] Step 2:

[0442] The terminal converts the user's entered lifestyle data into an appropriate format. It then sends the formatted data to the server via a secure communication protocol (e.g., HTTPS). The purpose of this step is to encode the input data and transmit it to the server while maintaining data integrity and confidentiality.

[0443] Step 3:

[0444] The server receives lifestyle data transmitted from the terminal. It then inputs this data into a generative AI model and begins data analysis. Based on the input data, the generative AI model uses pattern recognition algorithms to evaluate the user's health status and generate necessary health advice.

[0445] Step 4:

[0446] The server accesses external data sources based on the analysis results obtained by the generated AI model. Here, it retrieves the latest nutrition-related information and integrates additional data tailored to the user's specific health needs. This results in more precise and customized health information being output.

[0447] Step 5:

[0448] The server transmits integrated health information to the user's device. This information includes nutritional balance, exercise advice, and suggestions for improving lifestyle habits, and is provided in a format that the user can use in their daily life. The device displays the received information to the user and provides notifications to allow the user to easily access it.

[0449] Step 6:

[0450] Users adjust their lifestyle habits based on the health information presented. They input the results of these adjustments and their improvement experiences as feedback into the device. This feedback is sent to the server in digital format and used as data for generating health information in the future.

[0451] Through these steps, the system can provide personalized health advice tailored to the user's lifestyle in real time.

[0452] (Application Example 1)

[0453] 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."

[0454] Conventional health management systems have the problem that individuals find it burdensome to input and manage their health information in their daily lives. Furthermore, there is a challenge in providing users with immediate results of real-time lifestyle record analysis and appropriate health advice. Moreover, there is a need for a method that integrates daily life information and is easily usable in a home environment.

[0455] 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.

[0456] This invention includes a server that uses a generative model to analyze lifestyle data input by a user and generate personalized health information, a means for displaying the generated health information on a communication device, and an automated home machine that receives lifestyle data via voice input or a haptic display and presents the generated health information. This allows users to easily manage their daily health information, improves convenience in the home environment, and enables them to receive personalized health advice in real time.

[0457] A "generative model" is a technology that analyzes lifestyle data and generates personalized health information based on statistics and machine learning.

[0458] "User" refers to a person who provides lifestyle record data and receives health information based on that data.

[0459] "Lifestyle record data" refers to information about daily life, such as diet, exercise, and sleep, and is used to assess health status.

[0460] A "communication device" is an electronic device that displays generated health information and presents it to the user visually or audibly.

[0461] "External information sources" refer to third parties that provide the latest nutritional data and health information, and that provide information to supplement the generated health information.

[0462] "Automated machines in the home" are automated devices that receive user lifestyle data in a home environment and provide analysis results.

[0463] This invention is a system designed to support users in managing their health in their daily lives. The system mainly consists of a server, terminals, and automated machines in the home.

[0464] The server receives lifestyle data submitted by users and analyzes it using a generative AI model. The server plays a central role in data analysis, generating personalized health information based on data such as diet, exercise, and sleep. This generated health information is integrated with nutritional data obtained from external sources to provide more accurate and useful information.

[0465] The terminal receives generated health information provided by the server and presents it to the user visually or audibly. The terminal is designed to allow users to easily input lifestyle data through voice input and haptic displays.

[0466] Specifically, when a user voice-inputs something like, "I ate 150g of pasta for lunch today," an automated device in the home sends that information to a server. The server processes the data and performs analysis using a generative AI model. It then generates personalized health information, such as, "We recommend you eat more vegetables for dinner," and sends it to the device.

[0467] An example of a prompt message a user could enter would be: "My weight today is 68kg, I had 150g of pasta for lunch, and I took a 30-minute walk. Please give me some health advice."

[0468] This allows users to intuitively utilize the system in their daily lives and receive personalized health advice in real time.

[0469] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0470] Step 1:

[0471] Users input lifestyle data into the device using voice input or a haptic display. This data includes information such as meals, exercise, and sleep duration. The system is designed to be intuitive and allow for accurate recording of everyday lifestyle information.

[0472] Step 2:

[0473] The terminal digitizes the entered lifestyle record data and sends it to the server. This data processing includes converting audio data to text format and formatting the data as needed. The transmitted data is then used in subsequent analysis steps.

[0474] Step 3:

[0475] The server inputs the received lifestyle data into a generating AI model. This model learns from past data patterns and performs data calculations to optimize nutritional balance and exercise levels based on the received data. The output is personalized health information for each user.

[0476] Step 4:

[0477] The server retrieves the latest nutritional data from external sources and integrates it with the analysis results of the generated AI model. This process combines the externally acquired data with the user's individual data to establish the most beneficial health advice.

[0478] Step 5:

[0479] The generated health information is transmitted from the server to the terminal. The terminal presents this information to the user in an appropriate format, communicating its contents visually or audibly. Here, personalized health information obtained through analysis is presented, and feedback that is easy for the user to understand immediately is provided.

[0480] Step 6:

[0481] Users adjust their daily lives based on the presented health information and provide feedback to the server via their device. This feedback is used to improve the accuracy of future health information generation. The server collects this feedback and incorporates it into the generating AI model to improve the accuracy of future analyses.

[0482] 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.

[0483] As an embodiment of the present invention, a personalized health information provision system incorporating an emotion engine will be described. The system mainly consists of three elements: a user, a terminal, and a server, each playing a specific role.

[0484] Users input daily lifestyle data, including meals, exercise, and sleep, into a device application. They can also input their emotional state. This allows for a comprehensive understanding of not only the user's physical condition but also their emotional state.

[0485] The device transmits the entered lifestyle record data and emotional data to the server. During this process, the device packages the data in an appropriate format for efficient data transmission. Emotional data, in particular, is analyzed by an emotion engine and is therefore separately labeled before transmission.

[0486] The server uses a generative model to generate user health information based on the received data. The generative model compares accumulated historical data with current input data to assess health. In parallel, an emotion engine analyzes emotional data and creates health guidelines that reflect the user's emotional state. This emotion engine, for example, provides advice on relaxation and stress relief if the user is experiencing high levels of stress.

[0487] Next, the system collaborates with external sources to obtain additional information about nutritional balance, and integrates this information to form comprehensive health advice. The generated information is finally transmitted from the server to the terminal, which then presents the information to the user. For example, the server might generate specific guidance such as, "Your meal today was high in calories, and your current emotional state is somewhat stressful, so we recommend having a light dinner and trying to relax," and provide it to the user.

[0488] Finally, users can adjust their lifestyles based on the advice, and provide further feedback to improve the accuracy of future advice. This feedback data is stored on the server and used to improve the generative model. In this way, a system that incorporates an emotion engine can provide a service that takes into account both the user's health and emotions.

[0489] The following describes the processing flow.

[0490] Step 1:

[0491] Users input lifestyle data (such as meals, exercise, and sleep) and their emotional state at that time into the device. For example, they might record specific data such as "I ate salad and soup for lunch" or "I'm feeling a little anxious right now."

[0492] Step 2:

[0493] The terminal formats the user's entered lifestyle record data and emotional data and sends them to the server. The terminal verifies the data integrity and ensures that the data is in the correct format.

[0494] Step 3:

[0495] The server inputs data received from the terminal into a generative model to analyze the user's health status. The generative model generates individual health information based on the input data. In this process, past data trends and patterns are taken into consideration.

[0496] Step 4:

[0497] The server uses an emotion engine to analyze emotional data. The emotion engine quantifies the user's emotional state and generates appropriate health information and advice. For example, it might suggest relaxation techniques or deep breathing exercises to a user who is feeling stressed.

[0498] Step 5:

[0499] The server utilizes external sources to obtain the latest data on nutritional balance and supplements the generated health information. This supplementary information includes data on new nutrients and the health benefits of exercise.

[0500] Step 6:

[0501] The server sends generated health information and emotion-based advice to the terminal. The terminal displays this information in a way that is easy for the user to understand. For example, it might offer specific suggestions such as, "Try using stress-relieving ingredients in your dinner."

[0502] Step 7:

[0503] Users strive to improve their daily lives based on the health information they receive. Furthermore, they can provide subsequent feedback through their devices. This feedback is collected by the server and used to improve the accuracy of the generative model and emotion engine.

[0504] (Example 2)

[0505] 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."

[0506] Conventional health information systems are limited to providing information based on users' physiological data, making it difficult to provide comprehensive health guidelines that take emotional states into account. Furthermore, a lack of real-time data processing capabilities hinders the ability to provide prompt and effective guidance.

[0507] 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.

[0508] In this invention, the server includes means for analyzing lifestyle data and emotional data using a generative model to generate personalized health information, means for presenting health guidelines based on the generated health information and emotional state to the user's device, and means for acquiring nutritional information and supplementing the health information in cooperation with external information sources. This makes it possible to provide comprehensive and personalized health guidelines in real time, taking into account the user's emotional state and nutritional information.

[0509] A "generative model" is an algorithm that compares and analyzes past and present data to generate information tailored to the user's state.

[0510] "Lifestyle data" refers to records related to the user's daily activities, such as meals, exercise, and sleep.

[0511] "Emotional data" refers to information that indicates the user's psychological state, and is obtained through text and choices.

[0512] "Health information" refers to information about a user's health status and suggestions for lifestyle improvements, generated based on the user's lifestyle and emotional data.

[0513] "Health guidelines" are specific instructions and advice provided in consideration of the user's health information and emotional state.

[0514] "User equipment" refers to devices that users use to input or receive information.

[0515] "External information sources" refer to external databases or services accessed to obtain nutritional information or other relevant information.

[0516] As an embodiment of this system, we describe a personalized health information provision system that utilizes a generative AI model and an emotion engine. This system consists of three elements: user, terminal, and server, each playing its own unique role.

[0517] Users input lifestyle data related to their daily diet, exercise, and sleep into a device application. Simultaneously, they can also input data about their emotional state. For example, a user might input "Breakfast: toast and coffee," exercise: "30 minutes of jogging," sleep duration: "7 hours," and emotion: "no stress" through the app. This information is used to comprehensively understand the user's health and emotional state.

[0518] The terminal is responsible for converting the entered lifestyle and emotional data into an appropriate format and sending it to the server. During this process, emotional data is labeled for use in analysis by the emotion engine. The terminal uses a standard smartphone or tablet, and a dedicated health management app is used as the application.

[0519] The server receives data sent from the terminal and uses a generative AI model to generate health information optimized for the user. This generative AI model compares past lifestyle data with the input data to assess the user's health status. Furthermore, an emotion engine has the ability to analyze emotional data and create health guidelines that reflect the user's emotional state. For example, a prompt might read, "User A's meals today were high in calories, and emotional data indicates they are experiencing stress. Please provide health advice for this," and advice based on this would be generated.

[0520] This system also collaborates with external sources to acquire nutritional information and further enhance the generated health information. Finally, the information generated from the server is sent to the terminal and presented to the user. For example, it might provide advice such as, "Since today's meal was high in calories, we recommend having a light dinner. Try meditation for relaxation."

[0521] In this way, the system can provide personalized health guidance in real time based on the user's lifestyle data and emotional data.

[0522] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0523] Step 1:

[0524] Users input lifestyle data and emotional data into an application on their device. Specifically, users open the app and input details such as what they ate, exercise time, sleep time, and their emotional state. The input data is saved as "lifestyle data" and "emotional data," respectively.

[0525] Step 2:

[0526] The terminal receives input data from the user and converts it into an appropriate format. Specifically, an application within the terminal compiles the input lifestyle data into CSV format and labels the emotional data. This streamlines the transmission to the server. The output is the converted data file.

[0527] Step 3:

[0528] The device sends the formatted data to the server. The device's operation begins when the "Send" button is pressed in the app, and the data is sent to the server over the network. At this stage, the input is the converted data, and the output is the data that has arrived on the server.

[0529] Step 4:

[0530] The server analyzes the received data. Specifically, the server uses a generative AI model to generate personalized health information by comparing it with past data. An emotion engine also operates in parallel, generating guidelines based on emotional state. The input is data sent from the terminal, and the output is the generated health information and emotional guidelines.

[0531] Step 5:

[0532] The server sends the generated health information and guidelines to the terminal. The server's specific operation involves packaging the generated information into an appropriate format and sending it back to the terminal via the network. As output, the terminal receives information to be presented to the user.

[0533] Step 6:

[0534] The device presents the received information to the user. Specifically, it displays a notification on the screen and makes detailed information viewable. The user is then encouraged to adjust their lifestyle based on this information and provide feedback.

[0535] Step 7:

[0536] Users adjust their lifestyles based on the advice provided and input the results as feedback into the device. This helps improve the accuracy of future advice. The input is user feedback, and the output is information sent to the server and stored in the database.

[0537] (Application Example 2)

[0538] 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."

[0539] In modern society, there is a demand for personalized health information and comprehensive care that takes emotional states into consideration. However, conventional systems face the challenge of not being able to effectively integrate lifestyle data and emotional data, and to provide advice that considers not only the user's health but also their emotional state.

[0540] 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.

[0541] In this invention, the server includes means for analyzing user-inputted lifestyle data and emotional data using a generative model to generate personalized health and emotional information; means for presenting the generated health and emotional information to the user terminal; and means for collaborating with external information sources to obtain additional information regarding nutritional balance and to supplement the generated health and emotional information. This makes it possible to provide more accurate personalized health and emotional information that takes into account both the user's health and emotional state.

[0542] A "generative model" is a type of algorithm that generates specific information or advice based on data input from a user.

[0543] "Lifestyle data" refers to information about a user's daily activities, specifically data such as meals, exercise, and sleep.

[0544] "Emotional data" refers to information about a user's emotional state and is used to assess the user's psychological health.

[0545] "Health information" refers to personalized advice and reports regarding the user's health status.

[0546] "Emotional information" refers to advice and psychological health information that takes into account the user's emotional state.

[0547] "External information sources" refer to databases and resources that provide information other than that of the user, and are used by the system to generate more comprehensive information.

[0548] A "user terminal" is a device that a user directly operates to input and present information, and includes smartphones and tablets.

[0549] Modes for carrying out the invention

[0550] This invention is a system that analyzes a user's daily life record data and emotional data to provide personalized health and emotional information. The system mainly consists of a user terminal and a server.

[0551] Users input daily life records and emotional states through applications installed on their devices, such as smartphones and tablets. This data is transmitted to a server via the internet, and the server uses a generative AI model to analyze the received data.

[0552] On the server, a generative AI model is used to analyze the user's lifestyle data and emotional data to generate personalized health advice and emotional support. This process utilizes server applications written in Python or Flask, with the Google Cloud Natural Language API handling the emotional data analysis. Based on the analysis results, comprehensive advice is generated that considers not only health information but also emotional information.

[0553] Furthermore, the server collaborates with external sources to obtain additional information regarding nutritional balance. This reinforces the generated health and emotional information, presenting it to the user as more accurate and comprehensive advice.

[0554] Users adjust their lifestyles based on the health and emotional information provided, and then send feedback from their devices to the server to improve the accuracy of future advice.

[0555] For example, if a user inputs emotional data indicating they are "feeling stressed," the system might generate advice such as, "I recommend listening to relaxing music or taking a short walk." The following prompt can be used to generate such advice: "If the user's emotional state indicates high stress, compare this with past data and suggest the most suitable stress-relief methods."

[0556] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0557] Step 1:

[0558] Users input daily lifestyle data (such as meals, exercise, and sleep) and emotional data into an app installed on their smartphone or tablet. This data is collected on the device and converted to an appropriate data format before being sent to the server. Inputs include lifestyle data and emotional data entered by the user on the device. Outputs are formatted data.

[0559] Step 2:

[0560] The terminal sends formatted data to the server via the internet. The data processing involved is data packaging and conversion according to the transmission protocol. The input is the formatted data. The output is the data received by the server.

[0561] Step 3:

[0562] The server inputs data received from the user into a generating AI model and performs data analysis. Specifically, it uses Python to format the data and analyzes lifestyle data and emotional data. This analysis generates personalized health and emotional information. The input consists of lifestyle data and emotional data received by the server. The output is the analyzed personalized information.

[0563] Step 4:

[0564] The server utilizes the Google Cloud Natural Language API to perform detailed analysis of sentiment data and assess the user's psychological health. This data processing includes the process of extracting sentiment labels and related metrics from the sentiment data. The input is the user's sentiment data. The output is the analysis results, such as sentiment labels.

[0565] Step 5:

[0566] The server transmits the generated personalized health and emotional information to the user's terminal. It also integrates nutritional information obtained from external sources to provide the user with comprehensive health advice. Inputs include analyzed personalized information and nutritional information obtained from external sources. Output is comprehensive health advice.

[0567] Step 6:

[0568] Users adjust their lifestyles based on the health and emotional information they receive. They input the results as feedback into their device and send it to the server to improve future advice. The input is user feedback information, and the output is feedback data necessary for accuracy improvement.

[0569] 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.

[0570] 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.

[0571] 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.

[0572] [Fourth Embodiment]

[0573] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0574] 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.

[0575] 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).

[0576] 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.

[0577] 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.

[0578] 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).

[0579] 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.

[0580] 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.

[0581] 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.

[0582] 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.

[0583] 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.

[0584] 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.

[0585] 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".

[0586] This section describes an embodiment of this system. This system consists of a user, a terminal, and a server, and provides health information using a generative model based on individual lifestyle record data.

[0587] Users input daily lifestyle data, such as meals, exercise, and sleep, into their device. For example, they record detailed information in the application, such as "I ate 100g of oatmeal and one banana for breakfast." The entered data is then transmitted to the server via the device.

[0588] The server inputs the received lifestyle data into a generative model and performs data analysis. This generative model learns from past data patterns and has algorithms for evaluating the user's health status. Specifically, it generates personalized health information to maintain and improve the user's health, such as optimizing nutritional balance and warnings about lack of exercise. Furthermore, the server obtains the latest nutritional data from external sources and integrates this information to generate advice.

[0589] The generated health information is sent from the server to the user's device, which then presents this information to the user. Based on the presented information, the user can improve their daily life and provide feedback on their experience through the device.

[0590] This system allows users to easily manage their health in a way that suits their lifestyle. For example, if the generated health information says, "Your calorie intake today exceeded your target, so we recommend reducing the calories in your dinner," the user can adjust their dinner menu accordingly. This feedback is stored on the server and contributes to improving the accuracy of the generation model for future generations.

[0591] The following describes the processing flow.

[0592] Step 1:

[0593] Users input daily lifestyle data into a device application. This includes specific information such as meals, exercise, and sleep. For example, they might enter details like the menu and quantity of their breakfast, or the number of steps they walked in a day.

[0594] Step 2:

[0595] The terminal formats the user's entered lifestyle record data and sends it to the server in the appropriate format. The terminal also validates the data to ensure that the format is correct.

[0596] Step 3:

[0597] The server receives lifestyle record data sent from the terminal. After receiving the data, it inputs it into a generative model. This model analyzes the data based on past trends and predictive algorithms.

[0598] Step 4:

[0599] The generative model assesses the user's health status and generates personalized health information. This includes aspects such as calorie surplus or deficit, nutritional balance, and recommended exercise levels.

[0600] Step 5:

[0601] The server integrates health information generated by the generative model with external information sources to form more precise advice. For example, it uses external APIs to obtain the latest information on nutrients and incorporates it into the advice.

[0602] Step 6:

[0603] The server sends the final health information to the user's device. The information is then displayed on the device in an easy-to-understand format for the user.

[0604] Step 7:

[0605] Users can improve their daily lives based on health information presented through their devices. Furthermore, if users wish to provide feedback, they can do so through their devices to the server. This feedback is stored on the server and used to improve the generative model.

[0606] (Example 1)

[0607] 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".

[0608] In modern healthcare management, there is a lack of means to provide health information tailored to individual users. Furthermore, real-time data processing and the provision of highly accurate health information based on that data are difficult. Therefore, there is a need for a system that efficiently generates and provides personalized health information tailored to each user's lifestyle.

[0609] 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.

[0610] This invention includes a server that uses a generation algorithm to process lifestyle data entered by a user and generate customized health information, a means for displaying the generated health information on a portable electronic device, and a means for integrating with an external data source to obtain additional data on nutrient balance and enhance the generated health information. This enables users to receive specific health information tailored to their lifestyle in real time and use it to manage their health.

[0611] A "generative algorithm" is a computational method used to analyze lifestyle data obtained from users and generate customized health information.

[0612] A "user" is an individual or similar entity that uses the system to input their lifestyle data and obtain health information.

[0613] "Lifestyle data" refers to information about users' daily diet, exercise, sleep, etc., and serves as the basic data for generating health information.

[0614] "Customized health information" refers to specific and personalized health-related information generated through a generation algorithm based on the lifestyle data of each individual user.

[0615] "Portable electronic devices" refer to devices such as smartphones and tablets that are portable and used to display information.

[0616] "External data sources" refer to databases and information services located outside the system, and are used to obtain the latest data on nutrient balance.

[0617] "Nutrient balance" refers to the appropriate intake and harmony of various nutrients necessary for maintaining a healthy lifestyle, and is an indicator for enhancing health information provided to users.

[0618] This invention is based on a three-tiered structure consisting of a user, a terminal, and a server. The user inputs lifestyle data into a terminal such as a smartphone or tablet, and health information is obtained based on that data. Specifically, the user records information about their daily meals, exercise, and sleep in an application on the terminal. For example, they input detailed information such as, "I ate 100g of oatmeal and one banana for breakfast."

[0619] The terminal formats the data entered by the user and sends it to the server using a secure communication protocol (e.g., HTTPS). Upon receiving the data, the server analyzes it using a generative AI model and generates customized health information. This generative AI model incorporates an algorithm that learns from past data patterns, enabling it to generate advice tailored to the user's health condition.

[0620] Furthermore, the server accesses external data sources to obtain the latest data on nutrient balance and integrates it with the generated health information. This process allows users to receive a more accurate and detailed assessment of their health status.

[0621] The generated health information is sent from the server to the terminal. The terminal displays this information in an easy-to-understand format so that users can utilize it in their daily lives.

[0622] An example of a prompt message is, "Based on the user's lifestyle data, provide advice to optimize their nutritional balance." This allows the user to receive specific health advice tailored to their lifestyle, enabling them to adopt healthier habits.

[0623] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0624] Step 1:

[0625] Users input their daily lifestyle data into a dedicated application on their smartphone or tablet. This input data includes specific information such as details of their diet, exercise levels, and sleep quality. The entered data is stored digitally and prepared for the next processing step.

[0626] Step 2:

[0627] The terminal converts the user's entered lifestyle data into an appropriate format. It then sends the formatted data to the server via a secure communication protocol (e.g., HTTPS). The purpose of this step is to encode the input data and transmit it to the server while maintaining data integrity and confidentiality.

[0628] Step 3:

[0629] The server receives lifestyle data transmitted from the terminal. It then inputs this data into a generative AI model and begins data analysis. Based on the input data, the generative AI model uses pattern recognition algorithms to evaluate the user's health status and generate necessary health advice.

[0630] Step 4:

[0631] The server accesses external data sources based on the analysis results obtained by the generated AI model. Here, it retrieves the latest nutrition-related information and integrates additional data tailored to the user's specific health needs. This results in more precise and customized health information being output.

[0632] Step 5:

[0633] The server transmits integrated health information to the user's device. This information includes nutritional balance, exercise advice, and suggestions for improving lifestyle habits, and is provided in a format that the user can use in their daily life. The device displays the received information to the user and provides notifications to allow the user to easily access it.

[0634] Step 6:

[0635] Users adjust their lifestyle habits based on the health information presented. They input the results of these adjustments and their improvement experiences as feedback into the device. This feedback is sent to the server in digital format and used as data for generating health information in the future.

[0636] Through these steps, the system can provide personalized health advice tailored to the user's lifestyle in real time.

[0637] (Application Example 1)

[0638] 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".

[0639] Conventional health management systems have the problem that individuals find it burdensome to input and manage their health information in their daily lives. Furthermore, there is a challenge in providing users with immediate results of real-time lifestyle record analysis and appropriate health advice. Moreover, there is a need for a method that integrates daily life information and is easily usable in a home environment.

[0640] 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.

[0641] This invention includes a server that uses a generative model to analyze lifestyle data input by a user and generate personalized health information, a means for displaying the generated health information on a communication device, and an automated home machine that receives lifestyle data via voice input or a haptic display and presents the generated health information. This allows users to easily manage their daily health information, improves convenience in the home environment, and enables them to receive personalized health advice in real time.

[0642] A "generative model" is a technology that analyzes lifestyle data and generates personalized health information based on statistics and machine learning.

[0643] "User" refers to a person who provides lifestyle record data and receives health information based on that data.

[0644] "Lifestyle record data" refers to information about daily life, such as diet, exercise, and sleep, and is used to assess health status.

[0645] A "communication device" is an electronic device that displays generated health information and presents it to the user visually or audibly.

[0646] "External information sources" refer to third parties that provide the latest nutritional data and health information, and that provide information to supplement the generated health information.

[0647] "Automated machines in the home" are automated devices that receive user lifestyle data in a home environment and provide analysis results.

[0648] This invention is a system designed to support users in managing their health in their daily lives. The system mainly consists of a server, terminals, and automated machines in the home.

[0649] The server receives lifestyle data submitted by users and analyzes it using a generative AI model. The server plays a central role in data analysis, generating personalized health information based on data such as diet, exercise, and sleep. This generated health information is integrated with nutritional data obtained from external sources to provide more accurate and useful information.

[0650] The terminal receives generated health information provided by the server and presents it to the user visually or audibly. The terminal is designed to allow users to easily input lifestyle data through voice input and haptic displays.

[0651] Specifically, when a user voice-inputs something like, "I ate 150g of pasta for lunch today," an automated device in the home sends that information to a server. The server processes the data and performs analysis using a generative AI model. It then generates personalized health information, such as, "We recommend you eat more vegetables for dinner," and sends it to the device.

[0652] An example of a prompt message a user could enter would be: "My weight today is 68kg, I had 150g of pasta for lunch, and I took a 30-minute walk. Please give me some health advice."

[0653] This allows users to intuitively utilize the system in their daily lives and receive personalized health advice in real time.

[0654] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0655] Step 1:

[0656] Users input lifestyle data into the device using voice input or a haptic display. This data includes information such as meals, exercise, and sleep duration. The system is designed to be intuitive and allow for accurate recording of everyday lifestyle information.

[0657] Step 2:

[0658] The terminal digitizes the entered lifestyle record data and sends it to the server. This data processing includes converting audio data to text format and formatting the data as needed. The transmitted data is then used in subsequent analysis steps.

[0659] Step 3:

[0660] The server inputs the received lifestyle data into a generating AI model. This model learns from past data patterns and performs data calculations to optimize nutritional balance and exercise levels based on the received data. The output is personalized health information for each user.

[0661] Step 4:

[0662] The server retrieves the latest nutritional data from external sources and integrates it with the analysis results of the generated AI model. This process combines the externally acquired data with the user's individual data to establish the most beneficial health advice.

[0663] Step 5:

[0664] The generated health information is transmitted from the server to the terminal. The terminal presents this information to the user in an appropriate format, communicating its contents visually or audibly. Here, personalized health information obtained through analysis is presented, and feedback that is easy for the user to understand immediately is provided.

[0665] Step 6:

[0666] Users adjust their daily lives based on the presented health information and provide feedback to the server via their device. This feedback is used to improve the accuracy of future health information generation. The server collects this feedback and incorporates it into the generating AI model to improve the accuracy of future analyses.

[0667] 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.

[0668] As an embodiment of the present invention, a personalized health information provision system incorporating an emotion engine will be described. The system mainly consists of three elements: a user, a terminal, and a server, each playing a specific role.

[0669] Users input daily lifestyle data, including meals, exercise, and sleep, into a device application. They can also input their emotional state. This allows for a comprehensive understanding of not only the user's physical condition but also their emotional state.

[0670] The device transmits the entered lifestyle record data and emotional data to the server. During this process, the device packages the data in an appropriate format for efficient data transmission. Emotional data, in particular, is analyzed by an emotion engine and is therefore separately labeled before transmission.

[0671] The server uses a generative model to generate user health information based on the received data. The generative model compares accumulated historical data with current input data to assess health. In parallel, an emotion engine analyzes emotional data and creates health guidelines that reflect the user's emotional state. This emotion engine, for example, provides advice on relaxation and stress relief if the user is experiencing high levels of stress.

[0672] Next, the system collaborates with external sources to obtain additional information about nutritional balance, and integrates this information to form comprehensive health advice. The generated information is finally transmitted from the server to the terminal, which then presents the information to the user. For example, the server might generate specific guidance such as, "Your meal today was high in calories, and your current emotional state is somewhat stressful, so we recommend having a light dinner and trying to relax," and provide it to the user.

[0673] Finally, users can adjust their lifestyles based on the advice, and provide further feedback to improve the accuracy of future advice. This feedback data is stored on the server and used to improve the generative model. In this way, a system that incorporates an emotion engine can provide a service that takes into account both the user's health and emotions.

[0674] The following describes the processing flow.

[0675] Step 1:

[0676] Users input lifestyle data (such as meals, exercise, and sleep) and their emotional state at that time into the device. For example, they might record specific data such as "I ate salad and soup for lunch" or "I'm feeling a little anxious right now."

[0677] Step 2:

[0678] The terminal formats the user's entered lifestyle record data and emotional data and sends them to the server. The terminal verifies the data integrity and ensures that the data is in the correct format.

[0679] Step 3:

[0680] The server inputs data received from the terminal into a generative model to analyze the user's health status. The generative model generates individual health information based on the input data. In this process, past data trends and patterns are taken into consideration.

[0681] Step 4:

[0682] The server uses an emotion engine to analyze emotional data. The emotion engine quantifies the user's emotional state and generates appropriate health information and advice. For example, it might suggest relaxation techniques or deep breathing exercises to a user who is feeling stressed.

[0683] Step 5:

[0684] The server utilizes external sources to obtain the latest data on nutritional balance and supplements the generated health information. This supplementary information includes data on new nutrients and the health benefits of exercise.

[0685] Step 6:

[0686] The server sends generated health information and emotion-based advice to the terminal. The terminal displays this information in a way that is easy for the user to understand. For example, it might offer specific suggestions such as, "Try using stress-relieving ingredients in your dinner."

[0687] Step 7:

[0688] Users strive to improve their daily lives based on the health information they receive. Furthermore, they can provide subsequent feedback through their devices. This feedback is collected by the server and used to improve the accuracy of the generative model and emotion engine.

[0689] (Example 2)

[0690] 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".

[0691] Conventional health information systems are limited to providing information based on users' physiological data, making it difficult to provide comprehensive health guidelines that take emotional states into account. Furthermore, a lack of real-time data processing capabilities hinders the ability to provide prompt and effective guidance.

[0692] 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.

[0693] In this invention, the server includes means for analyzing lifestyle data and emotional data using a generative model to generate personalized health information, means for presenting health guidelines based on the generated health information and emotional state to the user's device, and means for acquiring nutritional information and supplementing the health information in cooperation with external information sources. This makes it possible to provide comprehensive and personalized health guidelines in real time, taking into account the user's emotional state and nutritional information.

[0694] A "generative model" is an algorithm that compares and analyzes past and present data to generate information tailored to the user's state.

[0695] "Lifestyle data" refers to records related to the user's daily activities, such as meals, exercise, and sleep.

[0696] "Emotional data" refers to information that indicates the user's psychological state, and is obtained through text and choices.

[0697] "Health information" refers to information about a user's health status and suggestions for lifestyle improvements, generated based on the user's lifestyle and emotional data.

[0698] "Health guidelines" are specific instructions and advice provided in consideration of the user's health information and emotional state.

[0699] "User equipment" refers to devices that users use to input or receive information.

[0700] "External information sources" refer to external databases or services accessed to obtain nutritional information or other relevant information.

[0701] As an embodiment of this system, we describe a personalized health information provision system that utilizes a generative AI model and an emotion engine. This system consists of three elements: user, terminal, and server, each playing its own unique role.

[0702] Users input lifestyle data related to their daily diet, exercise, and sleep into a device application. Simultaneously, they can also input data about their emotional state. For example, a user might input "Breakfast: toast and coffee," exercise: "30 minutes of jogging," sleep duration: "7 hours," and emotion: "no stress" through the app. This information is used to comprehensively understand the user's health and emotional state.

[0703] The terminal is responsible for converting the entered lifestyle and emotional data into an appropriate format and sending it to the server. During this process, emotional data is labeled for use in analysis by the emotion engine. The terminal uses a standard smartphone or tablet, and a dedicated health management app is used as the application.

[0704] The server receives data sent from the terminal and uses a generative AI model to generate health information optimized for the user. This generative AI model compares past lifestyle data with the input data to assess the user's health status. Furthermore, an emotion engine has the ability to analyze emotional data and create health guidelines that reflect the user's emotional state. For example, a prompt might read, "User A's meals today were high in calories, and emotional data indicates they are experiencing stress. Please provide health advice for this," and advice based on this would be generated.

[0705] This system also collaborates with external sources to acquire nutritional information and further enhance the generated health information. Finally, the information generated from the server is sent to the terminal and presented to the user. For example, it might provide advice such as, "Since today's meal was high in calories, we recommend having a light dinner. Try meditation for relaxation."

[0706] In this way, the system can provide personalized health guidance in real time based on the user's lifestyle data and emotional data.

[0707] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0708] Step 1:

[0709] Users input lifestyle data and emotional data into an application on their device. Specifically, users open the app and input details such as what they ate, exercise time, sleep time, and their emotional state. The input data is saved as "lifestyle data" and "emotional data," respectively.

[0710] Step 2:

[0711] The terminal receives input data from the user and converts it into an appropriate format. Specifically, an application within the terminal compiles the input lifestyle data into CSV format and labels the emotional data. This streamlines the transmission to the server. The output is the converted data file.

[0712] Step 3:

[0713] The device sends the formatted data to the server. The device's operation begins when the "Send" button is pressed in the app, and the data is sent to the server over the network. At this stage, the input is the converted data, and the output is the data that has arrived on the server.

[0714] Step 4:

[0715] The server analyzes the received data. Specifically, the server uses a generative AI model to generate personalized health information by comparing it with past data. An emotion engine also operates in parallel, generating guidelines based on emotional state. The input is data sent from the terminal, and the output is the generated health information and emotional guidelines.

[0716] Step 5:

[0717] The server sends the generated health information and guidelines to the terminal. The server's specific operation involves packaging the generated information into an appropriate format and sending it back to the terminal via the network. As output, the terminal receives information to be presented to the user.

[0718] Step 6:

[0719] The device presents the received information to the user. Specifically, it displays a notification on the screen and makes detailed information viewable. The user is then encouraged to adjust their lifestyle based on this information and provide feedback.

[0720] Step 7:

[0721] Users adjust their lifestyles based on the advice provided and input the results as feedback into the device. This helps improve the accuracy of future advice. The input is user feedback, and the output is information sent to the server and stored in the database.

[0722] (Application Example 2)

[0723] 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".

[0724] In modern society, there is a demand for personalized health information and comprehensive care that takes emotional states into consideration. However, conventional systems face the challenge of not being able to effectively integrate lifestyle data and emotional data, and to provide advice that considers not only the user's health but also their emotional state.

[0725] 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.

[0726] In this invention, the server includes means for analyzing user-inputted lifestyle data and emotional data using a generative model to generate personalized health and emotional information; means for presenting the generated health and emotional information to the user terminal; and means for collaborating with external information sources to obtain additional information regarding nutritional balance and to supplement the generated health and emotional information. This makes it possible to provide more accurate personalized health and emotional information that takes into account both the user's health and emotional state.

[0727] A "generative model" is a type of algorithm that generates specific information or advice based on data input from a user.

[0728] "Lifestyle data" refers to information about a user's daily activities, specifically data such as meals, exercise, and sleep.

[0729] "Emotional data" refers to information about a user's emotional state and is used to assess the user's psychological health.

[0730] "Health information" refers to personalized advice and reports regarding the user's health status.

[0731] "Emotional information" refers to advice and psychological health information that takes into account the user's emotional state.

[0732] "External information sources" refer to databases and resources that provide information other than that of the user, and are used by the system to generate more comprehensive information.

[0733] A "user terminal" is a device that a user directly operates to input and present information, and includes smartphones and tablets.

[0734] Modes for carrying out the invention

[0735] This invention is a system that analyzes a user's daily life record data and emotional data to provide personalized health and emotional information. The system mainly consists of a user terminal and a server.

[0736] Users input daily life records and emotional states through applications installed on their devices, such as smartphones and tablets. This data is transmitted to a server via the internet, and the server uses a generative AI model to analyze the received data.

[0737] On the server, a generative AI model is used to analyze the user's lifestyle data and emotional data to generate personalized health advice and emotional support. This process utilizes server applications written in Python or Flask, with the Google Cloud Natural Language API handling the emotional data analysis. Based on the analysis results, comprehensive advice is generated that considers not only health information but also emotional information.

[0738] Furthermore, the server collaborates with external sources to obtain additional information regarding nutritional balance. This reinforces the generated health and emotional information, presenting it to the user as more accurate and comprehensive advice.

[0739] Users adjust their lifestyles based on the health and emotional information provided, and then send feedback from their devices to the server to improve the accuracy of future advice.

[0740] For example, if a user inputs emotional data indicating they are "feeling stressed," the system might generate advice such as, "I recommend listening to relaxing music or taking a short walk." The following prompt can be used to generate such advice: "If the user's emotional state indicates high stress, compare this with past data and suggest the most suitable stress-relief methods."

[0741] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0742] Step 1:

[0743] Users input daily lifestyle data (such as meals, exercise, and sleep) and emotional data into an app installed on their smartphone or tablet. This data is collected on the device and converted to an appropriate data format before being sent to the server. Inputs include lifestyle data and emotional data entered by the user on the device. Outputs are formatted data.

[0744] Step 2:

[0745] The terminal sends formatted data to the server via the internet. The data processing involved is data packaging and conversion according to the transmission protocol. The input is the formatted data. The output is the data received by the server.

[0746] Step 3:

[0747] The server inputs data received from the user into a generating AI model and performs data analysis. Specifically, it uses Python to format the data and analyzes lifestyle data and emotional data. This analysis generates personalized health and emotional information. The input consists of lifestyle data and emotional data received by the server. The output is the analyzed personalized information.

[0748] Step 4:

[0749] The server utilizes the Google Cloud Natural Language API to perform detailed analysis of sentiment data and assess the user's psychological health. This data processing includes the process of extracting sentiment labels and related metrics from the sentiment data. The input is the user's sentiment data. The output is the analysis results, such as sentiment labels.

[0750] Step 5:

[0751] The server transmits the generated personalized health and emotional information to the user's terminal. It also integrates nutritional information obtained from external sources to provide the user with comprehensive health advice. Inputs include analyzed personalized information and nutritional information obtained from external sources. Output is comprehensive health advice.

[0752] Step 6:

[0753] Users adjust their lifestyles based on the health and emotional information they receive. They input the results as feedback into their device and send it to the server to improve future advice. The input is user feedback information, and the output is feedback data necessary for accuracy improvement.

[0754] 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.

[0755] 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.

[0756] 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.

[0757] 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.

[0758] 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.

[0759] 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.

[0760] 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.

[0761] 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.

[0762] 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."

[0763] 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.

[0764] 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.

[0765] 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.

[0766] 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.

[0767] 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.

[0768] 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.

[0769] 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.

[0770] 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.

[0771] 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.

[0772] 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.

[0773] 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.

[0774] 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.

[0775] The following is further disclosed regarding the embodiments described above.

[0776] (Claim 1)

[0777] A means of generating personalized health information by analyzing user-entered lifestyle data using a generative model,

[0778] A means of presenting the generated health information to the user terminal,

[0779] A means of collaborating with external information sources to obtain additional information on nutritional balance and supplement the generated health information,

[0780] A system that includes this.

[0781] (Claim 2)

[0782] The system according to claim 1, which collects user feedback and improves the accuracy of health information in subsequent visits.

[0783] (Claim 3)

[0784] The system according to claim 1, which enables efficient data transmission between a terminal and a server in order to process life record data in real time.

[0785] "Example 1"

[0786] (Claim 1)

[0787] A means for processing lifestyle data entered by users using a generation algorithm and generating customized health information,

[0788] A means for displaying the generated health information on a portable electronic device,

[0789] A means of integrating with external data sources to acquire additional data on nutrient balance and enhance the generated health information,

[0790] A data processing system that includes this.

[0791] (Claim 2)

[0792] A data processing system according to claim 1, which collects user response data and improves the accuracy of health information for subsequent visits.

[0793] (Claim 3)

[0794] A data processing system according to claim 1, which enables efficient data transmission between a portable electronic device and a computer device in order to process lifestyle data in real time.

[0795] "Application Example 1"

[0796] (Claim 1)

[0797] A means of generating personalized health information by analyzing lifestyle record data entered by users using a generative model,

[0798] A means for displaying the generated health information on a communication device,

[0799] A means of collaborating with external sources to obtain additional information on nutritional balance and supplement the generated health information,

[0800] A system including an automated machine for home use that receives lifestyle data via voice input or a haptic display and presents generated health information.

[0801] (Claim 2)

[0802] The system according to claim 1, which collects user feedback and improves the accuracy of health information for future use.

[0803] (Claim 3)

[0804] The system according to claim 1, which enables efficient information transmission between a communication device and a server in order to process real-time lifestyle data.

[0805] "Example 2 of combining an emotion engine"

[0806] (Claim 1)

[0807] A means of analyzing lifestyle data and emotional data using a generative model to generate personalized health information,

[0808] A means for presenting health guidelines based on generated health information and emotional state to the user's device,

[0809] A means of acquiring nutritional information and supplementing health information by collaborating with external information sources,

[0810] A system that includes this.

[0811] (Claim 2)

[0812] The system according to claim 1, which collects user feedback and improves the accuracy of health information in subsequent visits.

[0813] (Claim 3)

[0814] The system according to claim 1, which enables real-time data processing and efficient data transfer between devices and servers.

[0815] "Application example 2 when combining with an emotional engine"

[0816] (Claim 1)

[0817] A means for analyzing user-generated lifestyle data and emotional data using a generative model to generate personalized health and emotional information,

[0818] A means for presenting generated health information and emotional information to the user terminal,

[0819] A means of collaborating with external information sources to obtain additional information on nutritional balance and to supplement the generated health and emotional information,

[0820] A system that includes means to analyze the user's emotional state and provide advice that takes psychological health into consideration.

[0821] (Claim 2)

[0822] The system according to claim 1, which collects user feedback and improves the accuracy of health and emotional information in subsequent use.

[0823] (Claim 3)

[0824] The system according to claim 1, which enables efficient data transmission between a terminal and a server in order to process real-time lifestyle data and emotional data. [Explanation of Symbols]

[0825] 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 means of generating personalized health information by analyzing lifestyle record data entered by users using a generative model, A means for displaying the generated health information on a communication device, A means of collaborating with external sources to obtain additional information on nutritional balance and supplement the generated health information, A system including an automated machine for home use that receives lifestyle data via voice input or a haptic display and presents generated health information.

2. The system according to claim 1, which collects user feedback and improves the accuracy of health information for future use.

3. The system according to claim 1, which enables efficient information transmission between a communication device and a server in order to process real-time lifestyle data.

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