System

Abstract

A system is provided.SOLUTION: A system comprising: means for a user to input his / her physical condition and foods he / she wants to eat through a terminal; means for transmitting input data to a server; means for the server analyzing the input data and generating a list of appropriate ingredients and recipes; means for returning the menu plan generated by the server to the terminal; and means for the terminal displaying the proposed menu to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] In recent years, preventing lifestyle-related diseases and dementia has been attracting attention as an important health issue. However, it is difficult to provide appropriate nutritional management and exercise plans tailored to individual physical conditions and preferences, and conventional methods have low user retention. Furthermore, when specialized medical support is needed on a daily basis, there is a lack of systems that allow easy access to it. Therefore, there is a need for a comprehensive health management system that provides appropriate diet, exercise, brain training, and medical support based on individual health conditions and preferences. [Means for solving the problem]

[0005] The present invention provides a system that solves the above problems by the following means.

[0006] The system includes a means for a user to input the user's physical condition and what he / she wants to eat that day, a means for transmitting the input data to a server, a means for the server to analyze the input data and generate a list of suitable ingredients and recipes, a means for returning the menu plan generated by the server to a terminal, and a means for the terminal to display the suggested menu to the user.

[0007] The device further includes a means for the user to input age and weight, a means for transmitting the input data to a server, a means for the server to analyze the input data and generate an optimal exercise plan based on past exercise history, a means for the server to return the generated exercise plan to the terminal, and a means for the terminal to display the proposed exercise plan and implementation method to the user.

[0008] It also includes a means for the user to input that he or she wishes to start brain training, a means for transmitting the input data to a server, a means for the server to generate new training content based on past training history, a means for returning the generated training content to the terminal, and a means for playing back the training content provided by the terminal with audio to the user.

[0009] The system further includes means for a user to input a health-related question, means for transmitting the input data to a specialized medical support system, means for the specialized medical support system to generate an answer to the question and return it to a server, means for the server to transmit the received answer to a terminal, and means for the terminal to display the answer from the specialized medical support to the user.

[0010] This will make it easier for users to manage their individual health on a daily basis, preventing lifestyle-related diseases and dementia and providing appropriate medical support.

[0011] "User" refers to an individual who uses this system.

[0012] "Terminal" refers to a device through which a user inputs information and receives a display, such as a smartphone, tablet, or computer.

[0013] "Server" refers to a computer system that receives data sent by users and performs analysis and generation.

[0014] "Lifestyle-related diseases" is a general term for diseases that develop due to inappropriate lifestyle habits, including diabetes, high blood pressure, and heart disease.

[0015] A "menu plan" refers to a comprehensive list of menus for each day's meals.

[0016] "Exercise plan" refers to a plan for planning and proposing exercise content suitable for the user.

[0017] "Brain training" refers to exercises or activities designed to improve or maintain a user's brain function.

[0018] "Training content" refers to the specific tasks and activities of brain training.

[0019] A "specialized medical support system" refers to a system that provides medical expertise and advice.

[0020] "Ingredients" refers to the main ingredients or materials used to make a dish.

[0021] A "recipe" is a detailed description of the steps and ingredients required to prepare a particular dish.

[0022] "Nutrition management" refers to planning and instructing people to take in appropriate nutrients based on their individual health conditions. [Brief explanation of the drawings]

[0023] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0024] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0025] First, the terms used in the following description will be explained.

[0026] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0027] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0028] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0029] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0030] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0031] [First embodiment]

[0032] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0033] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0034] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0035] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0036] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0037] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0038] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0039] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0040] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0041] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0042] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0043] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0044] The present invention relates to a system that enables prevention of lifestyle-related diseases, exercise suggestions, brain training, and medical support. This system selects appropriate actions based on user input and provides the results to the user.

[0045] First, the user inputs their physical condition and what they want to eat that day through the device. For example, the user might input, "I'm feeling a bit tired today, so I'd like to eat something light." The device then sends this input data to the server. The server analyzes the received data, generates a list of appropriate ingredients and recipes, and suggests menu ideas such as "spinach and tomato salad" and "tofu and seaweed soup." The device then displays these menu ideas to the user.

[0046] Next, the user enters their age and weight through the device. For example, they might enter "45 years old, 70 kg." The device then sends this input data to the server, which then generates an optimal exercise plan based on the user's age, weight, and past exercise history. Specific exercises (for example, "30 minutes of light walking and 10 minutes of yoga stretching") are suggested based on the user's health condition. The device then displays this exercise plan to the user.

[0047] Furthermore, if the user wishes to start brain training, he / she may input, for example, "Start brain training." The device sends this request to the server, and the server generates new training content based on past training history. For example, it plays "A new folk tale, 'Urashima Taro,'" with audio. The device provides this training content to the user.

[0048] Furthermore, when a user inputs a question about their health, for example, "I feel like my blood pressure has been high recently," the device sends the input data to a specialized medical support system. The specialized medical support system generates an answer to the question and sends it back to the server. The server receives this answer and sends it to the device. The device then displays the answer to the user, saying, "Try to exercise moderately and eat a balanced diet."

[0049] A specific scenario for use would be for the user to enter their physical condition into the device in the morning, receive daily menu suggestions based on that information, review their exercise plan in the evening, and then do brain training at night. It would also be possible to immediately consult medical support if they felt unwell. This system is expected to make daily health management more personalized and provide continuous support to users.

[0050] The processing flow will be explained below.

[0051] Step 1:

[0052] The user inputs their physical condition and what they want to eat that day into the terminal.

[0053] Example: A user enters, "I'm feeling a bit tired today and would like to eat something light."

[0054] Step 2:

[0055] The terminal transmits the input data to the server.

[0056] The entered "physical condition" and "food preferences" data is sent to the server in JSON format.

[0057] Step 3:

[0058] The server parses the input data.

[0059] The server analyzes keywords such as "feeling tired" and "light food" and searches the database for relevant ingredients and recipes.

[0060] Step 4:

[0061] The server generates a list of suitable ingredients and recipes.

[0062] Based on the analysis results, recipes are generated using ingredients that are rich in vitamins and easy to digest.

[0063] Step 5:

[0064] The server sends the generated menu plan back to the terminal.

[0065] Send the menu plan to the device in JSON format.

[0066] Step 6:

[0067] The terminal displays the suggested menu to the user.

[0068] Example: Display "Spinach and Tomato Salad" and "Tofu and Seaweed Soup."

[0069] Step 1:

[0070] The user enters their age and weight into the terminal.

[0071] Example: User enters "45 years old, 70kg."

[0072] Step 2:

[0073] The terminal transmits the input data to the server.

[0074] The entered "age" and "weight" data is sent to the server in JSON format.

[0075] Step 3:

[0076] The server analyzes the input data and references past exercise history.

[0077] The user's past exercise history is retrieved from the database and checked to ensure that the same exercise is not repeated.

[0078] Step 4:

[0079] The server generates an optimal exercise plan.

[0080] Based on your age, weight, and past exercise history, it will suggest new exercises (for example, "30 minutes of easy walking and 10 minutes of yoga stretching").

[0081] Step 5:

[0082] The server returns the generated exercise plan to the terminal.

[0083] The exercise plan is sent to the device in JSON format.

[0084] Step 6:

[0085] The terminal displays the suggested exercise plan to the user.

[0086] A suggested exercise plan and implementation method is displayed to the user.

[0087] Step 1:

[0088] The user inputs into the terminal that he / she wants to start brain training.

[0089] Example: User types "Start Brain Training."

[0090] Step 2:

[0091] The device sends a request to the server.

[0092] Sends request data to the server in JSON format.

[0093] Step 3:

[0094] The server references past training history.

[0095] The system retrieves the user's past brain training history from a database and avoids repeating the same content.

[0096] Step 4:

[0097] The server generates new training content (fairy tales, original stories, rakugo, etc.).

[0098] Browse the knowledge base and generate new brain training challenges.

[0099] Step 5:

[0100] The server returns the generated training content to the terminal.

[0101] Send the training content to the device in JSON format.

[0102] Step 6:

[0103] The device provides the training content to the user with audio.

[0104] Example: Play "The New Folktale of Urashima Taro" with audio.

[0105] Step 1:

[0106] The user inputs a health-related question into the terminal.

[0107] Example: A user types, "My blood pressure feels high lately."

[0108] Step 2:

[0109] The terminal sends the input data to a specialized medical support system.

[0110] The input data is sent in JSON format to the API of a specialized medical support system.

[0111] Step 3:

[0112] A specialized medical support system generates answers to questions.

[0113] An answer is generated based on the medical information and sent back to the server.

[0114] Step 4:

[0115] The server sends the response received from the HELPO app in JSON format to the device.

[0116] The received response is sent to the device.

[0117] Step 5:

[0118] The terminal displays professional medical support answers to the user.

[0119] Example: Display "Make sure to exercise moderately and eat a balanced diet."

[0120] Example 1

[0121] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0122] Current health management systems struggle to provide personalized recommendations tailored to individual users' individual circumstances and needs. Furthermore, systems that address multiple areas such as dietary recommendations, exercise plans, brain training, and medical support are not integrated, requiring users to navigate multiple applications and services. Furthermore, the recommendations are general, with few specific advice based on individual lifestyle habits or past history.

[0123] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0124] In this invention, the server includes means for analyzing input data and using a generative AI model to generate a list of appropriate ingredients and recipes, means for analyzing input data and using a generative AI model to generate an optimal exercise plan based on past exercise history, and means for using a generative AI model to generate new training content based on past training history. This allows a single system to provide personalized suggestions tailored to each user's individual situation and needs, enabling more specific and actionable advice.

[0125] "User" refers to an individual who uses the system and is a consumer who provides input according to their health status and needs.

[0126] "Terminal" refers to a device through which a user makes input, and includes electronic devices such as smartphones, tablets, and personal computers.

[0127] "Server" refers to the central computing unit that receives and analyzes the data sent by the User, generates appropriate suggestions and sends them back to the Terminal.

[0128] "Input data" refers to information provided by the user through the terminal, including physical condition, dietary preferences, age, weight, requests to start brain training, health-related questions, etc.

[0129] A "generative AI model" refers to an algorithm that uses natural language processing and machine learning techniques to analyze input data and generate appropriate suggestions.

[0130] "Menu suggestions" refers to a list of ingredients and recipes suggested by a generative AI model based on the user's physical condition and dietary preferences.

[0131] "Exercise plan" refers to a specific exercise schedule suggested by a generative AI model based on a user's age, weight, and past exercise history.

[0132] "Training content" refers to the specific brain training content suggested by the generative AI model based on the user's past training history.

[0133] A "medical support system" refers to a system with specialized medical knowledge that is used to generate appropriate answers to health-related questions posed by users.

[0134] "Proposal" refers to the specific content that the server generates using the generative AI model and provides to the user via the terminal.

[0135] This invention relates to a system that enables prevention of lifestyle-related diseases, exercise suggestions, brain training, and medical support. This system selects appropriate actions based on user input and provides the results to the user.

[0136] First, the user inputs their physical condition and meal preferences for the day into the interface on their device. For example, the user might input, "I'm feeling a bit tired today, so I'd like to eat something light." The device then sends this input data to the server. The server analyzes the received data and uses a generative AI model to generate a list of appropriate ingredients and recipes. Specific menu suggestions generated include "spinach and tomato salad" and "tofu and seaweed soup." The device then displays these menu suggestions to the user.

[0137] Next, the user enters their age and weight through the device. For example, they might enter "45 years old, 70 kg." The device then sends this input data to the server. The server then analyzes the user's age, weight, and past exercise history, and uses a generative AI model to generate an optimal exercise plan. Specific exercises are suggested based on the user's health condition. A specific exercise plan might include "30 minutes of light walking and 10 minutes of yoga stretching." The device then displays this exercise plan to the user.

[0138] Next, the user inputs a request to start brain training into the device. For example, they input "Start brain training." The device then sends this request to the server. The server uses a generative AI model to generate new training content based on past training history. As a specific training content, it plays a new folk tale, "Urashima Taro," with audio. The device then provides this training content to the user.

[0139] Furthermore, when a user inputs a question about health, for example, "I feel like my blood pressure has been high recently," the terminal sends the input data to a specialized medical support system. The specialized medical support system generates an answer to the question and sends it back to the server. The server receives this answer and sends it to the terminal. The terminal then displays the answer to the user, saying, "Try to get moderate exercise and eat a balanced diet."

[0140] A specific scenario for use would be for the user to enter their physical condition into the device in the morning, receive daily menu suggestions based on that information, review their exercise plan in the evening, and then do brain training at night. It would also be possible to immediately consult medical support if they felt unwell. This system is expected to make daily health management more personalized and provide continuous support to users.

[0141] Example prompt sentence:

[0142] "I'm feeling a bit tired, so can you recommend some light recipes?"

[0143] "Please tell me an exercise plan that would be suitable for a 45-year-old, 70kg person."

[0144] "Please tell me about new brain training content."

[0145] "My blood pressure seems to be high recently. What can I do?"

[0146] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0147] Step 1:

[0148] The user inputs their physical condition and what they want to eat that day through the terminal. The input data provided is information such as "I'm feeling a bit tired today, so I'd like to eat something light." The terminal then sends this input data to the server. Specifically, the user enters the data using an input form on their smartphone or computer and presses the send button.

[0149] Step 2:

[0150] The server receives input data. The received data is "I'm feeling a bit tired today, so I'd like to eat something light." The server passes this data to the generative AI model, which generates a prompt to generate an appropriate menu list. Specifically, the server inputs the prompt "I'm feeling a bit tired, so please tell me some recipes for light dishes" into the generative AI model.

[0151] Step 3:

[0152] A generative AI model generates a menu list based on the prompt sentence. The model extracts appropriate information from a huge database and generates menu suggestions such as "spinach and tomato salad" or "tofu and seaweed soup." The data processing performed here involves analyzing the user's state from the input sentence and suggesting appropriate dishes.

[0153] Step 4:

[0154] The server returns the generated menu plan to the terminal. The returned data is a specific menu list of "spinach and tomato salad" and "tofu and wakame soup." The server returns the data using an HTTP response.

[0155] Step 5:

[0156] The device displays the suggested menu to the user. The device analyzes the received data and displays it on the user interface. Specifically, the device displays the names of the dishes "Spinach and Tomato Salad" and "Tofu and Seaweed Soup."

[0157] Step 6:

[0158] The user inputs their age and weight through the device. For example, they input "45 years old, 70 kg." The device then sends this input data to the server. Specifically, the user inputs the data using an input form on their smartphone or computer and presses the send button.

[0159] Step 7:

[0160] The server receives the input data. The received data is "45 years old, 70 kg." The server passes this data to the generative AI model and generates a prompt to generate the optimal exercise plan. Specifically, the server inputs the prompt "Please tell me an exercise plan suitable for a person who is 45 years old and weighs 70 kg" into the generative AI model.

[0161] Step 8:

[0162] The generative AI model generates an exercise plan based on the prompt sentence. The model extracts appropriate information from a vast database and generates an exercise plan such as "30 minutes of light walking" and "10 minutes of yoga stretching." The data processing performed here analyzes the user's physique and health condition from the input sentence and suggests appropriate exercises.

[0163] Step 9:

[0164] The server returns the generated exercise plan to the device. The returned data is a specific exercise plan consisting of "30 minutes of light walking" and "10 minutes of yoga stretching." The server returns the data using an HTTP response.

[0165] Step 10:

[0166] The device displays the proposed exercise plan to the user. The device analyzes the received data and displays it on the user interface. Specifically, the device displays the exercise names "30 minutes of easy walking" and "10 minutes of yoga stretching."

[0167] Step 11:

[0168] The user inputs a request to start brain training through the terminal. For example, the user inputs "Start brain training." The terminal sends this request to the server.

[0169] Step 12:

[0170] The server receives the request data. The received data is "Start brain training." The server passes this data to the generative AI model and generates a prompt to generate new training content. Specifically, the server inputs the prompt "Please tell me new brain training content" into the generative AI model.

[0171] Step 13:

[0172] The generative AI model generates brain training content based on the prompt. The model extracts appropriate training content and generates specific training content, such as "A new folktale, Urashima Taro." The data processing performed here refers to past training history and suggests new stimulating content.

[0173] Step 14:

[0174] The server returns the generated training content to the device. The returned data is the specific training content, "A New Folktale: Urashima Taro." The server returns the data using an HTTP response.

[0175] Step 15:

[0176] The training content provided by the device is played back to the user with audio. The device analyzes the received data and displays a play button on the user interface. Specifically, when the user presses the play button, a new folktale, "Urashima Taro," is played back aloud.

[0177] Step 16:

[0178] A user inputs a health-related question through the terminal. For example, the user inputs, "I feel like my blood pressure has been high recently." The terminal then sends this health-related question to a specialized medical support system.

[0179] Step 17:

[0180] A specialized medical support system receives the question data, analyzes it, and then generates an answer. For example, the generated answer might be, "Try to exercise moderately and eat a balanced diet." The data calculation performed here is to generate the optimal medical advice for the input question.

[0181] Step 18:

[0182] The server receives the response from the specialized medical support system and sends it back to the device. The returned data is specific advice such as "Try to exercise moderately and eat a balanced diet." The server returns the data using an HTTP response.

[0183] Step 19:

[0184] The device displays the answer from the specialized medical support system to the user. The device analyzes the received data and displays it on the user interface. Specifically, the device's display shows advice such as "Try to get moderate exercise and eat a balanced diet."

[0185] (Application example 1)

[0186] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0187] Conventional health management systems lacked the flexibility to accommodate individual user needs and physical conditions. They also lacked the ability to provide personalized meal suggestions and delivery based on user input, resulting in low user convenience. Furthermore, there was a need for a system that could go beyond simply suggesting meals and enable users to easily obtain healthy meals.

[0188] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0189] In this invention, the server includes a means for analyzing user input data and generating a list of appropriate ingredients and recipes, a means for creating an initial menu based on prompts generated using a generative AI model, and a means for delivering meals via an external service in accordance with the user's input. This allows for personalized menu suggestions based on the user's individual physical condition and food preferences, and for the menu to be easily obtained through a delivery service.

[0190] A "terminal" is an electronic device that allows a user to input data such as their physical condition and food preferences.

[0191] The "server" is a computer system that analyzes input data sent by users and generates appropriate ingredients, recipes, exercise plans, training content, etc.

[0192] "Input data" refers to information such as physical condition information, food preferences, age, weight, and intention to start brain training that the user inputs through the terminal.

[0193] A "list of ingredients and recipes" is a set of ingredients and cooking methods that the server generates based on the input data analyzed and suggests to the user.

[0194] A "menu plan" is a proposal of ingredients and recipes generated by the server, and is a menu provided to the user.

[0195] An "exercise plan" is a specific exercise plan generated by the server based on the user's age, weight, and past exercise history.

[0196] "Training content" refers to the specific content of the training generated by the server based on the brain training desired by the user.

[0197] A "generative AI model" is an artificial intelligence model that analyzes user input data and automatically generates appropriate ingredients, recipes, exercise plans, etc.

[0198] A "prompt sentence" is a pre-set input sentence for the generative AI model to analyze, and is a sentence that specifically instructs the model on the user's request.

[0199] A "delivery service" is a service that delivers meals based on ingredients and recipes selected by the user to the user's home via an external service.

[0200] This invention is a system that allows users to input specific health information and desired meal contents via a terminal, transmits the data to a server for analysis, suggests appropriate ingredients and recipes, creates exercise plans, provides brain training, and even delivers the suggested meals. The system consists of the following components:

[0201] First, the user uses a device (smartphone, tablet, etc.) to input their physical condition and desired foods for the day. This device is connected to the Internet, and the input data is sent to a server. The server receives the user data, analyzes it, and generates a list of appropriate ingredients and recipes. At this time, a generative AI model is used to generate prompts based on the user's physical condition and preferences.

[0202] The server then sends the generated menu plan back to the terminal, which displays it to the user and allows the user to select the menu they want. Furthermore, this system is linked to an external delivery service, so the user can have the menu they select delivered to their home.

[0203] For example, suppose a user types into their device, "I'm feeling a bit tired today, so I'd like to eat something light." This data is sent to the server, which analyzes it and generates menu suggestions such as "spinach and tomato salad" or "tofu and seaweed soup." At this time, a generative AI model is used to generate prompt sentences, which the server then optimizes.

[0204] An example of a generated prompt statement:

[0205] Health: I'm feeling a little tired this morning.

[0206] What I want to eat: Light food

[0207] Prompt: "If the person's health status is 'I feel a little tired this morning,' please suggest a healthy, light meal."

[0208] Based on this prompt, the server will suggest healthy menu items, allowing the user to select from them and easily obtain the selected menu items through a delivery service.

[0209] Users can also enter their age and weight, and this data is sent to the server for analysis. As a result, an exercise plan optimized for the user's health condition is generated and displayed on the device. This allows users to practice exercises that are tailored to their health condition.

[0210] If the user wishes to train their brain, they input this into the terminal. The server generates new training content based on the user's past training history and sends it back to the terminal. This training content is provided with audio, allowing the user to train their brain effectively.

[0211] In this way, this system can meet the diverse health management needs of users and support the provision of individualized, specific suggestions and their implementation.

[0212] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0213] Step 1:

[0214] The user uses the device to input information about their physical condition and what they want to eat. The input data is entered in the form of a specific string, such as "I'm feeling a bit tired today, so I'd like to eat something light."

[0215] Step 2:

[0216] The device sends the entered data to the server. The sent content consists of text data such as health information and food preferences.

[0217] Step 3:

[0218] The server analyzes the received input data and generates a prompt using a generative AI model. For example, a prompt might be generated such as, "If the user's physical condition is entered as 'I feel a little tired this morning,' please suggest a healthy, light meal."

[0219] Step 4:

[0220] The server generates a list of appropriate ingredients and recipes based on the generated prompt. The generated menu includes specific items such as "spinach and tomato salad" or "tofu and seaweed soup."

[0221] Step 5:

[0222] The server generates a menu plan and returns it to the terminal. The returned data is a text-format recipe list.

[0223] Step 6:

[0224] The terminal displays the returned menu plan to the user, who can then check the suggested menu on the screen and make a selection.

[0225] Step 7:

[0226] The user confirms the menu selection using the terminal, and then transmits the menu selection back to the server.

[0227] Step 8:

[0228] The server receives the menu selected by the user and links the selection to an external delivery service to process the order.

[0229] Step 9:

[0230] The delivery service prepares the specified menu and delivers it to the user. The delivery status and other information are sent to the terminal via the server so that the user can check it.

[0231] Step 10:

[0232] The user inputs their age and weight through the terminal, and this data is also entered in a specific text format, such as "45 years old, 70 kg."

[0233] Step 11:

[0234] The device sends the entered age and weight data to the server.

[0235] Step 12:

[0236] The server analyzes the received age and weight data and generates an optimal exercise plan based on past exercise history. For example, a specific exercise plan such as "30 minutes of light walking and 10 minutes of yoga stretching" is generated.

[0237] Step 13:

[0238] The server then sends the generated exercise plan back to the device. The returned data consists of specific instructions and a timeline.

[0239] Step 14:

[0240] The device displays the returned exercise plan to the user, who can then check and carry out the plan on the screen.

[0241] Step 15:

[0242] The user inputs through the terminal that he / she wants to start brain training. For example, this is input in the form of specific text such as "Start brain training."

[0243] Step 16:

[0244] The device sends the input brain training request to the server.

[0245] Step 17:

[0246] The server analyzes the received request and generates new training content based on past training history. For example, a new training content with audio for the folktale "Urashima Taro" is generated.

[0247] Step 18:

[0248] The server returns the generated training content to the terminal. The returned data is training content data including an audio file.

[0249] Step 19:

[0250] The device then provides the returned training content to the user, which is played back with audio, allowing the user to effectively train their brain.

[0251] The above processing steps constitute a system that allows the user to carry out effective health management.

[0252] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0253] The present invention combines a system that can provide lifestyle disease prevention, exercise suggestions, brain training, and medical support with an emotion engine that recognizes the user's emotions. This system selects appropriate actions based on the user's input and emotions, and provides the results to the user.

[0254] First, we will explain the specific flow when a user inputs their physical condition and what they want to eat that day. The user inputs their physical condition and what they want to eat that day into the device, for example, "I'm feeling a bit tired today, so I'd like to eat something light." The device sends this input data to the server, which then analyzes the user's input using an emotion engine to determine the user's emotional state (for example, "high stress"). The server analyzes the received data and the results of the emotion engine, and generates a list of appropriate ingredients and recipes. For example, "spinach and tomato salad" and "tofu and seaweed soup" are suggested. The device then displays this menu suggestion to the user.

[0255] Next, we will explain the specific flow when a user inputs their age and weight and an exercise plan is proposed. The user inputs "45 years old, 70 kg," and the device sends this data to the server. The emotion engine analyzes emotions based on the input text and the user's past data, and identifies the user's emotional state (for example, "low motivation"). The server generates an optimal exercise plan based on the age, weight, emotional state, and past exercise history. Specifically, it suggests "30 minutes of light walking and 10 minutes of yoga stretching." The device then displays this exercise plan to the user.

[0256] Furthermore, when a user wants to start brain training, they input, for example, "Start brain training." The device sends this request to the server, and the emotion engine analyzes the user's emotional state (for example, "I need to concentrate"). The server generates new training content based on past training history and emotional state, and plays a new folk tale, "Urashima Taro," with audio. The device then provides this training content to the user.

[0257] Or, if a user inputs a question about their health, for example, "I feel like my blood pressure has been high lately," the device sends the input data to a specialized medical support system, and the emotion engine analyzes the user's anxiety, etc. The specialized medical support system generates an answer to the question and sends it back to the server. The server sends this answer to the device, which then displays professional advice to the user, such as "Try to get moderate exercise and eat a balanced diet."

[0258] A specific usage scenario would be for a user to input their physical condition into the device in the morning, receive daily menu suggestions based on that information, check their exercise plan in the evening, and do brain training at night. Depending on their physical condition or emotional state, they may also be able to immediately consult medical support. By combining this system with an emotion engine, the goal is to make daily health management more personalized for users and enable continuous support that takes emotions into account.

[0259] The processing flow will be explained below.

[0260] Processing menu suggestions for lifestyle-related disease prevention

[0261] Step 1:

[0262] The user inputs their physical condition and what they want to eat that day into the terminal.

[0263] Example: A user enters, "I'm feeling a bit tired today and would like to eat something light."

[0264] Step 2:

[0265] The terminal transmits the input data to the server.

[0266] The entered "physical condition" and "food preferences" data is sent to the server in JSON format.

[0267] Step 3:

[0268] The device sends the input text to the emotion engine.

[0269] The emotion engine analyzes the user's emotional state from the input data, determining, for example, that they are "highly stressed."

[0270] Step 4:

[0271] The server parses the received data.

[0272] The server analyzes the keywords "feeling tired" and "light food" and searches the database for relevant ingredients and recipes.

[0273] Step 5:

[0274] The server generates a menu plan that reflects the emotional state.

[0275] Taking into account the results of the emotion engine (e.g., "high stress"), recipes with stress-reducing effects are selected.

[0276] Step 6:

[0277] The server sends the generated menu plan back to the terminal.

[0278] Send the menu plan to the device in JSON format.

[0279] Step 7:

[0280] The terminal displays the suggested menu to the user.

[0281] Example: Display "Spinach and Tomato Salad" and "Tofu and Seaweed Soup."

[0282] Processing exercise suggestions

[0283] Step 1:

[0284] The user enters their age and weight into the terminal.

[0285] Example: User enters "45 years old, 70kg."

[0286] Step 2:

[0287] The terminal transmits the input data to the server.

[0288] The entered "age" and "weight" data is sent to the server in JSON format.

[0289] Step 3:

[0290] The device sends the input data to the emotion engine.

[0291] The emotion engine analyzes the user's emotional state from the input data, determining, for example, that the user is "low motivation."

[0292] Step 4:

[0293] The server analyzes the input data and references past exercise history.

[0294] The user's past exercise history is retrieved from the database and checked to ensure that the same exercise is not repeated.

[0295] Step 5:

[0296] The server generates a motion plan that reflects the emotional state.

[0297] Taking into account the emotion engine result (e.g., "Low motivation"), it suggests moderate exercise, such as "30 minutes of easy walking and 10 minutes of yoga and stretching."

[0298] Step 6:

[0299] The server returns the generated exercise plan to the terminal.

[0300] The exercise plan is sent to the device in JSON format.

[0301] Step 7:

[0302] The terminal displays the suggested exercise plan to the user.

[0303] A suggested exercise plan and implementation method is displayed to the user.

[0304] Brain Training Processing

[0305] Step 1:

[0306] The user inputs into the terminal that he / she wants to start brain training.

[0307] Example: User types "Start Brain Training."

[0308] Step 2:

[0309] The device sends a request to the server.

[0310] Sends request data to the server in JSON format.

[0311] Step 3:

[0312] The device sends the input data to the emotion engine.

[0313] The emotion engine analyzes the user's emotional state from the input data, determining, for example, that "concentration is necessary."

[0314] Step 4:

[0315] The server references past training history.

[0316] The system retrieves the user's past brain training history from a database and avoids repeating the same content.

[0317] Step 5:

[0318] The server generates training content that reflects the emotional state.

[0319] It references the knowledge base and generates new brain training tasks based on the results of the emotion engine (e.g., "needs concentration").

[0320] Step 6:

[0321] The server returns the generated training content to the terminal.

[0322] Send the training content to the device in JSON format.

[0323] Step 7:

[0324] The device provides the training content to the user with audio.

[0325] Example: Play "The New Folktale of Urashima Taro" with audio.

[0326] Health Questions and Medical Support

[0327] Step 1:

[0328] The user inputs a health-related question into the terminal.

[0329] Example: A user types, "My blood pressure feels high lately."

[0330] Step 2:

[0331] The terminal sends the input data to a specialized medical support system.

[0332] The input data is sent in JSON format to the API of a specialized medical support system.

[0333] Step 3:

[0334] The device sends the input data to the emotion engine.

[0335] The emotion engine analyzes the user's emotional state and identifies feelings of anxiety, etc.

[0336] Step 4:

[0337] A specialized medical support system generates answers to questions.

[0338] An answer is generated based on the medical information and sent back to the server.

[0339] Step 5:

[0340] The server sends the response received from the specialized medical support system in JSON format to the terminal.

[0341] The received response is sent to the device.

[0342] Step 6:

[0343] The terminal displays professional medical support answers to the user.

[0344] Example: Display "Make sure to exercise moderately and eat a balanced diet."

[0345] Example 2

[0346] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0347] While conventional health management systems make suggestions based on user input data, they lack personalized support that takes into account the user's emotional state, making it difficult to improve user motivation and continuity. Furthermore, various types of support, such as exercise plans, meal suggestions, and brain training, are often provided individually, making it difficult to achieve comprehensive health management.

[0348] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for analyzing input data using an emotion analysis engine to determine the user's emotional state, means for generating a list of appropriate ingredients and recipes based on the analysis results, means for generating an optimal exercise plan based on the user's age, weight, emotional state, and past exercise history, and means for generating new training content based on the user's past training history and emotional state. This enables personalized suggestions and support that take the user's emotional state into consideration.

[0349] "User" means an individual who uses the system.

[0350] A "terminal" is a device that allows a user to interface with the system, such as a smartphone or PC.

[0351] "Input data" refers to information that a user provides to the system via a terminal, and includes physical condition, age, weight, and the like.

[0352] "Server" means a computer system that performs the central processing of the system and is responsible for collecting data, analyzing data, and generating results.

[0353] An "emotion analysis engine" is software that analyzes a user's emotional state based on input data.

[0354] "Emotional state" refers to a user's psychological state, such as stress level or motivation.

[0355] "Ingredients" refer to specific foods that are suggested to the user.

[0356] A "recipe" is a document or data that describes how to cook food using ingredients.

[0357] An "exercise plan" is a specific exercise plan suggested based on the user's health condition.

[0358] "Training content" refers to the specific brain training activities or programs provided to users.

[0359] The present invention is a system that enables the prevention of lifestyle-related diseases, exercise suggestions, brain training, and medical support. This system incorporates an emotion engine that recognizes the user's emotions, selects appropriate actions based on the user's input and emotions, and provides the results to the user. Details are described below.

[0360] Hardware and Software Use

[0361] This system consists of the following hardware and software:

[0362] Terminal: A device used by users to input information. Examples include smartphones and personal computers.

[0363] Server: A central system that receives and analyzes input data.

[0364] Sentiment analysis engine: Software that analyzes input text data and identifies the user's emotional state. It uses NLP (natural language processing) techniques.

[0365] Medical support system: A system that provides specialized medical information.

[0366] Specific operation of the system

[0367] The operation of the system will be explained using an example in which a user inputs their physical condition and what they want to eat that day. The user inputs, "I'm feeling a bit tired today, so I'd like to eat something light." The device sends this input data to the server, which uses an emotion analysis engine to analyze the user's emotional state (for example, "high stress"). The server then generates a list of appropriate ingredients and recipes based on the analysis results. Specifically, it suggests "spinach and tomato salad" and "tofu and seaweed soup." The device then displays this menu suggestion to the user.

[0368] Specific examples

[0369] Example prompt sentence:

[0370] The user enters "45 years old, 70 kg."

[0371] The device sends this data to the server, and the emotion engine analyzes it as "low motivation."

[0372] The server generates an optimal exercise plan based on age, weight, emotional state, and past exercise history, suggesting "30 minutes of light walking and 10 minutes of yoga stretching."

[0373] The device displays this exercise plan to the user.

[0374] Furthermore, if the user types "Start brain training," the same procedure is followed. The device sends the request to the server, and the emotion engine interprets it as "concentration required." The server generates new training content based on past training history and emotional state, and plays a new folk tale, "Urashima Taro," with audio. The device then provides this to the user.

[0375] This system allows users to input their physical condition into a device in the morning, receive daily menu suggestions based on that information, review their exercise plan in the evening, and then do brain training at night. Depending on their physical condition or emotional state, they can also immediately consult medical support. In this way, users' daily health management is personalized, and continuous support that takes their emotions into consideration is possible.

[0376] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0377] Step 1:

[0378] The user inputs their physical condition and what they want to eat that day into the input interface of the terminal.

[0379] Input: User's physical condition and food preferences (e.g., "I'm feeling a bit tired today, so I'd like to eat something light")

[0380] Output: Input data

[0381] Specific operation: The user enters text into a device such as a smartphone or PC and presses the send button.

[0382] Step 2:

[0383] The terminal transmits the received input data to the server.

[0384] Input: User-entered data

[0385] Output: The input data sent to the server

[0386] Specific operation: The device sends data to the server via the internet via the API.

[0387] Step 3:

[0388] The server uses an emotion analysis engine to analyze the input data.

[0389] Input: Input data sent to the server

[0390] Output: User's emotional state (e.g. "high stress")

[0391] Specific operation: The server launches an emotion analysis engine (NLP model) that analyzes input data and estimates the emotional state.

[0392] Step 4:

[0393] The server generates a list of appropriate ingredients and recipes based on the received data and the analysis results of the emotion engine.

[0394] Input: User input data and emotional state

[0395] Output: A list of ingredients or recipes (e.g., "Spinach and Tomato Salad" or "Tofu and Seaweed Soup")

[0396] How it works: The recipe generation algorithm in the server takes into account your emotional state and dietary preferences to narrow down the candidates.

[0397] Step 5:

[0398] The server returns the generated menu plan to the terminal.

[0399] Input: A list of ingredients and recipes generated

[0400] Output: Menu plan sent to device

[0401] Specific operation: The server sends data to the terminal as a response.

[0402] Step 6:

[0403] The terminal displays the suggested menu to the user.

[0404] Input: Menu plan sent from the server

[0405] Output: The menu displayed to the user

[0406] Specific operation: The measurement results are displayed on the device's user interface.

[0407] Step 7:

[0408] The user enters their age and weight into the device and an exercise plan is suggested.

[0409] Input: Age and weight (e.g., "45 years old, 70 kg")

[0410] Output: Input data

[0411] Specific operation: The user enters information into a device such as a smartphone or PC and presses the send button.

[0412] Step 8:

[0413] The terminal transmits the received input data to the server.

[0414] Input: User-entered data

[0415] Output: The input data sent to the server

[0416] Specific operation: The device sends data to the server via the internet via the API.

[0417] Step 9:

[0418] The server uses an emotion analysis engine to analyze emotions based on input data and past movement history.

[0419] Input: Input data sent to the server

[0420] Output: User's emotional state (e.g., "Low motivation")

[0421] What it does: The sentiment analysis engine analyzes past data and new input to identify emotional states.

[0422] Step 10:

[0423] The server generates an optimal exercise plan based on age, weight, emotional state and past exercise history.

[0424] Input: Age, weight, emotional state, past exercise history

[0425] Output: Exercise plan (e.g., "30 minutes of easy walking and 10 minutes of yoga and stretching")

[0426] Specific Actions: The movement plan generation algorithm uses this information to generate an optimal movement plan.

[0427] Step 11:

[0428] The server returns the generated exercise plan to the terminal.

[0429] Input: Generated movement plan

[0430] Output: Exercise plan sent to the device

[0431] Specific operation: The server sends data to the terminal as a response.

[0432] Step 12:

[0433] The terminal displays the suggested exercise plan to the user.

[0434] Input: Exercise plan sent from the server

[0435] Output: Exercise plan displayed to the user

[0436] Specific operation: The exercise plan is displayed on the device's user interface.

[0437] (Application example 2)

[0438] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0439] Conventional systems for preventing lifestyle-related diseases, suggesting exercise, brain training, and medical support often do not fully consider the user's physical condition or emotional state, resulting in a lack of personalized suggestions based on the user's emotions and physical condition. Furthermore, in brick-and-mortar stores, it is difficult to suggest ingredients and recipes based on the user's physical condition and emotions, so there are no means of effectively supporting health management and improving dietary content.

[0440] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for determining the user's emotional state using an emotion analysis engine that analyzes input data, means for generating a list of appropriate ingredients and recipes based on the emotional state, and means for returning the generated list to the terminal. This enables personalized health suggestions based on the user's physical condition and emotional state.

[0441] "User" refers to an individual who uses this system.

[0442] "Device" means the device used by a User to enter information or view suggestions, including, but not limited to, a smartphone, tablet, or PC.

[0443] "Server" refers to a computer system that receives, analyzes, and processes data sent by users.

[0444] "Sentiment analysis engine" refers to software or algorithms for determining a user's emotional state from input data.

[0445] "Emotional state" refers to the user's current psychological and emotional state, including, for example, stress and motivation.

[0446] A "list of ingredients and recipes" is a list containing ingredients and cooking methods suggested based on the user's physical and emotional state.

[0447] "Menu plan" refers to a list of suggested ingredients and recipes.

[0448] An "exercise plan" refers to a proposed exercise plan based on the user's physical condition and emotional state.

[0449] "Training content" refers to the specific brain training tasks and activities provided to users.

[0450] "With audio" refers to a method in which training content and notifications are provided to the user by audio.

[0451] To implement this invention, a system is constructed in which a user inputs their physical condition and food preferences for the day through a terminal. The terminal may be a smartphone, tablet, or PC. The data input to this terminal is sent to a server equipped with an emotion analysis engine. This server analyzes the user's input data and determines their emotional state.

[0452] The server uses a sentiment analysis engine to analyze the user's emotional state (e.g., high stress, low motivation, etc.). The sentiment analysis engine uses natural language processing techniques. The analysis results are used to generate ingredient and recipe lists, exercise plans, brain training content, and medical advice.

[0453] For example, if a user inputs "I'm feeling a bit tired today and would like to eat something light," this data is sent to the server, and the emotion analysis engine determines the emotional state as "high stress." Based on this emotional state, the server generates a list of suggestions, such as "spinach and tomato salad" and "tofu and seaweed soup." The generated list is sent back to the device and displayed to the user.

[0454] For example, if a user wants to input their age and weight and want an exercise plan suggested, they can enter "45 years old, 70 kg." This data is sent to the server, where the emotion analysis engine analyzes the user's emotional state. For example, if the user is judged to be "low motivation," the server will generate an exercise plan suggesting "30 minutes of light walking and 10 minutes of yoga stretching" based on this emotional state and past exercise history. The generated exercise plan is sent back to the device and displayed to the user.

[0455] Furthermore, if a user wants to start brain training, they input "Start brain training," and this data is sent to the server, where the emotion analysis engine analyzes the user's emotional state. For example, if the server determines that "concentration is necessary," it will play a new training content, "A new folk tale, 'Urashima Taro,'" with audio, based on this emotional state and past training history. As a result, the device provides this training content to the user.

[0456] Here's an example prompt that uses a generative AI model to analyze sentiment and determine what to suggest to the user:

[0457] "When a user inputs something like, 'I'm feeling a bit tired today and want to eat something light,' the app should analyze their emotional state and suggest appropriate ingredients and recipes."

[0458] The hardware for implementing this invention includes a user terminal (such as a smartphone, tablet, or PC), a server, and a computer system for running the sentiment analysis engine. The software includes Python 3.x and a requests library for sending API requests.

[0459] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0460] Step 1:

[0461] The user inputs their physical condition and what they want to eat into the terminal. For example, they might input, "I'm feeling a bit tired today, so I'd like to eat something light." This input data is acquired through an input form on the user terminal.

[0462] Step 2:

[0463] The device sends the input data to the server. Specifically, the data is sent as an HTTP POST request via a communication module built into the device. This request includes the user's input as a payload.

[0464] Step 3:

[0465] The input data received by the server is analyzed by a sentiment analysis engine. The sentiment analysis engine uses natural language processing technology to determine the user's emotional state. Here, the input data is analyzed in text format and an emotional tag such as "high stress" is generated.

[0466] Step 4:

[0467] The server generates a list of appropriate ingredients and recipes based on the user's emotional state and input data. The server uses an algorithm that takes into account the user's emotional state (e.g., "high stress") to search its internal database for light ingredients and recipes and generates the list.

[0468] Step 5:

[0469] The server then returns the generated list of ingredients and recipes to the device, again using an HTTP POST request. The returned data includes the suggested ingredients and recipe information.

[0470] Step 6:

[0471] The device displays the menu plan returned from the server to the user. Specifically, it uses a GUI (Graphical User Interface) component for display to show details of ingredients and recipes. Based on this information, the user can select appropriate ingredients and dishes.

[0472] For example, if a user inputs "I'm feeling a bit tired today and want to eat something light," the server analyzes this and generates an emotion tag called "high stress." The server then lists options like "spinach and tomato salad" and "tofu and seaweed soup" based on this emotion tag and returns this information. The device then displays it to the user.

[0473] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0474] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0475] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0476] [Second embodiment]

[0477] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0478] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0479] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0480] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0481] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0482] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0483] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0484] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0485] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0486] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0487] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0488] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0489] The present invention relates to a system that enables prevention of lifestyle-related diseases, exercise suggestions, brain training, and medical support. This system selects appropriate actions based on user input and provides the results to the user.

[0490] First, the user inputs their physical condition and what they want to eat that day through the device. For example, the user might input, "I'm feeling a bit tired today, so I'd like to eat something light." The device then sends this input data to the server. The server analyzes the received data, generates a list of appropriate ingredients and recipes, and suggests menu ideas such as "spinach and tomato salad" and "tofu and seaweed soup." The device then displays these menu ideas to the user.

[0491] Next, the user enters their age and weight through the device. For example, they might enter "45 years old, 70 kg." The device then sends this input data to the server, which then generates an optimal exercise plan based on the user's age, weight, and past exercise history. Specific exercises (for example, "30 minutes of light walking and 10 minutes of yoga stretching") are suggested based on the user's health condition. The device then displays this exercise plan to the user.

[0492] Furthermore, if the user wishes to start brain training, he / she may input, for example, "Start brain training." The device sends this request to the server, and the server generates new training content based on past training history. For example, it plays "A new folk tale, 'Urashima Taro,'" with audio. The device provides this training content to the user.

[0493] Furthermore, when a user inputs a question about their health, for example, "I feel like my blood pressure has been high recently," the device sends the input data to a specialized medical support system. The specialized medical support system generates an answer to the question and sends it back to the server. The server receives this answer and sends it to the device. The device then displays the answer to the user, saying, "Try to exercise moderately and eat a balanced diet."

[0494] A specific scenario for use would be for the user to enter their physical condition into the device in the morning, receive daily menu suggestions based on that information, review their exercise plan in the evening, and then do brain training at night. It would also be possible to immediately consult medical support if they felt unwell. This system is expected to make daily health management more personalized and provide continuous support to users.

[0495] The processing flow will be explained below.

[0496] Step 1:

[0497] The user inputs their physical condition and what they want to eat that day into the terminal.

[0498] Example: A user enters, "I'm feeling a bit tired today and would like to eat something light."

[0499] Step 2:

[0500] The terminal transmits the input data to the server.

[0501] The entered "physical condition" and "food preferences" data is sent to the server in JSON format.

[0502] Step 3:

[0503] The server parses the input data.

[0504] The server analyzes keywords such as "feeling tired" and "light food" and searches the database for relevant ingredients and recipes.

[0505] Step 4:

[0506] The server generates a list of suitable ingredients and recipes.

[0507] Based on the analysis results, recipes are generated using ingredients that are rich in vitamins and easy to digest.

[0508] Step 5:

[0509] The server sends the generated menu plan back to the terminal.

[0510] Send the menu plan to the device in JSON format.

[0511] Step 6:

[0512] The terminal displays the suggested menu to the user.

[0513] Example: Display "Spinach and Tomato Salad" and "Tofu and Seaweed Soup."

[0514] Step 1:

[0515] The user enters their age and weight into the terminal.

[0516] Example: User enters "45 years old, 70kg."

[0517] Step 2:

[0518] The terminal transmits the input data to the server.

[0519] The entered "age" and "weight" data is sent to the server in JSON format.

[0520] Step 3:

[0521] The server analyzes the input data and references past exercise history.

[0522] The user's past exercise history is retrieved from the database and checked to ensure that the same exercise is not repeated.

[0523] Step 4:

[0524] The server generates an optimal exercise plan.

[0525] Based on your age, weight, and past exercise history, it will suggest new exercises (for example, "30 minutes of easy walking and 10 minutes of yoga stretching").

[0526] Step 5:

[0527] The server returns the generated exercise plan to the terminal.

[0528] The exercise plan is sent to the device in JSON format.

[0529] Step 6:

[0530] The terminal displays the suggested exercise plan to the user.

[0531] A suggested exercise plan and implementation method is displayed to the user.

[0532] Step 1:

[0533] The user inputs into the terminal that he / she wants to start brain training.

[0534] Example: User types "Start Brain Training."

[0535] Step 2:

[0536] The device sends a request to the server.

[0537] Sends request data to the server in JSON format.

[0538] Step 3:

[0539] The server references past training history.

[0540] The system retrieves the user's past brain training history from a database and avoids repeating the same content.

[0541] Step 4:

[0542] The server generates new training content (fairy tales, original stories, rakugo, etc.).

[0543] Browse the knowledge base and generate new brain training challenges.

[0544] Step 5:

[0545] The server returns the generated training content to the terminal.

[0546] Send the training content to the device in JSON format.

[0547] Step 6:

[0548] The device provides the training content to the user with audio.

[0549] Example: Play "The New Folktale of Urashima Taro" with audio.

[0550] Step 1:

[0551] The user inputs a health-related question into the terminal.

[0552] Example: A user types, "My blood pressure feels high lately."

[0553] Step 2:

[0554] The terminal sends the input data to a specialized medical support system.

[0555] The input data is sent in JSON format to the API of a specialized medical support system.

[0556] Step 3:

[0557] A specialized medical support system generates answers to questions.

[0558] An answer is generated based on the medical information and sent back to the server.

[0559] Step 4:

[0560] The server sends the response received from the HELPO app in JSON format to the device.

[0561] The received response is sent to the device.

[0562] Step 5:

[0563] The terminal displays professional medical support answers to the user.

[0564] Example: Display "Make sure to exercise moderately and eat a balanced diet."

[0565] Example 1

[0566] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0567] Current health management systems struggle to provide personalized recommendations tailored to individual users' individual circumstances and needs. Furthermore, systems that address multiple areas such as dietary recommendations, exercise plans, brain training, and medical support are not integrated, requiring users to navigate multiple applications and services. Furthermore, the recommendations are general, with few specific advice based on individual lifestyle habits or past history.

[0568] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0569] In this invention, the server includes means for analyzing input data and using a generative AI model to generate a list of appropriate ingredients and recipes, means for analyzing input data and using a generative AI model to generate an optimal exercise plan based on past exercise history, and means for using a generative AI model to generate new training content based on past training history. This allows a single system to provide personalized suggestions tailored to each user's individual situation and needs, enabling more specific and actionable advice.

[0570] "User" refers to an individual who uses the system and is a consumer who provides input according to their health status and needs.

[0571] "Terminal" refers to a device through which a user makes input, and includes electronic devices such as smartphones, tablets, and personal computers.

[0572] "Server" refers to the central computing unit that receives and analyzes the data sent by the User, generates appropriate suggestions and sends them back to the Terminal.

[0573] "Input data" refers to information provided by the user through the terminal, including physical condition, dietary preferences, age, weight, requests to start brain training, health-related questions, etc.

[0574] A "generative AI model" refers to an algorithm that uses natural language processing and machine learning techniques to analyze input data and generate appropriate suggestions.

[0575] "Menu suggestions" refers to a list of ingredients and recipes suggested by a generative AI model based on the user's physical condition and dietary preferences.

[0576] "Exercise plan" refers to a specific exercise schedule suggested by a generative AI model based on a user's age, weight, and past exercise history.

[0577] "Training content" refers to the specific brain training content suggested by the generative AI model based on the user's past training history.

[0578] A "medical support system" refers to a system with specialized medical knowledge that is used to generate appropriate answers to health-related questions posed by users.

[0579] "Proposal" refers to the specific content that the server generates using the generative AI model and provides to the user via the terminal.

[0580] This invention relates to a system that enables prevention of lifestyle-related diseases, exercise suggestions, brain training, and medical support. This system selects appropriate actions based on user input and provides the results to the user.

[0581] First, the user inputs their physical condition and meal preferences for the day into the interface on their device. For example, the user might input, "I'm feeling a bit tired today, so I'd like to eat something light." The device then sends this input data to the server. The server analyzes the received data and uses a generative AI model to generate a list of appropriate ingredients and recipes. Specific menu suggestions generated include "spinach and tomato salad" and "tofu and seaweed soup." The device then displays these menu suggestions to the user.

[0582] Next, the user enters their age and weight through the device. For example, they might enter "45 years old, 70 kg." The device then sends this input data to the server. The server then analyzes the user's age, weight, and past exercise history, and uses a generative AI model to generate an optimal exercise plan. Specific exercises are suggested based on the user's health condition. A specific exercise plan might include "30 minutes of light walking and 10 minutes of yoga stretching." The device then displays this exercise plan to the user.

[0583] Next, the user inputs a request to start brain training into the device. For example, they input "Start brain training." The device then sends this request to the server. The server uses a generative AI model to generate new training content based on past training history. As a specific training content, it plays a new folk tale, "Urashima Taro," with audio. The device then provides this training content to the user.

[0584] Furthermore, when a user inputs a question about health, for example, "I feel like my blood pressure has been high recently," the terminal sends the input data to a specialized medical support system. The specialized medical support system generates an answer to the question and sends it back to the server. The server receives this answer and sends it to the terminal. The terminal then displays the answer to the user, saying, "Try to get moderate exercise and eat a balanced diet."

[0585] A specific scenario for use would be for the user to enter their physical condition into the device in the morning, receive daily menu suggestions based on that information, review their exercise plan in the evening, and then do brain training at night. It would also be possible to immediately consult medical support if they felt unwell. This system is expected to make daily health management more personalized and provide continuous support to users.

[0586] Example prompt sentence:

[0587] "I'm feeling a bit tired, so can you recommend some light recipes?"

[0588] "Please tell me an exercise plan that would be suitable for a 45-year-old, 70kg person."

[0589] "Please tell me about new brain training content."

[0590] "My blood pressure seems to be high recently. What can I do?"

[0591] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0592] Step 1:

[0593] The user inputs their physical condition and what they want to eat that day through the terminal. The input data provided is information such as "I'm feeling a bit tired today, so I'd like to eat something light." The terminal then sends this input data to the server. Specifically, the user enters the data using an input form on their smartphone or computer and presses the send button.

[0594] Step 2:

[0595] The server receives input data. The received data is "I'm feeling a bit tired today, so I'd like to eat something light." The server passes this data to the generative AI model, which generates a prompt to generate an appropriate menu list. Specifically, the server inputs the prompt "I'm feeling a bit tired, so please tell me some recipes for light dishes" into the generative AI model.

[0596] Step 3:

[0597] A generative AI model generates a menu list based on the prompt sentence. The model extracts appropriate information from a huge database and generates menu suggestions such as "spinach and tomato salad" or "tofu and seaweed soup." The data processing performed here involves analyzing the user's state from the input sentence and suggesting appropriate dishes.

[0598] Step 4:

[0599] The server returns the generated menu plan to the terminal. The returned data is a specific menu list of "spinach and tomato salad" and "tofu and wakame soup." The server returns the data using an HTTP response.

[0600] Step 5:

[0601] The device displays the suggested menu to the user. The device analyzes the received data and displays it on the user interface. Specifically, the device displays the names of the dishes "Spinach and Tomato Salad" and "Tofu and Seaweed Soup."

[0602] Step 6:

[0603] The user inputs their age and weight through the device. For example, they input "45 years old, 70 kg." The device then sends this input data to the server. Specifically, the user inputs the data using an input form on their smartphone or computer and presses the send button.

[0604] Step 7:

[0605] The server receives the input data. The received data is "45 years old, 70 kg." The server passes this data to the generative AI model and generates a prompt to generate the optimal exercise plan. Specifically, the server inputs the prompt "Please tell me an exercise plan suitable for a person who is 45 years old and weighs 70 kg" into the generative AI model.

[0606] Step 8:

[0607] The generative AI model generates an exercise plan based on the prompt sentence. The model extracts appropriate information from a vast database and generates an exercise plan such as "30 minutes of light walking" and "10 minutes of yoga stretching." The data processing performed here analyzes the user's physique and health condition from the input sentence and suggests appropriate exercises.

[0608] Step 9:

[0609] The server returns the generated exercise plan to the device. The returned data is a specific exercise plan consisting of "30 minutes of light walking" and "10 minutes of yoga stretching." The server returns the data using an HTTP response.

[0610] Step 10:

[0611] The device displays the proposed exercise plan to the user. The device analyzes the received data and displays it on the user interface. Specifically, the device displays the exercise names "30 minutes of easy walking" and "10 minutes of yoga stretching."

[0612] Step 11:

[0613] The user inputs a request to start brain training through the terminal. For example, the user inputs "Start brain training." The terminal sends this request to the server.

[0614] Step 12:

[0615] The server receives the request data. The received data is "Start brain training." The server passes this data to the generative AI model and generates a prompt to generate new training content. Specifically, the server inputs the prompt "Please tell me new brain training content" into the generative AI model.

[0616] Step 13:

[0617] The generative AI model generates brain training content based on the prompt. The model extracts appropriate training content and generates specific training content, such as "A new folktale, Urashima Taro." The data processing performed here refers to past training history and suggests new stimulating content.

[0618] Step 14:

[0619] The server returns the generated training content to the device. The returned data is the specific training content, "A New Folktale: Urashima Taro." The server returns the data using an HTTP response.

[0620] Step 15:

[0621] The training content provided by the device is played back to the user with audio. The device analyzes the received data and displays a play button on the user interface. Specifically, when the user presses the play button, a new folktale, "Urashima Taro," is played back aloud.

[0622] Step 16:

[0623] A user inputs a health-related question through the terminal. For example, the user inputs, "I feel like my blood pressure has been high recently." The terminal then sends this health-related question to a specialized medical support system.

[0624] Step 17:

[0625] A specialized medical support system receives the question data, analyzes it, and then generates an answer. For example, the generated answer might be, "Try to exercise moderately and eat a balanced diet." The data calculation performed here is to generate the optimal medical advice for the input question.

[0626] Step 18:

[0627] The server receives the response from the specialized medical support system and sends it back to the device. The returned data is specific advice such as "Try to exercise moderately and eat a balanced diet." The server returns the data using an HTTP response.

[0628] Step 19:

[0629] The device displays the answer from the specialized medical support system to the user. The device analyzes the received data and displays it on the user interface. Specifically, the device's display shows advice such as "Try to get moderate exercise and eat a balanced diet."

[0630] (Application example 1)

[0631] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0632] Conventional health management systems lacked the flexibility to accommodate individual user needs and physical conditions. They also lacked the ability to provide personalized meal suggestions and delivery based on user input, resulting in low user convenience. Furthermore, there was a need for a system that could go beyond simply suggesting meals and enable users to easily obtain healthy meals.

[0633] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0634] In this invention, the server includes a means for analyzing user input data and generating a list of appropriate ingredients and recipes, a means for creating an initial menu based on prompts generated using a generative AI model, and a means for delivering meals via an external service in accordance with the user's input. This allows for personalized menu suggestions based on the user's individual physical condition and food preferences, and for the menu to be easily obtained through a delivery service.

[0635] A "terminal" is an electronic device that allows a user to input data such as their physical condition and food preferences.

[0636] The "server" is a computer system that analyzes input data sent by users and generates appropriate ingredients, recipes, exercise plans, training content, etc.

[0637] "Input data" refers to information such as physical condition information, food preferences, age, weight, and intention to start brain training that the user inputs through the terminal.

[0638] A "list of ingredients and recipes" is a set of ingredients and cooking methods that the server generates based on the input data analyzed and suggests to the user.

[0639] A "menu plan" is a proposal of ingredients and recipes generated by the server, and is a menu provided to the user.

[0640] An "exercise plan" is a specific exercise plan generated by the server based on the user's age, weight, and past exercise history.

[0641] "Training content" refers to the specific content of the training generated by the server based on the brain training desired by the user.

[0642] A "generative AI model" is an artificial intelligence model that analyzes user input data and automatically generates appropriate ingredients, recipes, exercise plans, etc.

[0643] A "prompt sentence" is a pre-set input sentence for the generative AI model to analyze, and is a sentence that specifically instructs the model on the user's request.

[0644] A "delivery service" is a service that delivers meals based on ingredients and recipes selected by the user to the user's home via an external service.

[0645] This invention is a system that allows users to input specific health information and desired meal contents via a terminal, transmits the data to a server for analysis, suggests appropriate ingredients and recipes, creates exercise plans, provides brain training, and even delivers the suggested meals. This system consists of the following components:

[0646] First, the user uses a device (smartphone, tablet, etc.) to input their physical condition and desired foods for the day. This device is connected to the Internet, and the input data is sent to a server. The server receives the user data, analyzes it, and generates a list of appropriate ingredients and recipes. At this time, a generative AI model is used to generate prompts based on the user's physical condition and preferences.

[0647] The server then sends the generated menu plan back to the terminal, which displays it to the user and allows the user to select the menu they want. Furthermore, this system is linked to an external delivery service, so the menu selected by the user can be delivered to their home.

[0648] For example, suppose a user types into their device, "I'm feeling a bit tired today, so I'd like to eat something light." This data is sent to the server, which analyzes it and generates menu suggestions such as "spinach and tomato salad" or "tofu and seaweed soup." At this time, a generative AI model is used to generate prompt sentences, which the server then optimizes.

[0649] An example of a generated prompt statement:

[0650] Health: I'm feeling a little tired this morning.

[0651] What I want to eat: Light food

[0652] Prompt: "If the person's health status is 'I feel a little tired this morning,' please suggest a healthy, light meal."

[0653] Based on this prompt, the server will suggest healthy menu items, allowing the user to select from them and easily obtain the selected menu items through a delivery service.

[0654] Users can also enter their age and weight, and this data is sent to the server for analysis. As a result, an exercise plan optimized for the user's health condition is generated and displayed on the device, allowing users to practice exercises tailored to their health condition.

[0655] If the user wishes to train their brain, they input this into the terminal. The server generates new training content based on the user's past training history and sends it back to the terminal. This training content is provided with audio, allowing the user to train their brain effectively.

[0656] In this way, this system can meet the diverse health management needs of users and support the provision of individualized, specific suggestions and their implementation.

[0657] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0658] Step 1:

[0659] The user uses the device to input information about their physical condition and what they want to eat. The input data is entered in the form of a specific string, such as "I'm feeling a bit tired today, so I'd like to eat something light."

[0660] Step 2:

[0661] The device sends the entered data to the server. The sent content consists of text data such as health information and food preferences.

[0662] Step 3:

[0663] The server analyzes the received input data and generates a prompt using a generative AI model. For example, a prompt might be generated such as, "If the user's physical condition is entered as 'I feel a little tired this morning,' please suggest a healthy, light meal."

[0664] Step 4:

[0665] The server generates a list of appropriate ingredients and recipes based on the generated prompt. The generated menu includes specific items such as "spinach and tomato salad" or "tofu and seaweed soup."

[0666] Step 5:

[0667] The server generates a menu plan and returns it to the terminal. The returned data is a text-format recipe list.

[0668] Step 6:

[0669] The terminal displays the returned menu plan to the user, who can then check the suggested menu on the screen and make a selection.

[0670] Step 7:

[0671] The user confirms the menu selection using the terminal, and then transmits the menu selection back to the server.

[0672] Step 8:

[0673] The server receives the menu selected by the user and links the selection to an external delivery service to process the order.

[0674] Step 9:

[0675] The delivery service prepares the specified menu and delivers it to the user. The delivery status and other information are sent to the terminal via the server so that the user can check it.

[0676] Step 10:

[0677] The user inputs their age and weight through the terminal, and this data is also entered in a specific text format, such as "45 years old, 70 kg."

[0678] Step 11:

[0679] The device sends the entered age and weight data to the server.

[0680] Step 12:

[0681] The server analyzes the received age and weight data and generates an optimal exercise plan based on past exercise history. For example, a specific exercise plan such as "30 minutes of light walking and 10 minutes of yoga stretching" is generated.

[0682] Step 13:

[0683] The server then sends the generated exercise plan back to the device. The returned data consists of specific instructions and a timeline.

[0684] Step 14:

[0685] The device displays the returned exercise plan to the user, who can then check and carry out the plan on the screen.

[0686] Step 15:

[0687] The user inputs through the terminal that he / she wants to start brain training. For example, this is input in the form of specific text such as "Start brain training."

[0688] Step 16:

[0689] The device sends the input brain training request to the server.

[0690] Step 17:

[0691] The server analyzes the received request and generates new training content based on past training history. For example, a new training content with audio for the folktale "Urashima Taro" is generated.

[0692] Step 18:

[0693] The server returns the generated training content to the terminal. The returned data is training content data including an audio file.

[0694] Step 19:

[0695] The device then provides the returned training content to the user, which is played back with audio, allowing the user to effectively train their brain.

[0696] The above processing steps constitute a system that allows the user to carry out effective health management.

[0697] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0698] The present invention combines a system that can provide lifestyle disease prevention, exercise suggestions, brain training, and medical support with an emotion engine that recognizes the user's emotions. This system selects appropriate actions based on the user's input and emotions, and provides the results to the user.

[0699] First, we will explain the specific flow when a user inputs their physical condition and what they want to eat that day. The user inputs their physical condition and what they want to eat that day into the device, for example, "I'm feeling a bit tired today, so I'd like to eat something light." The device sends this input data to the server, which then analyzes the user's input using an emotion engine to determine the user's emotional state (for example, "high stress"). The server analyzes the received data and the results of the emotion engine, and generates a list of appropriate ingredients and recipes. For example, "spinach and tomato salad" and "tofu and seaweed soup" are suggested. The device then displays this menu suggestion to the user.

[0700] Next, we will explain the specific flow when a user inputs their age and weight and an exercise plan is proposed. The user inputs "45 years old, 70 kg," and the device sends this data to the server. The emotion engine analyzes emotions based on the input text and the user's past data, and identifies the user's emotional state (for example, "low motivation"). The server generates an optimal exercise plan based on the age, weight, emotional state, and past exercise history. Specifically, it suggests "30 minutes of light walking and 10 minutes of yoga stretching." The device then displays this exercise plan to the user.

[0701] Furthermore, when a user wants to start brain training, they input, for example, "Start brain training." The device sends this request to the server, and the emotion engine analyzes the user's emotional state (for example, "I need to concentrate"). The server generates new training content based on past training history and emotional state, and plays a new folk tale, "Urashima Taro," with audio. The device then provides this training content to the user.

[0702] Or, if a user inputs a question about their health, for example, "I feel like my blood pressure has been high lately," the device sends the input data to a specialized medical support system, and the emotion engine analyzes the user's anxiety, etc. The specialized medical support system generates an answer to the question and sends it back to the server. The server sends this answer to the device, which then displays professional advice to the user, such as "Try to get moderate exercise and eat a balanced diet."

[0703] A specific usage scenario would be for a user to input their physical condition into the device in the morning, receive daily menu suggestions based on that information, check their exercise plan in the evening, and do brain training at night. Depending on their physical condition or emotional state, they may also be able to immediately consult medical support. By combining this system with an emotion engine, the goal is to make daily health management more personalized for users and enable continuous support that takes emotions into account.

[0704] The processing flow will be explained below.

[0705] Processing menu suggestions for lifestyle-related disease prevention

[0706] Step 1:

[0707] The user inputs their physical condition and what they want to eat that day into the terminal.

[0708] Example: A user enters, "I'm feeling a bit tired today and would like to eat something light."

[0709] Step 2:

[0710] The terminal transmits the input data to the server.

[0711] The entered "physical condition" and "food preferences" data is sent to the server in JSON format.

[0712] Step 3:

[0713] The device sends the input text to the emotion engine.

[0714] The emotion engine analyzes the user's emotional state from the input data, determining, for example, that they are "highly stressed."

[0715] Step 4:

[0716] The server parses the received data.

[0717] The server analyzes the keywords "feeling tired" and "light food" and searches the database for relevant ingredients and recipes.

[0718] Step 5:

[0719] The server generates a menu plan that reflects the emotional state.

[0720] Taking into account the results of the emotion engine (e.g., "high stress"), recipes with stress-reducing effects are selected.

[0721] Step 6:

[0722] The server sends the generated menu plan back to the terminal.

[0723] Send the menu plan to the device in JSON format.

[0724] Step 7:

[0725] The terminal displays the suggested menu to the user.

[0726] Example: Display "Spinach and Tomato Salad" and "Tofu and Seaweed Soup."

[0727] Processing exercise suggestions

[0728] Step 1:

[0729] The user enters their age and weight into the terminal.

[0730] Example: User enters "45 years old, 70kg."

[0731] Step 2:

[0732] The terminal transmits the input data to the server.

[0733] The entered "age" and "weight" data is sent to the server in JSON format.

[0734] Step 3:

[0735] The device sends the input data to the emotion engine.

[0736] The emotion engine analyzes the user's emotional state from the input data, determining, for example, that the user is "low motivation."

[0737] Step 4:

[0738] The server analyzes the input data and references past exercise history.

[0739] The user's past exercise history is retrieved from the database and checked to ensure that the same exercise is not repeated.

[0740] Step 5:

[0741] The server generates a motion plan that reflects the emotional state.

[0742] Taking into account the emotion engine result (e.g., "Low motivation"), it suggests moderate exercise, such as "30 minutes of easy walking and 10 minutes of yoga and stretching."

[0743] Step 6:

[0744] The server returns the generated exercise plan to the terminal.

[0745] The exercise plan is sent to the device in JSON format.

[0746] Step 7:

[0747] The terminal displays the suggested exercise plan to the user.

[0748] A suggested exercise plan and implementation method is displayed to the user.

[0749] Brain Training Processing

[0750] Step 1:

[0751] The user inputs into the terminal that he / she wants to start brain training.

[0752] Example: User types "Start Brain Training."

[0753] Step 2:

[0754] The device sends a request to the server.

[0755] Sends request data to the server in JSON format.

[0756] Step 3:

[0757] The device sends the input data to the emotion engine.

[0758] The emotion engine analyzes the user's emotional state from the input data, determining, for example, that "concentration is necessary."

[0759] Step 4:

[0760] The server references past training history.

[0761] The system retrieves the user's past brain training history from a database and avoids repeating the same content.

[0762] Step 5:

[0763] The server generates training content that reflects the emotional state.

[0764] It references the knowledge base and generates new brain training tasks based on the results of the emotion engine (e.g., "needs concentration").

[0765] Step 6:

[0766] The server returns the generated training content to the terminal.

[0767] Send the training content to the device in JSON format.

[0768] Step 7:

[0769] The device provides the training content to the user with audio.

[0770] Example: Play "The New Folktale of Urashima Taro" with audio.

[0771] Health Questions and Medical Support

[0772] Step 1:

[0773] The user inputs a health-related question into the terminal.

[0774] Example: A user types, "My blood pressure feels high lately."

[0775] Step 2:

[0776] The terminal sends the input data to a specialized medical support system.

[0777] The input data is sent in JSON format to the API of a specialized medical support system.

[0778] Step 3:

[0779] The device sends the input data to the emotion engine.

[0780] The emotion engine analyzes the user's emotional state and identifies feelings of anxiety, etc.

[0781] Step 4:

[0782] A specialized medical support system generates answers to questions.

[0783] An answer is generated based on the medical information and sent back to the server.

[0784] Step 5:

[0785] The server sends the response received from the specialized medical support system in JSON format to the terminal.

[0786] The received response is sent to the device.

[0787] Step 6:

[0788] The terminal displays professional medical support answers to the user.

[0789] Example: Display "Make sure to exercise moderately and eat a balanced diet."

[0790] Example 2

[0791] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0792] While conventional health management systems make suggestions based on user input data, they lack personalized support that takes into account the user's emotional state, making it difficult to improve user motivation and continuity. Furthermore, various types of support, such as exercise plans, meal suggestions, and brain training, are often provided individually, making it difficult to achieve comprehensive health management.

[0793] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for analyzing input data using an emotion analysis engine to determine the user's emotional state, means for generating a list of appropriate ingredients and recipes based on the analysis results, means for generating an optimal exercise plan based on the user's age, weight, emotional state, and past exercise history, and means for generating new training content based on the user's past training history and emotional state. This enables personalized suggestions and support that take the user's emotional state into consideration.

[0794] "User" means an individual who uses the system.

[0795] A "terminal" is a device that allows a user to interface with the system, such as a smartphone or PC.

[0796] "Input data" refers to information that a user provides to the system via a terminal, and includes physical condition, age, weight, and the like.

[0797] "Server" means a computer system that performs the central processing of the system and is responsible for collecting data, analyzing data, and generating results.

[0798] An "emotion analysis engine" is software that analyzes a user's emotional state based on input data.

[0799] "Emotional state" refers to a user's psychological state, such as stress level or motivation.

[0800] "Ingredients" refer to specific foods that are suggested to the user.

[0801] A "recipe" is a document or data that describes how to cook food using ingredients.

[0802] An "exercise plan" is a specific exercise plan suggested based on the user's health condition.

[0803] "Training content" refers to the specific brain training activities or programs provided to users.

[0804] The present invention is a system that enables the prevention of lifestyle-related diseases, exercise suggestions, brain training, and medical support. This system incorporates an emotion engine that recognizes the user's emotions, selects appropriate actions based on the user's input and emotions, and provides the results to the user. Details are described below.

[0805] Hardware and Software Use

[0806] This system consists of the following hardware and software:

[0807] Terminal: A device used by users to input information. Examples include smartphones and personal computers.

[0808] Server: A central system that receives and analyzes input data.

[0809] Sentiment analysis engine: Software that analyzes input text data and identifies the user's emotional state. It uses NLP (natural language processing) techniques.

[0810] Medical support system: A system that provides specialized medical information.

[0811] Specific operation of the system

[0812] The operation of the system will be explained using an example in which a user inputs their physical condition and what they want to eat that day. The user inputs, "I'm feeling a bit tired today, so I'd like to eat something light." The device sends this input data to the server, which uses an emotion analysis engine to analyze the user's emotional state (for example, "high stress"). The server then generates a list of appropriate ingredients and recipes based on the analysis results. Specifically, it suggests "spinach and tomato salad" and "tofu and seaweed soup." The device then displays this menu suggestion to the user.

[0813] Specific examples

[0814] Example prompt sentence:

[0815] The user enters "45 years old, 70 kg."

[0816] The device sends this data to the server, and the emotion engine analyzes it as "low motivation."

[0817] The server generates an optimal exercise plan based on age, weight, emotional state, and past exercise history, suggesting "30 minutes of light walking and 10 minutes of yoga stretching."

[0818] The device displays this exercise plan to the user.

[0819] Furthermore, if the user types "Start brain training," the same procedure is followed. The device sends the request to the server, and the emotion engine interprets it as "concentration required." The server generates new training content based on past training history and emotional state, and plays a new folk tale, "Urashima Taro," with audio. The device then provides this to the user.

[0820] This system allows users to input their physical condition into a device in the morning, receive daily menu suggestions based on that information, review their exercise plan in the evening, and then do brain training at night. Depending on their physical condition or emotional state, they can also immediately consult medical support. In this way, users' daily health management is personalized, and continuous support that takes their emotions into consideration is possible.

[0821] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0822] Step 1:

[0823] The user inputs their physical condition and what they want to eat that day into the input interface of the terminal.

[0824] Input: User's physical condition and food preferences (e.g., "I'm feeling a bit tired today, so I'd like to eat something light")

[0825] Output: Input data

[0826] Specific operation: The user enters text into a device such as a smartphone or PC and presses the send button.

[0827] Step 2:

[0828] The terminal transmits the received input data to the server.

[0829] Input: User-entered data

[0830] Output: The input data sent to the server

[0831] Specific operation: The device sends data to the server via the internet via the API.

[0832] Step 3:

[0833] The server uses an emotion analysis engine to analyze the input data.

[0834] Input: Input data sent to the server

[0835] Output: User's emotional state (e.g., "high stress")

[0836] Specific operation: The server launches an emotion analysis engine (NLP model) that analyzes input data and estimates the emotional state.

[0837] Step 4:

[0838] The server generates a list of appropriate ingredients and recipes based on the received data and the analysis results of the emotion engine.

[0839] Input: User input data and emotional state

[0840] Output: A list of ingredients or recipes (e.g., "Spinach and Tomato Salad" or "Tofu and Seaweed Soup")

[0841] How it works: The recipe generation algorithm in the server narrows down the candidates by taking into account your emotional state and dietary preferences.

[0842] Step 5:

[0843] The server returns the generated menu plan to the terminal.

[0844] Input: A list of ingredients and recipes generated

[0845] Output: Menu plan sent to device

[0846] Specific operation: The server sends data to the terminal as a response.

[0847] Step 6:

[0848] The terminal displays the suggested menu to the user.

[0849] Input: Menu plan sent from the server

[0850] Output: The menu displayed to the user

[0851] Specific operation: The measurement results are displayed on the device's user interface.

[0852] Step 7:

[0853] The user enters their age and weight into the device and an exercise plan is suggested.

[0854] Input: Age and weight (e.g., "45 years old, 70 kg")

[0855] Output: Input data

[0856] Specific operation: The user enters information into a device such as a smartphone or PC and presses the send button.

[0857] Step 8:

[0858] The terminal transmits the received input data to the server.

[0859] Input: User-entered data

[0860] Output: The input data sent to the server

[0861] Specific operation: The device sends data to the server via the internet via the API.

[0862] Step 9:

[0863] The server uses an emotion analysis engine to analyze emotions based on input data and past movement history.

[0864] Input: Input data sent to the server

[0865] Output: User's emotional state (e.g., "Low motivation")

[0866] What it does: The sentiment analysis engine analyzes past data and new input to identify emotional states.

[0867] Step 10:

[0868] The server generates an optimal exercise plan based on age, weight, emotional state and past exercise history.

[0869] Input: Age, weight, emotional state, past exercise history

[0870] Output: Exercise plan (e.g., "30 minutes of easy walking and 10 minutes of yoga and stretching")

[0871] Specific Actions: The movement plan generation algorithm uses this information to generate an optimal movement plan.

[0872] Step 11:

[0873] The server returns the generated exercise plan to the terminal.

[0874] Input: Generated movement plan

[0875] Output: Exercise plan sent to the device

[0876] Specific operation: The server sends data to the terminal as a response.

[0877] Step 12:

[0878] The terminal displays the suggested exercise plan to the user.

[0879] Input: Exercise plan sent from the server

[0880] Output: Exercise plan displayed to the user

[0881] Specific operation: The exercise plan is displayed on the device's user interface.

[0882] (Application example 2)

[0883] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0884] Conventional systems for preventing lifestyle-related diseases, suggesting exercise, brain training, and medical support often do not fully consider the user's physical condition or emotional state, resulting in a lack of personalized suggestions based on the user's emotions and physical condition. Furthermore, in brick-and-mortar stores, it is difficult to suggest ingredients and recipes based on the user's physical condition and emotions, so there are no means of effectively supporting health management and improving dietary content.

[0885] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for determining the user's emotional state using an emotion analysis engine that analyzes input data, means for generating a list of appropriate ingredients and recipes based on the emotional state, and means for returning the generated list to the terminal. This enables personalized health suggestions based on the user's physical condition and emotional state.

[0886] "User" refers to an individual who uses this system.

[0887] "Device" means the device used by a User to enter information or view suggestions, including, but not limited to, a smartphone, tablet, or PC.

[0888] "Server" refers to a computer system that receives, analyzes, and processes data sent by users.

[0889] "Sentiment analysis engine" refers to software or algorithms for determining a user's emotional state from input data.

[0890] "Emotional state" refers to the user's current psychological and emotional state, including, for example, stress and motivation.

[0891] A "list of ingredients and recipes" is a list containing ingredients and cooking methods suggested based on the user's physical and emotional state.

[0892] "Menu plan" refers to a list of suggested ingredients and recipes.

[0893] An "exercise plan" refers to a proposed exercise plan based on the user's physical condition and emotional state.

[0894] "Training content" refers to the specific brain training tasks and activities provided to users.

[0895] "With audio" refers to a method in which training content and notifications are provided to the user by audio.

[0896] To implement this invention, a system is constructed in which a user inputs their physical condition and food preferences for the day through a terminal. The terminal may be a smartphone, tablet, or PC. The data input to this terminal is sent to a server equipped with an emotion analysis engine. This server analyzes the user's input data and determines their emotional state.

[0897] The server uses a sentiment analysis engine to analyze the user's emotional state (e.g., high stress, low motivation, etc.). The sentiment analysis engine uses natural language processing techniques. The analysis results are used to generate ingredient and recipe lists, exercise plans, brain training content, and medical advice.

[0898] For example, if a user inputs "I'm feeling a bit tired today and would like to eat something light," this data is sent to the server, and the emotion analysis engine determines the emotional state as "high stress." Based on this emotional state, the server generates a list of suggestions, such as "spinach and tomato salad" and "tofu and seaweed soup." The generated list is sent back to the device and displayed to the user.

[0899] For example, if a user wants to input their age and weight and want an exercise plan suggested, they can enter "45 years old, 70 kg." This data is sent to the server, where the emotion analysis engine analyzes the user's emotional state. For example, if the user is judged to be "low motivation," the server will generate an exercise plan suggesting "30 minutes of light walking and 10 minutes of yoga stretching" based on this emotional state and past exercise history. The generated exercise plan is sent back to the device and displayed to the user.

[0900] Furthermore, if a user wants to start brain training, they input "Start brain training," and this data is sent to the server, where the emotion analysis engine analyzes the user's emotional state. For example, if the server determines that "concentration is necessary," it will play a new training content, "A new folk tale, 'Urashima Taro,'" with audio, based on this emotional state and past training history. As a result, the device provides this training content to the user.

[0901] Here's an example prompt that uses a generative AI model to analyze sentiment and determine what to suggest to the user:

[0902] "When a user inputs something like, 'I'm feeling a bit tired today and want to eat something light,' analyze their emotional state and suggest appropriate ingredients and recipes."

[0903] The hardware for implementing this invention includes a user terminal (such as a smartphone, tablet, or PC), a server, and a computer system for running the sentiment analysis engine. The software includes Python 3.x and a requests library for sending API requests.

[0904] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0905] Step 1:

[0906] The user inputs their physical condition and what they want to eat into the terminal. For example, they might input, "I'm feeling a bit tired today, so I want to eat something light." This input data is acquired through an input form on the user terminal.

[0907] Step 2:

[0908] The device sends the input data to the server. Specifically, the data is sent as an HTTP POST request via a communication module built into the device. This request includes the user's input as a payload.

[0909] Step 3:

[0910] The input data received by the server is analyzed by a sentiment analysis engine. The sentiment analysis engine uses natural language processing technology to determine the user's emotional state. Here, the input data is analyzed in text format and an emotional tag such as "high stress" is generated.

[0911] Step 4:

[0912] The server generates a list of appropriate ingredients and recipes based on the user's emotional state and input data. The server uses an algorithm that takes into account the user's emotional state (e.g., "high stress") to search its internal database for light ingredients and recipes and generates the list.

[0913] Step 5:

[0914] The server then returns the generated list of ingredients and recipes to the device, again using an HTTP POST request. The returned data includes the suggested ingredients and recipe information.

[0915] Step 6:

[0916] The device displays the menu plan returned from the server to the user. Specifically, it uses a GUI (Graphical User Interface) component for display to show details of ingredients and recipes. Based on this information, the user can select appropriate ingredients and dishes.

[0917] For example, if a user inputs "I'm feeling a bit tired today and want to eat something light," the server analyzes this and generates an emotion tag called "high stress." The server then lists options like "spinach and tomato salad" and "tofu and seaweed soup" based on this emotion tag and returns this information. The device then displays it to the user.

[0918] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0919] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0920] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0921] [Third embodiment]

[0922] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0923] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0924] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0925] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0926] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0927] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0928] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0929] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0930] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0931] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0932] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0933] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0934] The present invention relates to a system that enables prevention of lifestyle-related diseases, exercise suggestions, brain training, and medical support. This system selects appropriate actions based on user input and provides the results to the user.

[0935] First, the user inputs their physical condition and what they want to eat that day through the device. For example, the user might input, "I'm feeling a bit tired today, so I'd like to eat something light." The device then sends this input data to the server. The server analyzes the received data, generates a list of appropriate ingredients and recipes, and suggests menu ideas such as "spinach and tomato salad" and "tofu and seaweed soup." The device then displays these menu ideas to the user.

[0936] Next, the user enters their age and weight through the device. For example, they might enter "45 years old, 70 kg." The device then sends this input data to the server, which then generates an optimal exercise plan based on the user's age, weight, and past exercise history. Specific exercises (for example, "30 minutes of light walking and 10 minutes of yoga stretching") are suggested based on the user's health condition. The device then displays this exercise plan to the user.

[0937] Furthermore, if the user wishes to start brain training, he / she may input, for example, "Start brain training." The device sends this request to the server, and the server generates new training content based on past training history. For example, it plays "A new folk tale, 'Urashima Taro,'" with audio. The device provides this training content to the user.

[0938] Furthermore, when a user inputs a question about their health, for example, "I feel like my blood pressure has been high recently," the device sends the input data to a specialized medical support system. The specialized medical support system generates an answer to the question and sends it back to the server. The server receives this answer and sends it to the device. The device then displays the answer to the user, saying, "Try to exercise moderately and eat a balanced diet."

[0939] A specific scenario for use would be for the user to enter their physical condition into the device in the morning, receive daily menu suggestions based on that information, review their exercise plan in the evening, and then do brain training at night. It would also be possible to immediately consult medical support if they felt unwell. This system is expected to make daily health management more personalized and provide continuous support to users.

[0940] The processing flow will be explained below.

[0941] Step 1:

[0942] The user inputs their physical condition and what they want to eat that day into the terminal.

[0943] Example: A user enters, "I'm feeling a bit tired today and would like to eat something light."

[0944] Step 2:

[0945] The terminal transmits the input data to the server.

[0946] The entered "physical condition" and "food preferences" data is sent to the server in JSON format.

[0947] Step 3:

[0948] The server parses the input data.

[0949] The server analyzes keywords such as "feeling tired" and "light food" and searches the database for relevant ingredients and recipes.

[0950] Step 4:

[0951] The server generates a list of suitable ingredients and recipes.

[0952] Based on the analysis results, recipes are generated using ingredients that are rich in vitamins and easy to digest.

[0953] Step 5:

[0954] The server sends the generated menu plan back to the terminal.

[0955] Send the menu plan to the device in JSON format.

[0956] Step 6:

[0957] The terminal displays the suggested menu to the user.

[0958] Example: Display "Spinach and Tomato Salad" and "Tofu and Seaweed Soup."

[0959] Step 1:

[0960] The user enters their age and weight into the terminal.

[0961] Example: User enters "45 years old, 70kg."

[0962] Step 2:

[0963] The terminal transmits the input data to the server.

[0964] The entered "age" and "weight" data is sent to the server in JSON format.

[0965] Step 3:

[0966] The server analyzes the input data and references past exercise history.

[0967] The user's past exercise history is retrieved from the database and checked to ensure that the same exercise is not repeated.

[0968] Step 4:

[0969] The server generates an optimal exercise plan.

[0970] Based on your age, weight, and past exercise history, it will suggest new exercises (for example, "30 minutes of easy walking and 10 minutes of yoga stretching").

[0971] Step 5:

[0972] The server returns the generated exercise plan to the terminal.

[0973] The exercise plan is sent to the device in JSON format.

[0974] Step 6:

[0975] The terminal displays the suggested exercise plan to the user.

[0976] A suggested exercise plan and implementation method is displayed to the user.

[0977] Step 1:

[0978] The user inputs into the terminal that he / she wants to start brain training.

[0979] Example: User types "Start Brain Training."

[0980] Step 2:

[0981] The device sends a request to the server.

[0982] Sends request data to the server in JSON format.

[0983] Step 3:

[0984] The server references past training history.

[0985] The system retrieves the user's past brain training history from a database and avoids repeating the same content.

[0986] Step 4:

[0987] The server generates new training content (fairy tales, original stories, rakugo, etc.).

[0988] Browse the knowledge base and generate new brain training challenges.

[0989] Step 5:

[0990] The server returns the generated training content to the terminal.

[0991] Send the training content to the device in JSON format.

[0992] Step 6:

[0993] The device provides the training content to the user with audio.

[0994] Example: Play "The New Folktale of Urashima Taro" with audio.

[0995] Step 1:

[0996] The user inputs a health-related question into the terminal.

[0997] Example: A user types, "My blood pressure feels high lately."

[0998] Step 2:

[0999] The terminal sends the input data to a specialized medical support system.

[1000] The input data is sent in JSON format to the API of a specialized medical support system.

[1001] Step 3:

[1002] A specialized medical support system generates answers to questions.

[1003] An answer is generated based on the medical information and sent back to the server.

[1004] Step 4:

[1005] The server sends the response received from the HELPO app in JSON format to the device.

[1006] The received response is sent to the device.

[1007] Step 5:

[1008] The terminal displays professional medical support answers to the user.

[1009] Example: Display "Make sure to exercise moderately and eat a balanced diet."

[1010] Example 1

[1011] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1012] Current health management systems struggle to provide personalized recommendations tailored to individual users' individual circumstances and needs. Furthermore, systems that address multiple areas such as dietary recommendations, exercise plans, brain training, and medical support are not integrated, requiring users to navigate multiple applications and services. Furthermore, the recommendations are general, with few specific advice based on individual lifestyle habits or past history.

[1013] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1014] In this invention, the server includes means for analyzing input data and using a generative AI model to generate a list of appropriate ingredients and recipes, means for analyzing input data and using a generative AI model to generate an optimal exercise plan based on past exercise history, and means for using a generative AI model to generate new training content based on past training history. This allows a single system to provide personalized suggestions tailored to each user's individual situation and needs, enabling more specific and actionable advice.

[1015] "User" refers to an individual who uses the system and is a consumer who provides input according to their health status and needs.

[1016] "Terminal" refers to a device through which a user makes input, and includes electronic devices such as smartphones, tablets, and personal computers.

[1017] "Server" refers to the central computing unit that receives and analyzes the data sent by the User, generates appropriate suggestions and sends them back to the Terminal.

[1018] "Input data" refers to information provided by the user through the terminal, including physical condition, dietary preferences, age, weight, requests to start brain training, health-related questions, etc.

[1019] A "generative AI model" refers to an algorithm that uses natural language processing and machine learning techniques to analyze input data and generate appropriate suggestions.

[1020] "Menu suggestions" refers to a list of ingredients and recipes suggested by a generative AI model based on the user's physical condition and dietary preferences.

[1021] "Exercise plan" refers to a specific exercise schedule suggested by a generative AI model based on a user's age, weight, and past exercise history.

[1022] "Training content" refers to the specific brain training content suggested by the generative AI model based on the user's past training history.

[1023] A "medical support system" refers to a system with specialized medical knowledge that is used to generate appropriate answers to health-related questions posed by users.

[1024] "Proposal" refers to the specific content that the server generates using the generative AI model and provides to the user via the terminal.

[1025] This invention relates to a system that enables prevention of lifestyle-related diseases, exercise suggestions, brain training, and medical support. This system selects appropriate actions based on user input and provides the results to the user.

[1026] First, the user inputs their physical condition and meal preferences for the day into the interface on their device. For example, the user might input, "I'm feeling a bit tired today, so I'd like to eat something light." The device then sends this input data to the server. The server analyzes the received data and uses a generative AI model to generate a list of appropriate ingredients and recipes. Specific menu suggestions generated include "spinach and tomato salad" and "tofu and seaweed soup." The device then displays these menu suggestions to the user.

[1027] Next, the user enters their age and weight through the device. For example, they might enter "45 years old, 70 kg." The device then sends this input data to the server. The server then analyzes the user's age, weight, and past exercise history, and uses a generative AI model to generate an optimal exercise plan. Specific exercises are suggested based on the user's health condition. A specific exercise plan might include "30 minutes of light walking and 10 minutes of yoga stretching." The device then displays this exercise plan to the user.

[1028] Next, the user inputs a request to start brain training into the device. For example, they input "Start brain training." The device then sends this request to the server. The server uses a generative AI model to generate new training content based on past training history. As a specific training content, it plays a new folk tale, "Urashima Taro," with audio. The device then provides this training content to the user.

[1029] Furthermore, when a user inputs a question about health, for example, "I feel like my blood pressure has been high recently," the terminal sends the input data to a specialized medical support system. The specialized medical support system generates an answer to the question and sends it back to the server. The server receives this answer and sends it to the terminal. The terminal then displays the answer to the user, saying, "Try to get moderate exercise and eat a balanced diet."

[1030] A specific scenario for use would be for the user to enter their physical condition into the device in the morning, receive daily menu suggestions based on that information, review their exercise plan in the evening, and then do brain training at night. It would also be possible to immediately consult medical support if they felt unwell. This system is expected to make daily health management more personalized and provide continuous support to users.

[1031] Example prompt sentence:

[1032] "I'm feeling a bit tired, so can you recommend some light recipes?"

[1033] "Please tell me an exercise plan that would be suitable for a 45-year-old, 70kg person."

[1034] "Please tell me about new brain training content."

[1035] "My blood pressure seems to be high recently. What can I do?"

[1036] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1037] Step 1:

[1038] The user inputs their physical condition and what they want to eat that day through the terminal. The input data provided is information such as "I'm feeling a bit tired today, so I'd like to eat something light." The terminal then sends this input data to the server. Specifically, the user enters the data using an input form on their smartphone or computer and presses the send button.

[1039] Step 2:

[1040] The server receives input data. The received data is "I'm feeling a bit tired today, so I'd like to eat something light." The server passes this data to the generative AI model, which generates a prompt to generate an appropriate menu list. Specifically, the server inputs the prompt "I'm feeling a bit tired, so please tell me some recipes for light dishes" into the generative AI model.

[1041] Step 3:

[1042] A generative AI model generates a menu list based on the prompt sentence. The model extracts appropriate information from a huge database and generates menu suggestions such as "spinach and tomato salad" or "tofu and seaweed soup." The data processing performed here involves analyzing the user's state from the input sentence and suggesting appropriate dishes.

[1043] Step 4:

[1044] The server returns the generated menu plan to the terminal. The returned data is a specific menu list of "spinach and tomato salad" and "tofu and wakame soup." The server returns the data using an HTTP response.

[1045] Step 5:

[1046] The device displays the suggested menu to the user. The device analyzes the received data and displays it on the user interface. Specifically, the device displays the names of the dishes "Spinach and Tomato Salad" and "Tofu and Seaweed Soup."

[1047] Step 6:

[1048] The user inputs their age and weight through the device. For example, they input "45 years old, 70 kg." The device then sends this input data to the server. Specifically, the user inputs the data using an input form on their smartphone or computer and presses the send button.

[1049] Step 7:

[1050] The server receives the input data. The received data is "45 years old, 70 kg." The server passes this data to the generative AI model and generates a prompt to generate the optimal exercise plan. Specifically, the server inputs the prompt "Please tell me an exercise plan suitable for a person who is 45 years old and weighs 70 kg" into the generative AI model.

[1051] Step 8:

[1052] The generative AI model generates an exercise plan based on the prompt sentence. The model extracts appropriate information from a vast database and generates an exercise plan such as "30 minutes of light walking" and "10 minutes of yoga stretching." The data processing performed here analyzes the user's physique and health condition from the input sentence and suggests appropriate exercises.

[1053] Step 9:

[1054] The server returns the generated exercise plan to the device. The returned data is a specific exercise plan consisting of "30 minutes of light walking" and "10 minutes of yoga stretching." The server returns the data using an HTTP response.

[1055] Step 10:

[1056] The device displays the proposed exercise plan to the user. The device analyzes the received data and displays it on the user interface. Specifically, the device displays the exercise names "30 minutes of easy walking" and "10 minutes of yoga stretching."

[1057] Step 11:

[1058] The user inputs a request to start brain training through the terminal. For example, the user inputs "Start brain training." The terminal sends this request to the server.

[1059] Step 12:

[1060] The server receives the request data. The received data is "Start brain training." The server passes this data to the generative AI model and generates a prompt to generate new training content. Specifically, the server inputs the prompt "Please tell me new brain training content" into the generative AI model.

[1061] Step 13:

[1062] The generative AI model generates brain training content based on the prompt. The model extracts appropriate training content and generates specific training content, such as "A new folktale, Urashima Taro." The data processing performed here refers to past training history and suggests new stimulating content.

[1063] Step 14:

[1064] The server returns the generated training content to the device. The returned data is the specific training content, "A New Folktale: Urashima Taro." The server returns the data using an HTTP response.

[1065] Step 15:

[1066] The training content provided by the device is played back to the user with audio. The device analyzes the received data and displays a play button on the user interface. Specifically, when the user presses the play button, a new folktale, "Urashima Taro," is played back aloud.

[1067] Step 16:

[1068] A user inputs a health-related question through the terminal. For example, the user inputs, "I feel like my blood pressure has been high recently." The terminal then sends this health-related question to a specialized medical support system.

[1069] Step 17:

[1070] A specialized medical support system receives the question data, analyzes it, and then generates an answer. For example, the generated answer might be, "Try to exercise moderately and eat a balanced diet." The data calculation performed here is to generate the optimal medical advice for the input question.

[1071] Step 18:

[1072] The server receives the response from the specialized medical support system and sends it back to the device. The returned data is specific advice such as "Try to exercise moderately and eat a balanced diet." The server returns the data using an HTTP response.

[1073] Step 19:

[1074] The device displays the answer from the specialized medical support system to the user. The device analyzes the received data and displays it on the user interface. Specifically, the device's display shows advice such as "Try to get moderate exercise and eat a balanced diet."

[1075] (Application example 1)

[1076] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1077] Conventional health management systems lacked the flexibility to accommodate individual user needs and physical conditions. They also lacked the ability to provide personalized meal suggestions and delivery based on user input, resulting in low user convenience. Furthermore, there was a need for a system that could go beyond simply suggesting meals and enable users to easily obtain healthy meals.

[1078] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1079] In this invention, the server includes a means for analyzing user input data and generating a list of appropriate ingredients and recipes, a means for creating an initial menu based on prompts generated using a generative AI model, and a means for delivering meals via an external service in accordance with the user's input. This allows for personalized menu suggestions based on the user's individual physical condition and food preferences, and for the menu to be easily obtained through a delivery service.

[1080] A "terminal" is an electronic device that allows a user to input data such as their physical condition and food preferences.

[1081] The "server" is a computer system that analyzes input data sent by users and generates appropriate ingredients, recipes, exercise plans, training content, etc.

[1082] "Input data" refers to information such as physical condition information, food preferences, age, weight, and intention to start brain training that the user inputs through the terminal.

[1083] A "list of ingredients and recipes" is a set of ingredients and cooking methods that the server generates based on the input data analyzed and suggests to the user.

[1084] A "menu plan" is a proposal of ingredients and recipes generated by the server, and is a menu provided to the user.

[1085] An "exercise plan" is a specific exercise plan generated by the server based on the user's age, weight, and past exercise history.

[1086] "Training content" refers to the specific content of the training generated by the server based on the brain training desired by the user.

[1087] A "generative AI model" is an artificial intelligence model that analyzes user input data and automatically generates appropriate ingredients, recipes, exercise plans, etc.

[1088] A "prompt sentence" is a pre-set input sentence for the generative AI model to analyze, and is a sentence that specifically instructs the model on the user's request.

[1089] A "delivery service" is a service that delivers meals based on ingredients and recipes selected by the user to the user's home via an external service.

[1090] This invention is a system that allows users to input specific health information and desired meal contents via a terminal, transmits the data to a server for analysis, suggests appropriate ingredients and recipes, creates exercise plans, provides brain training, and even delivers the suggested meals. This system consists of the following components:

[1091] First, the user uses a device (smartphone, tablet, etc.) to input their physical condition and desired foods for the day. This device is connected to the Internet, and the input data is sent to a server. The server receives the user data, analyzes it, and generates a list of appropriate ingredients and recipes. At this time, a generative AI model is used to generate prompts based on the user's physical condition and preferences.

[1092] The server then sends the generated menu plan back to the terminal, which displays it to the user and allows the user to select the menu they want. Furthermore, this system is linked to an external delivery service, so the menu selected by the user can be delivered to their home.

[1093] For example, suppose a user types into their device, "I'm feeling a bit tired today, so I'd like to eat something light." This data is sent to the server, which analyzes it and generates menu suggestions such as "spinach and tomato salad" or "tofu and seaweed soup." At this time, a generative AI model is used to generate prompt sentences, which the server then optimizes.

[1094] An example of a generated prompt statement:

[1095] Health: I'm feeling a little tired this morning.

[1096] What I want to eat: Light food

[1097] Prompt: "If the person's health status is 'I feel a little tired this morning,' please suggest a healthy, light meal."

[1098] Based on this prompt, the server will suggest healthy menu items, allowing the user to select from them and easily obtain the selected menu items through a delivery service.

[1099] Users can also enter their age and weight, and this data is sent to the server for analysis. As a result, an exercise plan optimized for the user's health condition is generated and displayed on the device, allowing users to practice exercises tailored to their health condition.

[1100] If the user wishes to train their brain, they input this into the terminal. The server generates new training content based on the user's past training history and sends it back to the terminal. This training content is provided with audio, allowing the user to train their brain effectively.

[1101] In this way, this system can meet the diverse health management needs of users and support the provision of individualized, specific suggestions and their implementation.

[1102] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1103] Step 1:

[1104] The user uses the device to input information about their physical condition and what they want to eat. The input data is entered in the form of a specific string, such as "I'm feeling a bit tired today, so I'd like to eat something light."

[1105] Step 2:

[1106] The device sends the entered data to the server. The sent content consists of text data such as health information and food preferences.

[1107] Step 3:

[1108] The server analyzes the received input data and generates a prompt using a generative AI model. For example, a prompt might be generated such as, "If the user's physical condition is entered as 'I feel a little tired this morning,' please suggest a healthy, light meal."

[1109] Step 4:

[1110] The server generates a list of appropriate ingredients and recipes based on the generated prompt. The generated menu includes specific items such as "spinach and tomato salad" or "tofu and seaweed soup."

[1111] Step 5:

[1112] The server generates a menu plan and returns it to the terminal. The returned data is a text-format recipe list.

[1113] Step 6:

[1114] The terminal displays the returned menu plan to the user, who can then check the suggested menu on the screen and make a selection.

[1115] Step 7:

[1116] The user confirms the menu selection using the terminal, and then transmits the menu selection back to the server.

[1117] Step 8:

[1118] The server receives the menu selected by the user and links the selection to an external delivery service to process the order.

[1119] Step 9:

[1120] The delivery service prepares the specified menu and delivers it to the user. The delivery status and other information are sent to the terminal via the server so that the user can check it.

[1121] Step 10:

[1122] The user inputs their age and weight through the terminal, and this data is also entered in a specific text format, such as "45 years old, 70 kg."

[1123] Step 11:

[1124] The device sends the entered age and weight data to the server.

[1125] Step 12:

[1126] The server analyzes the received age and weight data and generates an optimal exercise plan based on past exercise history. For example, a specific exercise plan such as "30 minutes of light walking and 10 minutes of yoga stretching" is generated.

[1127] Step 13:

[1128] The server then sends the generated exercise plan back to the device. The returned data consists of specific instructions and a timeline.

[1129] Step 14:

[1130] The device displays the returned exercise plan to the user, who can then check and carry out the plan on the screen.

[1131] Step 15:

[1132] The user inputs through the terminal that he / she wants to start brain training. For example, this is input in the form of specific text such as "Start brain training."

[1133] Step 16:

[1134] The device sends the input brain training request to the server.

[1135] Step 17:

[1136] The server analyzes the received request and generates new training content based on past training history. For example, a new training content with audio for the folktale "Urashima Taro" is generated.

[1137] Step 18:

[1138] The server returns the generated training content to the terminal. The returned data is training content data including an audio file.

[1139] Step 19:

[1140] The device then provides the returned training content to the user, which is played back with audio, allowing the user to effectively train their brain.

[1141] The above processing steps constitute a system that allows the user to carry out effective health management.

[1142] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1143] The present invention combines a system that can provide lifestyle disease prevention, exercise suggestions, brain training, and medical support with an emotion engine that recognizes the user's emotions. This system selects appropriate actions based on the user's input and emotions, and provides the results to the user.

[1144] First, we will explain the specific flow when a user inputs their physical condition and what they want to eat that day. The user inputs their physical condition and what they want to eat that day into the device, for example, "I'm feeling a bit tired today, so I'd like to eat something light." The device sends this input data to the server, which then analyzes the user's input using an emotion engine to determine the user's emotional state (for example, "high stress"). The server analyzes the received data and the results of the emotion engine, and generates a list of appropriate ingredients and recipes. For example, "spinach and tomato salad" and "tofu and seaweed soup" are suggested. The device then displays this menu suggestion to the user.

[1145] Next, we will explain the specific flow when a user inputs their age and weight and an exercise plan is proposed. The user inputs "45 years old, 70 kg," and the device sends this data to the server. The emotion engine analyzes emotions based on the input text and the user's past data, and identifies the user's emotional state (for example, "low motivation"). The server generates an optimal exercise plan based on the age, weight, emotional state, and past exercise history. Specifically, it suggests "30 minutes of light walking and 10 minutes of yoga stretching." The device then displays this exercise plan to the user.

[1146] Furthermore, when a user wants to start brain training, they input, for example, "Start brain training." The device sends this request to the server, and the emotion engine analyzes the user's emotional state (for example, "I need to concentrate"). The server generates new training content based on past training history and emotional state, and plays a new folk tale, "Urashima Taro," with audio. The device then provides this training content to the user.

[1147] Or, if a user inputs a question about their health, for example, "I feel like my blood pressure has been high lately," the device sends the input data to a specialized medical support system, and the emotion engine analyzes the user's anxiety, etc. The specialized medical support system generates an answer to the question and sends it back to the server. The server sends this answer to the device, which then displays professional advice to the user, such as "Try to get moderate exercise and eat a balanced diet."

[1148] A specific usage scenario would be for a user to input their physical condition into the device in the morning, receive daily menu suggestions based on that information, check their exercise plan in the evening, and do brain training at night. Depending on their physical condition or emotional state, they may also be able to immediately consult medical support. By combining this system with an emotion engine, the goal is to make daily health management more personalized for users and enable continuous support that takes emotions into account.

[1149] The processing flow will be explained below.

[1150] Processing menu suggestions for lifestyle-related disease prevention

[1151] Step 1:

[1152] The user inputs their physical condition and what they want to eat that day into the terminal.

[1153] Example: A user enters, "I'm feeling a bit tired today and would like to eat something light."

[1154] Step 2:

[1155] The terminal transmits the input data to the server.

[1156] The entered "physical condition" and "food preferences" data is sent to the server in JSON format.

[1157] Step 3:

[1158] The device sends the input text to the emotion engine.

[1159] The emotion engine analyzes the user's emotional state from the input data, determining, for example, that they are "highly stressed."

[1160] Step 4:

[1161] The server parses the received data.

[1162] The server analyzes the keywords "feeling tired" and "light food" and searches the database for relevant ingredients and recipes.

[1163] Step 5:

[1164] The server generates a menu plan that reflects the emotional state.

[1165] Taking into account the results of the emotion engine (e.g., "high stress"), recipes with stress-reducing effects are selected.

[1166] Step 6:

[1167] The server sends the generated menu plan back to the terminal.

[1168] Send the menu plan to the device in JSON format.

[1169] Step 7:

[1170] The terminal displays the suggested menu to the user.

[1171] Example: Display "Spinach and Tomato Salad" and "Tofu and Seaweed Soup."

[1172] Processing exercise suggestions

[1173] Step 1:

[1174] The user enters their age and weight into the terminal.

[1175] Example: User enters "45 years old, 70kg."

[1176] Step 2:

[1177] The terminal transmits the input data to the server.

[1178] The entered "age" and "weight" data is sent to the server in JSON format.

[1179] Step 3:

[1180] The device sends the input data to the emotion engine.

[1181] The emotion engine analyzes the user's emotional state from the input data, determining, for example, that the user is "low motivation."

[1182] Step 4:

[1183] The server analyzes the input data and references past exercise history.

[1184] The user's past exercise history is retrieved from the database and checked to ensure that the same exercise is not repeated.

[1185] Step 5:

[1186] The server generates a motion plan that reflects the emotional state.

[1187] Taking into account the emotion engine result (e.g., "Low motivation"), it suggests moderate exercise, such as "30 minutes of easy walking and 10 minutes of yoga and stretching."

[1188] Step 6:

[1189] The server returns the generated exercise plan to the terminal.

[1190] The exercise plan is sent to the device in JSON format.

[1191] Step 7:

[1192] The terminal displays the suggested exercise plan to the user.

[1193] A suggested exercise plan and implementation method is displayed to the user.

[1194] Brain Training Processing

[1195] Step 1:

[1196] The user inputs into the terminal that he / she wants to start brain training.

[1197] Example: User types "Start Brain Training."

[1198] Step 2:

[1199] The device sends a request to the server.

[1200] Sends request data to the server in JSON format.

[1201] Step 3:

[1202] The device sends the input data to the emotion engine.

[1203] The emotion engine analyzes the user's emotional state from the input data, determining, for example, that "concentration is necessary."

[1204] Step 4:

[1205] The server references past training history.

[1206] The system retrieves the user's past brain training history from a database and avoids repeating the same content.

[1207] Step 5:

[1208] The server generates training content that reflects the emotional state.

[1209] It references the knowledge base and generates new brain training tasks based on the results of the emotion engine (e.g., "needs concentration").

[1210] Step 6:

[1211] The server returns the generated training content to the terminal.

[1212] Send the training content to the device in JSON format.

[1213] Step 7:

[1214] The device provides the training content to the user with audio.

[1215] Example: Play "The New Folktale of Urashima Taro" with audio.

[1216] Health Questions and Medical Support

[1217] Step 1:

[1218] The user inputs a health-related question into the terminal.

[1219] Example: A user types, "My blood pressure feels high lately."

[1220] Step 2:

[1221] The terminal sends the input data to a specialized medical support system.

[1222] The input data is sent in JSON format to the API of a specialized medical support system.

[1223] Step 3:

[1224] The device sends the input data to the emotion engine.

[1225] The emotion engine analyzes the user's emotional state and identifies feelings of anxiety, etc.

[1226] Step 4:

[1227] A specialized medical support system generates answers to questions.

[1228] An answer is generated based on the medical information and sent back to the server.

[1229] Step 5:

[1230] The server sends the response received from the specialized medical support system in JSON format to the terminal.

[1231] The received response is sent to the device.

[1232] Step 6:

[1233] The terminal displays professional medical support answers to the user.

[1234] Example: Display "Make sure to exercise moderately and eat a balanced diet."

[1235] Example 2

[1236] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1237] While conventional health management systems make suggestions based on user input data, they lack personalized support that takes into account the user's emotional state, making it difficult to improve user motivation and continuity. Furthermore, various types of support, such as exercise plans, meal suggestions, and brain training, are often provided individually, making it difficult to achieve comprehensive health management.

[1238] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for analyzing input data using an emotion analysis engine to determine the user's emotional state, means for generating a list of appropriate ingredients and recipes based on the analysis results, means for generating an optimal exercise plan based on the user's age, weight, emotional state, and past exercise history, and means for generating new training content based on the user's past training history and emotional state. This enables personalized suggestions and support that take the user's emotional state into consideration.

[1239] "User" means an individual who uses the system.

[1240] A "terminal" is a device that allows a user to interface with the system, such as a smartphone or PC.

[1241] "Input data" refers to information that a user provides to the system via a terminal, and includes physical condition, age, weight, and the like.

[1242] "Server" means a computer system that performs the central processing of the system and is responsible for collecting data, analyzing data, and generating results.

[1243] An "emotion analysis engine" is software that analyzes a user's emotional state based on input data.

[1244] "Emotional state" refers to a user's psychological state, such as stress level or motivation.

[1245] "Ingredients" refer to specific foods that are suggested to the user.

[1246] A "recipe" is a document or data that describes how to cook food using ingredients.

[1247] An "exercise plan" is a specific exercise plan suggested based on the user's health condition.

[1248] "Training content" refers to the specific brain training activities or programs provided to users.

[1249] The present invention is a system that enables the prevention of lifestyle-related diseases, exercise suggestions, brain training, and medical support. This system incorporates an emotion engine that recognizes the user's emotions, selects appropriate actions based on the user's input and emotions, and provides the results to the user. Details are described below.

[1250] Hardware and Software Use

[1251] This system consists of the following hardware and software:

[1252] Terminal: A device used by users to input information. Examples include smartphones and personal computers.

[1253] Server: A central system that receives and analyzes input data.

[1254] Sentiment analysis engine: Software that analyzes input text data and identifies the user's emotional state. It uses NLP (natural language processing) techniques.

[1255] Medical support system: A system that provides specialized medical information.

[1256] Specific operation of the system

[1257] The operation of the system will be explained using an example in which a user inputs their physical condition and what they want to eat that day. The user inputs, "I'm feeling a bit tired today, so I'd like to eat something light." The device sends this input data to the server, which uses an emotion analysis engine to analyze the user's emotional state (for example, "high stress"). The server then generates a list of appropriate ingredients and recipes based on the analysis results. Specifically, it suggests "spinach and tomato salad" and "tofu and seaweed soup." The device then displays this menu suggestion to the user.

[1258] Specific examples

[1259] Example prompt sentence:

[1260] The user enters "45 years old, 70 kg."

[1261] The device sends this data to the server, and the emotion engine analyzes it as "low motivation."

[1262] The server generates an optimal exercise plan based on age, weight, emotional state, and past exercise history, suggesting "30 minutes of light walking and 10 minutes of yoga stretching."

[1263] The device displays this exercise plan to the user.

[1264] Furthermore, if the user types "Start brain training," the same procedure is followed. The device sends the request to the server, and the emotion engine interprets it as "concentration required." The server generates new training content based on past training history and emotional state, and plays a new folk tale, "Urashima Taro," with audio. The device then provides this to the user.

[1265] This system allows users to input their physical condition into a device in the morning, receive daily menu suggestions based on that information, review their exercise plan in the evening, and then do brain training at night. Depending on their physical condition or emotional state, they can also immediately consult medical support. In this way, users' daily health management is personalized, and continuous support that takes their emotions into consideration is possible.

[1266] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1267] Step 1:

[1268] The user inputs their physical condition and what they want to eat that day into the input interface of the terminal.

[1269] Input: User's physical condition and food preferences (e.g., "I'm feeling a bit tired today, so I'd like to eat something light")

[1270] Output: Input data

[1271] Specific operation: The user enters text into a device such as a smartphone or PC and presses the send button.

[1272] Step 2:

[1273] The terminal transmits the received input data to the server.

[1274] Input: User-entered data

[1275] Output: The input data sent to the server

[1276] Specific operation: The device sends data to the server via the internet via the API.

[1277] Step 3:

[1278] The server uses an emotion analysis engine to analyze the input data.

[1279] Input: Input data sent to the server

[1280] Output: User's emotional state (e.g., "high stress")

[1281] Specific operation: The server launches an emotion analysis engine (NLP model) that analyzes input data and estimates the emotional state.

[1282] Step 4:

[1283] The server generates a list of appropriate ingredients and recipes based on the received data and the analysis results of the emotion engine.

[1284] Input: User input data and emotional state

[1285] Output: A list of ingredients or recipes (e.g., "Spinach and Tomato Salad" or "Tofu and Seaweed Soup")

[1286] How it works: The recipe generation algorithm in the server narrows down the candidates by taking into account your emotional state and dietary preferences.

[1287] Step 5:

[1288] The server returns the generated menu plan to the terminal.

[1289] Input: A list of ingredients and recipes generated

[1290] Output: Menu plan sent to device

[1291] Specific operation: The server sends data to the terminal as a response.

[1292] Step 6:

[1293] The terminal displays the suggested menu to the user.

[1294] Input: Menu plan sent from the server

[1295] Output: The menu displayed to the user

[1296] Specific operation: The measurement results are displayed on the device's user interface.

[1297] Step 7:

[1298] The user enters their age and weight into the device and an exercise plan is suggested.

[1299] Input: Age and weight (e.g., "45 years old, 70 kg")

[1300] Output: Input data

[1301] Specific operation: The user enters information into a device such as a smartphone or PC and presses the send button.

[1302] Step 8:

[1303] The terminal transmits the received input data to the server.

[1304] Input: User-entered data

[1305] Output: The input data sent to the server

[1306] Specific operation: The device sends data to the server via the internet via the API.

[1307] Step 9:

[1308] The server uses an emotion analysis engine to analyze emotions based on input data and past movement history.

[1309] Input: Input data sent to the server

[1310] Output: User's emotional state (e.g., "Low motivation")

[1311] What it does: The sentiment analysis engine analyzes past data and new input to identify emotional states.

[1312] Step 10:

[1313] The server generates an optimal exercise plan based on age, weight, emotional state and past exercise history.

[1314] Input: Age, weight, emotional state, past exercise history

[1315] Output: Exercise plan (e.g., "30 minutes of easy walking and 10 minutes of yoga and stretching")

[1316] Specific Actions: The movement plan generation algorithm uses this information to generate an optimal movement plan.

[1317] Step 11:

[1318] The server returns the generated exercise plan to the terminal.

[1319] Input: Generated movement plan

[1320] Output: Exercise plan sent to the device

[1321] Specific operation: The server sends data to the terminal as a response.

[1322] Step 12:

[1323] The terminal displays the suggested exercise plan to the user.

[1324] Input: Exercise plan sent from the server

[1325] Output: Exercise plan displayed to the user

[1326] Specific operation: The exercise plan is displayed on the device's user interface.

[1327] (Application example 2)

[1328] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1329] Conventional systems for preventing lifestyle-related diseases, suggesting exercise, brain training, and medical support often do not fully consider the user's physical condition or emotional state, resulting in a lack of personalized suggestions based on the user's emotions and physical condition. Furthermore, in brick-and-mortar stores, it is difficult to suggest ingredients and recipes based on the user's physical condition and emotions, so there are no means of effectively supporting health management and improving dietary content.

[1330] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for determining the user's emotional state using an emotion analysis engine that analyzes input data, means for generating a list of appropriate ingredients and recipes based on the emotional state, and means for returning the generated list to the terminal. This enables personalized health suggestions based on the user's physical condition and emotional state.

[1331] "User" refers to an individual who uses this system.

[1332] "Device" means the device used by a User to enter information or view suggestions, including, but not limited to, a smartphone, tablet, or PC.

[1333] "Server" refers to a computer system that receives, analyzes, and processes data sent by users.

[1334] "Sentiment analysis engine" refers to software or algorithms for determining a user's emotional state from input data.

[1335] "Emotional state" refers to the user's current psychological and emotional state, including, for example, stress and motivation.

[1336] A "list of ingredients and recipes" is a list containing ingredients and cooking methods suggested based on the user's physical and emotional state.

[1337] "Menu plan" refers to a list of suggested ingredients and recipes.

[1338] An "exercise plan" refers to a proposed exercise plan based on the user's physical condition and emotional state.

[1339] "Training content" refers to the specific brain training tasks and activities provided to users.

[1340] "With audio" refers to a method in which training content and notifications are provided to the user by audio.

[1341] To implement this invention, a system is constructed in which a user inputs their physical condition and food preferences for the day through a terminal. The terminal may be a smartphone, tablet, or PC. The data input to this terminal is sent to a server equipped with an emotion analysis engine. This server analyzes the user's input data and determines their emotional state.

[1342] The server uses a sentiment analysis engine to analyze the user's emotional state (e.g., high stress, low motivation, etc.). The sentiment analysis engine uses natural language processing techniques. The analysis results are used to generate ingredient and recipe lists, exercise plans, brain training content, and medical advice.

[1343] For example, if a user inputs "I'm feeling a bit tired today and would like to eat something light," this data is sent to the server, and the emotion analysis engine determines the emotional state as "high stress." Based on this emotional state, the server generates a list of suggestions, such as "spinach and tomato salad" and "tofu and seaweed soup." The generated list is sent back to the device and displayed to the user.

[1344] For example, if a user wants to input their age and weight and want an exercise plan suggested, they can enter "45 years old, 70 kg." This data is sent to the server, where the emotion analysis engine analyzes the user's emotional state. For example, if the user is judged to be "low motivation," the server will generate an exercise plan suggesting "30 minutes of light walking and 10 minutes of yoga stretching" based on this emotional state and past exercise history. The generated exercise plan is sent back to the device and displayed to the user.

[1345] Furthermore, if a user wants to start brain training, they input "Start brain training," and this data is sent to the server, where the emotion analysis engine analyzes the user's emotional state. For example, if the server determines that "concentration is necessary," it will play a new training content, "A new folk tale, 'Urashima Taro,'" with audio, based on this emotional state and past training history. As a result, the device provides this training content to the user.

[1346] Here's an example prompt that uses a generative AI model to analyze sentiment and determine what to suggest to the user:

[1347] "When a user inputs something like, 'I'm feeling a bit tired today and want to eat something light,' analyze their emotional state and suggest appropriate ingredients and recipes."

[1348] The hardware for implementing this invention includes a user terminal (such as a smartphone, tablet, or PC), a server, and a computer system for running the sentiment analysis engine. The software includes Python 3.x and a requests library for sending API requests.

[1349] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1350] Step 1:

[1351] The user inputs their physical condition and what they want to eat into the terminal. For example, they might input, "I'm feeling a bit tired today, so I want to eat something light." This input data is acquired through an input form on the user terminal.

[1352] Step 2:

[1353] The device sends the input data to the server. Specifically, the data is sent as an HTTP POST request via a communication module built into the device. This request includes the user's input as a payload.

[1354] Step 3:

[1355] The input data received by the server is analyzed by a sentiment analysis engine. The sentiment analysis engine uses natural language processing technology to determine the user's emotional state. Here, the input data is analyzed in text format and an emotional tag such as "high stress" is generated.

[1356] Step 4:

[1357] The server generates a list of appropriate ingredients and recipes based on the user's emotional state and input data. The server uses an algorithm that takes into account the user's emotional state (e.g., "high stress") to search its internal database for light ingredients and recipes and generates the list.

[1358] Step 5:

[1359] The server then returns the generated list of ingredients and recipes to the device, again using an HTTP POST request. The returned data includes the suggested ingredients and recipe information.

[1360] Step 6:

[1361] The device displays the menu plan returned from the server to the user. Specifically, it uses a GUI (Graphical User Interface) component for display to show details of ingredients and recipes. Based on this information, the user can select appropriate ingredients and dishes.

[1362] For example, if a user inputs "I'm feeling a bit tired today and want to eat something light," the server analyzes this and generates an emotion tag called "high stress." The server then lists options like "spinach and tomato salad" and "tofu and seaweed soup" based on this emotion tag and returns this information. The device then displays it to the user.

[1363] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1364] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1365] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1366] [Fourth embodiment]

[1367] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1368] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1369] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1370] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1371] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1372] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1373] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1374] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1375] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1376] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1377] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1378] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1379] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1380] The present invention relates to a system that enables prevention of lifestyle-related diseases, exercise suggestions, brain training, and medical support. This system selects appropriate actions based on user input and provides the results to the user.

[1381] First, the user inputs their physical condition and what they want to eat that day through the device. For example, the user might input, "I'm feeling a bit tired today, so I'd like to eat something light." The device then sends this input data to the server. The server analyzes the received data, generates a list of appropriate ingredients and recipes, and suggests menu ideas such as "spinach and tomato salad" and "tofu and seaweed soup." The device then displays these menu ideas to the user.

[1382] Next, the user enters their age and weight through the device. For example, they might enter "45 years old, 70 kg." The device then sends this input data to the server, which then generates an optimal exercise plan based on the user's age, weight, and past exercise history. Specific exercises (for example, "30 minutes of light walking and 10 minutes of yoga stretching") are suggested based on the user's health condition. The device then displays this exercise plan to the user.

[1383] Furthermore, if the user wishes to start brain training, he / she may input, for example, "Start brain training." The device sends this request to the server, and the server generates new training content based on past training history. For example, it plays "A new folk tale, 'Urashima Taro,'" with audio. The device provides this training content to the user.

[1384] Furthermore, when a user inputs a question about their health, for example, "I feel like my blood pressure has been high recently," the device sends the input data to a specialized medical support system. The specialized medical support system generates an answer to the question and sends it back to the server. The server receives this answer and sends it to the device. The device then displays the answer to the user, saying, "Try to exercise moderately and eat a balanced diet."

[1385] A specific scenario for use would be for the user to enter their physical condition into the device in the morning, receive daily menu suggestions based on that information, review their exercise plan in the evening, and then do brain training at night. It would also be possible to immediately consult medical support if they felt unwell. This system is expected to make daily health management more personalized and provide continuous support to users.

[1386] The processing flow will be explained below.

[1387] Step 1:

[1388] The user inputs their physical condition and what they want to eat that day into the terminal.

[1389] Example: A user enters, "I'm feeling a bit tired today and would like to eat something light."

[1390] Step 2:

[1391] The terminal transmits the input data to the server.

[1392] The entered "physical condition" and "food preferences" data is sent to the server in JSON format.

[1393] Step 3:

[1394] The server parses the input data.

[1395] The server analyzes keywords such as "feeling tired" and "light food" and searches the database for relevant ingredients and recipes.

[1396] Step 4:

[1397] The server generates a list of suitable ingredients and recipes.

[1398] Based on the analysis results, recipes are generated using ingredients that are rich in vitamins and easy to digest.

[1399] Step 5:

[1400] The server sends the generated menu plan back to the terminal.

[1401] Send the menu plan to the device in JSON format.

[1402] Step 6:

[1403] The terminal displays the suggested menu to the user.

[1404] Example: Display "Spinach and Tomato Salad" and "Tofu and Seaweed Soup."

[1405] Step 1:

[1406] The user enters their age and weight into the terminal.

[1407] Example: User enters "45 years old, 70kg."

[1408] Step 2:

[1409] The terminal transmits the input data to the server.

[1410] The entered "age" and "weight" data is sent to the server in JSON format.

[1411] Step 3:

[1412] The server analyzes the input data and references past exercise history.

[1413] The user's past exercise history is retrieved from the database and checked to ensure that the same exercise is not repeated.

[1414] Step 4:

[1415] The server generates an optimal exercise plan.

[1416] Based on your age, weight, and past exercise history, it will suggest new exercises (for example, "30 minutes of easy walking and 10 minutes of yoga stretching").

[1417] Step 5:

[1418] The server returns the generated exercise plan to the terminal.

[1419] The exercise plan is sent to the device in JSON format.

[1420] Step 6:

[1421] The terminal displays the suggested exercise plan to the user.

[1422] A suggested exercise plan and implementation method is displayed to the user.

[1423] Step 1:

[1424] The user inputs into the terminal that he / she wants to start brain training.

[1425] Example: User types "Start Brain Training."

[1426] Step 2:

[1427] The device sends a request to the server.

[1428] Sends request data to the server in JSON format.

[1429] Step 3:

[1430] The server references past training history.

[1431] The system retrieves the user's past brain training history from a database and avoids repeating the same content.

[1432] Step 4:

[1433] The server generates new training content (fairy tales, original stories, rakugo, etc.).

[1434] Browse the knowledge base and generate new brain training challenges.

[1435] Step 5:

[1436] The server returns the generated training content to the terminal.

[1437] Send the training content to the device in JSON format.

[1438] Step 6:

[1439] The device provides the training content to the user with audio.

[1440] Example: Play "The New Folktale of Urashima Taro" with audio.

[1441] Step 1:

[1442] The user inputs a health-related question into the terminal.

[1443] Example: A user types, "My blood pressure feels high lately."

[1444] Step 2:

[1445] The terminal sends the input data to a specialized medical support system.

[1446] The input data is sent in JSON format to the API of a specialized medical support system.

[1447] Step 3:

[1448] A specialized medical support system generates answers to questions.

[1449] An answer is generated based on the medical information and sent back to the server.

[1450] Step 4:

[1451] The server sends the response received from the HELPO app in JSON format to the device.

[1452] The received response is sent to the device.

[1453] Step 5:

[1454] The terminal displays professional medical support answers to the user.

[1455] Example: Display "Make sure to exercise moderately and eat a balanced diet."

[1456] Example 1

[1457] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1458] Current health management systems struggle to provide personalized recommendations tailored to individual users' individual circumstances and needs. Furthermore, systems that address multiple areas such as dietary recommendations, exercise plans, brain training, and medical support are not integrated, requiring users to navigate multiple applications and services. Furthermore, the recommendations are general, with few specific advice based on individual lifestyle habits or past history.

[1459] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1460] In this invention, the server includes means for analyzing input data and using a generative AI model to generate a list of appropriate ingredients and recipes, means for analyzing input data and using a generative AI model to generate an optimal exercise plan based on past exercise history, and means for using a generative AI model to generate new training content based on past training history. This allows a single system to provide personalized suggestions tailored to each user's individual situation and needs, enabling more specific and actionable advice.

[1461] "User" refers to an individual who uses the system and is a consumer who provides input according to their health status and needs.

[1462] "Terminal" refers to a device through which a user makes input, and includes electronic devices such as smartphones, tablets, and personal computers.

[1463] "Server" refers to the central computing unit that receives and analyzes the data sent by the User, generates appropriate suggestions and sends them back to the Terminal.

[1464] "Input data" refers to information provided by the user through the terminal, including physical condition, dietary preferences, age, weight, requests to start brain training, health-related questions, etc.

[1465] A "generative AI model" refers to an algorithm that uses natural language processing and machine learning techniques to analyze input data and generate appropriate suggestions.

[1466] "Menu suggestions" refers to a list of ingredients and recipes suggested by a generative AI model based on the user's physical condition and dietary preferences.

[1467] "Exercise plan" refers to a specific exercise schedule suggested by a generative AI model based on a user's age, weight, and past exercise history.

[1468] "Training content" refers to the specific brain training content suggested by the generative AI model based on the user's past training history.

[1469] A "medical support system" refers to a system with specialized medical knowledge that is used to generate appropriate answers to health-related questions posed by users.

[1470] "Proposal" refers to the specific content that the server generates using the generative AI model and provides to the user via the terminal.

[1471] This invention relates to a system that enables prevention of lifestyle-related diseases, exercise suggestions, brain training, and medical support. This system selects appropriate actions based on user input and provides the results to the user.

[1472] First, the user inputs their physical condition and meal preferences for the day into the interface on their device. For example, the user might input, "I'm feeling a bit tired today, so I'd like to eat something light." The device then sends this input data to the server. The server analyzes the received data and uses a generative AI model to generate a list of appropriate ingredients and recipes. Specific menu suggestions generated include "spinach and tomato salad" and "tofu and seaweed soup." The device then displays these menu suggestions to the user.

[1473] Next, the user enters their age and weight through the device. For example, they might enter "45 years old, 70 kg." The device then sends this input data to the server. The server then analyzes the user's age, weight, and past exercise history, and uses a generative AI model to generate an optimal exercise plan. Specific exercises are suggested based on the user's health condition. A specific exercise plan might include "30 minutes of light walking and 10 minutes of yoga stretching." The device then displays this exercise plan to the user.

[1474] Next, the user inputs a request to start brain training into the device. For example, they input "Start brain training." The device then sends this request to the server. The server uses a generative AI model to generate new training content based on past training history. As a specific training content, it plays a new folk tale, "Urashima Taro," with audio. The device then provides this training content to the user.

[1475] Furthermore, when a user inputs a question about health, for example, "I feel like my blood pressure has been high recently," the terminal sends the input data to a specialized medical support system. The specialized medical support system generates an answer to the question and sends it back to the server. The server receives this answer and sends it to the terminal. The terminal then displays the answer to the user, saying, "Try to get moderate exercise and eat a balanced diet."

[1476] A specific scenario for use would be for the user to enter their physical condition into the device in the morning, receive daily menu suggestions based on that information, review their exercise plan in the evening, and then do brain training at night. It would also be possible to immediately consult medical support if they felt unwell. This system is expected to make daily health management more personalized and provide continuous support to users.

[1477] Example prompt sentence:

[1478] "I'm feeling a bit tired, so can you recommend some light recipes?"

[1479] "Please tell me an exercise plan that would be suitable for a 45-year-old, 70kg person."

[1480] "Please tell me about new brain training content."

[1481] "My blood pressure seems to be high recently. What can I do?"

[1482] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1483] Step 1:

[1484] The user inputs their physical condition and what they want to eat that day through the terminal. The input data provided is information such as "I'm feeling a bit tired today, so I'd like to eat something light." The terminal then sends this input data to the server. Specifically, the user enters the data using an input form on their smartphone or computer and presses the send button.

[1485] Step 2:

[1486] The server receives input data. The received data is "I'm feeling a bit tired today, so I'd like to eat something light." The server passes this data to the generative AI model, which generates a prompt to generate an appropriate menu list. Specifically, the server inputs the prompt "I'm feeling a bit tired, so please tell me some recipes for light dishes" into the generative AI model.

[1487] Step 3:

[1488] A generative AI model generates a menu list based on the prompt sentence. The model extracts appropriate information from a huge database and generates menu suggestions such as "spinach and tomato salad" or "tofu and seaweed soup." The data processing performed here involves analyzing the user's state from the input sentence and suggesting appropriate dishes.

[1489] Step 4:

[1490] The server returns the generated menu plan to the terminal. The returned data is a specific menu list of "spinach and tomato salad" and "tofu and wakame soup." The server returns the data using an HTTP response.

[1491] Step 5:

[1492] The device displays the suggested menu to the user. The device analyzes the received data and displays it on the user interface. Specifically, the device displays the names of the dishes "Spinach and Tomato Salad" and "Tofu and Seaweed Soup."

[1493] Step 6:

[1494] The user inputs their age and weight through the device. For example, they input "45 years old, 70 kg." The device then sends this input data to the server. Specifically, the user inputs the data using an input form on their smartphone or computer and presses the send button.

[1495] Step 7:

[1496] The server receives the input data. The received data is "45 years old, 70 kg." The server passes this data to the generative AI model and generates a prompt to generate the optimal exercise plan. Specifically, the server inputs the prompt "Please tell me an exercise plan suitable for a person who is 45 years old and weighs 70 kg" into the generative AI model.

[1497] Step 8:

[1498] The generative AI model generates an exercise plan based on the prompt sentence. The model extracts appropriate information from a vast database and generates an exercise plan such as "30 minutes of light walking" and "10 minutes of yoga stretching." The data processing performed here analyzes the user's physique and health condition from the input sentence and suggests appropriate exercises.

[1499] Step 9:

[1500] The server returns the generated exercise plan to the device. The returned data is a specific exercise plan consisting of "30 minutes of light walking" and "10 minutes of yoga stretching." The server returns the data using an HTTP response.

[1501] Step 10:

[1502] The device displays the proposed exercise plan to the user. The device analyzes the received data and displays it on the user interface. Specifically, the device displays the exercise names "30 minutes of easy walking" and "10 minutes of yoga stretching."

[1503] Step 11:

[1504] The user inputs a request to start brain training through the terminal. For example, the user inputs "Start brain training." The terminal sends this request to the server.

[1505] Step 12:

[1506] The server receives the request data. The received data is "Start brain training." The server passes this data to the generative AI model and generates a prompt to generate new training content. Specifically, the server inputs the prompt "Please tell me new brain training content" into the generative AI model.

[1507] Step 13:

[1508] The generative AI model generates brain training content based on the prompt. The model extracts appropriate training content and generates specific training content, such as "A new folktale, Urashima Taro." The data processing performed here refers to past training history and suggests new stimulating content.

[1509] Step 14:

[1510] The server returns the generated training content to the device. The returned data is the specific training content, "A New Folktale: Urashima Taro." The server returns the data using an HTTP response.

[1511] Step 15:

[1512] The training content provided by the device is played back to the user with audio. The device analyzes the received data and displays a play button on the user interface. Specifically, when the user presses the play button, a new folktale, "Urashima Taro," is played back aloud.

[1513] Step 16:

[1514] A user inputs a health-related question through the terminal. For example, the user inputs, "I feel like my blood pressure has been high recently." The terminal then sends this health-related question to a specialized medical support system.

[1515] Step 17:

[1516] A specialized medical support system receives the question data, analyzes it, and then generates an answer. For example, the generated answer might be, "Try to exercise moderately and eat a balanced diet." The data calculation performed here is to generate the optimal medical advice for the input question.

[1517] Step 18:

[1518] The server receives the response from the specialized medical support system and sends it back to the device. The returned data is specific advice such as "Try to exercise moderately and eat a balanced diet." The server returns the data using an HTTP response.

[1519] Step 19:

[1520] The device displays the answer from the specialized medical support system to the user. The device analyzes the received data and displays it on the user interface. Specifically, the device's display shows advice such as "Try to get moderate exercise and eat a balanced diet."

[1521] (Application example 1)

[1522] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1523] Conventional health management systems lacked the flexibility to accommodate individual user needs and physical conditions. They also lacked the ability to provide personalized meal suggestions and delivery based on user input, resulting in low user convenience. Furthermore, there was a need for a system that could go beyond simply suggesting meals and enable users to easily obtain healthy meals.

[1524] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1525] In this invention, the server includes a means for analyzing user input data and generating a list of appropriate ingredients and recipes, a means for creating an initial menu based on prompts generated using a generative AI model, and a means for delivering meals via an external service in accordance with the user's input. This allows for personalized menu suggestions based on the user's individual physical condition and food preferences, and for the menu to be easily obtained through a delivery service.

[1526] A "terminal" is an electronic device that allows a user to input data such as their physical condition and food preferences.

[1527] The "server" is a computer system that analyzes input data sent by users and generates appropriate ingredients, recipes, exercise plans, training content, etc.

[1528] "Input data" refers to information such as physical condition information, food preferences, age, weight, and intention to start brain training that the user inputs through the terminal.

[1529] A "list of ingredients and recipes" is a set of ingredients and cooking methods that the server generates based on the input data analyzed and suggests to the user.

[1530] A "menu plan" is a proposal of ingredients and recipes generated by the server, and is a menu provided to the user.

[1531] An "exercise plan" is a specific exercise plan generated by the server based on the user's age, weight, and past exercise history.

[1532] "Training content" refers to the specific content of the training generated by the server based on the brain training desired by the user.

[1533] A "generative AI model" is an artificial intelligence model that analyzes user input data and automatically generates appropriate ingredients, recipes, exercise plans, etc.

[1534] A "prompt sentence" is a pre-set input sentence for the generative AI model to analyze, and is a sentence that specifically instructs the model on the user's request.

[1535] A "delivery service" is a service that delivers meals based on ingredients and recipes selected by the user to the user's home via an external service.

[1536] This invention is a system that allows users to input specific health information and desired meal contents via a terminal, transmits the data to a server for analysis, suggests appropriate ingredients and recipes, creates exercise plans, provides brain training, and even delivers the suggested meals. This system consists of the following components:

[1537] First, the user uses a device (smartphone, tablet, etc.) to input their physical condition and desired foods for the day. This device is connected to the Internet, and the input data is sent to a server. The server receives the user data, analyzes it, and generates a list of appropriate ingredients and recipes. At this time, a generative AI model is used to generate prompts based on the user's physical condition and preferences.

[1538] The server then sends the generated menu plan back to the terminal, which displays it to the user and allows the user to select the menu they want. Furthermore, this system is linked to an external delivery service, so the menu selected by the user can be delivered to their home.

[1539] For example, suppose a user types into their device, "I'm feeling a bit tired today, so I'd like to eat something light." This data is sent to the server, which analyzes it and generates menu suggestions such as "spinach and tomato salad" or "tofu and seaweed soup." At this time, a generative AI model is used to generate prompt sentences, which the server then optimizes.

[1540] An example of a generated prompt statement:

[1541] Health: I'm feeling a little tired this morning.

[1542] What I want to eat: Light food

[1543] Prompt: "If the person's health status is 'I feel a little tired this morning,' please suggest a healthy, light meal."

[1544] Based on this prompt, the server will suggest healthy menu items, allowing the user to select from them and easily obtain the selected menu items through a delivery service.

[1545] Users can also enter their age and weight, and this data is sent to the server for analysis. As a result, an exercise plan optimized for the user's health condition is generated and displayed on the device, allowing users to practice exercises tailored to their health condition.

[1546] If the user wishes to train their brain, they input this into the terminal. The server generates new training content based on the user's past training history and sends it back to the terminal. This training content is provided with audio, allowing the user to train their brain effectively.

[1547] In this way, this system can meet the diverse health management needs of users and support the provision of individualized, specific suggestions and their implementation.

[1548] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1549] Step 1:

[1550] The user uses the device to input information about their physical condition and what they want to eat. The input data is entered in the form of a specific string, such as "I'm feeling a bit tired today, so I'd like to eat something light."

[1551] Step 2:

[1552] The device sends the entered data to the server. The sent content consists of text data such as health information and food preferences.

[1553] Step 3:

[1554] The server analyzes the received input data and generates a prompt using a generative AI model. For example, a prompt might be generated such as, "If the user's physical condition is entered as 'I feel a little tired this morning,' please suggest a healthy, light meal."

[1555] Step 4:

[1556] The server generates a list of appropriate ingredients and recipes based on the generated prompt. The generated menu includes specific items such as "spinach and tomato salad" or "tofu and seaweed soup."

[1557] Step 5:

[1558] The server generates a menu plan and returns it to the terminal. The returned data is a text-format recipe list.

[1559] Step 6:

[1560] The terminal displays the returned menu plan to the user, who can then check the suggested menu on the screen and make a selection.

[1561] Step 7:

[1562] The user confirms the menu selection using the terminal, and then transmits the menu selection back to the server.

[1563] Step 8:

[1564] The server receives the menu selected by the user and links the selection to an external delivery service to process the order.

[1565] Step 9:

[1566] The delivery service prepares the specified menu and delivers it to the user. The delivery status and other information are sent to the terminal via the server so that the user can check it.

[1567] Step 10:

[1568] The user inputs their age and weight through the terminal, and this data is also entered in a specific text format, such as "45 years old, 70 kg."

[1569] Step 11:

[1570] The device sends the entered age and weight data to the server.

[1571] Step 12:

[1572] The server analyzes the received age and weight data and generates an optimal exercise plan based on past exercise history. For example, a specific exercise plan such as "30 minutes of light walking and 10 minutes of yoga stretching" is generated.

[1573] Step 13:

[1574] The server then sends the generated exercise plan back to the device. The returned data consists of specific instructions and a timeline.

[1575] Step 14:

[1576] The device displays the returned exercise plan to the user, who can then check and carry out the plan on the screen.

[1577] Step 15:

[1578] The user inputs through the terminal that he / she wants to start brain training. For example, this is input in the form of specific text such as "Start brain training."

[1579] Step 16:

[1580] The device sends the input brain training request to the server.

[1581] Step 17:

[1582] The server analyzes the received request and generates new training content based on past training history. For example, a new training content with audio for the folktale "Urashima Taro" is generated.

[1583] Step 18:

[1584] The server returns the generated training content to the terminal. The returned data is training content data including an audio file.

[1585] Step 19:

[1586] The device then provides the returned training content to the user, which is played back with audio, allowing the user to effectively train their brain.

[1587] The above processing steps constitute a system that allows the user to carry out effective health management.

[1588] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1589] The present invention combines a system that can provide lifestyle disease prevention, exercise suggestions, brain training, and medical support with an emotion engine that recognizes the user's emotions. This system selects appropriate actions based on the user's input and emotions, and provides the results to the user.

[1590] First, we will explain the specific flow when a user inputs their physical condition and what they want to eat that day. The user inputs their physical condition and what they want to eat that day into the device, for example, "I'm feeling a bit tired today, so I'd like to eat something light." The device sends this input data to the server, which then analyzes the user's input using an emotion engine to determine the user's emotional state (for example, "high stress"). The server analyzes the received data and the results of the emotion engine, and generates a list of appropriate ingredients and recipes. For example, "spinach and tomato salad" and "tofu and seaweed soup" are suggested. The device then displays this menu suggestion to the user.

[1591] Next, we will explain the specific flow when a user inputs their age and weight and an exercise plan is proposed. The user inputs "45 years old, 70 kg," and the device sends this data to the server. The emotion engine analyzes emotions based on the input text and the user's past data, and identifies the user's emotional state (for example, "low motivation"). The server generates an optimal exercise plan based on the age, weight, emotional state, and past exercise history. Specifically, it suggests "30 minutes of light walking and 10 minutes of yoga stretching." The device then displays this exercise plan to the user.

[1592] Furthermore, when a user wants to start brain training, they input, for example, "Start brain training." The device sends this request to the server, and the emotion engine analyzes the user's emotional state (for example, "I need to concentrate"). The server generates new training content based on past training history and emotional state, and plays a new folk tale, "Urashima Taro," with audio. The device then provides this training content to the user.

[1593] Or, if a user inputs a question about their health, for example, "I feel like my blood pressure has been high lately," the device sends the input data to a specialized medical support system, and the emotion engine analyzes the user's anxiety, etc. The specialized medical support system generates an answer to the question and sends it back to the server. The server sends this answer to the device, which then displays professional advice to the user, such as "Try to get moderate exercise and eat a balanced diet."

[1594] A specific usage scenario would be for a user to input their physical condition into the device in the morning, receive daily menu suggestions based on that information, check their exercise plan in the evening, and do brain training at night. Depending on their physical condition or emotional state, they may also be able to immediately consult medical support. By combining this system with an emotion engine, the goal is to make daily health management more personalized for users and enable continuous support that takes emotions into account.

[1595] The processing flow will be explained below.

[1596] Processing menu suggestions for lifestyle-related disease prevention

[1597] Step 1:

[1598] The user inputs their physical condition and what they want to eat that day into the terminal.

[1599] Example: A user enters, "I'm feeling a bit tired today and would like to eat something light."

[1600] Step 2:

[1601] The terminal transmits the input data to the server.

[1602] The entered "physical condition" and "food preferences" data is sent to the server in JSON format.

[1603] Step 3:

[1604] The device sends the input text to the emotion engine.

[1605] The emotion engine analyzes the user's emotional state from the input data, determining, for example, that they are "highly stressed."

[1606] Step 4:

[1607] The server parses the received data.

[1608] The server analyzes the keywords "feeling tired" and "light food" and searches the database for relevant ingredients and recipes.

[1609] Step 5:

[1610] The server generates a menu plan that reflects the emotional state.

[1611] Taking into account the results of the emotion engine (e.g., "high stress"), recipes with stress-reducing effects are selected.

[1612] Step 6:

[1613] The server sends the generated menu plan back to the terminal.

[1614] Send the menu plan to the device in JSON format.

[1615] Step 7:

[1616] The terminal displays the suggested menu to the user.

[1617] Example: Display "Spinach and Tomato Salad" and "Tofu and Seaweed Soup."

[1618] Processing exercise suggestions

[1619] Step 1:

[1620] The user enters their age and weight into the terminal.

[1621] Example: User enters "45 years old, 70kg."

[1622] Step 2:

[1623] The terminal transmits the input data to the server.

[1624] The entered "age" and "weight" data is sent to the server in JSON format.

[1625] Step 3:

[1626] The device sends the input data to the emotion engine.

[1627] The emotion engine analyzes the user's emotional state from the input data, determining, for example, that the user is "low motivation."

[1628] Step 4:

[1629] The server analyzes the input data and references past exercise history.

[1630] The user's past exercise history is retrieved from the database and checked to ensure that the same exercise is not repeated.

[1631] Step 5:

[1632] The server generates a motion plan that reflects the emotional state.

[1633] Taking into account the emotion engine result (e.g., "Low motivation"), it suggests moderate exercise, such as "30 minutes of easy walking and 10 minutes of yoga and stretching."

[1634] Step 6:

[1635] The server returns the generated exercise plan to the terminal.

[1636] The exercise plan is sent to the device in JSON format.

[1637] Step 7:

[1638] The terminal displays the suggested exercise plan to the user.

[1639] A suggested exercise plan and implementation method is displayed to the user.

[1640] Brain Training Processing

[1641] Step 1:

[1642] The user inputs into the terminal that he / she wants to start brain training.

[1643] Example: User types "Start Brain Training."

[1644] Step 2:

[1645] The device sends a request to the server.

[1646] Sends request data to the server in JSON format.

[1647] Step 3:

[1648] The device sends the input data to the emotion engine.

[1649] The emotion engine analyzes the user's emotional state from the input data, determining, for example, that "concentration is necessary."

[1650] Step 4:

[1651] The server references past training history.

[1652] The system retrieves the user's past brain training history from a database and avoids repeating the same content.

[1653] Step 5:

[1654] The server generates training content that reflects the emotional state.

[1655] It references the knowledge base and generates new brain training tasks based on the results of the emotion engine (e.g., "needs concentration").

[1656] Step 6:

[1657] The server returns the generated training content to the terminal.

[1658] Send the training content to the device in JSON format.

[1659] Step 7:

[1660] The device provides the training content to the user with audio.

[1661] Example: Play "The New Folktale of Urashima Taro" with audio.

[1662] Health Questions and Medical Support

[1663] Step 1:

[1664] The user inputs a health-related question into the terminal.

[1665] Example: A user types, "My blood pressure feels high lately."

[1666] Step 2:

[1667] The terminal sends the input data to a specialized medical support system.

[1668] The input data is sent in JSON format to the API of a specialized medical support system.

[1669] Step 3:

[1670] The device sends the input data to the emotion engine.

[1671] The emotion engine analyzes the user's emotional state and identifies feelings of anxiety, etc.

[1672] Step 4:

[1673] A specialized medical support system generates answers to questions.

[1674] An answer is generated based on the medical information and sent back to the server.

[1675] Step 5:

[1676] The server sends the response received from the specialized medical support system in JSON format to the terminal.

[1677] The received response is sent to the device.

[1678] Step 6:

[1679] The terminal displays professional medical support answers to the user.

[1680] Example: Display "Make sure to exercise moderately and eat a balanced diet."

[1681] Example 2

[1682] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1683] While conventional health management systems make suggestions based on user input data, they lack personalized support that takes into account the user's emotional state, making it difficult to improve user motivation and continuity. Furthermore, various types of support, such as exercise plans, meal suggestions, and brain training, are often provided individually, making it difficult to achieve comprehensive health management.

[1684] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for analyzing input data using an emotion analysis engine to determine the user's emotional state, means for generating a list of appropriate ingredients and recipes based on the analysis results, means for generating an optimal exercise plan based on the user's age, weight, emotional state, and past exercise history, and means for generating new training content based on the user's past training history and emotional state. This enables personalized suggestions and support that take the user's emotional state into consideration.

[1685] "User" means an individual who uses the system.

[1686] A "terminal" is a device that allows a user to interface with the system, such as a smartphone or PC.

[1687] "Input data" refers to information that a user provides to the system via a terminal, and includes physical condition, age, weight, and the like.

[1688] "Server" means a computer system that performs the central processing of the system and is responsible for collecting data, analyzing data, and generating results.

[1689] An "emotion analysis engine" is software that analyzes a user's emotional state based on input data.

[1690] "Emotional state" refers to a user's psychological state, such as stress level or motivation.

[1691] "Ingredients" refer to specific foods that are suggested to the user.

[1692] A "recipe" is a document or data that describes how to cook food using ingredients.

[1693] An "exercise plan" is a specific exercise plan suggested based on the user's health condition.

[1694] "Training content" refers to the specific brain training activities or programs provided to users.

[1695] The present invention is a system that enables the prevention of lifestyle-related diseases, exercise suggestions, brain training, and medical support. This system incorporates an emotion engine that recognizes the user's emotions, selects appropriate actions based on the user's input and emotions, and provides the results to the user. Details are described below.

[1696] Hardware and Software Use

[1697] This system consists of the following hardware and software:

[1698] Terminal: A device used by users to input information. Examples include smartphones and personal computers.

[1699] Server: A central system that receives and analyzes input data.

[1700] Sentiment analysis engine: Software that analyzes input text data and identifies the user's emotional state. It uses NLP (natural language processing) techniques.

[1701] Medical support system: A system that provides specialized medical information.

[1702] Specific operation of the system

[1703] The operation of the system will be explained using an example in which a user inputs their physical condition and what they want to eat that day. The user inputs, "I'm feeling a bit tired today, so I'd like to eat something light." The device sends this input data to the server, which uses an emotion analysis engine to analyze the user's emotional state (for example, "high stress"). The server then generates a list of appropriate ingredients and recipes based on the analysis results. Specifically, it suggests "spinach and tomato salad" and "tofu and seaweed soup." The device then displays this menu suggestion to the user.

[1704] Specific examples

[1705] Example prompt sentence:

[1706] The user enters "45 years old, 70 kg."

[1707] The device sends this data to the server, and the emotion engine analyzes it as "low motivation."

[1708] The server generates an optimal exercise plan based on age, weight, emotional state, and past exercise history, suggesting "30 minutes of light walking and 10 minutes of yoga stretching."

[1709] The device displays this exercise plan to the user.

[1710] Furthermore, if the user types "Start brain training," the same procedure is followed. The device sends the request to the server, and the emotion engine interprets it as "concentration required." The server generates new training content based on past training history and emotional state, and plays a new folk tale, "Urashima Taro," with audio. The device then provides this to the user.

[1711] This system allows users to input their physical condition into a device in the morning, receive daily menu suggestions based on that information, review their exercise plan in the evening, and then do brain training at night. Depending on their physical condition or emotional state, they can also immediately consult medical support. In this way, users' daily health management is personalized, and continuous support that takes their emotions into consideration is possible.

[1712] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1713] Step 1:

[1714] The user inputs their physical condition and what they want to eat that day into the input interface of the terminal.

[1715] Input: User's physical condition and food preferences (e.g., "I'm feeling a bit tired today, so I'd like to eat something light")

[1716] Output: Input data

[1717] Specific operation: The user enters text into a device such as a smartphone or PC and presses the send button.

[1718] Step 2:

[1719] The terminal transmits the received input data to the server.

[1720] Input: User-entered data

[1721] Output: The input data sent to the server

[1722] Specific operation: The device sends data to the server via the internet via the API.

[1723] Step 3:

[1724] The server uses an emotion analysis engine to analyze the input data.

[1725] Input: Input data sent to the server

[1726] Output: User's emotional state (e.g., "high stress")

[1727] Specific operation: The server launches an emotion analysis engine (NLP model) that analyzes input data and estimates the emotional state.

[1728] Step 4:

[1729] The server generates a list of appropriate ingredients and recipes based on the received data and the analysis results of the emotion engine.

[1730] Input: User input data and emotional state

[1731] Output: A list of ingredients or recipes (e.g., "Spinach and Tomato Salad" or "Tofu and Seaweed Soup")

[1732] How it works: The recipe generation algorithm in the server narrows down the candidates by taking into account your emotional state and dietary preferences.

[1733] Step 5:

[1734] The server returns the generated menu plan to the terminal.

[1735] Input: A list of ingredients and recipes generated

[1736] Output: Menu plan sent to device

[1737] Specific operation: The server sends data to the terminal as a response.

[1738] Step 6:

[1739] The terminal displays the suggested menu to the user.

[1740] Input: Menu plan sent from the server

[1741] Output: The menu displayed to the user

[1742] Specific operation: The measurement results are displayed on the device's user interface.

[1743] Step 7:

[1744] The user enters their age and weight into the device and an exercise plan is suggested.

[1745] Input: Age and weight (e.g., "45 years old, 70 kg")

[1746] Output: Input data

[1747] Specific operation: The user enters information into a device such as a smartphone or PC and presses the send button.

[1748] Step 8:

[1749] The terminal transmits the received input data to the server.

[1750] Input: User-entered data

[1751] Output: The input data sent to the server

[1752] Specific operation: The device sends data to the server via the internet via the API.

[1753] Step 9:

[1754] The server uses an emotion analysis engine to analyze emotions based on input data and past movement history.

[1755] Input: Input data sent to the server

[1756] Output: User's emotional state (e.g., "Low motivation")

[1757] What it does: The sentiment analysis engine analyzes past data and new input to identify emotional states.

[1758] Step 10:

[1759] The server generates an optimal exercise plan based on age, weight, emotional state and past exercise history.

[1760] Input: Age, weight, emotional state, past exercise history

[1761] Output: Exercise plan (e.g., "30 minutes of easy walking and 10 minutes of yoga and stretching")

[1762] Specific Actions: The movement plan generation algorithm uses this information to generate an optimal movement plan.

[1763] Step 11:

[1764] The server returns the generated exercise plan to the terminal.

[1765] Input: Generated movement plan

[1766] Output: Exercise plan sent to the device

[1767] Specific operation: The server sends data to the terminal as a response.

[1768] Step 12:

[1769] The terminal displays the suggested exercise plan to the user.

[1770] Input: Exercise plan sent from the server

[1771] Output: Exercise plan displayed to the user

[1772] Specific operation: The exercise plan is displayed on the device's user interface.

[1773] (Application example 2)

[1774] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1775] Conventional systems for preventing lifestyle-related diseases, suggesting exercise, brain training, and medical support often do not fully consider the user's physical condition or emotional state, resulting in a lack of personalized suggestions based on the user's emotions and physical condition. Furthermore, in brick-and-mortar stores, it is difficult to suggest ingredients and recipes based on the user's physical condition and emotions, so there are no means of effectively supporting health management and improving dietary content.

[1776] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for determining the user's emotional state using an emotion analysis engine that analyzes input data, means for generating a list of appropriate ingredients and recipes based on the emotional state, and means for returning the generated list to the terminal. This enables personalized health suggestions based on the user's physical condition and emotional state.

[1777] "User" refers to an individual who uses this system.

[1778] "Device" means the device used by a User to enter information or view suggestions, including, but not limited to, a smartphone, tablet, or PC.

[1779] "Server" refers to a computer system that receives, analyzes, and processes data sent by users.

[1780] "Sentiment analysis engine" refers to software or algorithms for determining a user's emotional state from input data.

[1781] "Emotional state" refers to the user's current psychological and emotional state, including, for example, stress and motivation.

[1782] A "list of ingredients and recipes" is a list containing ingredients and cooking methods suggested based on the user's physical and emotional state.

[1783] "Menu plan" refers to a list of suggested ingredients and recipes.

[1784] An "exercise plan" refers to a proposed exercise plan based on the user's physical condition and emotional state.

[1785] "Training content" refers to the specific brain training tasks and activities provided to users.

[1786] "With audio" refers to a method in which training content and notifications are provided to the user by audio.

[1787] To implement this invention, a system is constructed in which a user inputs their physical condition and food preferences for the day through a terminal. The terminal may be a smartphone, tablet, or PC. The data input to this terminal is sent to a server equipped with an emotion analysis engine. This server analyzes the user's input data and determines their emotional state.

[1788] The server uses a sentiment analysis engine to analyze the user's emotional state (e.g., high stress, low motivation, etc.). The sentiment analysis engine uses natural language processing techniques. The analysis results are used to generate ingredient and recipe lists, exercise plans, brain training content, and medical advice.

[1789] For example, if a user inputs "I'm feeling a bit tired today and would like to eat something light," this data is sent to the server, and the emotion analysis engine determines the emotional state as "high stress." Based on this emotional state, the server generates a list of suggestions, such as "spinach and tomato salad" and "tofu and seaweed soup." The generated list is sent back to the device and displayed to the user.

[1790] For example, if a user wants to input their age and weight and want an exercise plan suggested, they can enter "45 years old, 70 kg." This data is sent to the server, where the emotion analysis engine analyzes the user's emotional state. For example, if the user is judged to be "low motivation," the server will generate an exercise plan suggesting "30 minutes of light walking and 10 minutes of yoga stretching" based on this emotional state and past exercise history. The generated exercise plan is sent back to the device and displayed to the user.

[1791] Furthermore, if a user wants to start brain training, they input "Start brain training," and this data is sent to the server, where the emotion analysis engine analyzes the user's emotional state. For example, if the server determines that "concentration is necessary," it will play a new training content, "A new folk tale, 'Urashima Taro,'" with audio, based on this emotional state and past training history. As a result, the device provides this training content to the user.

[1792] Here's an example prompt that uses a generative AI model to analyze sentiment and determine what to suggest to the user:

[1793] "When a user inputs something like, 'I'm feeling a bit tired today and want to eat something light,' analyze their emotional state and suggest appropriate ingredients and recipes."

[1794] The hardware for implementing this invention includes a user terminal (such as a smartphone, tablet, or PC), a server, and a computer system for running the sentiment analysis engine. The software includes Python 3.x and a requests library for sending API requests.

[1795] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1796] Step 1:

[1797] The user inputs their physical condition and what they want to eat into the terminal. For example, they might input, "I'm feeling a bit tired today, so I want to eat something light." This input data is acquired through an input form on the user terminal.

[1798] Step 2:

[1799] The device sends the input data to the server. Specifically, the data is sent as an HTTP POST request via a communication module built into the device. This request includes the user's input as a payload.

[1800] Step 3:

[1801] The input data received by the server is analyzed by a sentiment analysis engine. The sentiment analysis engine uses natural language processing technology to determine the user's emotional state. Here, the input data is analyzed in text format and an emotional tag such as "high stress" is generated.

[1802] Step 4:

[1803] The server generates a list of appropriate ingredients and recipes based on the user's emotional state and input data. The server uses an algorithm that takes into account the user's emotional state (e.g., "high stress") to search its internal database for light ingredients and recipes and generates the list.

[1804] Step 5:

[1805] The server then returns the generated list of ingredients and recipes to the device, again using an HTTP POST request. The returned data includes the suggested ingredients and recipe information.

[1806] Step 6:

[1807] The device displays the menu plan returned from the server to the user. Specifically, it uses a GUI (Graphical User Interface) component for display to show details of ingredients and recipes. Based on this information, the user can select appropriate ingredients and dishes.

[1808] For example, if a user inputs "I'm feeling a bit tired today and want to eat something light," the server analyzes this and generates an emotion tag called "high stress." The server then lists options like "spinach and tomato salad" and "tofu and seaweed soup" based on this emotion tag and returns this information. The device then displays it to the user.

[1809] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1810] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1811] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1812] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1813] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1814] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1815] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1816] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1817] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1818] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1819] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1820] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1821] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1822] 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.

[1823] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1824] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1825] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1826] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1827] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1828] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1829] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1830] The following is further disclosed regarding the above embodiment.

[1831] (Claim 1)

[1832] A means for users to input their physical condition and what they want to eat that day through a terminal,

[1833] means for transmitting the input data to a server;

[1834] means for the server to analyze input data and generate a list of suitable ingredients and recipes;

[1835] A means for returning the menu plan generated by the server to the terminal;

[1836] means for displaying the proposed menu to the user by the terminal;

[1837] A system including:

[1838] (Claim 2)

[1839] A means for a user to input age and weight through a terminal;

[1840] means for transmitting the input data to a server;

[1841] A means for the server to analyze input data and generate an optimal exercise plan based on past exercise history;

[1842] A means for returning the exercise plan generated by the server to the terminal;

[1843] a means for displaying the proposed exercise plan and implementation method to the user by the terminal;

[1844] 10. The system of claim 1, comprising:

[1845] (Claim 3)

[1846] A means for a user to input through a terminal that he / she wants to start brain training;

[1847] means for transmitting the input data to a server;

[1848] a means for generating new training content based on past training history by the server;

[1849] means for returning the training content generated by the server to the terminal;

[1850] means for reproducing the training content provided by the terminal to the user with audio;

[1851] 10. The system of claim 1, comprising:

[1852] (Claim 4)

[1853] a means for a user to input health-related questions via a terminal;

[1854] means for transmitting the input data to a specialized medical support system;

[1855] means for the specialized medical support system to generate answers to questions and return them to a server;

[1856] means for transmitting the response received by the server to a terminal;

[1857] means for the terminal to display professional medical support answers to the user;

[1858] 10. The system of claim 1, comprising:

[1859] "Example 1"

[1860] (Claim 1)

[1861] A means for users to input their physical condition and what they want to eat that day through a terminal,

[1862] means for transmitting the input data to a server;

[1863] means for the server to analyze input data and use a generative AI model to generate a list of suitable ingredients and recipes;

[1864] A means for returning the menu plan generated by the server to the terminal;

[1865] means for displaying the proposed menu to the user by the terminal;

[1866] A system including:

[1867] (Claim 2)

[1868] A means for a user to input age and weight through a terminal;

[1869] means for transmitting the input data to a server;

[1870] means for the server to analyze the input data and use a generative AI model to generate an optimal exercise plan based on past exercise history;

[1871] A means for returning the exercise plan generated by the server to the terminal;

[1872] a means for displaying the proposed exercise plan and implementation method to the user by the terminal;

[1873] 10. The system of claim 1, comprising:

[1874] (Claim 3)

[1875] A means for a user to input through a terminal that he / she wants to start brain training;

[1876] means for transmitting the input data to a server;

[1877] means for the server to use a generative AI model to generate new training content based on past training history;

[1878] means for returning the training content generated by the server to the terminal;

[1879] means for reproducing the training content provided by the terminal to the user with audio;

[1880] 10. The system of claim 1, comprising:

[1881] "Application Example 1"

[1882] (Claim 1)

[1883] A means for users to input their physical condition and what they want to eat that day through a terminal,

[1884] means for transmitting the input data to a server;

[1885] means for the server to analyze input data and generate a list of suitable ingredients and recipes;

[1886] A means for returning the menu plan generated by the server to the terminal;

[1887] means for displaying the proposed menu to the user by the terminal;

[1888] a means for generating an initial menu based on a prompt sentence generated using the generative AI model;

[1889] a means for delivering meals through an external service in response to user input;

[1890] A system including:

[1891] (Claim 2)

[1892] A means for a user to input age and weight through a terminal;

[1893] means for transmitting the input data to a server;

[1894] A means for the server to analyze input data and generate an optimal exercise plan based on past exercise history;

[1895] A means for returning the exercise plan generated by the server to the terminal;

[1896] a means for displaying the proposed exercise plan and implementation method to the user by the terminal;

[1897] 10. The system of claim 1, comprising:

[1898] (Claim 3)

[1899] A means for a user to input through a terminal that he / she wants to start brain training;

[1900] means for transmitting the input data to a server;

[1901] a means for generating new training content based on past training history by the server;

[1902] means for returning the training content generated by the server to the terminal;

[1903] means for reproducing the training content provided by the terminal to the user with audio;

[1904] 10. The system of claim 1, comprising:

[1905] "Example 2: Combining Emotion Engines"

[1906] (Claim 1)

[1907] A means for users to input their physical condition and what they want to eat that day through a terminal,

[1908] means for transmitting the input data to a server;

[1909] means for the server to analyze input data using an emotion analysis engine and determine the emotional state of the user;

[1910] A means for the server to generate a list of suitable ingredients and recipes based on the analysis results;

[1911] A means for returning the menu plan generated by the server to the terminal;

[1912] means for displaying the proposed menu to the user by the terminal;

[1913] A system including:

[1914] (Claim 2)

[1915] A means for a user to input age and weight through a terminal;

[1916] means for transmitting the input data to a server;

[1917] a means for the server to determine the user's emotional state based on the input data and past exercise history using an emotion analysis engine;

[1918] A means for the server to generate an optimal exercise plan based on age, weight, emotional state, and past exercise history;

[1919] A means for returning the exercise plan generated by the server to the terminal;

[1920] a means for displaying the proposed exercise plan and implementation method to the user by the terminal;

[1921] 10. The system of claim 1, comprising:

[1922] (Claim 3)

[1923] A means for a user to input through a terminal that he / she wants to start brain training;

[1924] means for transmitting the input data to a server;

[1925] means for the server to determine the emotional state of a user using an emotion analysis engine;

[1926] a means for generating new training content based on past training history and emotional state by the server;

[1927] means for returning the training content generated by the server to the terminal;

[1928] means for reproducing the training content provided by the terminal to the user with audio;

[1929] 10. The system of claim 1, comprising:

[1930] "Application example 2 when combining emotion engines"

[1931] (Claim 1)

[1932] A means for users to input their physical condition and what they want to eat that day through a terminal,

[1933] means for transmitting the input data to a server;

[1934] means for determining the emotional state of a user by using a sentiment analysis engine in the server to analyze input data;

[1935] means for generating a list of suitable ingredients and recipes based on the emotional state of the server;

[1936] A means for returning the menu plan generated by the server to the terminal;

[1937] means for displaying the proposed menu to the user by the terminal;

[1938] A system including:

[1939] (Claim 2)

[1940] A means for a user to input age and weight through a terminal;

[1941] means for transmitting the input data to a server;

[1942] means for determining the emotional state of a user by using a sentiment analysis engine in the server to analyze input data;

[1943] A means for generating an optimal exercise plan based on input data and emotional state, taking into account past exercise history;

[1944] A means for returning the exercise plan generated by the server to the terminal;

[1945] a means for displaying the proposed exercise plan and implementation method to the user by the terminal;

[1946] 10. The system of claim 1, comprising:

[1947] (Claim 3)

[1948] A means for a user to input through a terminal that he / she wants to start brain training;

[1949] means for transmitting the input data to a server;

[1950] means for determining the emotional state of a user by using a sentiment analysis engine in the server to analyze input data;

[1951] means for generating new training content based on the emotional state and past training history of the server;

[1952] means for returning the training content generated by the server to the terminal;

[1953] means for reproducing the training content provided by the terminal to the user with audio;

[1954] 10. The system of claim 1, comprising: [Explanation of symbols]

[1955] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for users to input their physical condition and what they want to eat that day through a terminal, means for transmitting the input data to a server; means for the server to analyze input data and generate a list of suitable ingredients and recipes; A means for returning the menu plan generated by the server to the terminal; means for displaying the proposed menu to the user by the terminal; A system including:

2. A means for a user to input age and weight through a terminal; means for transmitting the input data to a server; A means for the server to analyze input data and generate an optimal exercise plan based on past exercise history; A means for returning the exercise plan generated by the server to the terminal; a means for displaying the proposed exercise plan and implementation method to the user by the terminal; The system of claim 1 , comprising:

3. A means for a user to input through a terminal that he / she wants to start brain training; means for transmitting the input data to a server; a means for generating new training content based on past training history by the server; means for returning the training content generated by the server to the terminal; means for reproducing the training content provided by the terminal to the user with audio; The system of claim 1 , comprising:

4. a means for a user to input health-related questions via a terminal; means for transmitting the input data to a specialized medical support system; means for the specialized medical support system to generate answers to questions and return them to a server; means for transmitting the response received by the server to a terminal; means for the terminal to display professional medical support answers to the user; The system of claim 1 , comprising:

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