system

Abstract

We provide the system. [Solution] A means by which the user inputs their basic information and daily diet and exercise data via a terminal, A server stores data received from users in a database and provides a means to retrieve past data. A means by which a server sends user data to a generative AI model to generate individually optimized advice and motivational messages, The server sends generated advice and motivational messages to the user, and the terminal has a means to display them. A system that includes this.

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, 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 that responds 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, the health management and motivation maintenance of employees are important issues for improving corporate productivity and reducing the turnover rate. However, providing personal training by experts for each employee is costly in terms of time and economy and is not practical. In addition, due to the lack of individual training plans, employees have difficulty in self-management methods and find it difficult to maintain exercise habits and healthy eating habits. Against this background, there is a need for a system that can easily provide individual exercise and diet advice and maintain motivation.

Means for Solving the Problems

[0005] The present invention is solved by a system that includes means for a user to input their basic information and daily diet and exercise data via a terminal, means for a server to store the data received from the user in a database and retrieve past data, means for the server to send the user's data to a generative AI model to generate individually optimized advice and motivational messages, and means for the server to send the generated advice and motivational messages to the user and display them on the terminal. This system makes it easier for employees to maintain healthy lifestyle habits because they can easily receive professional and individually customized advice. Furthermore, motivational messages are provided daily to maintain motivation, enabling continuous health management, which in turn leads to improved employee well-being, increased productivity, and reduced employee turnover.

[0006] A "user" is a person who uses the system to input their basic information and daily diet and exercise data through their device.

[0007] A "terminal" is a device used by users to input information and display received advice and motivational messages.

[0008] A "server" is a computer system that stores data received from users in a database, sends that data to a generative AI model to generate advice and motivational messages, and then sends those messages to the terminal.

[0009] A "database" is a data storage system for saving basic user information, past diet and exercise records, and generated advice and motivational messages.

[0010] A "generative AI model" is an artificial intelligence algorithm that analyzes user input data and generates personalized advice and motivational messages to maintain motivation.

[0011] "Advice" refers to suggestions regarding exercise and diet optimized for the user to achieve their goals.

[0012] An "inspirational message" is a message that conveys words of encouragement and support to help users maintain their motivation and continue healthy lifestyle habits. [Brief explanation of the drawing]

[0013] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13]It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when the 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 the emotion engine is combined.

Mode for Carrying Out the Invention

[0014] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be one type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), etc.

[0017] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disk (e.g., hard disk), or magnetic tape, etc.

[0019] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0020] 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."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] 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.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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".

[0034] The system of this invention begins with the user inputting their basic information and daily diet and exercise data through a terminal. The server receives this data and stores it in a database. It also simultaneously acquires the user's past data and sends it to a generative AI model. Based on the input data, this AI model generates individually optimized exercise and diet advice, as well as motivational messages to enhance the user's motivation. The generated content is then sent back to the terminal via the server and displayed on the user's terminal.

[0035] An example of the detailed processing of this system is shown below.

[0036] Program overview and specific examples

[0037] Entering and saving user information

[0038] When a user uses the system for the first time, they enter their basic information (e.g., age, gender, weight, height, exercise experience, health status, goals, etc.) through the terminal's UI. They also enter their daily meals (e.g., what they ate for breakfast) and exercise records (e.g., 20 minutes of jogging).

[0039] The device collects this data and sends it to the server.

[0040] The server receives user input data and stores it in the database. The stored data is managed along with past records.

[0041] Generating advice and motivational messages

[0042] The server retrieves historical data based on user requests and sends it to a generative AI model. This data includes the user's basic information, past dietary habits, exercise history, and weight fluctuations.

[0043] Generative AI models analyze input data and generate personalized exercise advice and dietary suggestions. For example, one user might receive specific advice such as, "Today, I recommend 30 minutes of walking and 20 minutes of strength training. Natto and eggs are good sources of protein, so please continue with them."

[0044] Furthermore, the generative AI model also generates encouraging messages to maintain user motivation. For example, these might include messages like, "Great start! If you keep going, you'll definitely reach your goal."

[0045] Notifications of advice and motivational messages

[0046] The server processes the advice and motivational messages received from the generative AI model and sends them to the user's terminal.

[0047] The device displays received advice and messages on its screen. This allows users to receive specific guidance regarding their daily activities and diet. Furthermore, encouraging messages help maintain motivation.

[0048] Specific example

[0049] Let's consider the case where a user (Mr. Tanaka) uses the system.

[0050] Ms. Tanaka enters the details of her breakfast (rice, natto, and egg) and her exercise record for today (20 minutes of jogging) into the terminal. Next, she presses the button to request exercise advice.

[0051] The terminal collects input data and sends it to the server.

[0052] The server saves the received data to a database and retrieves Tanaka's past data (last week's exercise records and weight fluctuations) and sends it to a generative AI model.

[0053] The generative AI model analyzes Tanaka's data and generates advice such as, "Today, I recommend 30 minutes of walking and 20 minutes of strength training. Natto and eggs are good sources of protein, so please continue with this," and an encouraging message such as, "That's a great start! If you keep it up, you will definitely reach your goal."

[0054] The server receives the generated advice and motivational messages and sends them to the terminal.

[0055] The device displays this information on its screen, allowing Ms. Tanaka to see it, obtain specific guidance regarding her exercise and diet for the day, and maintain her motivation through encouraging messages.

[0056] In this way, the system provides personalized health management advice and motivational support to each user, ultimately contributing to improved well-being and a better work environment.

[0057] The following describes the processing flow.

[0058] Step 1:

[0059] The user accesses the device's UI and enters their basic information. This basic information includes age, gender, weight, height, exercise experience, health status, and goals.

[0060] Step 2:

[0061] The terminal sends the entered basic information to the server. Specifically, it uses an HTTP request to pass the user's information to the server.

[0062] Step 3:

[0063] The server stores the received user's basic information in a database. This ensures that the user's basic information is managed securely.

[0064] Step 4:

[0065] To record their daily activities, users input details about their meals and exercise through the device's UI. For example, they might input data about eating rice, natto, and eggs for breakfast, or going for a 20-minute jog.

[0066] Step 5:

[0067] The device collects daily activity data and sends it to the server. This involves using HTTP requests again to pass the data to the server.

[0068] Step 6:

[0069] The server receives daily activity data and stores it in a database. This stored data is later referenced and used to generate advice.

[0070] Step 7:

[0071] The server retrieves the user's basic information and past exercise and dietary data from the database. This prepares the data for transmission to the generative AI model.

[0072] Step 8:

[0073] The server sends the acquired data to the generative AI model. Specifically, it sends a request containing the user's data to the AI ​​model's API endpoint.

[0074] Step 9:

[0075] A generative AI model analyzes the received data and generates personalized exercise and dietary advice. For example, it might generate advice such as, "Today, I recommend 30 minutes of walking and 20 minutes of strength training. Natto and eggs are good sources of protein, so please continue with them."

[0076] Step 10:

[0077] Generative AI models generate encouraging messages to maintain user motivation. For example, they might generate a message like, "Great start! If you keep going, you'll definitely reach your goal."

[0078] Step 11:

[0079] The server receives advice and motivational messages from the generative AI model. This includes procedures for processing the API responses from the generative AI model.

[0080] Step 12:

[0081] The server sends received advice and motivational messages to the terminal. This includes identifying the user ID and sending the data to the corresponding terminal.

[0082] Step 13:

[0083] The device displays received advice and motivational messages on the user's screen. This allows users to receive specific guidance on exercise and diet, as well as boost their motivation.

[0084] This series of steps allows users to receive personalized exercise and dietary advice, effectively manage their daily activities, and maintain motivation.

[0085] (Example 1)

[0086] 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."

[0087] Traditional systems have complex data entry and management processes for user health management, making it difficult to provide individually optimized advice. Furthermore, they lack effective feedback to maintain user motivation. As a result, there are challenges in smoothly improving users' health and achieving their goals.

[0088] 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.

[0089] In this invention, the server includes means for the user to input basic information and daily diet and exercise data, means for the terminal to transmit data collected by the terminal to the server, and means for the server to store the received data in a database and retrieve past data. This enables efficient collection and storage of data from the user, and makes it possible to provide individually optimized exercise and diet advice and motivational messages using a generative AI model. Users can easily receive this advice and messages through their terminal, resulting in more efficient health management and improved motivation.

[0090] A "user" refers to someone who uses the system to input basic information, daily diet, and exercise data, and receives advice.

[0091] A "terminal" refers to a device that allows users to input data, communicate with a server, and receive and display advice and motivational messages.

[0092] A "server" refers to a device that receives data sent by users, stores it in a database, and then sends that data to a generative AI model.

[0093] A "database" refers to information storage used by a server to store and manage data received from users, as well as historical data.

[0094] A "generative AI model" refers to an artificial intelligence program that generates personalized health advice and motivational messages based on user input data.

[0095] "Advice" refers to recommendations regarding exercise and diet that are generated by generative AI models to help users manage their health.

[0096] An "inspirational message" refers to a message generated by a generative AI model that aims to boost user motivation and encourage continuous health improvement.

[0097] Modes for carrying out the invention

[0098] The system of this invention allows users to input their basic information and daily diet and exercise data in order to manage their health, and based on that, it provides personalized advice and motivational messages to enhance their motivation.

[0099] Hardware and software configuration

[0100] The device provides an interface for the user to input information. This includes smartphones, tablets, and personal computers. The entered data is transmitted to a server via the internet.

[0101] The server is equipped with a high-performance processor and sufficient storage capacity to receive data sent by users. The server also uses a database management system (DBMS) to store and manage the received data and historical data.

[0102] The generative AI model is built using Python's TENSORFLOW® and PyTorch libraries and runs on a server. The AI ​​model analyzes user input data and generates individually customized advice and motivational messages.

[0103] Details of data processing and calculations

[0104] The terminal uses standard data formats such as JSON and XML to send user input data to the server in real time. To ensure security, data transmission uses the HTTPS protocol.

[0105] The server temporarily stores received data in cache memory and performs error checking. Once consistency is confirmed, it is saved to the database. The saved data includes basic information, daily meal details, exercise records, and weight fluctuations.

[0106] The generative AI model receives a comprehensive dataset, including historically stored data, each time a request is made, and performs analysis. The model learns each user's unique health status, dietary history, and exercise habits, and then generates individually optimized advice.

[0107] Examples of specific prompt statements include the following:

[0108] "Please enter your basic information. Example: Age, Gender, Weight, Height."

[0109] "Please enter what you ate today. Example: What I ate for breakfast."

[0110] "Please enter your exercise plan for today. Example: 20 minutes of jogging."

[0111] Specific example

[0112] Consider a scenario where a user is using the system for the first time. The user enters basic information such as age (30 years old), gender (male), and weight (70 kg) through the device's UI. They also enter daily details of their meals (e.g., rice, natto, and eggs for breakfast) and exercise records (e.g., 20 minutes of jogging).

[0113] The device sends this data to the server in real time. The server stores the received data in a database, and when the user requests exercise advice, it sends it, along with past data, to a generative AI model.

[0114] The generative AI model analyzes user data and generates specific advice such as, "Today, I recommend a 30-minute walk and 20 minutes of strength training. Natto and eggs are good sources of protein, so please continue with this." It also provides encouraging messages like, "Great start! If you keep going, you will definitely reach your goal."

[0115] The server sends generated advice and motivational messages to the device, which displays them on the screen. Users can review these and use them to help manage their daily health.

[0116] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0117] Step 1:

[0118] The user accesses a UI form on their device and enters basic information. This basic information includes age, gender, weight, height, exercise experience, health status, and goals. They also enter daily meal details and exercise records. The input data is in text and numerical format, and the device packages this data into JSON format. Input data: basic information, meal details, exercise records. Output data: JSON format data.

[0119] Step 2:

[0120] The terminal sends user-entered data to the server over the network. HTTPS is used as the transmission protocol to ensure data security. Input data: User's data in JSON format. Output data: Transmission request to the server.

[0121] Step 3:

[0122] The server temporarily stores the JSON data received from the terminal in cache memory and performs an integrity check. After using an error checking program to confirm that the data is accurate, it is saved to the database. Input data: JSON data received from the terminal. Output data: Data whose integrity has been checked.

[0123] Step 4:

[0124] The server uses a database management system (DBMS) to permanently store data that has passed integrity checks in the database. The stored data is timestamped and tagged to facilitate management. Input data: Data whose integrity has been verified. Output data: Data stored in the database.

[0125] Step 5:

[0126] The user sends a request for exercise advice to the server via their device. This request is initiated by the user clicking a button on the interface. Input data: Exercise advice request. Output data: Request to the server.

[0127] Step 6:

[0128] After receiving a request from a user, the server retrieves the user's past data from the database. This data includes basic information, dietary details, exercise records, weight fluctuations, etc. Input data: Exercise advice request. Output data: Set of past data.

[0129] Step 7:

[0130] The server sends the acquired historical data to a generative AI model. The AI ​​model uses Python's TensorFlow or PyTorch libraries, and the model, built using these libraries, performs the data analysis. Input data: User's historical data. Output data: Data sent to the AI ​​model.

[0131] Step 8:

[0132] Generative AI models analyze submitted data to generate personalized advice and motivational messages. The AI ​​model analyzes input data and generates specific advice and messages tailored to each user's health condition. Input data: User's historical data. Output data: Individually optimized advice and motivational messages.

[0133] Step 9:

[0134] The server re-checks the integrity of the advice and motivational messages received from the generative AI model and saves them to the database. It then sends them to the user's terminal. Input data: Advice and motivational messages from the generative AI model. Output data: Data sent to the user's terminal.

[0135] Step 10:

[0136] The device displays advice and motivational messages received from the server on its screen. Users review this information to help manage their daily health. The motivational messages also help maintain their motivation. Input data: Advice and motivational messages from the server. Output data: Advice and messages displayed on the screen.

[0137] (Application Example 1)

[0138] 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."

[0139] Conventional fitness gym training support systems have difficulty providing optimal exercise menus and dietary advice tailored to each user's individual condition and goals, and thus have difficulty maintaining users' sustained motivation. As a result, users may not be able to perform effective training and may have difficulty achieving their goals. This invention aims to solve these problems.

[0140] 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.

[0141] In this invention, the server includes means for the user to input their basic information and daily diet and exercise data through a terminal; means for the server to store the data received from the user in a database and retrieve past data; means for the server to transmit the user's data to a generative AI model to generate individually optimized exercise menus and dietary advice, as well as motivational messages; and means for the server to transmit the generated exercise menus and dietary advice, as well as motivational messages, to the user, which the terminal displays. As a result, the user can receive individually optimized exercise and dietary advice, maintain motivation through motivational messages, and continue effective training.

[0142] A "terminal" is a device that a user uses to input their basic information and daily diet and exercise data.

[0143] A "server" is a central processing unit that stores data received from users in a database, retrieves past data, and sends it to generative AI models.

[0144] A "database" is an information storage system that stores and manages a user's basic information, daily diet and exercise data, and historical data.

[0145] A "generative AI model" is an artificial intelligence program that analyzes user data and generates personalized exercise menus, dietary advice, and motivational messages.

[0146] A "personally optimized exercise program" is an optimal exercise plan tailored to each individual user, based on their weight, goals, past exercise records, and dietary habits.

[0147] "Dietary advice" refers to specific meal suggestions recommended to the user based on their basic information and daily eating data.

[0148] An "inspirational message" is a message of encouragement or support generated to maintain and enhance the user's motivation.

[0149] The system of this invention begins with the user inputting their basic information and daily diet and exercise data via a terminal. Specifically, the user inputs their information using a smartphone app, and this input data is sent to a server. The server stores the received data in a database. It also retrieves past data and sends it to a generative AI model. Based on the input data, the generative AI model generates personalized exercise menus, dietary advice, and motivational messages for each user.

[0150] The hardware used consists of smartphones (user terminals) and servers, and the software is primarily developed using Python. Flask (a web framework) is used on the server side, and MongoDB or PostgreSQL are used for the database. The generative AI models include AI algorithms specialized in natural language processing and data analysis.

[0151] The server stores the user's input data in a database. The stored data is managed along with past records and sent to a generative AI model. The generative AI model analyzes the input data and generates personalized exercise menus and dietary advice. For example, some users might receive specific advice such as, "Today, I recommend 30 minutes of walking and 20 minutes of strength training. Natto and eggs are good sources of protein, so please continue with that." The generative AI model also generates motivational messages to help users stay motivated. For example, it might include messages like, "Great start! If you keep going, you'll definitely reach your goal."

[0152] Examples of prompts to send to a generative AI model are as follows:

[0153] "Based on the user's basic information and daily diet and exercise data, generate personalized exercise plans, dietary advice, and motivational messages."

[0154] The server sends the generated exercise menu, dietary advice, and motivational messages to the user's device. The user's device receives this information and displays it on the screen. This allows the user to receive specific guidance on their daily activities and diet. The motivational messages also help maintain their motivation.

[0155] As a concrete example, if a user enters the details of their breakfast (rice, natto, egg) and their exercise record for the day (20 minutes of jogging) into their device, that data is sent to the server. The server stores the data in a database and sends it, along with past data, to a generative AI model. The generative AI model analyzes this data and generates personalized advice and motivational messages for the user. The server sends the generated results to the device, which then displays them to the user. For example, the advice might say, "I recommend 30 minutes of walking and 20 minutes of strength training today. Natto and eggs are good sources of protein, so please continue with that," and the motivational message might say, "Great start! If you keep it up, you'll definitely reach your goal."

[0156] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0157] Step 1:

[0158] Users input their basic information (age, gender, weight, height, exercise experience, health status, goals) and daily food and exercise data via their device. This input data includes details such as what they ate for breakfast and the exercise they did that day. The system obtains basic user information and daily activity data as input.

[0159] Step 2:

[0160] The terminal collects data entered by the user and sends it to the server. The data transmission process involves converting the input data into an appropriate format, such as JSON, and sending it to the server using an HTTP POST request. Basic user information and daily activity data are used as input, and the data sent to the server is generated as output.

[0161] Step 3:

[0162] The server saves the received data to the database. During the saving process, the received data is converted into the required format and stored in the database along with existing user data. The input is data received from the terminal, and the output is data recorded in the database.

[0163] Step 4:

[0164] The server retrieves historical data from the database. This includes the user's exercise and dietary data from the past few weeks or months. The retrieved data is used as input data for a generative AI model. The input is the query results from the database, and the output is historical user data.

[0165] Step 5:

[0166] The server sends user data to a generative AI model. This data includes the user's basic information, current diet and exercise data, and historical data. The generative AI model analyzes this input data to generate personalized exercise and dietary advice, as well as motivational messages. The input is the user's complete dataset, and the output is the generated exercise menu, advice, and motivational messages.

[0167] Step 6:

[0168] Generative AI models analyze received data and generate individually optimized advice and motivational messages. During the analysis process, natural language processing and data analysis algorithms are used to generate user-specific feedback. User data is the input, and the generated content is the output.

[0169] Step 7:

[0170] The server sends exercise menus, dietary advice, and motivational messages received from the generative AI model to the user's terminal. The transmission process involves converting the generated data into an appropriate format and sending it to the user's terminal using an HTTP POST request. The input consists of the generated exercise menus, advice, and motivational messages, while the output is the data sent to the user's terminal.

[0171] Step 8:

[0172] The terminal displays exercise menus, dietary advice, and motivational messages received from the server. The display process includes visualizing the received data in an easy-to-understand format for the user and displaying it on the screen. The input is data received from the server, and the output is the content displayed to the user.

[0173] Through these steps, users can receive personalized exercise and dietary advice, maintain motivation through encouraging messages, and continue effective training.

[0174] 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.

[0175] The present invention combines an emotion engine with means for the user to input their basic information and daily diet and exercise data through a terminal, means for a server to store the data received from the user in a database and retrieve past data, means for the server to send the user's data to a generative AI model to generate individually optimized advice and motivational messages, and means for the server to send the generated advice and motivational messages to the user and display them on the terminal. The emotion engine has the function of recognizing emotions from the user's input data and sending it to the generative AI model to adjust the advice and motivational messages based on those emotions.

[0176] Program overview and specific examples

[0177] Entering and saving user information

[0178] When a user uses the system for the first time, they will enter their basic information (age, gender, weight, height, exercise experience, health status, goals, etc.) through the terminal's UI. They will also enter their daily meal details and exercise records.

[0179] The device collects this data and sends it to the server.

[0180] The server stores basic information and daily activity data received from users in a database. This ensures that users' basic information and historical data are securely stored.

[0181] Emotion recognition and data analysis

[0182] The emotion engine on the server recognizes emotions from the user's input data. For example, emotion recognition is performed through text analysis. If the user inputs facial expressions or voice data, that data is also analyzed.

[0183] The emotion engine recognizes the emotion and sends it to the generative AI model. This enables the generation of advice and motivational messages that reflect the user's emotional state.

[0184] Generating advice and motivational messages

[0185] The server retrieves the user's basic information and past exercise and diet data from the database and sends this information to a generative AI model. The results of the emotion engine are also included.

[0186] A generative AI model analyzes the data and generates personalized exercise and dietary advice for the user. Specific advice such as, "Today, I recommend 30 minutes of walking and 20 minutes of strength training. Natto and eggs are good sources of protein, so please continue with them," is provided.

[0187] Furthermore, generative AI models can also generate motivational messages based on emotions. For example, if a user is feeling a little down, a message like, "Today might be a tough day, but let's keep going!" will be generated.

[0188] Notifications of advice and motivational messages

[0189] The server receives advice and motivational messages from the generative AI model and sends them to the user's device.

[0190] The device displays received advice and motivational messages on the user's screen. This allows the user to receive appropriate guidance and encouragement.

[0191] Specific example

[0192] The following is a specific example from the case of user Tanaka.

[0193] Ms. Tanaka entered her breakfast details (rice, natto, and egg) and exercise record (20 minutes of jogging) into the terminal. In addition, Ms. Tanaka entered her facial expression data via the camera.

[0194] The terminal sends input data and facial expression data to the server.

[0195] The server stores the received data in a database and then uses an emotion engine to recognize Tanaka's emotions. This emotion result, along with the data, is then sent to a generative AI model.

[0196] The generative AI model analyzes Tanaka's data and generates advice and motivational messages based on Tanaka's emotions. For example, it generates specific advice such as, "Today I recommend 30 minutes of walking and 20 minutes of strength training. Natto and eggs are good sources of protein, so please continue with that," and a message like, "You seem a little tired. But if you keep going, you'll definitely see results. Keep it up!"

[0197] The server sends the generated advice and motivational messages to the terminal.

[0198] The device displays this information on its screen, allowing Ms. Tanaka to see it, obtain specific guidance regarding her exercise and diet for the day, and maintain her motivation through emotionally sensitive and encouraging messages.

[0199] In this way, by combining emotional engines, it is possible to create a system that provides more personalized advice that responds to the user's emotional state. This allows users to manage their health more effectively and maintain their motivation.

[0200] The following describes the processing flow.

[0201] Step 1:

[0202] The user accesses the device's UI and enters their basic information. This basic information includes age, gender, weight, height, exercise experience, health status, and goals.

[0203] Step 2:

[0204] The terminal sends the entered basic information to the server. Specifically, it uses an HTTP request to pass the user's information to the server.

[0205] Step 3:

[0206] The server stores the received user's basic information in a database. This ensures that the user's basic information is managed securely.

[0207] Step 4:

[0208] To record their daily activities, users input details about their meals and exercise through the device's UI. For example, they might input data about eating rice, natto, and eggs for breakfast, or going for a 20-minute jog.

[0209] Step 5:

[0210] The user inputs facial expression data via the camera using their device. Additionally, if audio data is also to be collected, a microphone is used.

[0211] Step 6:

[0212] The device collects daily activity data, facial expression data, and voice data, and sends them to the server. This involves using HTTP requests again to pass the data to the server.

[0213] Step 7:

[0214] The server receives daily activity data, facial expression data, and voice data, and stores them in a database. The stored data is later used for emotion recognition and advice generation.

[0215] Step 8:

[0216] The server uses an emotion engine to recognize emotions from user input data, facial expression data, and voice data. For example, it performs text analysis, facial expression analysis, and voice analysis to estimate the user's emotional state.

[0217] Step 9:

[0218] The emotion engine recognizes emotions, which are then compiled on a server and sent to a generative AI model. This data also includes the user's emotional state.

[0219] Step 10:

[0220] The server retrieves the user's basic information and past exercise and diet data from the database and sends this information, along with sentiment results, to a generative AI model. This involves sending requests to the AI ​​model's API endpoint.

[0221] Step 11:

[0222] The generative AI model analyzes the received data and generates personalized exercise and dietary advice. For example, it might generate specific advice such as, "Today, I recommend 30 minutes of walking and 20 minutes of strength training. Natto and eggs are good sources of protein, so please continue with them."

[0223] Step 12:

[0224] The generative AI model also generates encouraging messages based on the user's emotional state. For example, if the user is feeling a little down, it will generate a message such as, "Today may be a tough day, but let's keep going!"

[0225] Step 13:

[0226] The server processes advice and motivational messages received from the generative AI model and sends them to the user's terminal. This includes identifying the user's ID and sending the corresponding data.

[0227] Step 14:

[0228] The device displays received advice and motivational messages on the user's screen. This allows the user to receive specific guidance and encouragement.

[0229] This series of steps allows users to receive personalized exercise and dietary advice, effectively manage their daily activities, and maintain motivation. Furthermore, the emotional engine provides individualized feedback that takes the user's emotions into consideration.

[0230] (Example 2)

[0231] 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".

[0232] Traditional health management systems have struggled to provide advice and motivational messages that are tailored to users' emotions and individual circumstances. This has made it difficult to maintain user motivation and to implement personalized health management.

[0233] 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.

[0234] In this invention, the server includes means for the user to input their basic information and daily diet and exercise data through a terminal; means for the server to store the data received from the user in a database and retrieve past data; means for the server to send the user's data to a generating AI model to generate individually optimized advice and motivational messages; means for the server to send the generated advice and motivational messages to the user and for the terminal to display them; and means for an emotion engine to recognize emotions from the user's input data and send them to the generating AI model to adjust the advice and motivational messages based on the user's emotions. This makes it possible to provide advice and motivational messages that are tailored to the user's emotions and individual circumstances.

[0235] A "terminal" is a device used by users to input basic information and daily diet and exercise data, and to communicate with the server.

[0236] A "server" is a central device that stores data received from users and works in conjunction with generative AI models and emotion engines to generate and send advice and motivational messages.

[0237] A "database" is a storage device that securely stores a user's basic information and daily activity data, allowing it to be retrieved and analyzed later.

[0238] A "generative AI model" is an algorithm that generates individually optimized advice and motivational messages based on the user's basic information, past data, and emotional results from an emotion engine.

[0239] "Advice" refers to specific action guidelines provided by the generative AI model for the user's health management.

[0240] An "encouraging message" is a message of encouragement or support provided by a generative AI model based on the user's emotional state.

[0241] An "emotion engine" is an analysis device or software that recognizes emotions from data input by the user and sends the results to a generating AI model.

[0242] A "user" is an individual who uses the system to input their basic information and daily activity data, and receives generated advice and motivational messages.

[0243] The present invention provides a system comprising: means for the user to input their basic information and daily diet and exercise data through a terminal; means for a server to store the data received from the user in a database and retrieve past data; means for the server to send the user's data to a generating AI model to generate individually optimized advice and motivational messages; and means for the server to send the generated advice and motivational messages to the user, which are then displayed on the terminal. Furthermore, by combining it with an emotion engine, the system has the function of recognizing emotions from the user's input data and sending it to the generating AI model to adjust the advice and motivational messages based on the user's emotions.

[0244] Entering and saving user information

[0245] When a user logs into the system for the first time, they enter their basic information (age, gender, weight, height, exercise experience, health status, goals, etc.) through the terminal's user interface (UI). They also enter their daily diet and exercise records. The terminal collects this data and sends it to the server. The server stores the basic information and daily activity data received from the user in a database. This ensures that the user's basic information and past data are securely stored.

[0246] Emotion recognition and data analysis

[0247] An emotion engine embedded in the server recognizes emotions from user input data. For example, it recognizes emotions through text analysis. If the user inputs facial expressions or voice data, that data is also analyzed. By sending the emotion results recognized by the emotion engine to a generation AI model, it becomes possible to generate advice and encouragement messages that reflect the user's emotional state.

[0248] Generating advice and motivational messages

[0249] The server retrieves the user's basic information and past exercise and diet data from the database and sends it to a generative AI model. This includes the results of the emotion engine. The generative AI model analyzes the data and generates exercise and diet advice optimized for the user. For example, it might provide specific advice such as, "Today, I recommend 30 minutes of walking and 20 minutes of strength training. Natto and eggs are good sources of protein, so please continue with them." The generative AI model also generates motivational messages based on emotions. For example, if the user is feeling a little down, it might generate a message like, "Today might be a tough day, but let's keep going!"

[0250] Notification of advice and motivational messages

[0251] The server sends advice and motivational messages received from the generated AI model to the user's device. The device displays the received advice and motivational messages on the user's screen. This allows the user to receive appropriate guidance and encouragement.

[0252] Specific example

[0253] The following is a specific example for a particular individual user. This individual inputs their breakfast details (rice, natto, egg) and exercise record (20 minutes of jogging) into the device. In addition, this individual inputs facial expression data via the camera. The device sends the input data and facial expression data to the server. The server stores the received data in a database and uses an emotion engine to recognize the individual's emotions. It sends the data, along with the emotion result, to a generating AI model. The generating AI model analyzes the individual's data and generates advice and motivational messages based on the individual's emotions. For example, it might generate specific advice such as, "I recommend 30 minutes of walking and 20 minutes of strength training today. Natto and eggs are good sources of protein, so please continue," and a message such as, "You seem a little tired. But if you keep going, you will surely see results. Keep it up!" The server sends the generated advice and motivational message to the device. The device displays this on the screen, and the individual can see it and receive specific guidance regarding today's exercise and diet, as well as maintain motivation with an emotionally sensitive motivational message.

[0254] Example of a prompt

[0255] 1. "The user entered their breakfast details (rice, natto, egg) and exercise record (20 minutes of jogging), and collected facial expression data using a camera. Based on this data, please generate optimal advice and motivational messages."

[0256] 2. "Please advise on what kind of encouraging message to send to a user who is feeling down."

[0257] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0258] Step 1:

[0259] Users input basic information (age, gender, weight, height, exercise experience, health status, goals, etc.) through their device, and also input their daily meal details and exercise records. This collects basic information and daily data from the user on the device. The device then transmits the entered basic information and meal / exercise data to the server.

[0260] Step 2:

[0261] The server saves basic information and daily meal and exercise data received from the terminal to a database. This allows the user's past data to be accumulated and securely stored on the server. Within the server, the operation of writing received data to the database is performed.

[0262] Step 3:

[0263] The emotion engine embedded in the server analyzes the input data sent by the user. Specifically, it may recognize emotions from the words entered by the user through text analysis, or, if facial expression data or voice data is input, it may analyze these to identify emotions. Through this analysis process, the emotion engine extracts the user's emotional state as data. The output of the emotion engine may include emotional states such as "joy," "sadness," or "fatigue."

[0264] Step 4:

[0265] The server compiles the user's basic information, past exercise and diet data, along with the emotion engine's output, and sends it to the generative AI model. The server combines all the necessary data into a single request and sends it to the generative AI model. Through this communication, the generative AI model receives the user's overall data and prepares it for analysis.

[0266] Step 5:

[0267] The generation AI model analyzes the submitted data and generates personalized advice and motivational messages based on the user's basic information, past data, and emotional state. Examples of advice include, "Today, I recommend a 30-minute walk and 20 minutes of strength training. Natto and eggs are good sources of protein, so please continue with them." Examples of emotionally-based motivational messages include, "You seem a little tired. But if you keep going, you'll definitely see results. Keep it up!"

[0268] Step 6:

[0269] The server sends advice and motivational messages received from the generated AI model to the user's device. The server receives the output from the generated AI model and resends it to the appropriate device for each user. This process prepares the user's device to receive the messages.

[0270] Step 7:

[0271] The device displays received advice and motivational messages on the user's screen. Users can view these messages to gain specific guidance on exercise and diet for the day, and maintain motivation through emotionally resonant motivational messages. For example, the user might review the message displayed on the device screen and act accordingly.

[0272] (Application Example 2)

[0273] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0274] Traditionally, systems have existed that allow users to input daily diet and exercise data to manage their health and receive personalized advice. However, these systems failed to consider the user's emotional state, resulting in a lack of effective encouragement and motivation. Furthermore, the absence of electronic payment functionality linked to health management made it difficult for users to easily purchase necessary goods and services. The objective of this invention is to solve these problems.

[0275] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for the user to input their basic information and daily diet and exercise data through a terminal, means for the server to store the data received from the user in a database and retrieve past data, means for the server to transmit the user's data to a generative AI model to generate individually optimized advice and motivational messages, means for the emotion engine to analyze the emotional data input by the user, transmit the analysis results to the generative AI model to generate individually optimized emotion-based advice and motivational messages, means for notifying the generated advice and motivational messages on the terminal, and means for purchasing health-related goods and services through an electronic payment function. As a result, individually optimized advice and motivational messages that reflect the user's emotional state are generated, making it possible to increase the user's motivation. In addition, the purchase of health-related goods and services can be made seamlessly through the electronic payment function, further promoting the user's health management.

[0276] A "user" is the individual who uses the system and inputs their own health management data.

[0277] A "terminal" is an electronic device used by users to input basic information and daily diet and exercise data.

[0278] "Basic information" refers to data such as the user's age, gender, weight, height, exercise experience, health status, goals, etc.

[0279] "Server" refers to a central computer system that stores and analyzes data received from users and transmits it to the generative AI model.

[0280] "Database" refers to an information management system for storing data received from users, generated advice, and motivational messages.

[0281] "Generative AI model" refers to an artificial intelligence model that analyzes user data and generates individually optimized advice and motivational messages.

[0282] "Advice" refers to specific instructions and recommendations regarding exercise and diet provided to the user by the generative AI model.

[0283] "Motivational message" refers to a message provided by the generative AI model to encourage the user, taking into account the user's emotional state.

[0284] "Emotion engine" refers to an engine that analyzes emotions from the data input by the user and transmits the results to the generative AI model.

[0285] "Emotion data" refers to information regarding emotions read from the user's input data.

[0286] "Electronic payment function" refers to an online payment function for the user to seamlessly purchase health - necessary goods and services. <00​​​​​​​​

[0289] "Items" refer to specific items such as food and fitness equipment that users need to manage their health.

[0290] "Services" refer to activities such as training guidance and nutritional counseling that users utilize to support their health management.

[0291] This invention is a system for users to effectively manage their health. The details of the system will be described based on the following procedure.

[0292] System Configuration

[0293] Hardware:

[0294] 1. User terminal: A device such as a smartphone or tablet that allows the user to input data and has a camera function.

[0295] 2. Server: A computer system that performs data processing and storage.

[0296] software:

[0297] 1. Database: A system that stores the user's basic information and daily diet and exercise data.

[0298] 2. Generative AI Models: Artificial intelligence models that analyze user data and generate individually optimized advice and motivational messages.

[0299] 3. Emotion Engine: An engine that analyzes emotions from user input data and sends the results to a generative AI model.

[0300] 4. Electronic payment function: An online payment system for users to purchase health-related goods and services.

[0301] System Operation Overview

[0302] Input and Saving of User Information

[0303] The user inputs their basic information (age, gender, weight, height, exercise experience, health status, goals, etc.) through the UI of the terminal. In addition, the user inputs daily diet content and exercise records, and further inputs facial expression data through the camera. The terminal sends this data to the server, and the server saves the received data in the database.

[0304] Emotion Recognition and Data Analysis

[0305] The emotion engine on the server recognizes emotions from the user's input data and sends the recognition results to the generative AI model. The generative AI model analyzes the user's basic information, past exercise and diet data, and emotional state sent from the server, and generates individually optimized advice and emotion-based encouragement messages.

[0306] Notification of Advice and Encouragement Messages

[0307] The server sends the advice and encouragement messages received from the generative AI model to the user's terminal, and the terminal displays this on the screen. The user can obtain appropriate action guidelines and encouragement based on this.

[0308] Electronic Payment Function

[0309] The user uses the online payment function to purchase appropriate items and services according to the advice. This enables seamless purchase of foods and fitness goods necessary for health.

[0310] Specific Example

[0311] For example, a user might input meal data such as "rice, natto, and eggs" and record that they jogged for 20 minutes. They also input facial expression data via camera. The device sends this data to a server, which uses an emotion engine to recognize the user's emotions. The server then sends this recognition result along with the data to a generative AI model. The generative AI model performs analysis and generates specific advice such as, "Today, I recommend 30 minutes of walking and 20 minutes of strength training. Natto and eggs are good sources of protein, so please continue this," as well as an emotion-based motivational message such as, "You seem a little tired. But if you keep going, you'll definitely see results. Keep it up!" The server sends these to the device, and the user checks them on the screen.

[0312] Example of a prompt

[0313] "Please tell us about your exercise and meals today. Also, please point your face towards the camera and input your facial expression."

[0314] The above describes the embodiments for carrying out this invention.

[0315] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0316] Step 1:

[0317] Users input basic information (age, gender, weight, height, exercise experience, health status, goals) and daily meal and exercise data through the device's UI. The device collects this data and sends it to the server. This input data includes text-based meal details, exercise details, and facial expression data to interpret emotions. Examples of data collected by the device include "rice, natto, eggs" and "20 minutes of jogging."

[0318] Step 2:

[0319] The server saves basic information and daily data received from users to a database. The input for this save operation is the user's basic information and daily diet and exercise data, and the output is the saved database record. During saving, the data is linked to a specific user ID in the database.

[0320] Step 3:

[0321] An emotion engine on the server analyzes the user's emotions from their input data. The emotion engine takes text data and facial expression data as input, and outputs the analysis results regarding emotions. For example, it might produce an analysis result such as, "Today's emotional state is somewhat depressed."

[0322] Step 4:

[0323] The server sends the emotion results recognized by the emotion engine to the generative AI model. The input data sent includes the user's basic information, past exercise and diet data, and emotion analysis results, while the output is the data analyzed by the generative AI model.

[0324] Step 5:

[0325] Generative AI models analyze input data and generate user-optimized advice and emotion-based motivational messages. For example, if the input data is "Weight 70kg, weight loss goal, 20 minutes of jogging, emotional state: tired," the output messages would be "I recommend 30 minutes of walking and 20 minutes of strength training today. Natto and eggs are good sources of protein, so please continue," and "You seem a little tired. But if you keep going, you will surely see results. Keep it up!"

[0326] Step 6:

[0327] The server sends advice and motivational messages received from the generative AI model to the user's device. The input data sent is the generated message, and the output is a notification message displayed on the device.

[0328] Step 7:

[0329] The device displays advice and motivational messages received from the server on the user's screen. Specifically, the device's display shows messages such as, "Today, we recommend 30 minutes of walking and 20 minutes of strength training," and "You seem a little tired, but if you keep going, you'll definitely see results. Keep it up!"

[0330] Step 8:

[0331] Users use the electronic payment function on their device to purchase health-related goods and services based on the advice they receive. For example, when purchasing fitness equipment online based on a notification message, the purchase process and payment are seamless.

[0332] The above are the specific processing steps for carrying out this invention.

[0333] 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.

[0334] 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.

[0335] 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.

[0336] [Second Embodiment]

[0337] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0338] 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.

[0339] 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).

[0340] 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.

[0341] 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.

[0342] 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).

[0343] 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.

[0344] 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.

[0345] 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.

[0346] 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.

[0347] 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.

[0348] 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".

[0349] The system of this invention begins with the user inputting their basic information and daily diet and exercise data through a terminal. The server receives this data and stores it in a database. It also simultaneously acquires the user's past data and sends it to a generative AI model. Based on the input data, this AI model generates individually optimized exercise and diet advice, as well as motivational messages to enhance the user's motivation. The generated content is then sent back to the terminal via the server and displayed on the user's terminal.

[0350] An example of the detailed processing of this system is shown below.

[0351] Program overview and specific examples

[0352] Entering and saving user information

[0353] When a user uses the system for the first time, they enter their basic information (e.g., age, gender, weight, height, exercise experience, health status, goals, etc.) through the terminal's UI. They also enter their daily meals (e.g., what they ate for breakfast) and exercise records (e.g., 20 minutes of jogging).

[0354] The device collects this data and sends it to the server.

[0355] The server receives user input data and stores it in the database. The stored data is managed along with past records.

[0356] Generating advice and motivational messages

[0357] The server retrieves historical data based on user requests and sends it to a generative AI model. This data includes the user's basic information, past dietary habits, exercise history, and weight fluctuations.

[0358] Generative AI models analyze input data and generate personalized exercise advice and dietary suggestions. For example, one user might receive specific advice such as, "Today, I recommend 30 minutes of walking and 20 minutes of strength training. Natto and eggs are good sources of protein, so please continue with them."

[0359] Furthermore, the generative AI model also generates encouraging messages to maintain user motivation. For example, these might include messages like, "Great start! If you keep going, you'll definitely reach your goal."

[0360] Notifications of advice and motivational messages

[0361] The server processes the advice and motivational messages received from the generative AI model and sends them to the user's terminal.

[0362] The device displays received advice and messages on its screen. This allows users to receive specific guidance regarding their daily activities and diet. Furthermore, encouraging messages help maintain motivation.

[0363] Specific example

[0364] Let's consider the case where a user (Mr. Tanaka) uses the system.

[0365] Ms. Tanaka enters the details of her breakfast (rice, natto, and egg) and her exercise record for today (20 minutes of jogging) into the terminal. Next, she presses the button to request exercise advice.

[0366] The terminal collects input data and sends it to the server.

[0367] The server saves the received data to a database and retrieves Tanaka's past data (last week's exercise records and weight fluctuations) and sends it to a generative AI model.

[0368] The generative AI model analyzes Tanaka's data and generates advice such as, "Today, I recommend 30 minutes of walking and 20 minutes of strength training. Natto and eggs are good sources of protein, so please continue with this," and an encouraging message such as, "That's a great start! If you keep it up, you will definitely reach your goal."

[0369] The server receives the generated advice and motivational messages and sends them to the terminal.

[0370] The device displays this information on its screen, allowing Ms. Tanaka to see it, obtain specific guidance regarding her exercise and diet for the day, and maintain her motivation through encouraging messages.

[0371] In this way, the system provides personalized health management advice and motivational support to each user, ultimately contributing to improved well-being and a better work environment.

[0372] The following describes the processing flow.

[0373] Step 1:

[0374] The user accesses the device's UI and enters their basic information. This basic information includes age, gender, weight, height, exercise experience, health status, and goals.

[0375] Step 2:

[0376] The terminal sends the entered basic information to the server. Specifically, it uses an HTTP request to pass the user's information to the server.

[0377] Step 3:

[0378] The server stores the received user's basic information in a database. This ensures that the user's basic information is managed securely.

[0379] Step 4:

[0380] To record their daily activities, users input details about their meals and exercise through the device's UI. For example, they might input data about eating rice, natto, and eggs for breakfast, or going for a 20-minute jog.

[0381] Step 5:

[0382] The device collects daily activity data and sends it to the server. This involves using HTTP requests again to pass the data to the server.

[0383] Step 6:

[0384] The server receives daily activity data and stores it in a database. This stored data is later referenced and used to generate advice.

[0385] Step 7:

[0386] The server retrieves the user's basic information and past exercise and dietary data from the database. This prepares the data for transmission to the generative AI model.

[0387] Step 8:

[0388] The server sends the acquired data to the generative AI model. Specifically, it sends a request containing the user's data to the AI ​​model's API endpoint.

[0389] Step 9:

[0390] A generative AI model analyzes the received data and generates personalized exercise and dietary advice. For example, it might generate advice such as, "Today, I recommend 30 minutes of walking and 20 minutes of strength training. Natto and eggs are good sources of protein, so please continue with them."

[0391] Step 10:

[0392] Generative AI models generate encouraging messages to maintain user motivation. For example, they might generate a message like, "Great start! If you keep going, you'll definitely reach your goal."

[0393] Step 11:

[0394] The server receives advice and motivational messages from the generative AI model. This includes procedures for processing the API responses from the generative AI model.

[0395] Step 12:

[0396] The server sends received advice and motivational messages to the terminal. This includes identifying the user ID and sending the data to the corresponding terminal.

[0397] Step 13:

[0398] The device displays received advice and motivational messages on the user's screen. This allows users to receive specific guidance on exercise and diet, as well as boost their motivation.

[0399] This series of steps allows users to receive personalized exercise and dietary advice, effectively manage their daily activities, and maintain motivation.

[0400] (Example 1)

[0401] 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."

[0402] Traditional systems have complex data entry and management processes for user health management, making it difficult to provide individually optimized advice. Furthermore, they lack effective feedback to maintain user motivation. As a result, there are challenges in smoothly improving users' health and achieving their goals.

[0403] 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.

[0404] In this invention, the server includes means for the user to input basic information and daily diet and exercise data, means for the terminal to transmit data collected by the terminal to the server, and means for the server to store the received data in a database and retrieve past data. This enables efficient collection and storage of data from the user, and makes it possible to provide individually optimized exercise and diet advice and motivational messages using a generative AI model. Users can easily receive this advice and messages through their terminal, resulting in more efficient health management and improved motivation.

[0405] A "user" refers to someone who uses the system to input basic information, daily diet, and exercise data, and receives advice.

[0406] A "terminal" refers to a device that allows users to input data, communicate with a server, and receive and display advice and motivational messages.

[0407] A "server" refers to a device that receives data sent by users, stores it in a database, and then sends that data to a generative AI model.

[0408] A "database" refers to information storage used by a server to store and manage data received from users, as well as historical data.

[0409] A "generative AI model" refers to an artificial intelligence program that generates personalized health advice and motivational messages based on user input data.

[0410] "Advice" refers to recommendations regarding exercise and diet that are generated by generative AI models to help users manage their health.

[0411] An "inspirational message" refers to a message generated by a generative AI model that aims to boost user motivation and encourage continuous health improvement.

[0412] Modes for carrying out the invention

[0413] The system of this invention allows users to input their basic information and daily diet and exercise data in order to manage their health, and based on that, it provides personalized advice and motivational messages to enhance their motivation.

[0414] Hardware and software configuration

[0415] The device provides an interface for the user to input information. This includes smartphones, tablets, and personal computers. The entered data is transmitted to a server via the internet.

[0416] The server is equipped with a high-performance processor and sufficient storage capacity to receive data sent by users. The server also uses a database management system (DBMS) to store and manage the received data and historical data.

[0417] The generative AI model is built using Python's TensorFlow and PyTorch libraries and runs on a server. The AI ​​model analyzes user input data and generates individually customized advice and motivational messages.

[0418] Details of data processing and calculations

[0419] The terminal uses standard data formats such as JSON and XML to send user input data to the server in real time. To ensure security, data transmission uses the HTTPS protocol.

[0420] The server temporarily stores received data in cache memory and performs error checking. Once consistency is confirmed, it is saved to the database. The saved data includes basic information, daily meal details, exercise records, and weight fluctuations.

[0421] The generative AI model receives a comprehensive dataset, including historically stored data, each time a request is made, and performs analysis. The model learns each user's unique health status, dietary history, and exercise habits, and then generates individually optimized advice.

[0422] Examples of specific prompt statements include the following:

[0423] "Please enter your basic information. Example: Age, Gender, Weight, Height."

[0424] "Please enter what you ate today. Example: What I ate for breakfast."

[0425] "Please enter your exercise plan for today. Example: 20 minutes of jogging."

[0426] Specific example

[0427] Consider a scenario where a user is using the system for the first time. The user enters basic information such as age (30 years old), gender (male), and weight (70 kg) through the device's UI. They also enter daily details of their meals (e.g., rice, natto, and eggs for breakfast) and exercise records (e.g., 20 minutes of jogging).

[0428] The device sends this data to the server in real time. The server stores the received data in a database, and when the user requests exercise advice, it sends it, along with past data, to a generative AI model.

[0429] The generative AI model analyzes user data and generates specific advice such as, "Today, I recommend a 30-minute walk and 20 minutes of strength training. Natto and eggs are good sources of protein, so please continue with this." It also provides encouraging messages like, "Great start! If you keep going, you will definitely reach your goal."

[0430] The server sends generated advice and motivational messages to the device, which displays them on the screen. Users can review these and use them to help manage their daily health.

[0431] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0432] Step 1:

[0433] The user accesses a UI form on their device and enters basic information. This basic information includes age, gender, weight, height, exercise experience, health status, and goals. They also enter daily meal details and exercise records. The input data is in text and numerical format, and the device packages this data into JSON format. Input data: basic information, meal details, exercise records. Output data: JSON format data.

[0434] Step 2:

[0435] The terminal sends user-entered data to the server over the network. HTTPS is used as the transmission protocol to ensure data security. Input data: User's data in JSON format. Output data: Transmission request to the server.

[0436] Step 3:

[0437] The server temporarily stores the JSON data received from the terminal in cache memory and performs an integrity check. After using an error checking program to confirm that the data is accurate, it is saved to the database. Input data: JSON data received from the terminal. Output data: Data whose integrity has been checked.

[0438] Step 4:

[0439] The server uses a database management system (DBMS) to permanently store data that has passed integrity checks in the database. The stored data is timestamped and tagged to facilitate management. Input data: Data whose integrity has been verified. Output data: Data stored in the database.

[0440] Step 5:

[0441] The user sends a request for exercise advice to the server via their device. This request is initiated by the user clicking a button on the interface. Input data: Exercise advice request. Output data: Request to the server.

[0442] Step 6:

[0443] After receiving a request from a user, the server retrieves the user's past data from the database. This data includes basic information, dietary details, exercise records, weight fluctuations, etc. Input data: Exercise advice request. Output data: Set of past data.

[0444] Step 7:

[0445] The server sends the acquired historical data to a generative AI model. The AI ​​model uses Python's TensorFlow or PyTorch libraries, and the model, built using these libraries, performs the data analysis. Input data: User's historical data. Output data: Data sent to the AI ​​model.

[0446] Step 8:

[0447] Generative AI models analyze submitted data to generate personalized advice and motivational messages. The AI ​​model analyzes input data and generates specific advice and messages tailored to each user's health condition. Input data: User's historical data. Output data: Individually optimized advice and motivational messages.

[0448] Step 9:

[0449] The server re-checks the integrity of the advice and motivational messages received from the generative AI model and saves them to the database. It then sends them to the user's terminal. Input data: Advice and motivational messages from the generative AI model. Output data: Data sent to the user's terminal.

[0450] Step 10:

[0451] The device displays advice and motivational messages received from the server on its screen. Users review this information to help manage their daily health. The motivational messages also help maintain their motivation. Input data: Advice and motivational messages from the server. Output data: Advice and messages displayed on the screen.

[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 smart glasses 214 will be referred to as the "terminal."

[0454] Conventional fitness gym training support systems have difficulty providing optimal exercise menus and dietary advice tailored to each user's individual condition and goals, and thus have difficulty maintaining users' sustained motivation. As a result, users may not be able to perform effective training and may have difficulty achieving their goals. This invention aims to solve these problems.

[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] In this invention, the server includes means for the user to input their basic information and daily diet and exercise data through a terminal; means for the server to store the data received from the user in a database and retrieve past data; means for the server to transmit the user's data to a generative AI model to generate individually optimized exercise menus and dietary advice, as well as motivational messages; and means for the server to transmit the generated exercise menus and dietary advice, as well as motivational messages, to the user, which the terminal displays. As a result, the user can receive individually optimized exercise and dietary advice, maintain motivation through motivational messages, and continue effective training.

[0457] A "terminal" is a device that a user uses to input their basic information and daily diet and exercise data.

[0458] A "server" is a central processing unit that stores data received from users in a database, retrieves past data, and sends it to generative AI models.

[0459] A "database" is an information storage system that stores and manages a user's basic information, daily diet and exercise data, and historical data.

[0460] A "generative AI model" is an artificial intelligence program that analyzes user data and generates personalized exercise menus, dietary advice, and motivational messages.

[0461] A "personally optimized exercise program" is an optimal exercise plan tailored to each individual user, based on their weight, goals, past exercise records, and dietary habits.

[0462] "Dietary advice" refers to specific meal suggestions recommended to the user based on their basic information and daily eating data.

[0463] An "inspirational message" is a message of encouragement or support generated to maintain and enhance the user's motivation.

[0464] The system of this invention begins with the user inputting their basic information and daily diet and exercise data via a terminal. Specifically, the user inputs their information using a smartphone app, and this input data is sent to a server. The server stores the received data in a database. It also retrieves past data and sends it to a generative AI model. Based on the input data, the generative AI model generates personalized exercise menus, dietary advice, and motivational messages for each user.

[0465] The hardware used consists of smartphones (user terminals) and servers, and the software is primarily developed using Python. Flask (a web framework) is used on the server side, and MongoDB or PostgreSQL are used for the database. The generative AI models include AI algorithms specialized in natural language processing and data analysis.

[0466] The server stores the user's input data in a database. The stored data is managed along with past records and sent to a generative AI model. The generative AI model analyzes the input data and generates personalized exercise menus and dietary advice. For example, some users might receive specific advice such as, "Today, I recommend 30 minutes of walking and 20 minutes of strength training. Natto and eggs are good sources of protein, so please continue with that." The generative AI model also generates motivational messages to help users stay motivated. For example, it might include messages like, "Great start! If you keep going, you'll definitely reach your goal."

[0467] Examples of prompts to send to a generative AI model are as follows:

[0468] "Based on the user's basic information and daily diet and exercise data, generate personalized exercise plans, dietary advice, and motivational messages."

[0469] The server sends the generated exercise menu, dietary advice, and motivational messages to the user's device. The user's device receives this information and displays it on the screen. This allows the user to receive specific guidance on their daily activities and diet. The motivational messages also help maintain their motivation.

[0470] As a concrete example, if a user enters the details of their breakfast (rice, natto, egg) and their exercise record for the day (20 minutes of jogging) into their device, that data is sent to the server. The server stores the data in a database and sends it, along with past data, to a generative AI model. The generative AI model analyzes this data and generates personalized advice and motivational messages for the user. The server sends the generated results to the device, which then displays them to the user. For example, the advice might say, "I recommend 30 minutes of walking and 20 minutes of strength training today. Natto and eggs are good sources of protein, so please continue with that," and the motivational message might say, "Great start! If you keep it up, you'll definitely reach your goal."

[0471] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0472] Step 1:

[0473] Users input their basic information (age, gender, weight, height, exercise experience, health status, goals) and daily food and exercise data via their device. This input data includes details such as what they ate for breakfast and the exercise they did that day. The system obtains basic user information and daily activity data as input.

[0474] Step 2:

[0475] The terminal collects data entered by the user and sends it to the server. The data transmission process involves converting the input data into an appropriate format, such as JSON, and sending it to the server using an HTTP POST request. Basic user information and daily activity data are used as input, and the data sent to the server is generated as output.

[0476] Step 3:

[0477] The server saves the received data to the database. During the saving process, the received data is converted into the required format and stored in the database along with existing user data. The input is data received from the terminal, and the output is data recorded in the database.

[0478] Step 4:

[0479] The server retrieves historical data from the database. This includes the user's exercise and dietary data from the past few weeks or months. The retrieved data is used as input data for a generative AI model. The input is the query results from the database, and the output is historical user data.

[0480] Step 5:

[0481] The server sends user data to a generative AI model. This data includes the user's basic information, current diet and exercise data, and historical data. The generative AI model analyzes this input data to generate personalized exercise and dietary advice, as well as motivational messages. The input is the user's complete dataset, and the output is the generated exercise menu, advice, and motivational messages.

[0482] Step 6:

[0483] Generative AI models analyze received data and generate individually optimized advice and motivational messages. During the analysis process, natural language processing and data analysis algorithms are used to generate user-specific feedback. User data is the input, and the generated content is the output.

[0484] Step 7:

[0485] The server sends exercise menus, dietary advice, and motivational messages received from the generative AI model to the user's terminal. The transmission process involves converting the generated data into an appropriate format and sending it to the user's terminal using an HTTP POST request. The input consists of the generated exercise menus, advice, and motivational messages, while the output is the data sent to the user's terminal.

[0486] Step 8:

[0487] The terminal displays exercise menus, dietary advice, and motivational messages received from the server. The display process includes visualizing the received data in an easy-to-understand format for the user and displaying it on the screen. The input is data received from the server, and the output is the content displayed to the user.

[0488] Through these steps, users can receive personalized exercise and dietary advice, maintain motivation through encouraging messages, and continue effective training.

[0489] 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.

[0490] The present invention combines an emotion engine with means for the user to input their basic information and daily diet and exercise data through a terminal, means for a server to store the data received from the user in a database and retrieve past data, means for the server to send the user's data to a generative AI model to generate individually optimized advice and motivational messages, and means for the server to send the generated advice and motivational messages to the user and display them on the terminal. The emotion engine has the function of recognizing emotions from the user's input data and sending it to the generative AI model to adjust the advice and motivational messages based on those emotions.

[0491] Program overview and specific examples

[0492] Entering and saving user information

[0493] When a user uses the system for the first time, they will enter their basic information (age, gender, weight, height, exercise experience, health status, goals, etc.) through the terminal's UI. They will also enter their daily meal details and exercise records.

[0494] The device collects this data and sends it to the server.

[0495] The server stores basic information and daily activity data received from users in a database. This ensures that users' basic information and historical data are securely stored.

[0496] Emotion recognition and data analysis

[0497] The emotion engine on the server recognizes emotions from the user's input data. For example, emotion recognition is performed through text analysis. If the user inputs facial expressions or voice data, that data is also analyzed.

[0498] The emotion engine recognizes the emotion and sends it to the generative AI model. This enables the generation of advice and motivational messages that reflect the user's emotional state.

[0499] Generating advice and motivational messages

[0500] The server retrieves the user's basic information and past exercise and diet data from the database and sends this information to a generative AI model. The results of the emotion engine are also included.

[0501] A generative AI model analyzes the data and generates personalized exercise and dietary advice for the user. Specific advice such as, "Today, I recommend 30 minutes of walking and 20 minutes of strength training. Natto and eggs are good sources of protein, so please continue with them," is provided.

[0502] Furthermore, generative AI models can also generate motivational messages based on emotions. For example, if a user is feeling a little down, a message like, "Today might be a tough day, but let's keep going!" will be generated.

[0503] Notifications of advice and motivational messages

[0504] The server receives advice and motivational messages from the generative AI model and sends them to the user's device.

[0505] The device displays received advice and motivational messages on the user's screen. This allows the user to receive appropriate guidance and encouragement.

[0506] Specific example

[0507] The following is a specific example from the case of user Tanaka.

[0508] Ms. Tanaka entered her breakfast details (rice, natto, and egg) and exercise record (20 minutes of jogging) into the terminal. In addition, Ms. Tanaka entered her facial expression data via the camera.

[0509] The terminal sends input data and facial expression data to the server.

[0510] The server stores the received data in a database and then uses an emotion engine to recognize Tanaka's emotions. This emotion result, along with the data, is then sent to a generative AI model.

[0511] The generative AI model analyzes Tanaka's data and generates advice and motivational messages based on Tanaka's emotions. For example, it generates specific advice such as, "Today I recommend 30 minutes of walking and 20 minutes of strength training. Natto and eggs are good sources of protein, so please continue with that," and a message like, "You seem a little tired. But if you keep going, you'll definitely see results. Keep it up!"

[0512] The server sends the generated advice and motivational messages to the terminal.

[0513] The device displays this information on its screen, allowing Ms. Tanaka to see it, obtain specific guidance regarding her exercise and diet for the day, and maintain her motivation through emotionally sensitive and encouraging messages.

[0514] In this way, by combining emotional engines, it is possible to create a system that provides more personalized advice that responds to the user's emotional state. This allows users to manage their health more effectively and maintain their motivation.

[0515] The following describes the processing flow.

[0516] Step 1:

[0517] The user accesses the device's UI and enters their basic information. This basic information includes age, gender, weight, height, exercise experience, health status, and goals.

[0518] Step 2:

[0519] The terminal sends the entered basic information to the server. Specifically, it uses an HTTP request to pass the user's information to the server.

[0520] Step 3:

[0521] The server stores the received user's basic information in a database. This ensures that the user's basic information is managed securely.

[0522] Step 4:

[0523] To record their daily activities, users input details about their meals and exercise through the device's UI. For example, they might input data about eating rice, natto, and eggs for breakfast, or going for a 20-minute jog.

[0524] Step 5:

[0525] The user inputs facial expression data via the camera using their device. Additionally, if audio data is also to be collected, a microphone is used.

[0526] Step 6:

[0527] The device collects daily activity data, facial expression data, and voice data, and sends them to the server. This involves using HTTP requests again to pass the data to the server.

[0528] Step 7:

[0529] The server receives daily activity data, facial expression data, and voice data, and stores them in a database. The stored data is later used for emotion recognition and advice generation.

[0530] Step 8:

[0531] The server uses an emotion engine to recognize emotions from user input data, facial expression data, and voice data. For example, it performs text analysis, facial expression analysis, and voice analysis to estimate the user's emotional state.

[0532] Step 9:

[0533] The emotion engine recognizes emotions, which are then compiled on a server and sent to a generative AI model. This data also includes the user's emotional state.

[0534] Step 10:

[0535] The server retrieves the user's basic information and past exercise and diet data from the database and sends this information, along with sentiment results, to a generative AI model. This involves sending requests to the AI ​​model's API endpoint.

[0536] Step 11:

[0537] The generative AI model analyzes the received data and generates personalized exercise and dietary advice. For example, it might generate specific advice such as, "Today, I recommend 30 minutes of walking and 20 minutes of strength training. Natto and eggs are good sources of protein, so please continue with them."

[0538] Step 12:

[0539] The generative AI model also generates encouraging messages based on the user's emotional state. For example, if the user is feeling a little down, it will generate a message such as, "Today may be a tough day, but let's keep going!"

[0540] Step 13:

[0541] The server processes advice and motivational messages received from the generative AI model and sends them to the user's terminal. This includes identifying the user's ID and sending the corresponding data.

[0542] Step 14:

[0543] The device displays received advice and motivational messages on the user's screen. This allows the user to receive specific guidance and encouragement.

[0544] This series of steps allows users to receive personalized exercise and dietary advice, effectively manage their daily activities, and maintain motivation. Furthermore, the emotional engine provides individualized feedback that takes the user's emotions into consideration.

[0545] (Example 2)

[0546] 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".

[0547] Traditional health management systems have struggled to provide advice and motivational messages that are tailored to users' emotions and individual circumstances. This has made it difficult to maintain user motivation and to implement personalized health management.

[0548] 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.

[0549] In this invention, the server includes means for the user to input their basic information and daily diet and exercise data through a terminal; means for the server to store the data received from the user in a database and retrieve past data; means for the server to send the user's data to a generating AI model to generate individually optimized advice and motivational messages; means for the server to send the generated advice and motivational messages to the user and for the terminal to display them; and means for an emotion engine to recognize emotions from the user's input data and send them to the generating AI model to adjust the advice and motivational messages based on the user's emotions. This makes it possible to provide advice and motivational messages that are tailored to the user's emotions and individual circumstances.

[0550] A "terminal" is a device used by users to input basic information and daily diet and exercise data, and to communicate with the server.

[0551] A "server" is a central device that stores data received from users and works in conjunction with generative AI models and emotion engines to generate and send advice and motivational messages.

[0552] A "database" is a storage device that securely stores a user's basic information and daily activity data, allowing it to be retrieved and analyzed later.

[0553] A "generative AI model" is an algorithm that generates individually optimized advice and motivational messages based on the user's basic information, past data, and emotional results from an emotion engine.

[0554] "Advice" refers to specific action guidelines provided by the generative AI model for the user's health management.

[0555] An "encouraging message" is a message of encouragement or support provided by a generative AI model based on the user's emotional state.

[0556] An "emotion engine" is an analysis device or software that recognizes emotions from data input by the user and sends the results to a generating AI model.

[0557] A "user" is an individual who uses the system to input their basic information and daily activity data, and receives generated advice and motivational messages.

[0558] The present invention provides a system comprising: means for the user to input their basic information and daily diet and exercise data through a terminal; means for a server to store the data received from the user in a database and retrieve past data; means for the server to send the user's data to a generating AI model to generate individually optimized advice and motivational messages; and means for the server to send the generated advice and motivational messages to the user, which are then displayed on the terminal. Furthermore, by combining it with an emotion engine, the system has the function of recognizing emotions from the user's input data and sending it to the generating AI model to adjust the advice and motivational messages based on the user's emotions.

[0559] Entering and saving user information

[0560] When a user logs into the system for the first time, they enter their basic information (age, gender, weight, height, exercise experience, health status, goals, etc.) through the terminal's user interface (UI). They also enter their daily diet and exercise records. The terminal collects this data and sends it to the server. The server stores the basic information and daily activity data received from the user in a database. This ensures that the user's basic information and past data are securely stored.

[0561] Emotion recognition and data analysis

[0562] An emotion engine embedded in the server recognizes emotions from user input data. For example, it recognizes emotions through text analysis. If the user inputs facial expressions or voice data, that data is also analyzed. By sending the emotion results recognized by the emotion engine to a generation AI model, it becomes possible to generate advice and encouragement messages that reflect the user's emotional state.

[0563] Generating advice and motivational messages

[0564] The server retrieves the user's basic information and past exercise and diet data from the database and sends it to a generative AI model. This includes the results of the emotion engine. The generative AI model analyzes the data and generates exercise and diet advice optimized for the user. For example, it might provide specific advice such as, "Today, I recommend 30 minutes of walking and 20 minutes of strength training. Natto and eggs are good sources of protein, so please continue with them." The generative AI model also generates motivational messages based on emotions. For example, if the user is feeling a little down, it might generate a message like, "Today might be a tough day, but let's keep going!"

[0565] Notification of advice and motivational messages

[0566] The server sends advice and motivational messages received from the generated AI model to the user's device. The device displays the received advice and motivational messages on the user's screen. This allows the user to receive appropriate guidance and encouragement.

[0567] Specific example

[0568] The following is a specific example for a particular individual user. This individual inputs their breakfast details (rice, natto, egg) and exercise record (20 minutes of jogging) into the device. In addition, this individual inputs facial expression data via the camera. The device sends the input data and facial expression data to the server. The server stores the received data in a database and uses an emotion engine to recognize the individual's emotions. It sends the data, along with the emotion result, to a generating AI model. The generating AI model analyzes the individual's data and generates advice and motivational messages based on the individual's emotions. For example, it might generate specific advice such as, "I recommend 30 minutes of walking and 20 minutes of strength training today. Natto and eggs are good sources of protein, so please continue," and a message such as, "You seem a little tired. But if you keep going, you will surely see results. Keep it up!" The server sends the generated advice and motivational message to the device. The device displays this on the screen, and the individual can see it and receive specific guidance regarding today's exercise and diet, as well as maintain motivation with an emotionally sensitive motivational message.

[0569] Example of a prompt

[0570] 1. "The user entered their breakfast details (rice, natto, egg) and exercise record (20 minutes of jogging), and collected facial expression data using a camera. Based on this data, please generate optimal advice and motivational messages."

[0571] 2. "Please advise on what kind of encouraging message to send to a user who is feeling down."

[0572] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0573] Step 1:

[0574] Users input basic information (age, gender, weight, height, exercise experience, health status, goals, etc.) through their device, and also input their daily meal details and exercise records. This collects basic information and daily data from the user on the device. The device then transmits the entered basic information and meal / exercise data to the server.

[0575] Step 2:

[0576] The server saves basic information and daily meal and exercise data received from the terminal to a database. This allows the user's past data to be accumulated and securely stored on the server. Within the server, the operation of writing received data to the database is performed.

[0577] Step 3:

[0578] The emotion engine embedded in the server analyzes the input data sent by the user. Specifically, it may recognize emotions from the words entered by the user through text analysis, or, if facial expression data or voice data is input, it may analyze these to identify emotions. Through this analysis process, the emotion engine extracts the user's emotional state as data. The output of the emotion engine may include emotional states such as "joy," "sadness," or "fatigue."

[0579] Step 4:

[0580] The server compiles the user's basic information, past exercise and diet data, along with the emotion engine's output, and sends it to the generative AI model. The server combines all the necessary data into a single request and sends it to the generative AI model. Through this communication, the generative AI model receives the user's overall data and prepares it for analysis.

[0581] Step 5:

[0582] The generation AI model analyzes the submitted data and generates personalized advice and motivational messages based on the user's basic information, past data, and emotional state. Examples of advice include, "Today, I recommend a 30-minute walk and 20 minutes of strength training. Natto and eggs are good sources of protein, so please continue with them." Examples of emotionally-based motivational messages include, "You seem a little tired. But if you keep going, you'll definitely see results. Keep it up!"

[0583] Step 6:

[0584] The server sends advice and motivational messages received from the generated AI model to the user's device. The server receives the output from the generated AI model and resends it to the appropriate device for each user. This process prepares the user's device to receive the messages.

[0585] Step 7:

[0586] The device displays received advice and motivational messages on the user's screen. Users can view these messages to gain specific guidance on exercise and diet for the day, and maintain motivation through emotionally resonant motivational messages. For example, the user might review the message displayed on the device screen and act accordingly.

[0587] (Application Example 2)

[0588] 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."

[0589] Traditionally, systems have existed that allow users to input daily diet and exercise data to manage their health and receive personalized advice. However, these systems failed to consider the user's emotional state, resulting in a lack of effective encouragement and motivation. Furthermore, the absence of electronic payment functionality linked to health management made it difficult for users to easily purchase necessary goods and services. The objective of this invention is to solve these problems.

[0590] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for the user to input their basic information and daily diet and exercise data through a terminal, means for the server to store the data received from the user in a database and retrieve past data, means for the server to transmit the user's data to a generative AI model to generate individually optimized advice and motivational messages, means for the emotion engine to analyze the emotional data input by the user, transmit the analysis results to the generative AI model to generate individually optimized emotion-based advice and motivational messages, means for notifying the generated advice and motivational messages on the terminal, and means for purchasing health-related goods and services through an electronic payment function. As a result, individually optimized advice and motivational messages that reflect the user's emotional state are generated, making it possible to increase the user's motivation. In addition, the purchase of health-related goods and services can be made seamlessly through the electronic payment function, further promoting the user's health management.

[0591] A "user" is the individual who uses the system and inputs their own health management data.

[0592] A "terminal" is an electronic device used by users to input basic information and daily diet and exercise data.

[0593] "Basic information" refers to data such as the user's age, gender, weight, height, exercise experience, health status, and goals.

[0594] A "server" is a central computing system that stores and analyzes data received from users and transmits it to generative AI models.

[0595] A "database" is an information management system used to store data received from users, as well as advice and motivational messages that are generated.

[0596] A "generative AI model" is an artificial intelligence model that analyzes user data and generates personalized advice and motivational messages.

[0597] "Advice" refers to specific instructions and recommendations regarding exercise and diet provided to the user by a generative AI model.

[0598] An "encouraging message" is a message provided by a generative AI model that takes the user's emotional state into consideration, in order to encourage the user.

[0599] An "emotion engine" is an engine that analyzes emotions from data entered by the user and sends the results to a generative AI model.

[0600] "Emotional data" refers to information about emotions that is derived from user input data.

[0601] "Electronic payment functionality" refers to online payment features that allow users to seamlessly purchase health-related goods and services.

[0602] "Notifications" refer to the act of displaying generated advice and motivational messages on the user's device.

[0603] "Motivation" refers to the willingness or motivation a user has to continue managing their health.

[0604] "Items" refer to specific items such as food and fitness equipment that users need to manage their health.

[0605] "Services" refer to activities such as training guidance and nutritional counseling that users utilize to support their health management.

[0606] This invention is a system for users to effectively manage their health. The details of the system will be described based on the following procedure.

[0607] System Configuration

[0608] Hardware:

[0609] 1. User terminal: A device such as a smartphone or tablet that allows the user to input data and has a camera function.

[0610] 2. Server: A computer system that performs data processing and storage.

[0611] software:

[0612] 1. Database: A system that stores the user's basic information and daily diet and exercise data.

[0613] 2. Generative AI Models: Artificial intelligence models that analyze user data and generate individually optimized advice and motivational messages.

[0614] 3. Emotion Engine: An engine that analyzes emotions from user input data and sends the results to a generative AI model.

[0615] 4. Electronic payment function: An online payment system for users to purchase health-related goods and services.

[0616] System Operation Overview

[0617] Entering and saving user information

[0618] Users input their basic information (age, gender, weight, height, exercise experience, health status, goals, etc.) through the device's UI. They also input their daily diet and exercise records, and further input facial expression data via the camera. The device sends this data to the server, which stores the received data in a database.

[0619] Emotion recognition and data analysis

[0620] The emotion engine on the server recognizes emotions from the user's input data and sends the recognition results to a generative AI model. The generative AI model analyzes the user's basic information, past exercise and diet data, and emotional state sent from the server to generate individually optimized advice and emotion-based motivational messages.

[0621] Notifications of advice and motivational messages

[0622] The server sends advice and motivational messages received from the generative AI model to the user's device, which then displays them on the screen. The user uses this information to obtain appropriate guidance and encouragement.

[0623] Electronic payment function

[0624] Users utilize online payment features to purchase appropriate goods and services based on the advice they receive. This allows for seamless purchases of essential health foods, fitness equipment, and other items.

[0625] Specific example

[0626] For example, a user might input meal data such as "rice, natto, and eggs" and record that they jogged for 20 minutes. They also input facial expression data via camera. The device sends this data to a server, which uses an emotion engine to recognize the user's emotions. The server then sends this recognition result along with the data to a generative AI model. The generative AI model performs analysis and generates specific advice such as, "Today, I recommend 30 minutes of walking and 20 minutes of strength training. Natto and eggs are good sources of protein, so please continue this," as well as an emotion-based motivational message such as, "You seem a little tired. But if you keep going, you'll definitely see results. Keep it up!" The server sends these to the device, and the user checks them on the screen.

[0627] Example of a prompt

[0628] "Please tell us about your exercise and meals today. Also, please point your face towards the camera and input your facial expression."

[0629] The above describes the embodiments for carrying out this invention.

[0630] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0631] Step 1:

[0632] Users input basic information (age, gender, weight, height, exercise experience, health status, goals) and daily meal and exercise data through the device's UI. The device collects this data and sends it to the server. This input data includes text-based meal details, exercise details, and facial expression data to interpret emotions. Examples of data collected by the device include "rice, natto, eggs" and "20 minutes of jogging."

[0633] Step 2:

[0634] The server saves basic information and daily data received from users to a database. The input for this save operation is the user's basic information and daily diet and exercise data, and the output is the saved database record. During saving, the data is linked to a specific user ID in the database.

[0635] Step 3:

[0636] An emotion engine on the server analyzes the user's emotions from their input data. The emotion engine takes text data and facial expression data as input, and outputs the analysis results regarding emotions. For example, it might produce an analysis result such as, "Today's emotional state is somewhat depressed."

[0637] Step 4:

[0638] The server sends the emotion results recognized by the emotion engine to the generative AI model. The input data sent includes the user's basic information, past exercise and diet data, and emotion analysis results, while the output is the data analyzed by the generative AI model.

[0639] Step 5:

[0640] Generative AI models analyze input data and generate user-optimized advice and emotion-based motivational messages. For example, if the input data is "Weight 70kg, weight loss goal, 20 minutes of jogging, emotional state: tired," the output messages would be "I recommend 30 minutes of walking and 20 minutes of strength training today. Natto and eggs are good sources of protein, so please continue," and "You seem a little tired. But if you keep going, you will surely see results. Keep it up!"

[0641] Step 6:

[0642] The server sends advice and motivational messages received from the generative AI model to the user's device. The input data sent is the generated message, and the output is a notification message displayed on the device.

[0643] Step 7:

[0644] The device displays advice and motivational messages received from the server on the user's screen. Specifically, the device's display shows messages such as, "Today, we recommend 30 minutes of walking and 20 minutes of strength training," and "You seem a little tired, but if you keep going, you'll definitely see results. Keep it up!"

[0645] Step 8:

[0646] Users use the electronic payment function on their device to purchase health-related goods and services based on the advice they receive. For example, when purchasing fitness equipment online based on a notification message, the purchase process and payment are seamless.

[0647] The above are the specific processing steps for carrying out this invention.

[0648] 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.

[0649] 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.

[0650] 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.

[0651] [Third Embodiment]

[0652] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0653] 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.

[0654] 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).

[0655] 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.

[0656] 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.

[0657] 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).

[0658] 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.

[0659] 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.

[0660] 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.

[0661] 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.

[0662] 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.

[0663] 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".

[0664] The system of this invention begins with the user inputting their basic information and daily diet and exercise data through a terminal. The server receives this data and stores it in a database. It also simultaneously acquires the user's past data and sends it to a generative AI model. Based on the input data, this AI model generates individually optimized exercise and diet advice, as well as motivational messages to enhance the user's motivation. The generated content is then sent back to the terminal via the server and displayed on the user's terminal.

[0665] An example of the detailed processing of this system is shown below.

[0666] Program overview and specific examples

[0667] Entering and saving user information

[0668] When a user uses the system for the first time, they enter their basic information (e.g., age, gender, weight, height, exercise experience, health status, goals, etc.) through the terminal's UI. They also enter their daily meals (e.g., what they ate for breakfast) and exercise records (e.g., 20 minutes of jogging).

[0669] The device collects this data and sends it to the server.

[0670] The server receives user input data and stores it in the database. The stored data is managed along with past records.

[0671] Generating advice and motivational messages

[0672] The server retrieves historical data based on user requests and sends it to a generative AI model. This data includes the user's basic information, past dietary habits, exercise history, and weight fluctuations.

[0673] Generative AI models analyze input data and generate personalized exercise advice and dietary suggestions. For example, one user might receive specific advice such as, "Today, I recommend 30 minutes of walking and 20 minutes of strength training. Natto and eggs are good sources of protein, so please continue with them."

[0674] Furthermore, the generative AI model also generates encouraging messages to maintain user motivation. For example, these might include messages like, "Great start! If you keep going, you'll definitely reach your goal."

[0675] Notifications of advice and motivational messages

[0676] The server processes the advice and motivational messages received from the generative AI model and sends them to the user's terminal.

[0677] The device displays received advice and messages on its screen. This allows users to receive specific guidance regarding their daily activities and diet. Furthermore, encouraging messages help maintain motivation.

[0678] Specific example

[0679] Let's consider the case where a user (Mr. Tanaka) uses the system.

[0680] Ms. Tanaka enters the details of her breakfast (rice, natto, and egg) and her exercise record for today (20 minutes of jogging) into the terminal. Next, she presses the button to request exercise advice.

[0681] The terminal collects input data and sends it to the server.

[0682] The server saves the received data to a database and retrieves Tanaka's past data (last week's exercise records and weight fluctuations) and sends it to a generative AI model.

[0683] The generative AI model analyzes Tanaka's data and generates advice such as, "Today, I recommend 30 minutes of walking and 20 minutes of strength training. Natto and eggs are good sources of protein, so please continue with this," and an encouraging message such as, "That's a great start! If you keep it up, you will definitely reach your goal."

[0684] The server receives the generated advice and motivational messages and sends them to the terminal.

[0685] The device displays this information on its screen, allowing Ms. Tanaka to see it, obtain specific guidance regarding her exercise and diet for the day, and maintain her motivation through encouraging messages.

[0686] In this way, the system provides personalized health management advice and motivational support to each user, ultimately contributing to improved well-being and a better work environment.

[0687] The following describes the processing flow.

[0688] Step 1:

[0689] The user accesses the device's UI and enters their basic information. This basic information includes age, gender, weight, height, exercise experience, health status, and goals.

[0690] Step 2:

[0691] The terminal sends the entered basic information to the server. Specifically, it uses an HTTP request to pass the user's information to the server.

[0692] Step 3:

[0693] The server stores the received user's basic information in a database. This ensures that the user's basic information is managed securely.

[0694] Step 4:

[0695] To record their daily activities, users input details about their meals and exercise through the device's UI. For example, they might input data about eating rice, natto, and eggs for breakfast, or going for a 20-minute jog.

[0696] Step 5:

[0697] The device collects daily activity data and sends it to the server. This involves using HTTP requests again to pass the data to the server.

[0698] Step 6:

[0699] The server receives daily activity data and stores it in a database. This stored data is later referenced and used to generate advice.

[0700] Step 7:

[0701] The server retrieves the user's basic information and past exercise and dietary data from the database. This prepares the data for transmission to the generative AI model.

[0702] Step 8:

[0703] The server sends the acquired data to the generative AI model. Specifically, it sends a request containing the user's data to the AI ​​model's API endpoint.

[0704] Step 9:

[0705] A generative AI model analyzes the received data and generates personalized exercise and dietary advice. For example, it might generate advice such as, "Today, I recommend 30 minutes of walking and 20 minutes of strength training. Natto and eggs are good sources of protein, so please continue with them."

[0706] Step 10:

[0707] Generative AI models generate encouraging messages to maintain user motivation. For example, they might generate a message like, "Great start! If you keep going, you'll definitely reach your goal."

[0708] Step 11:

[0709] The server receives advice and motivational messages from the generative AI model. This includes procedures for processing the API responses from the generative AI model.

[0710] Step 12:

[0711] The server sends received advice and motivational messages to the terminal. This includes identifying the user ID and sending the data to the corresponding terminal.

[0712] Step 13:

[0713] The device displays received advice and motivational messages on the user's screen. This allows users to receive specific guidance on exercise and diet, as well as boost their motivation.

[0714] This series of steps allows users to receive personalized exercise and dietary advice, effectively manage their daily activities, and maintain motivation.

[0715] (Example 1)

[0716] 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."

[0717] Traditional systems have complex data entry and management processes for user health management, making it difficult to provide individually optimized advice. Furthermore, they lack effective feedback to maintain user motivation. As a result, there are challenges in smoothly improving users' health and achieving their goals.

[0718] 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.

[0719] In this invention, the server includes means for the user to input basic information and daily diet and exercise data, means for the terminal to transmit data collected by the terminal to the server, and means for the server to store the received data in a database and retrieve past data. This enables efficient collection and storage of data from the user, and makes it possible to provide individually optimized exercise and diet advice and motivational messages using a generative AI model. Users can easily receive this advice and messages through their terminal, resulting in more efficient health management and improved motivation.

[0720] A "user" refers to someone who uses the system to input basic information, daily diet, and exercise data, and receives advice.

[0721] A "terminal" refers to a device that allows users to input data, communicate with a server, and receive and display advice and motivational messages.

[0722] A "server" refers to a device that receives data sent by users, stores it in a database, and then sends that data to a generative AI model.

[0723] A "database" refers to information storage used by a server to store and manage data received from users, as well as historical data.

[0724] A "generative AI model" refers to an artificial intelligence program that generates personalized health advice and motivational messages based on user input data.

[0725] "Advice" refers to recommendations regarding exercise and diet that are generated by generative AI models to help users manage their health.

[0726] An "inspirational message" refers to a message generated by a generative AI model that aims to boost user motivation and encourage continuous health improvement.

[0727] Modes for carrying out the invention

[0728] The system of this invention allows users to input their basic information and daily diet and exercise data in order to manage their health, and based on that, it provides personalized advice and motivational messages to enhance their motivation.

[0729] Hardware and software configuration

[0730] The device provides an interface for the user to input information. This includes smartphones, tablets, and personal computers. The entered data is transmitted to a server via the internet.

[0731] The server is equipped with a high-performance processor and sufficient storage capacity to receive data sent by users. The server also uses a database management system (DBMS) to store and manage the received data and historical data.

[0732] The generative AI model is built using Python's TensorFlow and PyTorch libraries and runs on a server. The AI ​​model analyzes user input data and generates individually customized advice and motivational messages.

[0733] Details of data processing and calculations

[0734] The terminal uses standard data formats such as JSON and XML to send user input data to the server in real time. To ensure security, data transmission uses the HTTPS protocol.

[0735] The server temporarily stores received data in cache memory and performs error checking. Once consistency is confirmed, it is saved to the database. The saved data includes basic information, daily meal details, exercise records, and weight fluctuations.

[0736] The generative AI model receives a comprehensive dataset, including historically stored data, each time a request is made, and performs analysis. The model learns each user's unique health status, dietary history, and exercise habits, and then generates individually optimized advice.

[0737] Examples of specific prompt statements include the following:

[0738] "Please enter your basic information. Example: Age, Gender, Weight, Height."

[0739] "Please enter what you ate today. Example: What I ate for breakfast."

[0740] "Please enter your exercise plan for today. Example: 20 minutes of jogging."

[0741] Specific example

[0742] Consider a scenario where a user is using the system for the first time. The user enters basic information such as age (30 years old), gender (male), and weight (70 kg) through the device's UI. They also enter daily details of their meals (e.g., rice, natto, and eggs for breakfast) and exercise records (e.g., 20 minutes of jogging).

[0743] The device sends this data to the server in real time. The server stores the received data in a database, and when the user requests exercise advice, it sends it, along with past data, to a generative AI model.

[0744] The generative AI model analyzes user data and generates specific advice such as, "Today, I recommend a 30-minute walk and 20 minutes of strength training. Natto and eggs are good sources of protein, so please continue with this." It also provides encouraging messages like, "Great start! If you keep going, you will definitely reach your goal."

[0745] The server sends generated advice and motivational messages to the device, which displays them on the screen. Users can review these and use them to help manage their daily health.

[0746] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0747] Step 1:

[0748] The user accesses a UI form on their device and enters basic information. This basic information includes age, gender, weight, height, exercise experience, health status, and goals. They also enter daily meal details and exercise records. The input data is in text and numerical format, and the device packages this data into JSON format. Input data: basic information, meal details, exercise records. Output data: JSON format data.

[0749] Step 2:

[0750] The terminal sends user-entered data to the server over the network. HTTPS is used as the transmission protocol to ensure data security. Input data: User's data in JSON format. Output data: Transmission request to the server.

[0751] Step 3:

[0752] The server temporarily stores the JSON data received from the terminal in cache memory and performs an integrity check. After using an error checking program to confirm that the data is accurate, it is saved to the database. Input data: JSON data received from the terminal. Output data: Data whose integrity has been checked.

[0753] Step 4:

[0754] The server uses a database management system (DBMS) to permanently store data that has passed integrity checks in the database. The stored data is timestamped and tagged to facilitate management. Input data: Data whose integrity has been verified. Output data: Data stored in the database.

[0755] Step 5:

[0756] The user sends a request for exercise advice to the server via their device. This request is initiated by the user clicking a button on the interface. Input data: Exercise advice request. Output data: Request to the server.

[0757] Step 6:

[0758] After receiving a request from a user, the server retrieves the user's past data from the database. This data includes basic information, dietary details, exercise records, weight fluctuations, etc. Input data: Exercise advice request. Output data: Set of past data.

[0759] Step 7:

[0760] The server sends the acquired historical data to a generative AI model. The AI ​​model uses Python's TensorFlow or PyTorch libraries, and the model, built using these libraries, performs the data analysis. Input data: User's historical data. Output data: Data sent to the AI ​​model.

[0761] Step 8:

[0762] Generative AI models analyze submitted data to generate personalized advice and motivational messages. The AI ​​model analyzes input data and generates specific advice and messages tailored to each user's health condition. Input data: User's historical data. Output data: Individually optimized advice and motivational messages.

[0763] Step 9:

[0764] The server re-checks the integrity of the advice and motivational messages received from the generative AI model and saves them to the database. It then sends them to the user's terminal. Input data: Advice and motivational messages from the generative AI model. Output data: Data sent to the user's terminal.

[0765] Step 10:

[0766] The device displays advice and motivational messages received from the server on its screen. Users review this information to help manage their daily health. The motivational messages also help maintain their motivation. Input data: Advice and motivational messages from the server. Output data: Advice and messages displayed on the screen.

[0767] (Application Example 1)

[0768] 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."

[0769] Conventional fitness gym training support systems have difficulty providing optimal exercise menus and dietary advice tailored to each user's individual condition and goals, and thus have difficulty maintaining users' sustained motivation. As a result, users may not be able to perform effective training and may have difficulty achieving their goals. This invention aims to solve these problems.

[0770] 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.

[0771] In this invention, the server includes means for the user to input their basic information and daily diet and exercise data through a terminal; means for the server to store the data received from the user in a database and retrieve past data; means for the server to transmit the user's data to a generative AI model to generate individually optimized exercise menus and dietary advice, as well as motivational messages; and means for the server to transmit the generated exercise menus and dietary advice, as well as motivational messages, to the user, which the terminal displays. As a result, the user can receive individually optimized exercise and dietary advice, maintain motivation through motivational messages, and continue effective training.

[0772] A "terminal" is a device that a user uses to input their basic information and daily diet and exercise data.

[0773] A "server" is a central processing unit that stores data received from users in a database, retrieves past data, and sends it to generative AI models.

[0774] A "database" is an information storage system that stores and manages a user's basic information, daily diet and exercise data, and historical data.

[0775] A "generative AI model" is an artificial intelligence program that analyzes user data and generates personalized exercise menus, dietary advice, and motivational messages.

[0776] A "personally optimized exercise program" is an optimal exercise plan tailored to each individual user, based on their weight, goals, past exercise records, and dietary habits.

[0777] "Dietary advice" refers to specific meal suggestions recommended to the user based on their basic information and daily eating data.

[0778] An "inspirational message" is a message of encouragement or support generated to maintain and enhance the user's motivation.

[0779] The system of this invention begins with the user inputting their basic information and daily diet and exercise data via a terminal. Specifically, the user inputs their information using a smartphone app, and this input data is sent to a server. The server stores the received data in a database. It also retrieves past data and sends it to a generative AI model. Based on the input data, the generative AI model generates personalized exercise menus, dietary advice, and motivational messages for each user.

[0780] The hardware used consists of smartphones (user terminals) and servers, and the software is primarily developed using Python. Flask (a web framework) is used on the server side, and MongoDB or PostgreSQL are used for the database. The generative AI models include AI algorithms specialized in natural language processing and data analysis.

[0781] The server stores the user's input data in a database. The stored data is managed along with past records and sent to a generative AI model. The generative AI model analyzes the input data and generates personalized exercise menus and dietary advice. For example, some users might receive specific advice such as, "Today, I recommend 30 minutes of walking and 20 minutes of strength training. Natto and eggs are good sources of protein, so please continue with that." The generative AI model also generates motivational messages to help users stay motivated. For example, it might include messages like, "Great start! If you keep going, you'll definitely reach your goal."

[0782] Examples of prompts to send to a generative AI model are as follows:

[0783] "Based on the user's basic information and daily diet and exercise data, generate personalized exercise plans, dietary advice, and motivational messages."

[0784] The server sends the generated exercise menu, dietary advice, and motivational messages to the user's device. The user's device receives this information and displays it on the screen. This allows the user to receive specific guidance on their daily activities and diet. The motivational messages also help maintain their motivation.

[0785] As a concrete example, if a user enters the details of their breakfast (rice, natto, egg) and their exercise record for the day (20 minutes of jogging) into their device, that data is sent to the server. The server stores the data in a database and sends it, along with past data, to a generative AI model. The generative AI model analyzes this data and generates personalized advice and motivational messages for the user. The server sends the generated results to the device, which then displays them to the user. For example, the advice might say, "I recommend 30 minutes of walking and 20 minutes of strength training today. Natto and eggs are good sources of protein, so please continue with that," and the motivational message might say, "Great start! If you keep it up, you'll definitely reach your goal."

[0786] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0787] Step 1:

[0788] Users input their basic information (age, gender, weight, height, exercise experience, health status, goals) and daily food and exercise data via their device. This input data includes details such as what they ate for breakfast and the exercise they did that day. The system obtains basic user information and daily activity data as input.

[0789] Step 2:

[0790] The terminal collects data entered by the user and sends it to the server. The data transmission process involves converting the input data into an appropriate format, such as JSON, and sending it to the server using an HTTP POST request. Basic user information and daily activity data are used as input, and the data sent to the server is generated as output.

[0791] Step 3:

[0792] The server saves the received data to the database. During the saving process, the received data is converted into the required format and stored in the database along with existing user data. The input is data received from the terminal, and the output is data recorded in the database.

[0793] Step 4:

[0794] The server retrieves historical data from the database. This includes the user's exercise and dietary data from the past few weeks or months. The retrieved data is used as input data for a generative AI model. The input is the query results from the database, and the output is historical user data.

[0795] Step 5:

[0796] The server sends user data to a generative AI model. This data includes the user's basic information, current diet and exercise data, and historical data. The generative AI model analyzes this input data to generate personalized exercise and dietary advice, as well as motivational messages. The input is the user's complete dataset, and the output is the generated exercise menu, advice, and motivational messages.

[0797] Step 6:

[0798] Generative AI models analyze received data and generate individually optimized advice and motivational messages. During the analysis process, natural language processing and data analysis algorithms are used to generate user-specific feedback. User data is the input, and the generated content is the output.

[0799] Step 7:

[0800] The server sends exercise menus, dietary advice, and motivational messages received from the generative AI model to the user's terminal. The transmission process involves converting the generated data into an appropriate format and sending it to the user's terminal using an HTTP POST request. The input consists of the generated exercise menus, advice, and motivational messages, while the output is the data sent to the user's terminal.

[0801] Step 8:

[0802] The terminal displays exercise menus, dietary advice, and motivational messages received from the server. The display process includes visualizing the received data in an easy-to-understand format for the user and displaying it on the screen. The input is data received from the server, and the output is the content displayed to the user.

[0803] Through these steps, users can receive personalized exercise and dietary advice, maintain motivation through encouraging messages, and continue effective training.

[0804] 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.

[0805] The present invention combines an emotion engine with means for the user to input their basic information and daily diet and exercise data through a terminal, means for a server to store the data received from the user in a database and retrieve past data, means for the server to send the user's data to a generative AI model to generate individually optimized advice and motivational messages, and means for the server to send the generated advice and motivational messages to the user and display them on the terminal. The emotion engine has the function of recognizing emotions from the user's input data and sending it to the generative AI model to adjust the advice and motivational messages based on those emotions.

[0806] Program overview and specific examples

[0807] Entering and saving user information

[0808] When a user uses the system for the first time, they will enter their basic information (age, gender, weight, height, exercise experience, health status, goals, etc.) through the terminal's UI. They will also enter their daily meal details and exercise records.

[0809] The device collects this data and sends it to the server.

[0810] The server stores basic information and daily activity data received from users in a database. This ensures that users' basic information and historical data are securely stored.

[0811] Emotion recognition and data analysis

[0812] The emotion engine on the server recognizes emotions from the user's input data. For example, emotion recognition is performed through text analysis. If the user inputs facial expressions or voice data, that data is also analyzed.

[0813] The emotion engine recognizes the emotion and sends it to the generative AI model. This enables the generation of advice and motivational messages that reflect the user's emotional state.

[0814] Generating advice and motivational messages

[0815] The server retrieves the user's basic information and past exercise and diet data from the database and sends this information to a generative AI model. The results of the emotion engine are also included.

[0816] A generative AI model analyzes the data and generates personalized exercise and dietary advice for the user. Specific advice such as, "Today, I recommend 30 minutes of walking and 20 minutes of strength training. Natto and eggs are good sources of protein, so please continue with them," is provided.

[0817] Furthermore, generative AI models can also generate motivational messages based on emotions. For example, if a user is feeling a little down, a message like, "Today might be a tough day, but let's keep going!" will be generated.

[0818] Notifications of advice and motivational messages

[0819] The server receives advice and motivational messages from the generative AI model and sends them to the user's device.

[0820] The device displays received advice and motivational messages on the user's screen. This allows the user to receive appropriate guidance and encouragement.

[0821] Specific example

[0822] The following is a specific example from the case of user Tanaka.

[0823] Ms. Tanaka entered her breakfast details (rice, natto, and egg) and exercise record (20 minutes of jogging) into the terminal. In addition, Ms. Tanaka entered her facial expression data via the camera.

[0824] The terminal sends input data and facial expression data to the server.

[0825] The server stores the received data in a database and then uses an emotion engine to recognize Tanaka's emotions. This emotion result, along with the data, is then sent to a generative AI model.

[0826] The generative AI model analyzes Tanaka's data and generates advice and motivational messages based on Tanaka's emotions. For example, it generates specific advice such as, "Today I recommend 30 minutes of walking and 20 minutes of strength training. Natto and eggs are good sources of protein, so please continue with that," and a message like, "You seem a little tired. But if you keep going, you'll definitely see results. Keep it up!"

[0827] The server sends the generated advice and motivational messages to the terminal.

[0828] The device displays this information on its screen, allowing Ms. Tanaka to see it, obtain specific guidance regarding her exercise and diet for the day, and maintain her motivation through emotionally sensitive and encouraging messages.

[0829] In this way, by combining emotional engines, it is possible to create a system that provides more personalized advice that responds to the user's emotional state. This allows users to manage their health more effectively and maintain their motivation.

[0830] The following describes the processing flow.

[0831] Step 1:

[0832] The user accesses the device's UI and enters their basic information. This basic information includes age, gender, weight, height, exercise experience, health status, and goals.

[0833] Step 2:

[0834] The terminal sends the entered basic information to the server. Specifically, it uses an HTTP request to pass the user's information to the server.

[0835] Step 3:

[0836] The server stores the received user's basic information in a database. This ensures that the user's basic information is managed securely.

[0837] Step 4:

[0838] To record their daily activities, users input details about their meals and exercise through the device's UI. For example, they might input data about eating rice, natto, and eggs for breakfast, or going for a 20-minute jog.

[0839] Step 5:

[0840] The user inputs facial expression data via the camera using their device. Additionally, if audio data is also to be collected, a microphone is used.

[0841] Step 6:

[0842] The device collects daily activity data, facial expression data, and voice data, and sends them to the server. This involves using HTTP requests again to pass the data to the server.

[0843] Step 7:

[0844] The server receives daily activity data, facial expression data, and voice data, and stores them in a database. The stored data is later used for emotion recognition and advice generation.

[0845] Step 8:

[0846] The server uses an emotion engine to recognize emotions from user input data, facial expression data, and voice data. For example, it performs text analysis, facial expression analysis, and voice analysis to estimate the user's emotional state.

[0847] Step 9:

[0848] The emotion engine recognizes emotions, which are then compiled on a server and sent to a generative AI model. This data also includes the user's emotional state.

[0849] Step 10:

[0850] The server retrieves the user's basic information and past exercise and diet data from the database and sends this information, along with sentiment results, to a generative AI model. This involves sending requests to the AI ​​model's API endpoint.

[0851] Step 11:

[0852] The generative AI model analyzes the received data and generates personalized exercise and dietary advice. For example, it might generate specific advice such as, "Today, I recommend 30 minutes of walking and 20 minutes of strength training. Natto and eggs are good sources of protein, so please continue with them."

[0853] Step 12:

[0854] The generative AI model also generates encouraging messages based on the user's emotional state. For example, if the user is feeling a little down, it will generate a message such as, "Today may be a tough day, but let's keep going!"

[0855] Step 13:

[0856] The server processes advice and motivational messages received from the generative AI model and sends them to the user's terminal. This includes identifying the user's ID and sending the corresponding data.

[0857] Step 14:

[0858] The device displays received advice and motivational messages on the user's screen. This allows the user to receive specific guidance and encouragement.

[0859] This series of steps allows users to receive personalized exercise and dietary advice, effectively manage their daily activities, and maintain motivation. Furthermore, the emotional engine provides individualized feedback that takes the user's emotions into consideration.

[0860] (Example 2)

[0861] 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."

[0862] Traditional health management systems have struggled to provide advice and motivational messages that are tailored to users' emotions and individual circumstances. This has made it difficult to maintain user motivation and to implement personalized health management.

[0863] 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.

[0864] In this invention, the server includes means for the user to input their basic information and daily diet and exercise data through a terminal; means for the server to store the data received from the user in a database and retrieve past data; means for the server to send the user's data to a generating AI model to generate individually optimized advice and motivational messages; means for the server to send the generated advice and motivational messages to the user and for the terminal to display them; and means for an emotion engine to recognize emotions from the user's input data and send them to the generating AI model to adjust the advice and motivational messages based on the user's emotions. This makes it possible to provide advice and motivational messages that are tailored to the user's emotions and individual circumstances.

[0865] A "terminal" is a device used by users to input basic information and daily diet and exercise data, and to communicate with the server.

[0866] A "server" is a central device that stores data received from users and works in conjunction with generative AI models and emotion engines to generate and send advice and motivational messages.

[0867] A "database" is a storage device that securely stores a user's basic information and daily activity data, allowing it to be retrieved and analyzed later.

[0868] A "generative AI model" is an algorithm that generates individually optimized advice and motivational messages based on the user's basic information, past data, and emotional results from an emotion engine.

[0869] "Advice" refers to specific action guidelines provided by the generative AI model for the user's health management.

[0870] An "encouraging message" is a message of encouragement or support provided by a generative AI model based on the user's emotional state.

[0871] An "emotion engine" is an analysis device or software that recognizes emotions from data input by the user and sends the results to a generating AI model.

[0872] A "user" is an individual who uses the system to input their basic information and daily activity data, and receives generated advice and motivational messages.

[0873] The present invention provides a system comprising: means for the user to input their basic information and daily diet and exercise data through a terminal; means for a server to store the data received from the user in a database and retrieve past data; means for the server to send the user's data to a generating AI model to generate individually optimized advice and motivational messages; and means for the server to send the generated advice and motivational messages to the user, which are then displayed on the terminal. Furthermore, by combining it with an emotion engine, the system has the function of recognizing emotions from the user's input data and sending it to the generating AI model to adjust the advice and motivational messages based on the user's emotions.

[0874] Entering and saving user information

[0875] When a user logs into the system for the first time, they enter their basic information (age, gender, weight, height, exercise experience, health status, goals, etc.) through the terminal's user interface (UI). They also enter their daily diet and exercise records. The terminal collects this data and sends it to the server. The server stores the basic information and daily activity data received from the user in a database. This ensures that the user's basic information and past data are securely stored.

[0876] Emotion recognition and data analysis

[0877] An emotion engine embedded in the server recognizes emotions from user input data. For example, it recognizes emotions through text analysis. If the user inputs facial expressions or voice data, that data is also analyzed. By sending the emotion results recognized by the emotion engine to a generation AI model, it becomes possible to generate advice and encouragement messages that reflect the user's emotional state.

[0878] Generating advice and motivational messages

[0879] The server retrieves the user's basic information and past exercise and diet data from the database and sends it to a generative AI model. This includes the results of the emotion engine. The generative AI model analyzes the data and generates exercise and diet advice optimized for the user. For example, it might provide specific advice such as, "Today, I recommend 30 minutes of walking and 20 minutes of strength training. Natto and eggs are good sources of protein, so please continue with them." The generative AI model also generates motivational messages based on emotions. For example, if the user is feeling a little down, it might generate a message like, "Today might be a tough day, but let's keep going!"

[0880] Notification of advice and motivational messages

[0881] The server sends advice and motivational messages received from the generated AI model to the user's device. The device displays the received advice and motivational messages on the user's screen. This allows the user to receive appropriate guidance and encouragement.

[0882] Specific example

[0883] The following is a specific example for a particular individual user. This individual inputs their breakfast details (rice, natto, egg) and exercise record (20 minutes of jogging) into the device. In addition, this individual inputs facial expression data via the camera. The device sends the input data and facial expression data to the server. The server stores the received data in a database and uses an emotion engine to recognize the individual's emotions. It sends the data, along with the emotion result, to a generating AI model. The generating AI model analyzes the individual's data and generates advice and motivational messages based on the individual's emotions. For example, it might generate specific advice such as, "I recommend 30 minutes of walking and 20 minutes of strength training today. Natto and eggs are good sources of protein, so please continue," and a message such as, "You seem a little tired. But if you keep going, you will surely see results. Keep it up!" The server sends the generated advice and motivational message to the device. The device displays this on the screen, and the individual can see it and receive specific guidance regarding today's exercise and diet, as well as maintain motivation with an emotionally sensitive motivational message.

[0884] Example of a prompt

[0885] 1. "The user entered their breakfast details (rice, natto, egg) and exercise record (20 minutes of jogging), and collected facial expression data using a camera. Based on this data, please generate optimal advice and motivational messages."

[0886] 2. "Please advise on what kind of encouraging message to send to a user who is feeling down."

[0887] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0888] Step 1:

[0889] Users input basic information (age, gender, weight, height, exercise experience, health status, goals, etc.) through their device, and also input their daily meal details and exercise records. This collects basic information and daily data from the user on the device. The device then transmits the entered basic information and meal / exercise data to the server.

[0890] Step 2:

[0891] The server saves basic information and daily meal and exercise data received from the terminal to a database. This allows the user's past data to be accumulated and securely stored on the server. Within the server, the operation of writing received data to the database is performed.

[0892] Step 3:

[0893] The emotion engine embedded in the server analyzes the input data sent by the user. Specifically, it may recognize emotions from the words entered by the user through text analysis, or, if facial expression data or voice data is input, it may analyze these to identify emotions. Through this analysis process, the emotion engine extracts the user's emotional state as data. The output of the emotion engine may include emotional states such as "joy," "sadness," or "fatigue."

[0894] Step 4:

[0895] The server compiles the user's basic information, past exercise and diet data, along with the emotion engine's output, and sends it to the generative AI model. The server combines all the necessary data into a single request and sends it to the generative AI model. Through this communication, the generative AI model receives the user's overall data and prepares it for analysis.

[0896] Step 5:

[0897] The generation AI model analyzes the submitted data and generates personalized advice and motivational messages based on the user's basic information, past data, and emotional state. Examples of advice include, "Today, I recommend a 30-minute walk and 20 minutes of strength training. Natto and eggs are good sources of protein, so please continue with them." Examples of emotionally-based motivational messages include, "You seem a little tired. But if you keep going, you'll definitely see results. Keep it up!"

[0898] Step 6:

[0899] The server sends advice and motivational messages received from the generated AI model to the user's device. The server receives the output from the generated AI model and resends it to the appropriate device for each user. This process prepares the user's device to receive the messages.

[0900] Step 7:

[0901] The device displays received advice and motivational messages on the user's screen. Users can view these messages to gain specific guidance on exercise and diet for the day, and maintain motivation through emotionally resonant motivational messages. For example, the user might review the message displayed on the device screen and act accordingly.

[0902] (Application Example 2)

[0903] 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."

[0904] Traditionally, systems have existed that allow users to input daily diet and exercise data to manage their health and receive personalized advice. However, these systems failed to consider the user's emotional state, resulting in a lack of effective encouragement and motivation. Furthermore, the absence of electronic payment functionality linked to health management made it difficult for users to easily purchase necessary goods and services. The objective of this invention is to solve these problems.

[0905] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for the user to input their basic information and daily diet and exercise data through a terminal, means for the server to store the data received from the user in a database and retrieve past data, means for the server to transmit the user's data to a generative AI model to generate individually optimized advice and motivational messages, means for the emotion engine to analyze the emotional data input by the user, transmit the analysis results to the generative AI model to generate individually optimized emotion-based advice and motivational messages, means for notifying the generated advice and motivational messages on the terminal, and means for purchasing health-related goods and services through an electronic payment function. As a result, individually optimized advice and motivational messages that reflect the user's emotional state are generated, making it possible to increase the user's motivation. In addition, the purchase of health-related goods and services can be made seamlessly through the electronic payment function, further promoting the user's health management.

[0906] A "user" is the individual who uses the system and inputs their own health management data.

[0907] A "terminal" is an electronic device used by users to input basic information and daily diet and exercise data.

[0908] "Basic information" refers to data such as the user's age, gender, weight, height, exercise experience, health status, and goals.

[0909] A "server" is a central computing system that stores and analyzes data received from users and transmits it to generative AI models.

[0910] A "database" is an information management system used to store data received from users, as well as advice and motivational messages that are generated.

[0911] A "generative AI model" is an artificial intelligence model that analyzes user data and generates personalized advice and motivational messages.

[0912] "Advice" refers to specific instructions and recommendations regarding exercise and diet provided to the user by a generative AI model.

[0913] An "encouraging message" is a message provided by a generative AI model that takes the user's emotional state into consideration, in order to encourage the user.

[0914] An "emotion engine" is an engine that analyzes emotions from data entered by the user and sends the results to a generative AI model.

[0915] "Emotional data" refers to information about emotions that is derived from user input data.

[0916] "Electronic payment functionality" refers to online payment features that allow users to seamlessly purchase health-related goods and services.

[0917] "Notifications" refer to the act of displaying generated advice and motivational messages on the user's device.

[0918] "Motivation" refers to the willingness or motivation a user has to continue managing their health.

[0919] "Items" refer to specific items such as food and fitness equipment that users need to manage their health.

[0920] "Services" refer to activities such as training guidance and nutritional counseling that users utilize to support their health management.

[0921] This invention is a system for users to effectively manage their health. The details of the system will be described based on the following procedure.

[0922] System Configuration

[0923] Hardware:

[0924] 1. User terminal: A device such as a smartphone or tablet that allows the user to input data and has a camera function.

[0925] 2. Server: A computer system that performs data processing and storage.

[0926] software:

[0927] 1. Database: A system that stores the user's basic information and daily diet and exercise data.

[0928] 2. Generative AI Models: Artificial intelligence models that analyze user data and generate individually optimized advice and motivational messages.

[0929] 3. Emotion Engine: An engine that analyzes emotions from user input data and sends the results to a generative AI model.

[0930] 4. Electronic payment function: An online payment system for users to purchase health-related goods and services.

[0931] System Operation Overview

[0932] Entering and saving user information

[0933] Users input their basic information (age, gender, weight, height, exercise experience, health status, goals, etc.) through the device's UI. They also input their daily diet and exercise records, and further input facial expression data via the camera. The device sends this data to the server, which stores the received data in a database.

[0934] Emotion recognition and data analysis

[0935] The emotion engine on the server recognizes emotions from the user's input data and sends the recognition results to a generative AI model. The generative AI model analyzes the user's basic information, past exercise and diet data, and emotional state sent from the server to generate individually optimized advice and emotion-based motivational messages.

[0936] Notifications of advice and motivational messages

[0937] The server sends advice and motivational messages received from the generative AI model to the user's device, which then displays them on the screen. The user uses this information to obtain appropriate guidance and encouragement.

[0938] Electronic payment function

[0939] Users utilize online payment features to purchase appropriate goods and services based on the advice they receive. This allows for seamless purchases of essential health foods, fitness equipment, and other items.

[0940] Specific example

[0941] For example, a user might input meal data such as "rice, natto, and eggs" and record that they jogged for 20 minutes. They also input facial expression data via camera. The device sends this data to a server, which uses an emotion engine to recognize the user's emotions. The server then sends this recognition result along with the data to a generative AI model. The generative AI model performs analysis and generates specific advice such as, "Today, I recommend 30 minutes of walking and 20 minutes of strength training. Natto and eggs are good sources of protein, so please continue this," as well as an emotion-based motivational message such as, "You seem a little tired. But if you keep going, you'll definitely see results. Keep it up!" The server sends these to the device, and the user checks them on the screen.

[0942] Example of a prompt

[0943] "Please tell us about your exercise and meals today. Also, please point your face towards the camera and input your facial expression."

[0944] The above describes the embodiments for carrying out this invention.

[0945] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0946] Step 1:

[0947] Users input basic information (age, gender, weight, height, exercise experience, health status, goals) and daily meal and exercise data through the device's UI. The device collects this data and sends it to the server. This input data includes text-based meal details, exercise details, and facial expression data to interpret emotions. Examples of data collected by the device include "rice, natto, eggs" and "20 minutes of jogging."

[0948] Step 2:

[0949] The server saves basic information and daily data received from users to a database. The input for this save operation is the user's basic information and daily diet and exercise data, and the output is the saved database record. During saving, the data is linked to a specific user ID in the database.

[0950] Step 3:

[0951] An emotion engine on the server analyzes the user's emotions from their input data. The emotion engine takes text data and facial expression data as input, and outputs the analysis results regarding emotions. For example, it might produce an analysis result such as, "Today's emotional state is somewhat depressed."

[0952] Step 4:

[0953] The server sends the emotion results recognized by the emotion engine to the generative AI model. The input data sent includes the user's basic information, past exercise and diet data, and emotion analysis results, while the output is the data analyzed by the generative AI model.

[0954] Step 5:

[0955] Generative AI models analyze input data and generate user-optimized advice and emotion-based motivational messages. For example, if the input data is "Weight 70kg, weight loss goal, 20 minutes of jogging, emotional state: tired," the output messages would be "I recommend 30 minutes of walking and 20 minutes of strength training today. Natto and eggs are good sources of protein, so please continue," and "You seem a little tired. But if you keep going, you will surely see results. Keep it up!"

[0956] Step 6:

[0957] The server sends advice and motivational messages received from the generative AI model to the user's device. The input data sent is the generated message, and the output is a notification message displayed on the device.

[0958] Step 7:

[0959] The device displays advice and motivational messages received from the server on the user's screen. Specifically, the device's display shows messages such as, "Today, we recommend 30 minutes of walking and 20 minutes of strength training," and "You seem a little tired, but if you keep going, you'll definitely see results. Keep it up!"

[0960] Step 8:

[0961] Users use the electronic payment function on their device to purchase health-related goods and services based on the advice they receive. For example, when purchasing fitness equipment online based on a notification message, the purchase process and payment are seamless.

[0962] The above are the specific processing steps for carrying out this invention.

[0963] 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.

[0964] 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.

[0965] 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.

[0966] [Fourth Embodiment]

[0967] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0968] 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.

[0969] 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).

[0970] 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.

[0971] 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.

[0972] 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).

[0973] 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.

[0974] 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.

[0975] 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.

[0976] 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.

[0977] 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.

[0978] 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.

[0979] 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".

[0980] The system of this invention begins with the user inputting their basic information and daily diet and exercise data through a terminal. The server receives this data and stores it in a database. It also simultaneously acquires the user's past data and sends it to a generative AI model. Based on the input data, this AI model generates individually optimized exercise and diet advice, as well as motivational messages to enhance the user's motivation. The generated content is then sent back to the terminal via the server and displayed on the user's terminal.

[0981] An example of the detailed processing of this system is shown below.

[0982] Program overview and specific examples

[0983] Entering and saving user information

[0984] When a user uses the system for the first time, they enter their basic information (e.g., age, gender, weight, height, exercise experience, health status, goals, etc.) through the terminal's UI. They also enter their daily meals (e.g., what they ate for breakfast) and exercise records (e.g., 20 minutes of jogging).

[0985] The device collects this data and sends it to the server.

[0986] The server receives user input data and stores it in the database. The stored data is managed along with past records.

[0987] Generating advice and motivational messages

[0988] The server retrieves historical data based on user requests and sends it to a generative AI model. This data includes the user's basic information, past dietary habits, exercise history, and weight fluctuations.

[0989] Generative AI models analyze input data and generate personalized exercise advice and dietary suggestions. For example, one user might receive specific advice such as, "Today, I recommend 30 minutes of walking and 20 minutes of strength training. Natto and eggs are good sources of protein, so please continue with them."

[0990] Furthermore, the generative AI model also generates encouraging messages to maintain user motivation. For example, these might include messages like, "Great start! If you keep going, you'll definitely reach your goal."

[0991] Notifications of advice and motivational messages

[0992] The server processes the advice and motivational messages received from the generative AI model and sends them to the user's terminal.

[0993] The device displays received advice and messages on its screen. This allows users to receive specific guidance regarding their daily activities and diet. Furthermore, encouraging messages help maintain motivation.

[0994] Specific example

[0995] Let's consider the case where a user (Mr. Tanaka) uses the system.

[0996] Ms. Tanaka enters the details of her breakfast (rice, natto, and egg) and her exercise record for today (20 minutes of jogging) into the terminal. Next, she presses the button to request exercise advice.

[0997] The terminal collects input data and sends it to the server.

[0998] The server saves the received data to a database and retrieves Tanaka's past data (last week's exercise records and weight fluctuations) and sends it to a generative AI model.

[0999] The generative AI model analyzes Tanaka's data and generates advice such as, "Today, I recommend 30 minutes of walking and 20 minutes of strength training. Natto and eggs are good sources of protein, so please continue with this," and an encouraging message such as, "That's a great start! If you keep it up, you will definitely reach your goal."

[1000] The server receives the generated advice and motivational messages and sends them to the terminal.

[1001] The device displays this information on its screen, allowing Ms. Tanaka to see it, obtain specific guidance regarding her exercise and diet for the day, and maintain her motivation through encouraging messages.

[1002] In this way, the system provides personalized health management advice and motivational support to each user, ultimately contributing to improved well-being and a better work environment.

[1003] The following describes the processing flow.

[1004] Step 1:

[1005] The user accesses the device's UI and enters their basic information. This basic information includes age, gender, weight, height, exercise experience, health status, and goals.

[1006] Step 2:

[1007] The terminal sends the entered basic information to the server. Specifically, it uses an HTTP request to pass the user's information to the server.

[1008] Step 3:

[1009] The server stores the received user's basic information in a database. This ensures that the user's basic information is managed securely.

[1010] Step 4:

[1011] To record their daily activities, users input details about their meals and exercise through the device's UI. For example, they might input data about eating rice, natto, and eggs for breakfast, or going for a 20-minute jog.

[1012] Step 5:

[1013] The device collects daily activity data and sends it to the server. This involves using HTTP requests again to pass the data to the server.

[1014] Step 6:

[1015] The server receives daily activity data and stores it in a database. This stored data is later referenced and used to generate advice.

[1016] Step 7:

[1017] The server retrieves the user's basic information and past exercise and dietary data from the database. This prepares the data for transmission to the generative AI model.

[1018] Step 8:

[1019] The server sends the acquired data to the generative AI model. Specifically, it sends a request containing the user's data to the AI ​​model's API endpoint.

[1020] Step 9:

[1021] A generative AI model analyzes the received data and generates personalized exercise and dietary advice. For example, it might generate advice such as, "Today, I recommend 30 minutes of walking and 20 minutes of strength training. Natto and eggs are good sources of protein, so please continue with them."

[1022] Step 10:

[1023] Generative AI models generate encouraging messages to maintain user motivation. For example, they might generate a message like, "Great start! If you keep going, you'll definitely reach your goal."

[1024] Step 11:

[1025] The server receives advice and motivational messages from the generative AI model. This includes procedures for processing the API responses from the generative AI model.

[1026] Step 12:

[1027] The server sends received advice and motivational messages to the terminal. This includes identifying the user ID and sending the data to the corresponding terminal.

[1028] Step 13:

[1029] The device displays received advice and motivational messages on the user's screen. This allows users to receive specific guidance on exercise and diet, as well as boost their motivation.

[1030] This series of steps allows users to receive personalized exercise and dietary advice, effectively manage their daily activities, and maintain motivation.

[1031] (Example 1)

[1032] 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".

[1033] Traditional systems have complex data entry and management processes for user health management, making it difficult to provide individually optimized advice. Furthermore, they lack effective feedback to maintain user motivation. As a result, there are challenges in smoothly improving users' health and achieving their goals.

[1034] 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.

[1035] In this invention, the server includes means for the user to input basic information and daily diet and exercise data, means for the terminal to transmit data collected by the terminal to the server, and means for the server to store the received data in a database and retrieve past data. This enables efficient collection and storage of data from the user, and makes it possible to provide individually optimized exercise and diet advice and motivational messages using a generative AI model. Users can easily receive this advice and messages through their terminal, resulting in more efficient health management and improved motivation.

[1036] A "user" refers to someone who uses the system to input basic information, daily diet, and exercise data, and receives advice.

[1037] A "terminal" refers to a device that allows users to input data, communicate with a server, and receive and display advice and motivational messages.

[1038] A "server" refers to a device that receives data sent by users, stores it in a database, and then sends that data to a generative AI model.

[1039] A "database" refers to information storage used by a server to store and manage data received from users, as well as historical data.

[1040] A "generative AI model" refers to an artificial intelligence program that generates personalized health advice and motivational messages based on user input data.

[1041] "Advice" refers to recommendations regarding exercise and diet that are generated by generative AI models to help users manage their health.

[1042] An "inspirational message" refers to a message generated by a generative AI model that aims to boost user motivation and encourage continuous health improvement.

[1043] Modes for carrying out the invention

[1044] The system of this invention allows users to input their basic information and daily diet and exercise data in order to manage their health, and based on that, it provides personalized advice and motivational messages to enhance their motivation.

[1045] Hardware and software configuration

[1046] The device provides an interface for the user to input information. This includes smartphones, tablets, and personal computers. The entered data is transmitted to a server via the internet.

[1047] The server is equipped with a high-performance processor and sufficient storage capacity to receive data sent by users. The server also uses a database management system (DBMS) to store and manage the received data and historical data.

[1048] The generative AI model is built using Python's TensorFlow and PyTorch libraries and runs on a server. The AI ​​model analyzes user input data and generates individually customized advice and motivational messages.

[1049] Details of data processing and calculations

[1050] The terminal uses standard data formats such as JSON and XML to send user input data to the server in real time. To ensure security, data transmission uses the HTTPS protocol.

[1051] The server temporarily stores received data in cache memory and performs error checking. Once consistency is confirmed, it is saved to the database. The saved data includes basic information, daily meal details, exercise records, and weight fluctuations.

[1052] The generative AI model receives a comprehensive dataset, including historically stored data, each time a request is made, and performs analysis. The model learns each user's unique health status, dietary history, and exercise habits, and then generates individually optimized advice.

[1053] Examples of specific prompt statements include the following:

[1054] "Please enter your basic information. Example: Age, Gender, Weight, Height."

[1055] "Please enter what you ate today. Example: What I ate for breakfast."

[1056] "Please enter your exercise plan for today. Example: 20 minutes of jogging."

[1057] Specific example

[1058] Consider a scenario where a user is using the system for the first time. The user enters basic information such as age (30 years old), gender (male), and weight (70 kg) through the device's UI. They also enter daily details of their meals (e.g., rice, natto, and eggs for breakfast) and exercise records (e.g., 20 minutes of jogging).

[1059] The device sends this data to the server in real time. The server stores the received data in a database, and when the user requests exercise advice, it sends it, along with past data, to a generative AI model.

[1060] The generative AI model analyzes user data and generates specific advice such as, "Today, I recommend a 30-minute walk and 20 minutes of strength training. Natto and eggs are good sources of protein, so please continue with this." It also provides encouraging messages like, "Great start! If you keep going, you will definitely reach your goal."

[1061] The server sends generated advice and motivational messages to the device, which displays them on the screen. Users can review these and use them to help manage their daily health.

[1062] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1063] Step 1:

[1064] The user accesses a UI form on their device and enters basic information. This basic information includes age, gender, weight, height, exercise experience, health status, and goals. They also enter daily meal details and exercise records. The input data is in text and numerical format, and the device packages this data into JSON format. Input data: basic information, meal details, exercise records. Output data: JSON format data.

[1065] Step 2:

[1066] The terminal sends user-entered data to the server over the network. HTTPS is used as the transmission protocol to ensure data security. Input data: User's data in JSON format. Output data: Transmission request to the server.

[1067] Step 3:

[1068] The server temporarily stores the JSON data received from the terminal in cache memory and performs an integrity check. After using an error checking program to confirm that the data is accurate, it is saved to the database. Input data: JSON data received from the terminal. Output data: Data whose integrity has been checked.

[1069] Step 4:

[1070] The server uses a database management system (DBMS) to permanently store data that has passed integrity checks in the database. The stored data is timestamped and tagged to facilitate management. Input data: Data whose integrity has been verified. Output data: Data stored in the database.

[1071] Step 5:

[1072] The user sends a request for exercise advice to the server via their device. This request is initiated by the user clicking a button on the interface. Input data: Exercise advice request. Output data: Request to the server.

[1073] Step 6:

[1074] After receiving a request from a user, the server retrieves the user's past data from the database. This data includes basic information, dietary details, exercise records, weight fluctuations, etc. Input data: Exercise advice request. Output data: Set of past data.

[1075] Step 7:

[1076] The server sends the acquired historical data to a generative AI model. The AI ​​model uses Python's TensorFlow or PyTorch libraries, and the model, built using these libraries, performs the data analysis. Input data: User's historical data. Output data: Data sent to the AI ​​model.

[1077] Step 8:

[1078] Generative AI models analyze submitted data to generate personalized advice and motivational messages. The AI ​​model analyzes input data and generates specific advice and messages tailored to each user's health condition. Input data: User's historical data. Output data: Individually optimized advice and motivational messages.

[1079] Step 9:

[1080] The server re-checks the integrity of the advice and motivational messages received from the generative AI model and saves them to the database. It then sends them to the user's terminal. Input data: Advice and motivational messages from the generative AI model. Output data: Data sent to the user's terminal.

[1081] Step 10:

[1082] The device displays advice and motivational messages received from the server on its screen. Users review this information to help manage their daily health. The motivational messages also help maintain their motivation. Input data: Advice and motivational messages from the server. Output data: Advice and messages displayed on the screen.

[1083] (Application Example 1)

[1084] 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".

[1085] Conventional fitness gym training support systems have difficulty providing optimal exercise menus and dietary advice tailored to each user's individual condition and goals, and thus have difficulty maintaining users' sustained motivation. As a result, users may not be able to perform effective training and may have difficulty achieving their goals. This invention aims to solve these problems.

[1086] 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.

[1087] In this invention, the server includes means for the user to input their basic information and daily diet and exercise data through a terminal; means for the server to store the data received from the user in a database and retrieve past data; means for the server to transmit the user's data to a generative AI model to generate individually optimized exercise menus and dietary advice, as well as motivational messages; and means for the server to transmit the generated exercise menus and dietary advice, as well as motivational messages, to the user, which the terminal displays. As a result, the user can receive individually optimized exercise and dietary advice, maintain motivation through motivational messages, and continue effective training.

[1088] A "terminal" is a device that a user uses to input their basic information and daily diet and exercise data.

[1089] A "server" is a central processing unit that stores data received from users in a database, retrieves past data, and sends it to generative AI models.

[1090] A "database" is an information storage system that stores and manages a user's basic information, daily diet and exercise data, and historical data.

[1091] A "generative AI model" is an artificial intelligence program that analyzes user data and generates personalized exercise menus, dietary advice, and motivational messages.

[1092] A "personally optimized exercise program" is an optimal exercise plan tailored to each individual user, based on their weight, goals, past exercise records, and dietary habits.

[1093] "Dietary advice" refers to specific meal suggestions recommended to the user based on their basic information and daily eating data.

[1094] An "inspirational message" is a message of encouragement or support generated to maintain and enhance the user's motivation.

[1095] The system of this invention begins with the user inputting their basic information and daily diet and exercise data via a terminal. Specifically, the user inputs their information using a smartphone app, and this input data is sent to a server. The server stores the received data in a database. It also retrieves past data and sends it to a generative AI model. Based on the input data, the generative AI model generates personalized exercise menus, dietary advice, and motivational messages for each user.

[1096] The hardware used consists of smartphones (user terminals) and servers, and the software is primarily developed using Python. Flask (a web framework) is used on the server side, and MongoDB or PostgreSQL are used for the database. The generative AI models include AI algorithms specialized in natural language processing and data analysis.

[1097] The server stores the user's input data in a database. The stored data is managed along with past records and sent to a generative AI model. The generative AI model analyzes the input data and generates personalized exercise menus and dietary advice. For example, some users might receive specific advice such as, "Today, I recommend 30 minutes of walking and 20 minutes of strength training. Natto and eggs are good sources of protein, so please continue with that." The generative AI model also generates motivational messages to help users stay motivated. For example, it might include messages like, "Great start! If you keep going, you'll definitely reach your goal."

[1098] Examples of prompts to send to a generative AI model are as follows:

[1099] "Based on the user's basic information and daily diet and exercise data, generate personalized exercise plans, dietary advice, and motivational messages."

[1100] The server sends the generated exercise menu, dietary advice, and motivational messages to the user's device. The user's device receives this information and displays it on the screen. This allows the user to receive specific guidance on their daily activities and diet. The motivational messages also help maintain their motivation.

[1101] As a concrete example, if a user enters the details of their breakfast (rice, natto, egg) and their exercise record for the day (20 minutes of jogging) into their device, that data is sent to the server. The server stores the data in a database and sends it, along with past data, to a generative AI model. The generative AI model analyzes this data and generates personalized advice and motivational messages for the user. The server sends the generated results to the device, which then displays them to the user. For example, the advice might say, "I recommend 30 minutes of walking and 20 minutes of strength training today. Natto and eggs are good sources of protein, so please continue with that," and the motivational message might say, "Great start! If you keep it up, you'll definitely reach your goal."

[1102] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1103] Step 1:

[1104] Users input their basic information (age, gender, weight, height, exercise experience, health status, goals) and daily food and exercise data via their device. This input data includes details such as what they ate for breakfast and the exercise they did that day. The system obtains basic user information and daily activity data as input.

[1105] Step 2:

[1106] The terminal collects data entered by the user and sends it to the server. The data transmission process involves converting the input data into an appropriate format, such as JSON, and sending it to the server using an HTTP POST request. Basic user information and daily activity data are used as input, and the data sent to the server is generated as output.

[1107] Step 3:

[1108] The server saves the received data to the database. During the saving process, the received data is converted into the required format and stored in the database along with existing user data. The input is data received from the terminal, and the output is data recorded in the database.

[1109] Step 4:

[1110] The server retrieves historical data from the database. This includes the user's exercise and dietary data from the past few weeks or months. The retrieved data is used as input data for a generative AI model. The input is the query results from the database, and the output is historical user data.

[1111] Step 5:

[1112] The server sends user data to a generative AI model. This data includes the user's basic information, current diet and exercise data, and historical data. The generative AI model analyzes this input data to generate personalized exercise and dietary advice, as well as motivational messages. The input is the user's complete dataset, and the output is the generated exercise menu, advice, and motivational messages.

[1113] Step 6:

[1114] Generative AI models analyze received data and generate individually optimized advice and motivational messages. During the analysis process, natural language processing and data analysis algorithms are used to generate user-specific feedback. User data is the input, and the generated content is the output.

[1115] Step 7:

[1116] The server sends exercise menus, dietary advice, and motivational messages received from the generative AI model to the user's terminal. The transmission process involves converting the generated data into an appropriate format and sending it to the user's terminal using an HTTP POST request. The input consists of the generated exercise menus, advice, and motivational messages, while the output is the data sent to the user's terminal.

[1117] Step 8:

[1118] The terminal displays exercise menus, dietary advice, and motivational messages received from the server. The display process includes visualizing the received data in an easy-to-understand format for the user and displaying it on the screen. The input is data received from the server, and the output is the content displayed to the user.

[1119] Through these steps, users can receive personalized exercise and dietary advice, maintain motivation through encouraging messages, and continue effective training.

[1120] 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.

[1121] The present invention combines an emotion engine with means for the user to input their basic information and daily diet and exercise data through a terminal, means for a server to store the data received from the user in a database and retrieve past data, means for the server to send the user's data to a generative AI model to generate individually optimized advice and motivational messages, and means for the server to send the generated advice and motivational messages to the user and display them on the terminal. The emotion engine has the function of recognizing emotions from the user's input data and sending it to the generative AI model to adjust the advice and motivational messages based on those emotions.

[1122] Program overview and specific examples

[1123] Entering and saving user information

[1124] When a user uses the system for the first time, they will enter their basic information (age, gender, weight, height, exercise experience, health status, goals, etc.) through the terminal's UI. They will also enter their daily meal details and exercise records.

[1125] The device collects this data and sends it to the server.

[1126] The server stores basic information and daily activity data received from users in a database. This ensures that users' basic information and historical data are securely stored.

[1127] Emotion recognition and data analysis

[1128] The emotion engine on the server recognizes emotions from the user's input data. For example, emotion recognition is performed through text analysis. If the user inputs facial expressions or voice data, that data is also analyzed.

[1129] The emotion engine recognizes the emotion and sends it to the generative AI model. This enables the generation of advice and motivational messages that reflect the user's emotional state.

[1130] Generating advice and motivational messages

[1131] The server retrieves the user's basic information and past exercise and diet data from the database and sends this information to a generative AI model. The results of the emotion engine are also included.

[1132] A generative AI model analyzes the data and generates personalized exercise and dietary advice for the user. Specific advice such as, "Today, I recommend 30 minutes of walking and 20 minutes of strength training. Natto and eggs are good sources of protein, so please continue with them," is provided.

[1133] Furthermore, generative AI models can also generate motivational messages based on emotions. For example, if a user is feeling a little down, a message like, "Today might be a tough day, but let's keep going!" will be generated.

[1134] Notifications of advice and motivational messages

[1135] The server receives advice and motivational messages from the generative AI model and sends them to the user's device.

[1136] The device displays received advice and motivational messages on the user's screen. This allows the user to receive appropriate guidance and encouragement.

[1137] Specific example

[1138] The following is a specific example from the case of user Tanaka.

[1139] Ms. Tanaka entered her breakfast details (rice, natto, and egg) and exercise record (20 minutes of jogging) into the terminal. In addition, Ms. Tanaka entered her facial expression data via the camera.

[1140] The terminal sends input data and facial expression data to the server.

[1141] The server stores the received data in a database and then uses an emotion engine to recognize Tanaka's emotions. This emotion result, along with the data, is then sent to a generative AI model.

[1142] The generative AI model analyzes Tanaka's data and generates advice and motivational messages based on Tanaka's emotions. For example, it generates specific advice such as, "Today I recommend 30 minutes of walking and 20 minutes of strength training. Natto and eggs are good sources of protein, so please continue with that," and a message like, "You seem a little tired. But if you keep going, you'll definitely see results. Keep it up!"

[1143] The server sends the generated advice and motivational messages to the terminal.

[1144] The device displays this information on its screen, allowing Ms. Tanaka to see it, obtain specific guidance regarding her exercise and diet for the day, and maintain her motivation through emotionally sensitive and encouraging messages.

[1145] In this way, by combining emotional engines, it is possible to create a system that provides more personalized advice that responds to the user's emotional state. This allows users to manage their health more effectively and maintain their motivation.

[1146] The following describes the processing flow.

[1147] Step 1:

[1148] The user accesses the device's UI and enters their basic information. This basic information includes age, gender, weight, height, exercise experience, health status, and goals.

[1149] Step 2:

[1150] The terminal sends the entered basic information to the server. Specifically, it uses an HTTP request to pass the user's information to the server.

[1151] Step 3:

[1152] The server stores the received user's basic information in a database. This ensures that the user's basic information is managed securely.

[1153] Step 4:

[1154] To record their daily activities, users input details about their meals and exercise through the device's UI. For example, they might input data about eating rice, natto, and eggs for breakfast, or going for a 20-minute jog.

[1155] Step 5:

[1156] The user inputs facial expression data via the camera using their device. Additionally, if audio data is also to be collected, a microphone is used.

[1157] Step 6:

[1158] The device collects daily activity data, facial expression data, and voice data, and sends them to the server. This involves using HTTP requests again to pass the data to the server.

[1159] Step 7:

[1160] The server receives daily activity data, facial expression data, and voice data, and stores them in a database. The stored data is later used for emotion recognition and advice generation.

[1161] Step 8:

[1162] The server uses an emotion engine to recognize emotions from user input data, facial expression data, and voice data. For example, it performs text analysis, facial expression analysis, and voice analysis to estimate the user's emotional state.

[1163] Step 9:

[1164] The emotion engine recognizes emotions, which are then compiled on a server and sent to a generative AI model. This data also includes the user's emotional state.

[1165] Step 10:

[1166] The server retrieves the user's basic information and past exercise and diet data from the database and sends this information, along with sentiment results, to a generative AI model. This involves sending requests to the AI ​​model's API endpoint.

[1167] Step 11:

[1168] The generative AI model analyzes the received data and generates personalized exercise and dietary advice. For example, it might generate specific advice such as, "Today, I recommend 30 minutes of walking and 20 minutes of strength training. Natto and eggs are good sources of protein, so please continue with them."

[1169] Step 12:

[1170] The generative AI model also generates encouraging messages based on the user's emotional state. For example, if the user is feeling a little down, it will generate a message such as, "Today may be a tough day, but let's keep going!"

[1171] Step 13:

[1172] The server processes advice and motivational messages received from the generative AI model and sends them to the user's terminal. This includes identifying the user's ID and sending the corresponding data.

[1173] Step 14:

[1174] The device displays received advice and motivational messages on the user's screen. This allows the user to receive specific guidance and encouragement.

[1175] This series of steps allows users to receive personalized exercise and dietary advice, effectively manage their daily activities, and maintain motivation. Furthermore, the emotional engine provides individualized feedback that takes the user's emotions into consideration.

[1176] (Example 2)

[1177] 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".

[1178] Traditional health management systems have struggled to provide advice and motivational messages that are tailored to users' emotions and individual circumstances. This has made it difficult to maintain user motivation and to implement personalized health management.

[1179] 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.

[1180] In this invention, the server includes means for the user to input their basic information and daily diet and exercise data through a terminal; means for the server to store the data received from the user in a database and retrieve past data; means for the server to send the user's data to a generating AI model to generate individually optimized advice and motivational messages; means for the server to send the generated advice and motivational messages to the user and for the terminal to display them; and means for an emotion engine to recognize emotions from the user's input data and send them to the generating AI model to adjust the advice and motivational messages based on the user's emotions. This makes it possible to provide advice and motivational messages that are tailored to the user's emotions and individual circumstances.

[1181] A "terminal" is a device used by users to input basic information and daily diet and exercise data, and to communicate with the server.

[1182] A "server" is a central device that stores data received from users and works in conjunction with generative AI models and emotion engines to generate and send advice and motivational messages.

[1183] A "database" is a storage device that securely stores a user's basic information and daily activity data, allowing it to be retrieved and analyzed later.

[1184] A "generative AI model" is an algorithm that generates individually optimized advice and motivational messages based on the user's basic information, past data, and emotional results from an emotion engine.

[1185] "Advice" refers to specific action guidelines provided by the generative AI model for the user's health management.

[1186] An "encouraging message" is a message of encouragement or support provided by a generative AI model based on the user's emotional state.

[1187] An "emotion engine" is an analysis device or software that recognizes emotions from data input by the user and sends the results to a generating AI model.

[1188] A "user" is an individual who uses the system to input their basic information and daily activity data, and receives generated advice and motivational messages.

[1189] The present invention provides a system comprising: means for the user to input their basic information and daily diet and exercise data through a terminal; means for a server to store the data received from the user in a database and retrieve past data; means for the server to send the user's data to a generating AI model to generate individually optimized advice and motivational messages; and means for the server to send the generated advice and motivational messages to the user, which are then displayed on the terminal. Furthermore, by combining it with an emotion engine, the system has the function of recognizing emotions from the user's input data and sending it to the generating AI model to adjust the advice and motivational messages based on the user's emotions.

[1190] Entering and saving user information

[1191] When a user logs into the system for the first time, they enter their basic information (age, gender, weight, height, exercise experience, health status, goals, etc.) through the terminal's user interface (UI). They also enter their daily diet and exercise records. The terminal collects this data and sends it to the server. The server stores the basic information and daily activity data received from the user in a database. This ensures that the user's basic information and past data are securely stored.

[1192] Emotion recognition and data analysis

[1193] An emotion engine embedded in the server recognizes emotions from user input data. For example, it recognizes emotions through text analysis. If the user inputs facial expressions or voice data, that data is also analyzed. By sending the emotion results recognized by the emotion engine to a generation AI model, it becomes possible to generate advice and encouragement messages that reflect the user's emotional state.

[1194] Generating advice and motivational messages

[1195] The server retrieves the user's basic information and past exercise and diet data from the database and sends it to a generative AI model. This includes the results of the emotion engine. The generative AI model analyzes the data and generates exercise and diet advice optimized for the user. For example, it might provide specific advice such as, "Today, I recommend 30 minutes of walking and 20 minutes of strength training. Natto and eggs are good sources of protein, so please continue with them." The generative AI model also generates motivational messages based on emotions. For example, if the user is feeling a little down, it might generate a message like, "Today might be a tough day, but let's keep going!"

[1196] Notification of advice and motivational messages

[1197] The server sends advice and motivational messages received from the generated AI model to the user's device. The device displays the received advice and motivational messages on the user's screen. This allows the user to receive appropriate guidance and encouragement.

[1198] Specific example

[1199] The following is a specific example for a particular individual user. This individual inputs their breakfast details (rice, natto, egg) and exercise record (20 minutes of jogging) into the device. In addition, this individual inputs facial expression data via the camera. The device sends the input data and facial expression data to the server. The server stores the received data in a database and uses an emotion engine to recognize the individual's emotions. It sends the data, along with the emotion result, to a generating AI model. The generating AI model analyzes the individual's data and generates advice and motivational messages based on the individual's emotions. For example, it might generate specific advice such as, "I recommend 30 minutes of walking and 20 minutes of strength training today. Natto and eggs are good sources of protein, so please continue," and a message such as, "You seem a little tired. But if you keep going, you will surely see results. Keep it up!" The server sends the generated advice and motivational message to the device. The device displays this on the screen, and the individual can see it and receive specific guidance regarding today's exercise and diet, as well as maintain motivation with an emotionally sensitive motivational message.

[1200] Example of a prompt

[1201] 1. "The user entered their breakfast details (rice, natto, egg) and exercise record (20 minutes of jogging), and collected facial expression data using a camera. Based on this data, please generate optimal advice and motivational messages."

[1202] 2. "Please advise on what kind of encouraging message to send to a user who is feeling down."

[1203] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1204] Step 1:

[1205] Users input basic information (age, gender, weight, height, exercise experience, health status, goals, etc.) through their device, and also input their daily meal details and exercise records. This collects basic information and daily data from the user on the device. The device then transmits the entered basic information and meal / exercise data to the server.

[1206] Step 2:

[1207] The server saves basic information and daily meal and exercise data received from the terminal to a database. This allows the user's past data to be accumulated and securely stored on the server. Within the server, the operation of writing received data to the database is performed.

[1208] Step 3:

[1209] The emotion engine embedded in the server analyzes the input data sent by the user. Specifically, it may recognize emotions from the words entered by the user through text analysis, or, if facial expression data or voice data is input, it may analyze these to identify emotions. Through this analysis process, the emotion engine extracts the user's emotional state as data. The output of the emotion engine may include emotional states such as "joy," "sadness," or "fatigue."

[1210] Step 4:

[1211] The server compiles the user's basic information, past exercise and diet data, along with the emotion engine's output, and sends it to the generative AI model. The server combines all the necessary data into a single request and sends it to the generative AI model. Through this communication, the generative AI model receives the user's overall data and prepares it for analysis.

[1212] Step 5:

[1213] The generation AI model analyzes the submitted data and generates personalized advice and motivational messages based on the user's basic information, past data, and emotional state. Examples of advice include, "Today, I recommend a 30-minute walk and 20 minutes of strength training. Natto and eggs are good sources of protein, so please continue with them." Examples of emotionally-based motivational messages include, "You seem a little tired. But if you keep going, you'll definitely see results. Keep it up!"

[1214] Step 6:

[1215] The server sends advice and motivational messages received from the generated AI model to the user's device. The server receives the output from the generated AI model and resends it to the appropriate device for each user. This process prepares the user's device to receive the messages.

[1216] Step 7:

[1217] The device displays received advice and motivational messages on the user's screen. Users can view these messages to gain specific guidance on exercise and diet for the day, and maintain motivation through emotionally resonant motivational messages. For example, the user might review the message displayed on the device screen and act accordingly.

[1218] (Application Example 2)

[1219] 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".

[1220] Traditionally, systems have existed that allow users to input daily diet and exercise data to manage their health and receive personalized advice. However, these systems failed to consider the user's emotional state, resulting in a lack of effective encouragement and motivation. Furthermore, the absence of electronic payment functionality linked to health management made it difficult for users to easily purchase necessary goods and services. The objective of this invention is to solve these problems.

[1221] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for the user to input their basic information and daily diet and exercise data through a terminal, means for the server to store the data received from the user in a database and retrieve past data, means for the server to transmit the user's data to a generative AI model to generate individually optimized advice and motivational messages, means for the emotion engine to analyze the emotional data input by the user, transmit the analysis results to the generative AI model to generate individually optimized emotion-based advice and motivational messages, means for notifying the generated advice and motivational messages on the terminal, and means for purchasing health-related goods and services through an electronic payment function. As a result, individually optimized advice and motivational messages that reflect the user's emotional state are generated, making it possible to increase the user's motivation. In addition, the purchase of health-related goods and services can be made seamlessly through the electronic payment function, further promoting the user's health management.

[1222] A "user" is the individual who uses the system and inputs their own health management data.

[1223] A "terminal" is an electronic device used by users to input basic information and daily diet and exercise data.

[1224] "Basic information" refers to data such as the user's age, gender, weight, height, exercise experience, health status, and goals.

[1225] A "server" is a central computing system that stores and analyzes data received from users and transmits it to generative AI models.

[1226] A "database" is an information management system used to store data received from users, as well as advice and motivational messages that are generated.

[1227] A "generative AI model" is an artificial intelligence model that analyzes user data and generates personalized advice and motivational messages.

[1228] "Advice" refers to specific instructions and recommendations regarding exercise and diet provided to the user by a generative AI model.

[1229] An "encouraging message" is a message provided by a generative AI model that takes the user's emotional state into consideration, in order to encourage the user.

[1230] An "emotion engine" is an engine that analyzes emotions from data entered by the user and sends the results to a generative AI model.

[1231] "Emotional data" refers to information about emotions that is derived from user input data.

[1232] "Electronic payment functionality" refers to online payment features that allow users to seamlessly purchase health-related goods and services.

[1233] "Notifications" refer to the act of displaying generated advice and motivational messages on the user's device.

[1234] "Motivation" refers to the willingness or motivation a user has to continue managing their health.

[1235] "Items" refer to specific items such as food and fitness equipment that users need to manage their health.

[1236] "Services" refer to activities such as training guidance and nutritional counseling that users utilize to support their health management.

[1237] This invention is a system for users to effectively manage their health. The details of the system will be described based on the following procedure.

[1238] System Configuration

[1239] Hardware:

[1240] 1. User terminal: A device such as a smartphone or tablet that allows the user to input data and has a camera function.

[1241] 2. Server: A computer system that performs data processing and storage.

[1242] software:

[1243] 1. Database: A system that stores the user's basic information and daily diet and exercise data.

[1244] 2. Generative AI Models: Artificial intelligence models that analyze user data and generate individually optimized advice and motivational messages.

[1245] 3. Emotion Engine: An engine that analyzes emotions from user input data and sends the results to a generative AI model.

[1246] 4. Electronic payment function: An online payment system for users to purchase health-related goods and services.

[1247] System Operation Overview

[1248] Entering and saving user information

[1249] Users input their basic information (age, gender, weight, height, exercise experience, health status, goals, etc.) through the device's UI. They also input their daily diet and exercise records, and further input facial expression data via the camera. The device sends this data to the server, which stores the received data in a database.

[1250] Emotion recognition and data analysis

[1251] The emotion engine on the server recognizes emotions from the user's input data and sends the recognition results to a generative AI model. The generative AI model analyzes the user's basic information, past exercise and diet data, and emotional state sent from the server to generate individually optimized advice and emotion-based motivational messages.

[1252] Notifications of advice and motivational messages

[1253] The server sends advice and motivational messages received from the generative AI model to the user's device, which then displays them on the screen. The user uses this information to obtain appropriate guidance and encouragement.

[1254] Electronic payment function

[1255] Users utilize online payment features to purchase appropriate goods and services based on the advice they receive. This allows for seamless purchases of essential health foods, fitness equipment, and other items.

[1256] Specific example

[1257] For example, a user might input meal data such as "rice, natto, and eggs" and record that they jogged for 20 minutes. They also input facial expression data via camera. The device sends this data to a server, which uses an emotion engine to recognize the user's emotions. The server then sends this recognition result along with the data to a generative AI model. The generative AI model performs analysis and generates specific advice such as, "Today, I recommend 30 minutes of walking and 20 minutes of strength training. Natto and eggs are good sources of protein, so please continue this," as well as an emotion-based motivational message such as, "You seem a little tired. But if you keep going, you'll definitely see results. Keep it up!" The server sends these to the device, and the user checks them on the screen.

[1258] Example of a prompt

[1259] "Please tell us about your exercise and meals today. Also, please point your face towards the camera and input your facial expression."

[1260] The above describes the embodiments for carrying out this invention.

[1261] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1262] Step 1:

[1263] Users input basic information (age, gender, weight, height, exercise experience, health status, goals) and daily meal and exercise data through the device's UI. The device collects this data and sends it to the server. This input data includes text-based meal details, exercise details, and facial expression data to interpret emotions. Examples of data collected by the device include "rice, natto, eggs" and "20 minutes of jogging."

[1264] Step 2:

[1265] The server saves basic information and daily data received from users to a database. The input for this save operation is the user's basic information and daily diet and exercise data, and the output is the saved database record. During saving, the data is linked to a specific user ID in the database.

[1266] Step 3:

[1267] An emotion engine on the server analyzes the user's emotions from their input data. The emotion engine takes text data and facial expression data as input, and outputs the analysis results regarding emotions. For example, it might produce an analysis result such as, "Today's emotional state is somewhat depressed."

[1268] Step 4:

[1269] The server sends the emotion results recognized by the emotion engine to the generative AI model. The input data sent includes the user's basic information, past exercise and diet data, and emotion analysis results, while the output is the data analyzed by the generative AI model.

[1270] Step 5:

[1271] Generative AI models analyze input data and generate user-optimized advice and emotion-based motivational messages. For example, if the input data is "Weight 70kg, weight loss goal, 20 minutes of jogging, emotional state: tired," the output messages would be "I recommend 30 minutes of walking and 20 minutes of strength training today. Natto and eggs are good sources of protein, so please continue," and "You seem a little tired. But if you keep going, you will surely see results. Keep it up!"

[1272] Step 6:

[1273] The server sends advice and motivational messages received from the generative AI model to the user's device. The input data sent is the generated message, and the output is a notification message displayed on the device.

[1274] Step 7:

[1275] The device displays advice and motivational messages received from the server on the user's screen. Specifically, the device's display shows messages such as, "Today, we recommend 30 minutes of walking and 20 minutes of strength training," and "You seem a little tired, but if you keep going, you'll definitely see results. Keep it up!"

[1276] Step 8:

[1277] Users use the electronic payment function on their device to purchase health-related goods and services based on the advice they receive. For example, when purchasing fitness equipment online based on a notification message, the purchase process and payment are seamless.

[1278] The above are the specific processing steps for carrying out this invention.

[1279] 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.

[1280] 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.

[1281] 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.

[1282] 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.

[1283] 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. In the upper and lower directions of the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. Also, the upper side of the concentric circles is where "pleasant" emotions are located, and the lower side is where "unpleasant" emotions are located. In this way, 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.

[1284] 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.

[1285] 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.

[1286] 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.

[1287] 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."

[1288] 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.

[1289] 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.

[1290] 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.

[1291] 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.

[1292] 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.

[1293] 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.

[1294] 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.

[1295] 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.

[1296] 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.

[1297] 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.

[1298] 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.

[1299] 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 as being incorporated by reference.

[1300] The following is further disclosed regarding the embodiments described above.

[1301] (Claim 1)

[1302] A means for users to input their basic information and daily diet and exercise data through their device,

[1303] A server stores data received from users in a database and provides a means to retrieve past data.

[1304] A means by which a server sends user data to a generative AI model to generate individually optimized advice and motivational messages,

[1305] The server sends generated advice and motivational messages to the user, and the terminal has a means to display them.

[1306] A system that includes this.

[1307] (Claim 2)

[1308] The system according to claim 1, wherein a generative AI model generates individually optimized advice based on the user's weight, goals, past exercise records, and diet.

[1309] (Claim 3)

[1310] The system according to claim 1, wherein the server stores advice and motivational messages received from a generative AI model in a database and uses them to continuously increase the user's motivation.

[1311] "Example 1"

[1312] (Claim 1)

[1313] A means for users to input their basic information and daily diet and exercise data through their device,

[1314] A means by which a device sends data collected from the user to a server,

[1315] A server stores data received from users in a database and provides a means to retrieve past data.

[1316] A means by which a server retrieves a user's past data based on a user's request and sends it to a generative AI model,

[1317] A generative AI model provides a means for generating individually optimized advice and motivational messages based on input data,

[1318] A means for the server to process advice and motivational messages received from a generative AI model and send them to the user's terminal,

[1319] A means for displaying advice and encouragement messages received by the device,

[1320] A system that includes this.

[1321] (Claim 2)

[1322] The system according to claim 1, wherein a generative AI model generates individually optimized advice based on the user's weight, goals, past exercise records, and diet.

[1323] (Claim 3)

[1324] The system according to claim 1, wherein the server stores advice and motivational messages received from a generative AI model in a database and uses them to continuously increase the user's motivation.

[1325] "Application Example 1"

[1326] (Claim 1)

[1327] A means for users to input their basic information and daily diet and exercise data through their device,

[1328] A server stores data received from users in a database and provides a means to retrieve past data.

[1329] A means by which a server transmits user data to a generative AI model to generate individually optimized exercise menus and dietary advice, as well as motivational messages.

[1330] The server sends generated exercise menus and dietary advice, as well as motivational messages, to the user, and the terminal has means to display them.

[1331] A system that includes this.

[1332] (Claim 2)

[1333] The system according to claim 1, wherein a generative AI model generates an individually optimized exercise menu and dietary advice based on the user's weight, goals, past exercise records, and dietary content.

[1334] (Claim 3)

[1335] The system according to claim 1, in which the server stores exercise menus and dietary advice, as well as motivational messages received from a generative AI model, in a database and uses them to continuously enhance the user's motivation.

[1336] "Example 2 of combining an emotion engine"

[1337] (Claim 1)

[1338] A means for users to input their basic information and daily diet and exercise data through their device,

[1339] A server stores data received from a user in a database and provides a means to retrieve past data.

[1340] A means by which a server sends user data to a generating AI model to generate individually optimized advice and motivational messages,

[1341] The server sends generated advice and motivational messages to the user, and the terminal has a means to display them.

[1342] A means of adjusting advice and motivational messages based on the user's emotions by having an emotion engine recognize emotions from user input data and send them to a generative AI model,

[1343] A system that includes this.

[1344] (Claim 2)

[1345] The system according to claim 1, wherein the generating AI model generates individually optimized advice based on the user's weight, goals, past exercise records, and diet.

[1346] (Claim 3)

[1347] The system according to claim 1, wherein the server stores advice and motivational messages received from a generated AI model in a database and uses them to continuously increase the user's motivation.

[1348] "Application example 2 when combining with an emotional engine"

[1349] (Claim 1)

[1350] A means for users to input their basic information and daily diet and exercise data through their device,

[1351] A server stores data received from users in a database and provides a means to retrieve past data.

[1352] A means by which a server sends user data to a generative AI model to generate individually optimized advice and motivational messages,

[1353] The server sends generated advice and motivational messages to the user, and the terminal has a means to display them.

[1354] A means for analyzing emotional data entered by a user using an emotion engine, sending the analysis results to a generative AI model, and generating individually optimized emotion-based advice and motivational messages,

[1355] A means of notifying the generated advice and motivational messages on the device,

[1356] Electronic payment functions provide a means of purchasing health-related goods and services,

[1357] A system that includes this.

[1358] (Claim 2)

[1359] The system according to claim 1, wherein a generative AI model generates individually optimized advice based on the user's weight, goals, past exercise records, and diet.

[1360] (Claim 3)

[1361] The system according to claim 1, wherein the server stores advice and motivational messages received from a generative AI model in a database and uses them to continuously increase the user's motivation. [Explanation of symbols]

[1362] 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 for users to input their basic information and daily diet and exercise data through their device, A server stores data received from users in a database and provides a means to retrieve past data. A means by which a server sends user data to a generative AI model to generate individually optimized advice and motivational messages, The server sends generated advice and motivational messages to the user, and the terminal has a means to display them. A system that includes this.

2. The system according to claim 1, wherein a generative AI model generates individually optimized advice based on the user's weight, goals, past exercise records, and diet.

3. The system according to claim 1, wherein the server stores advice and motivational messages received from a generative AI model in a database and uses it to continuously increase the user's motivation.

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