system

Abstract

We provide the system. [Solution] Means for receiving users' personal data and health data, A method for generating nutritional supplement suggestions by using a machine learning model to analyze received data, A means of presenting the generated proposals to the user using natural language processing technology, A means of receiving feedback from users and using it to improve machine learning models, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In recent years, there has been an increasing interest in health, and an appropriate selection of dietary supplements based on individual nutritional needs is required. However, it is difficult for users to select the most suitable dietary supplement for themselves from a vast amount of information, and it is necessary to consider individual health conditions and lifestyles in this selection. Therefore, it is desired to provide a system that can accurately and efficiently propose dietary supplements suitable for each individual.

Means for Solving the Problems

[0005] This invention provides a system that receives users' personal and health data, analyzes this data using a machine learning model, and generates personalized nutritional supplement recommendations for each user. Furthermore, the generated recommendations are presented in an easily understandable format using natural language processing technology. In addition, by collecting user feedback and using it to improve the accuracy of the machine learning model, the quality of the recommendations can be continuously improved.

[0006] "User" refers to an individual who uses the system to receive recommendations for nutritional supplements based on their personal health condition.

[0007] "Personal data" refers to data that includes individual attribute information such as the user's age, gender, and medical history.

[0008] "Health data" refers to data that includes blood test results and lifestyle information that indicate the user's health status.

[0009] A "machine learning model" refers to a set of algorithms used to make predictions and classifications based on collected data.

[0010] "Analysis" refers to data processing techniques used to analyze data received from users and select appropriate nutritional supplements.

[0011] "Nutritional supplements" refer to products containing vitamins, minerals, and other nutrients that are taken for the purpose of maintaining or improving health.

[0012] "Suggestions" refer to the optimal nutritional supplement options presented as a result of analysis based on the user's input data.

[0013] "Natural language processing technology" refers to artificial intelligence technology used to enable computers to understand and generate human language.

[0014] "Feedback" refers to the opinions and impressions that users provide regarding suggestions they receive.

[0015] "Improving accuracy" refers to enhancing the system's capabilities so that the proposed content can respond more accurately to the expectations of users.

Brief Explanation of Drawings

[0016] ​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

[0017] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] [[ID=1,6]]In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0020] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0021] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0022] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0029] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0036] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0037] This invention is based on a system that combines various technologies to offer nutritional supplements optimized for individual users. First, the user enters their personal and health data via a dedicated application or web interface. This includes information on age, gender, lifestyle, and, if necessary, recent biopsy results.

[0038] When a user enters data, the terminal converts this information into the appropriate format and sends it to the server. The server verifies the data received from the user to ensure its quality and integrity. Next, the server analyzes the data using a machine learning model. This model utilizes an extensive medical research database and is trained to identify the most suitable nutritional supplements for each user.

[0039] The suggestions derived from the analysis are provided to the user in the form of explanations in natural language. This makes it easier for the user to understand the background and reasons for the selection of the suggestions. In addition, the server receives feedback from the user and uses this information to continuously improve the machine learning model. As a result, the system can make more accurate suggestions that reflect user feedback.

[0040] As a concrete example, suppose a 35-year-old female user who wishes to become pregnant uses this system and inputs her blood test results and current lifestyle habits. Based on this information, the server suggests combinations of nutrients, including folic acid, and explains the reasons in detail. Furthermore, if the user provides feedback on the suggestions or points of concern, the server collects this information and uses it to improve the model.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] Users use their devices to launch a dedicated application or web interface and enter their personal and health data. This data includes age, gender, lifestyle, and recent test results.

[0044] Step 2:

[0045] The terminal receives data entered by the user, formats it into a standard format, and then verifies the data's integrity and the presence of missing values. After verification, it sends the data to the server.

[0046] Step 3:

[0047] The server receives data sent from the terminal and performs data quality verification. If inconsistencies or defects are found, it generates an error message and sends it back to the terminal.

[0048] Step 4:

[0049] The server inputs data into a machine learning model and performs analysis based on the user's health status. During this process, it refers to accumulated medical research data to identify the most suitable nutritional supplements.

[0050] Step 5:

[0051] The server describes the suggested nutritional supplements generated based on the analysis results in natural language and creates explanatory text in an easy-to-understand format.

[0052] Step 6:

[0053] The server sends the generated proposal to the terminal and presents it to the user.

[0054] Step 7:

[0055] Users can view proposals on their devices and provide feedback on them. This feedback includes opinions and additional requests regarding the proposals.

[0056] Step 8:

[0057] The device receives feedback from the user and sends it to the server.

[0058] Step 9:

[0059] The server analyzes the feedback and uses it as training data for machine learning models to improve the accuracy of future suggestions.

[0060] (Example 1)

[0061] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0062] When providing personalized nutritional recommendations, it is essential to appropriately utilize diverse biometric and lifestyle information of users to deliver highly accurate recommendations. Furthermore, a system is needed to facilitate understanding of the recommendations, effectively incorporate user feedback, and improve the accuracy of the recommendations.

[0063] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0064] In this invention, the server includes means for receiving the user's biometric and lifestyle information, means for analyzing the received information using a machine learning processing device to generate nutritional suggestions, and means for presenting the generated suggestions to the user using natural language conversion technology. This makes it possible to provide personalized suggestions to the user while improving the accuracy of the suggestions by utilizing feedback.

[0065] "User biometric information" refers to individual-specific physical data, including bioanalysis results and information related to health maintenance.

[0066] "Lifestyle information" refers to data related to the user's daily life, including information on lifestyle habits, diet, exercise, etc.

[0067] A "machine learning processing device" is a computing device that learns from input data, analyzes patterns, and makes suggestions aligned with a specific purpose.

[0068] "Nutrient recommendations" are guidelines that show the combination of necessary nutrients based on the user's information.

[0069] "Natural language conversion technology" is a technology that converts machine-generated information into a natural language format that is easy for humans to understand.

[0070] "Feedback" refers to the evaluations and opinions that users provide regarding suggestions, and contributes to the improvement of the system.

[0071] In this invention, users can input their biometric and lifestyle information using a dedicated application or web interface. This information includes age, gender, weight, activity level, diet, and even the results of biological analyses such as blood tests. The terminal is responsible for formatting the input information into an appropriate format and transmitting it to the server.

[0072] The server uses a highly trained generative AI model to analyze the received data. This AI model has been trained on a large amount of medical research data and can suggest optimal nutrients to the user. The server then uses natural language conversion technology to express the suggested content in a human-readable format and present it to the user.

[0073] As a concrete example, a 35-year-old female user planning a pregnancy can use this system to input her blood test data and current eating habits. The server analyzes this information and suggests that folic acid is particularly important, recommending its intake.

[0074] Furthermore, when users provide feedback on the effectiveness and impression of the suggestions, the server can collect this feedback and use it to improve the machine learning model. This allows the system to continuously improve its accuracy.

[0075] An example of a prompt message given to the generative AI model is, "A 35-year-old woman wants to become pregnant. Please suggest nutrients including folic acid and explain the reasons why."

[0076] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0077] Step 1:

[0078] Users input biometric and lifestyle information via a dedicated application or web interface. This input includes age, gender, weight, activity level, dietary habits, and blood test results. The entered data is stored as digital data in text and numerical formats.

[0079] Step 2:

[0080] The terminal receives information entered by the user and formats the data. This formatting process converts the input data into a standardized format and checks for missing or outlier values. The output of this process is user data converted into an analyzable format.

[0081] Step 3:

[0082] The terminal transfers the formatted data to the server. This data transfer uses a dedicated protocol to ensure secure communication. The server receives this data and prepares it for the next analysis step.

[0083] Step 4:

[0084] The server analyzes the received data using a generating AI model. During this analysis phase, the AI ​​model uses the user's biometric and lifestyle information as input to perform calculations to identify the optimal combination of nutrients. As a result of the analysis, a list of suggested nutrients optimized for the user is output. The AI ​​model is given a prompt such as, "Please suggest the most effective nutrients based on the following user data."

[0085] Step 5:

[0086] The server uses natural language conversion technology to formalize the generated suggestions and presents them to the user in easily understandable language. The output suggestions are meaningful to the user and include explanations of specific nutritional supplements and the reasons behind their recommendations.

[0087] Step 6:

[0088] Users review the proposed content and provide feedback. This feedback includes the usefulness of the proposal and the results of its implementation. User feedback is sent to the server via the device.

[0089] Step 7:

[0090] The server uses the received feedback to improve its machine learning model. Based on the feedback, it adjusts the model's parameters to improve the accuracy of future suggestions. As a result, an improved AI model is generated.

[0091] (Application Example 1)

[0092] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0093] In modern society, there is a growing demand for easily accessible meals optimized for the individual health conditions and lifestyles of each user. However, there is a lack of efficient and reliable means to recommend and quickly deliver nutritious meals tailored to each user. In particular, providing personalized meals based on health information requires a system that analyzes user data and appropriately incorporates feedback.

[0094] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0095] In this invention, the server includes means for receiving user attribute information and health information, means for performing analysis using a machine learning algorithm based on the received information to generate suggestions for highly nutritious meals, and means for processing requests for meals suggested by affiliated restaurants. This makes it possible to quickly suggest and provide personalized, highly nutritious meals to users.

[0096] "User attribute information" refers to individual information about each user, such as age, gender, occupation, and living environment.

[0097] "Health information" refers to information that indicates the user's health status, and includes biological test results, medical history, and current dietary and exercise habits.

[0098] A "machine learning algorithm" is a computational method for analyzing data and discovering patterns, and is used to solve complex problems.

[0099] "Suggesting nutritious meals" involves selecting and recommending meal menus with the optimal nutritional balance based on the user's health information and attribute information.

[0100] A "food and beverage establishment" refers to a partner that provides meals to users, and includes places such as restaurants and food service establishments.

[0101] "Request processing" refers to the procedure for receiving orders from customers and preparing and serving meals accordingly.

[0102] This document describes a mode for carrying out the invention. To realize the system of this invention, a network-connected client terminal, a server system, and an order processing system for affiliated restaurants are required. Users input their attribute information and health information into a specific application using a client terminal (e.g., a smartphone or computer). This application is implemented using a cross-platform development tool such as React Native and securely transmits user data to the server.

[0103] The server uses the Python programming language and machine learning algorithms based on the Scikit-Learn library to analyze the received data. This analysis generates a highly nutritious meal plan optimized for the user. The generated suggestions are presented to the user using natural language processing libraries such as spaCy or NLTK, and explained in an easy-to-understand language.

[0104] When a user accepts a meal suggestion, a request is sent to the order processing system of a partner restaurant. The partner restaurant prepares the meal based on the request and delivers it to the specified time and location. After delivery is complete, user feedback is collected and the information is updated on the server. This allows the machine learning algorithm to continuously improve.

[0105] As a concrete example, a health-conscious user in their 30s inputs information about their exercise habits and preferred foods into the application. Based on this information, the server suggests a meal plan that is high in protein and low in carbohydrates. An example of a prompt text for the generating AI model is, "Suggest a high-protein, low-carbohydrate meal menu." Based on this suggestion, the user can order the menu from a nearby restaurant and have it delivered to their home.

[0106] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0107] Step 1:

[0108] Users open the application using a client device and enter attribute and health information. This data includes age, gender, exercise habits, health status, and dietary preferences. This information is temporarily stored in a database within the application.

[0109] Step 2:

[0110] The terminal converts the input information into JSON format and sends it to the server via a secure protocol (e.g., HTTPS). The server converts the received data into a parseable format and stores it in a database.

[0111] Step 3:

[0112] The server executes machine learning algorithms using Python and Scikit-Learn to analyze the received user data. In this step, it calculates optimal meal suggestions for each individual user based on a dataset of nutritional and health information. The output is a recommended meal menu.

[0113] Step 4:

[0114] The server translates the generated meal suggestions into understandable language using natural language processing techniques. To achieve this, it uses libraries such as spaCy and NLTK to create prompts for the generative AI model. The output is the suggestion details in natural language format.

[0115] Step 5:

[0116] The server sends the proposal details to the client terminal and displays them in the user application. The user can then accept the proposal or specify what needs to be corrected.

[0117] Step 6:

[0118] If the proposal is accepted, the terminal sends an order request to the partner restaurant. The order includes user information and accepted menu details. The accepted order information is then passed to the restaurant's system.

[0119] Step 7:

[0120] After delivery is complete, the user provides feedback through the application. The device sends the feedback to a server where it is recorded. The server uses this feedback to update the machine learning algorithm model and improve accuracy for the next time.

[0121] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0122] This invention is a system that proposes nutritional supplements further optimized for individual users by combining an emotion engine. Users input their personal and health data via a terminal. This includes age, gender, lifestyle, and recent biopsy results. The input data is formatted by the terminal and sent to the server.

[0123] This system uses a machine learning model to analyze data received from terminals, and also incorporates an emotion engine. The emotion engine analyzes the user's emotional state and incorporates it as a factor influencing the data analysis results. This allows for suggestions that take the user's psychological aspects into account.

[0124] The server generates nutritional supplement recommendations based on the analysis. These recommendations are presented to the user via the terminal, written in a user-friendly format using natural language processing technology. The recommendations also reflect the user's emotional state and are adjusted to be emotionally relatable.

[0125] Users can review the presented suggestions and select appropriate nutritional supplements based on that information. Furthermore, they can provide emotional feedback on the suggestions via the emotion engine. This feedback is sent to the server and used to improve the machine learning model and the emotion engine.

[0126] As a concrete example, suppose a salaried worker with a high stress level uses this system. The system analyzes his health data and emotional state and suggests supplements containing nutrients to cope with stress, such as B vitamins. The suggestions include an explanation of why the supplements are effective, and are presented in positive language that takes his emotional state into account.

[0127] Thus, embodiments of the present invention provide a method that comprehensively considers the user's health and emotional aspects and assists in selecting more effective nutritional supplements.

[0128] The following describes the processing flow.

[0129] Step 1:

[0130] Users input their personal and health data via the device. This includes biometric test results and health information from their daily lives. The device also accepts input of the user's emotional state.

[0131] Step 2:

[0132] The terminal converts the input data into a standard format, verifies the data's integrity and validity, and then sends it to the server.

[0133] Step 3:

[0134] The server receives the incoming data and first checks if any data is missing. If there are inconsistencies, it returns an error message to the terminal.

[0135] Step 4:

[0136] The server passes the data to a machine learning model, which then begins an analysis based on the user's health status. This model references a wide range of medical data to suggest appropriate nutritional supplements.

[0137] Step 5:

[0138] The server uses an emotion engine to analyze the user's emotional state. The analysis results are then used to adjust the nutritional supplement recommendations to reflect emotional factors.

[0139] Step 6:

[0140] The server integrates the results of machine learning models and an emotion engine to generate nutritional supplement recommendations. These recommendations are then explained in a user-friendly format using natural language processing techniques.

[0141] Step 7:

[0142] Once the proposal is complete, the server sends the information to the terminal and presents it to the user.

[0143] Step 8:

[0144] Users can view suggestions on their devices to help them with meal choices and health management. The suggestions are personalized based on the user's emotional status.

[0145] Step 9:

[0146] Users input emotional feedback on a proposal into their device and send it to the server. This feedback is used to improve future proposals.

[0147] Step 10:

[0148] The server analyzes user feedback and uses it to improve the emotion engine and machine learning models, thereby increasing the accuracy of future suggestions.

[0149] (Example 2)

[0150] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0151] In modern society, there is a demand for nutritional supplements optimized for each individual user. However, conventional systems have a problem in that they do not take into account the user's emotions or psychological state, resulting in limited acceptance and effectiveness of the recommendations. Therefore, there is a need for individualized recommendations that reflect not only the user's health condition but also their emotional state.

[0152] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0153] This invention includes a server that receives the user's personal data and health data, formats the data, and transmits it to an information processing device; a server that analyzes the received data using a machine learning model and an emotion analysis device to generate personalized nutritional supplement recommendations; and a server that presents the generated recommendations in a language format that takes into account the user's psychological state using natural language processing technology. This makes it possible to propose nutritional supplements that take into account the user's unique emotions and psychological state, thereby increasing the effectiveness and acceptability of the recommendations.

[0154] "User's personal data" refers to attribute information related to an individual, including information such as age, gender, and lifestyle.

[0155] "Health data" refers to information about a user's health status, including biological test results and medical diagnostic data.

[0156] An "information processing device" is a device used for data analysis and computation, and refers to servers and computer systems.

[0157] A "machine learning model" is an algorithmic model that learns specific patterns based on large amounts of data and uses them to perform new data analysis.

[0158] An "emotion analysis device" is a device that analyzes the emotional state of a user and reflects the results in data processing.

[0159] "Natural language processing technology" is a technology that enables computers to understand and generate human language, and to convey information in an easy-to-understand manner.

[0160] "Personalized nutritional supplements" are nutritional supplements that are suggested to users based on their specific health and emotional conditions.

[0161] "Emotional feedback" refers to the emotional reactions and evaluations that users give to suggestions, and is information used to improve the system.

[0162] Users input personal and health data via a terminal. This data includes age, gender, lifestyle, and biometric test results. The terminal formats the input data, encrypts it, and prepares it for transmission to the server. The server receives this data, stores it in a database, and then performs data analysis using machine learning models and sentiment analyzers. The machine learning models analyze the user's health status and identify necessary nutrients. The sentiment analyzer analyzes the user's emotional state and incorporates it as a factor influencing nutritional supplement recommendations. Based on these analysis results, the server uses natural language processing technology to generate nutritional supplement recommendations in a format that takes the user's psychological state into account. The recommendations are presented to the user via the terminal, allowing the user to select appropriate nutritional supplements. Furthermore, users provide emotional feedback on the recommendations via the terminal and send it to the server. This feedback is used to improve the machine learning models and sentiment analyzers.

[0163] Specifically, the server side incorporates AI libraries and natural language processing technologies for data analysis and sentiment analysis. On the terminal side, an application runs to build the user interface, managing data input and the display of suggested content.

[0164] As a concrete example, consider a user who is a salaried worker with a high stress level. This user uses their device to input their fatigue level and stress level from a recent health checkup. Based on this information, the system suggests supplements containing B vitamins. These suggestions are presented in positive language, along with explanations that they help with fatigue recovery and stress reduction. An example of a prompt message sent to the server would be: "A 30-year-old male who works a desk job five days a week and has recently reported feeling stressed. Recent test results show a slight deficiency in B vitamins. Use the emotion engine to generate suggestions for nutritional supplements that can help reduce stress."

[0165] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0166] Step 1:

[0167] The terminal accepts personal and health data input from the user. The data entered by the user includes age, gender, lifestyle, and recent biopsy results. The terminal formats this data and converts it into a format easily analyzable by the information processing device. The output of this step is data in a consistent format.

[0168] Step 2:

[0169] The terminal encrypts the formatted data and sends it to the server. Data transmission is performed using a secure communication protocol, ensuring data confidentiality. The server verifies the data received from the terminal and stores it in a database. The input to this step is the formatted data, and the output is the stored data.

[0170] Step 3:

[0171] The server initiates analysis using a machine learning model based on the stored data. The machine learning model analyzes the user's health status and identifies necessary nutrients, drawing on past data. Furthermore, the server uses an emotion analyzer to detect the user's emotional state. This emotional state is incorporated as a factor influencing the recommendation of nutritional supplements. The output of this step is the analysis results, including necessary nutrients and emotional state.

[0172] Step 4:

[0173] The server combines the results of machine learning models and sentiment analysis, and uses natural language processing techniques to create personalized nutritional supplement recommendations for the user. The generated recommendations are clearly explained, and the content is emotionally resonant, using positive language. The output of this step is a well-customized recommendation.

[0174] Step 5:

[0175] The terminal receives suggestions sent from the server and displays them to the user. The user can review the suggestions and select the corresponding nutritional supplements. Furthermore, the user can send feedback on the suggestions through the terminal. In this step, the input is the suggestions from the server, and the output is the user's feedback.

[0176] Step 6:

[0177] The server receives user feedback and records it in a database. The received feedback is used for the continuous improvement of the machine learning model and sentiment analysis device. The input to this step is user feedback, and the output is information on improvements to the model and device.

[0178] (Application Example 2)

[0179] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0180] Modern consumers seek personalized nutritional supplement recommendations based on their individual health and emotional states, but traditional systems struggle to consider emotional states and therefore cannot provide optimal recommendations. Furthermore, presenting recommendations in a user-friendly format is difficult, hindering the improvement of the purchasing experience.

[0181] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0182] In this invention, the server includes means for acquiring and receiving the user's biometric data and emotional state, means for performing analysis based on the received data using a machine learning model and an emotion analysis engine to generate suggestions for nutritional supplements, and means for presenting the generated suggestions to a portable terminal using natural language processing technology. This makes it possible to generate personalized suggestions that take into account the user's emotional state and present them in a way that is easy for the user to understand.

[0183] A "user" refers to an individual who uses the system to input health data and emotional status and receives personalized suggestions.

[0184] "Biometric data" refers to information that indicates the user's health status, such as age, gender, lifestyle, and recent biopsy results.

[0185] "Emotional state" refers to information that indicates the user's current emotions and psychological state, and is the data analyzed for personalizing suggestions.

[0186] A "machine learning model" refers to a technology that learns from large amounts of data and uses that data to make predictions and perform analyses on new inputs.

[0187] A "sentiment analysis engine" refers to a software configuration that analyzes the user's emotional state and adjusts the suggested content based on that analysis.

[0188] "Natural language processing technology" refers to methodologies and techniques that enable computers to understand and generate human language.

[0189] A "portable device" refers to a device, such as a smartphone or tablet, that is portable and allows the user to directly operate it to receive information.

[0190] "Feedback" refers to the responses and opinions that users give in response to suggestions, and the information used to improve the system.

[0191] This invention is a system that provides recommendations for nutritional supplements optimized based on the health data and emotional state of individual users. This system mainly consists of three elements: a server, a portable terminal, and the user.

[0192] Portable devices, such as smartphones and tablets, allow users to input their health data and emotional state. This includes age, gender, lifestyle, and recent biopsy results. The data entered on the user's portable device is transmitted to a server via the internet.

[0193] The server plays a central role in analyzing the received data. Specifically, it uses machine learning models and sentiment analysis engines based on Python. By utilizing libraries such as Scikit-learn and TENSORFLOW®, it efficiently processes large amounts of data and generates recommendations for nutritional supplements best suited to each individual user. Furthermore, it leverages natural language processing technologies, including Natural Language Toolkit (NLTK) and GPT-3®, to generate recommendations in a language format that is easy for users to understand. The recommendations are adjusted according to the user's emotional state, making them more readily accepted.

[0194] Users can receive and review suggestions via devices such as smartphones. They can also provide feedback on the suggestions, which is then sent back to the server to help improve the machine learning models and sentiment analysis engine.

[0195] As a concrete example, if a user inputs the emotion "I'm stressed" on their smartphone, the server will suggest a nutritional supplement containing B vitamins. In this case, the suggestion would include positive words such as, "Please try this supplement, which is expected to have a relaxing effect!" An example of input to the generating AI model would be a prompt message like, "User's emotional state is stressed, high. Age: 35, Gender: Male. Generate suggestion for a B vitamin supplement:"

[0196] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0197] Step 1:

[0198] The device receives health data and emotional state from the user as input. Through a smartphone application, the user can input their age, gender, lifestyle, biometric test results, and select their emotional state. This input data is transmitted in a formatted form via the internet and reaches the server.

[0199] Step 2:

[0200] The server receives data from the terminal as input and performs data preprocessing. This involves formatting the received data, imputing missing values, and converting it into a format usable by machine learning models. Python libraries such as Pandas and NumPy are used for this process.

[0201] Step 3:

[0202] The server takes formatted data as input and performs analysis using a machine learning model. Algorithms using Scikit-learn and TensorFlow analyze the user's health status and suggest suitable nutritional supplements. The output is a list of specific nutritional supplements suggested.

[0203] Step 4:

[0204] The server adjusts the output using an emotion analysis engine based on the analysis results. Using a generative AI model, linguistic adjustments are made to ensure the suggestions match the user's emotional state. OpenAI's GPT-3, among others, is used in this process, and an example of a generated prompt is: "User's emotional state is stressed, high. Age: 35, Gender: Male. Generate suggestion for vitamin B complex supplements:"

[0205] Step 5:

[0206] The server outputs suggestions, refined using natural language processing technology, to a portable device. Using tools such as the Natural Language Toolkit (NLTK), the suggestions are sent to the device in a user-friendly format. Users can then review these suggestions on their own devices.

[0207] Step 6:

[0208] Users enter feedback on the suggestion via their device. This feedback includes whether they accepted the suggested nutritional supplement and their emotional reaction to the suggestion. This feedback is then formatted again and sent to the server.

[0209] Step 7:

[0210] The server receives user feedback as input and uses it to improve the machine learning model and sentiment analysis engine. The feedback data is incorporated into the model's learning process and becomes data to improve the accuracy of future suggestions.

[0211] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0212] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0213] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0214] [Second Embodiment]

[0215] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0216] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0217] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0218] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0219] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0220] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0221] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0222] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0223] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0224] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0225] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0226] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0227] This invention is based on a system that combines various technologies to offer nutritional supplements optimized for individual users. First, the user enters their personal and health data via a dedicated application or web interface. This includes information on age, gender, lifestyle, and, if necessary, recent biopsy results.

[0228] When a user enters data, the terminal converts this information into the appropriate format and sends it to the server. The server verifies the data received from the user to ensure its quality and integrity. Next, the server analyzes the data using a machine learning model. This model utilizes an extensive medical research database and is trained to identify the most suitable nutritional supplements for each user.

[0229] The suggestions derived from the analysis are provided to the user in the form of explanations in natural language. This makes it easier for the user to understand the background and reasons for the selection of the suggestions. In addition, the server receives feedback from the user and uses this information to continuously improve the machine learning model. As a result, the system can make more accurate suggestions that reflect user feedback.

[0230] As a concrete example, suppose a 35-year-old female user who wishes to become pregnant uses this system and inputs her blood test results and current lifestyle habits. Based on this information, the server suggests combinations of nutrients, including folic acid, and explains the reasons in detail. Furthermore, if the user provides feedback on the suggestions or points of concern, the server collects this information and uses it to improve the model.

[0231] The following describes the processing flow.

[0232] Step 1:

[0233] Users use their devices to launch a dedicated application or web interface and enter their personal and health data. This data includes age, gender, lifestyle, and recent test results.

[0234] Step 2:

[0235] The terminal receives data entered by the user, formats it into a standard format, and then verifies the data's integrity and the presence of missing values. After verification, it sends the data to the server.

[0236] Step 3:

[0237] The server receives data sent from the terminal and performs data quality verification. If inconsistencies or defects are found, it generates an error message and sends it back to the terminal.

[0238] Step 4:

[0239] The server inputs data into a machine learning model and performs analysis based on the user's health status. During this process, it refers to accumulated medical research data to identify the most suitable nutritional supplements.

[0240] Step 5:

[0241] The server describes the suggested nutritional supplements generated based on the analysis results in natural language and creates explanatory text in an easy-to-understand format.

[0242] Step 6:

[0243] The server sends the generated proposal to the terminal and presents it to the user.

[0244] Step 7:

[0245] Users can view proposals on their devices and provide feedback on them. This feedback includes opinions and additional requests regarding the proposals.

[0246] Step 8:

[0247] The device receives feedback from the user and sends it to the server.

[0248] Step 9:

[0249] The server analyzes the feedback and uses it as training data for machine learning models to improve the accuracy of future suggestions.

[0250] (Example 1)

[0251] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0252] When providing personalized nutritional recommendations, it is essential to appropriately utilize diverse biometric and lifestyle information of users to deliver highly accurate recommendations. Furthermore, a system is needed to facilitate understanding of the recommendations, effectively incorporate user feedback, and improve the accuracy of the recommendations.

[0253] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0254] In this invention, the server includes means for receiving the user's biometric and lifestyle information, means for analyzing the received information using a machine learning processing device to generate nutritional suggestions, and means for presenting the generated suggestions to the user using natural language conversion technology. This makes it possible to provide personalized suggestions to the user while improving the accuracy of the suggestions by utilizing feedback.

[0255] "User biometric information" refers to individual-specific physical data, including bioanalysis results and information related to health maintenance.

[0256] "Lifestyle information" refers to data related to the user's daily life, including information on lifestyle habits, diet, exercise, etc.

[0257] A "machine learning processing device" is a computing device that learns from input data, analyzes patterns, and makes suggestions aligned with a specific purpose.

[0258] "Nutrient recommendations" are guidelines that show the combination of necessary nutrients based on the user's information.

[0259] "Natural language conversion technology" is a technology that converts machine-generated information into a natural language format that is easy for humans to understand.

[0260] "Feedback" refers to the evaluations and opinions that users provide regarding suggestions, and contributes to the improvement of the system.

[0261] In this invention, users can input their biometric and lifestyle information using a dedicated application or web interface. This information includes age, gender, weight, activity level, diet, and even the results of biological analyses such as blood tests. The terminal is responsible for formatting the input information into an appropriate format and transmitting it to the server.

[0262] The server uses a highly trained generative AI model to analyze the received data. This AI model has been trained on a large amount of medical research data and can suggest optimal nutrients to the user. The server then uses natural language conversion technology to express the suggested content in a human-readable format and present it to the user.

[0263] As a concrete example, a 35-year-old female user planning a pregnancy can use this system to input her blood test data and current eating habits. The server analyzes this information and suggests that folic acid is particularly important, recommending its intake.

[0264] Furthermore, when users provide feedback on the effectiveness and impression of the suggestions, the server can collect this feedback and use it to improve the machine learning model. This allows the system to continuously improve its accuracy.

[0265] An example of a prompt message given to the generative AI model is, "A 35-year-old woman wants to become pregnant. Please suggest nutrients including folic acid and explain the reasons why."

[0266] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0267] Step 1:

[0268] Users input biometric and lifestyle information via a dedicated application or web interface. This input includes age, gender, weight, activity level, dietary habits, and blood test results. The entered data is stored as digital data in text and numerical formats.

[0269] Step 2:

[0270] The terminal receives information entered by the user and formats the data. This formatting process converts the input data into a standardized format and checks for missing or outlier values. The output of this process is user data converted into an analyzable format.

[0271] Step 3:

[0272] The terminal transfers the formatted data to the server. This data transfer uses a dedicated protocol to ensure secure communication. The server receives this data and prepares it for the next analysis step.

[0273] Step 4:

[0274] The server analyzes the received data using a generating AI model. During this analysis phase, the AI ​​model uses the user's biometric and lifestyle information as input to perform calculations to identify the optimal combination of nutrients. As a result of the analysis, a list of suggested nutrients optimized for the user is output. The AI ​​model is given a prompt such as, "Please suggest the most effective nutrients based on the following user data."

[0275] Step 5:

[0276] The server formalizes the generated proposal using natural language conversion technology and presents it to the user in easy-to-understand words. The output proposal becomes meaningful text for the user and includes content explaining specific nutritional supplements and the reasons therefor.

[0277] Step 6:

[0278] The user reviews the proposed content and provides feedback. The feedback includes the usefulness of the proposal and the results of implementation, etc. The feedback from the user is sent to the server via the terminal.

[0279] Step 7:

[0280] The server utilizes the received feedback to improve the machine learning model. Based on the feedback, the parameters of the model are adjusted to improve the proposal accuracy for subsequent times. As a result of this output, an improved AI model is generated.

[0281] (Application Example 1)

[0282] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses

[0283] In modern society, there is a demand to easily provide meals optimized for the health conditions and lifestyle habits of individual users. However, there is a lack of means to efficiently and reliably recommend nutritionally valuable meals according to each user and provide them promptly. In particular, in order to provide individualized meals based on health information, a system that analyzes user information and appropriately reflects feedback is necessary.

[0284] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following respective means.

[0285] In this invention, the server includes means for receiving the user's attribute information and health information, means for performing analysis using a machine learning algorithm based on the received information, and generating a proposal for a nutritious diet, and means for processing requests for the diet proposed by the partnered food service establishments. This makes it possible to quickly propose an individualized and nutritious diet to the user and enable its provision.

[0286] The "user's attribute information" refers to individual information about each user, such as age, gender, occupation, and living environment.

[0287] The "health information" refers to information indicating the user's health status, including biological test results, medical history, current diet and exercise habits, etc.

[0288] The "machine learning algorithm" is a computational method for analyzing data and discovering patterns, and is used to solve complex problems.

[0289] The "proposal for a nutritious diet" refers to selecting and recommending a diet menu with an optimal nutritional balance based on the user's health information and attribute information.

[0290] The "food service establishment" is a partner for providing meals to the user, and includes places such as restaurants and food delivery services.

[0291] The "request processing" is a procedure for receiving an order from the user and preparing and providing a meal accordingly.

[0292] [[ID= - 27]] This document describes a mode for carrying out the invention. To realize the system of this invention, a network-connected client terminal, a server system, and an order processing system for affiliated restaurants are required. Users input their attribute information and health information into a specific application using a client terminal (e.g., a smartphone or computer). This application is implemented using a cross-platform development tool such as React Native and securely transmits user data to the server.

[0293] The server uses the Python programming language and machine learning algorithms based on the Scikit-Learn library to analyze the received data. This analysis generates a highly nutritious meal plan optimized for the user. The generated suggestions are presented to the user using natural language processing libraries such as spaCy or NLTK, and explained in an easy-to-understand language.

[0294] When a user accepts a meal suggestion, a request is sent to the order processing system of a partner restaurant. The partner restaurant prepares the meal based on the request and delivers it to the specified time and location. After delivery is complete, user feedback is collected and the information is updated on the server. This allows the machine learning algorithm to continuously improve.

[0295] As a concrete example, a health-conscious user in their 30s inputs information about their exercise habits and preferred foods into the application. Based on this information, the server suggests a meal plan that is high in protein and low in carbohydrates. An example of a prompt text for the generating AI model is, "Suggest a high-protein, low-carbohydrate meal menu." Based on this suggestion, the user can order the menu from a nearby restaurant and have it delivered to their home.

[0296] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0297] Step 1:

[0298] The user opens an application using a client terminal and enters attribute information and health information. The input data includes age, gender, exercise habits, health status, food preferences, etc. This information is temporarily stored in a database within the application.

[0299] Step 2:

[0300] The terminal converts the input information into JSON format and sends it to the server via a secure protocol (e.g., HTTPS). The server converts the received data into an analyzable format and stores it in the database.

[0301] Step 3:

[0302] The server executes a machine learning algorithm using Python and Scikit-Learn to analyze the received user data. In this step, based on the dataset of nutritional and health information, the optimal diet recommendations for each individual user are calculated. The output is the recommended diet menu.

[0303] Step 4:

[0304] The server converts the generated diet recommendations into an easy-to-understand language using natural language processing techniques.For this, libraries such as spaCy and NLTK are used to create the prompt text for the generative AI model. The output is the details of the recommendations in natural language format.

[0305] Step 5:

[0306] The recommendation details are sent from the server to the client terminal and displayed in the user application. The user can accept the recommendation or specify points for modification

[0307] Step 6:

[0308] If the proposal is accepted, the terminal sends an order request to the partner restaurant. The order includes user information and accepted menu details. The accepted order information is then passed to the restaurant's system.

[0309] Step 7:

[0310] After delivery is complete, the user provides feedback through the application. The device sends the feedback to a server where it is recorded. The server uses this feedback to update the machine learning algorithm model and improve accuracy for the next time.

[0311] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0312] This invention is a system that proposes nutritional supplements further optimized for individual users by combining an emotion engine. Users input their personal and health data via a terminal. This includes age, gender, lifestyle, and recent biopsy results. The input data is formatted by the terminal and sent to the server.

[0313] This system uses a machine learning model to analyze data received from terminals, and also incorporates an emotion engine. The emotion engine analyzes the user's emotional state and incorporates it as a factor influencing the data analysis results. This allows for suggestions that take the user's psychological aspects into account.

[0314] The server generates nutritional supplement recommendations based on the analysis. These recommendations are presented to the user via the terminal, written in a user-friendly format using natural language processing technology. The recommendations also reflect the user's emotional state and are adjusted to be emotionally relatable.

[0315] Users can review the presented suggestions and select appropriate nutritional supplements based on that information. Furthermore, they can provide emotional feedback on the suggestions via the emotion engine. This feedback is sent to the server and used to improve the machine learning model and the emotion engine.

[0316] As a concrete example, suppose a salaried worker with a high stress level uses this system. The system analyzes his health data and emotional state and suggests supplements containing nutrients to cope with stress, such as B vitamins. The suggestions include an explanation of why the supplements are effective, and are presented in positive language that takes his emotional state into account.

[0317] Thus, embodiments of the present invention provide a method that comprehensively considers the user's health and emotional aspects and assists in selecting more effective nutritional supplements.

[0318] The following describes the processing flow.

[0319] Step 1:

[0320] Users input their personal and health data via the device. This includes biometric test results and health information from their daily lives. The device also accepts input of the user's emotional state.

[0321] Step 2:

[0322] The terminal converts the input data into a standard format, verifies the data's integrity and validity, and then sends it to the server.

[0323] Step 3:

[0324] The server receives the incoming data and first checks if any data is missing. If there are inconsistencies, it returns an error message to the terminal.

[0325] Step 4:

[0326] The server passes the data to a machine learning model, which then begins an analysis based on the user's health status. This model references a wide range of medical data to suggest appropriate nutritional supplements.

[0327] Step 5:

[0328] The server uses an emotion engine to analyze the user's emotional state. The analysis results are then used to adjust the nutritional supplement recommendations to reflect emotional factors.

[0329] Step 6:

[0330] The server integrates the results of machine learning models and an emotion engine to generate nutritional supplement recommendations. These recommendations are then explained in a user-friendly format using natural language processing techniques.

[0331] Step 7:

[0332] Once the proposal is complete, the server sends the information to the terminal and presents it to the user.

[0333] Step 8:

[0334] Users can view suggestions on their devices to help them with meal choices and health management. The suggestions are personalized based on the user's emotional status.

[0335] Step 9:

[0336] Users input emotional feedback on a proposal into their device and send it to the server. This feedback is used to improve future proposals.

[0337] Step 10:

[0338] The server analyzes user feedback and uses it to improve the emotion engine and machine learning models, thereby increasing the accuracy of future suggestions.

[0339] (Example 2)

[0340] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0341] In modern society, there is a demand for nutritional supplements optimized for each individual user. However, conventional systems have a problem in that they do not take into account the user's emotions or psychological state, resulting in limited acceptance and effectiveness of the recommendations. Therefore, there is a need for individualized recommendations that reflect not only the user's health condition but also their emotional state.

[0342] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0343] This invention includes a server that receives the user's personal data and health data, formats the data, and transmits it to an information processing device; a server that analyzes the received data using a machine learning model and an emotion analysis device to generate personalized nutritional supplement recommendations; and a server that presents the generated recommendations in a language format that takes into account the user's psychological state using natural language processing technology. This makes it possible to propose nutritional supplements that take into account the user's unique emotions and psychological state, thereby increasing the effectiveness and acceptability of the recommendations.

[0344] "User's personal data" refers to attribute information related to an individual, including information such as age, gender, and lifestyle.

[0345] "Health data" refers to information about a user's health status, including biological test results and medical diagnostic data.

[0346] An "information processing device" is a device used for data analysis and computation, and refers to servers and computer systems.

[0347] A "machine learning model" is an algorithmic model that learns specific patterns based on large amounts of data and uses them to perform new data analysis.

[0348] An "emotion analysis device" is a device that analyzes the emotional state of a user and reflects the results in data processing.

[0349] "Natural language processing technology" is a technology that enables computers to understand and generate human language, and to convey information in an easy-to-understand manner.

[0350] "Personalized nutritional supplements" are nutritional supplements that are suggested to users based on their specific health and emotional conditions.

[0351] "Emotional feedback" refers to the emotional reactions and evaluations that users give to suggestions, and is information used to improve the system.

[0352] Users input personal and health data via a terminal. This data includes age, gender, lifestyle, and biometric test results. The terminal formats the input data, encrypts it, and prepares it for transmission to the server. The server receives this data, stores it in a database, and then performs data analysis using machine learning models and sentiment analyzers. The machine learning models analyze the user's health status and identify necessary nutrients. The sentiment analyzer analyzes the user's emotional state and incorporates it as a factor influencing nutritional supplement recommendations. Based on these analysis results, the server uses natural language processing technology to generate nutritional supplement recommendations in a format that takes the user's psychological state into account. The recommendations are presented to the user via the terminal, allowing the user to select appropriate nutritional supplements. Furthermore, users provide emotional feedback on the recommendations via the terminal and send it to the server. This feedback is used to improve the machine learning models and sentiment analyzers.

[0353] Specifically, the server side incorporates AI libraries and natural language processing technologies for data analysis and sentiment analysis. On the terminal side, an application runs to build the user interface, managing data input and the display of suggested content.

[0354] As a concrete example, consider a user who is a salaried worker with a high stress level. This user uses their device to input their fatigue level and stress level from a recent health checkup. Based on this information, the system suggests supplements containing B vitamins. These suggestions are presented in positive language, along with explanations that they help with fatigue recovery and stress reduction. An example of a prompt message sent to the server would be: "A 30-year-old male who works a desk job five days a week and has recently reported feeling stressed. Recent test results show a slight deficiency in B vitamins. Use the emotion engine to generate suggestions for nutritional supplements that can help reduce stress."

[0355] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0356] Step 1:

[0357] The terminal accepts personal and health data input from the user. The data entered by the user includes age, gender, lifestyle, and recent biopsy results. The terminal formats this data and converts it into a format easily analyzable by the information processing device. The output of this step is data in a consistent format.

[0358] Step 2:

[0359] The terminal encrypts the formatted data and sends it to the server. Data transmission is performed using a secure communication protocol, ensuring data confidentiality. The server verifies the data received from the terminal and stores it in a database. The input to this step is the formatted data, and the output is the stored data.

[0360] Step 3:

[0361] The server initiates analysis using a machine learning model based on the stored data. The machine learning model analyzes the user's health status and identifies necessary nutrients, drawing on past data. Furthermore, the server uses an emotion analyzer to detect the user's emotional state. This emotional state is incorporated as a factor influencing the recommendation of nutritional supplements. The output of this step is the analysis results, including necessary nutrients and emotional state.

[0362] Step 4:

[0363] The server combines the results of machine learning models and sentiment analysis, and uses natural language processing techniques to create personalized nutritional supplement recommendations for the user. The generated recommendations are clearly explained, and the content is emotionally resonant, using positive language. The output of this step is a well-customized recommendation.

[0364] Step 5:

[0365] The terminal receives suggestions sent from the server and displays them to the user. The user can review the suggestions and select the corresponding nutritional supplements. Furthermore, the user can send feedback on the suggestions through the terminal. In this step, the input is the suggestions from the server, and the output is the user's feedback.

[0366] Step 6:

[0367] The server receives user feedback and records it in a database. The received feedback is used for the continuous improvement of the machine learning model and sentiment analysis device. The input to this step is user feedback, and the output is information on improvements to the model and device.

[0368] (Application Example 2)

[0369] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0370] Modern consumers seek personalized nutritional supplement recommendations based on their individual health and emotional states, but traditional systems struggle to consider emotional states and therefore cannot provide optimal recommendations. Furthermore, presenting recommendations in a user-friendly format is difficult, hindering the improvement of the purchasing experience.

[0371] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0372] In this invention, the server includes means for acquiring and receiving the user's biometric data and emotional state, means for performing analysis based on the received data using a machine learning model and an emotion analysis engine to generate suggestions for nutritional supplements, and means for presenting the generated suggestions to a portable terminal using natural language processing technology. This makes it possible to generate personalized suggestions that take into account the user's emotional state and present them in a way that is easy for the user to understand.

[0373] A "user" refers to an individual who uses the system to input health data and emotional status and receives personalized suggestions.

[0374] "Biometric data" refers to information that indicates the user's health status, such as age, gender, lifestyle, and recent biopsy results.

[0375] "Emotional state" refers to information that indicates the user's current emotions and psychological state, and is the data analyzed for personalizing suggestions.

[0376] A "machine learning model" refers to a technology that learns from large amounts of data and uses that data to make predictions and perform analyses on new inputs.

[0377] A "sentiment analysis engine" refers to a software configuration that analyzes the user's emotional state and adjusts the suggested content based on that analysis.

[0378] "Natural language processing technology" refers to methodologies and techniques that enable computers to understand and generate human language.

[0379] A "portable device" refers to a device, such as a smartphone or tablet, that is portable and allows the user to directly operate it to receive information.

[0380] "Feedback" refers to the responses and opinions that users give in response to suggestions, and the information used to improve the system.

[0381] This invention is a system that provides recommendations for nutritional supplements optimized based on the health data and emotional state of individual users. This system mainly consists of three elements: a server, a portable terminal, and the user.

[0382] Portable devices, such as smartphones and tablets, allow users to input their health data and emotional state. This includes age, gender, lifestyle, and recent biopsy results. The data entered on the user's portable device is transmitted to a server via the internet.

[0383] The server plays a central role in analyzing the received data. Specifically, it uses machine learning models and sentiment analysis engines based on Python. By utilizing libraries such as Scikit-learn and TensorFlow, it efficiently processes large amounts of data and generates recommendations for nutritional supplements best suited to each individual user. Furthermore, it leverages natural language processing technologies, including Natural Language Toolkit (NLTK) and GPT-3, to generate recommendations in a language format that is easy for users to understand. The recommendations are adjusted according to the user's emotional state, making them more readily accepted.

[0384] Users can receive and review suggestions via devices such as smartphones. They can also provide feedback on the suggestions, which is then sent back to the server to help improve the machine learning models and sentiment analysis engine.

[0385] As a concrete example, if a user inputs the emotion "I'm stressed" on their smartphone, the server will suggest a nutritional supplement containing B vitamins. In this case, the suggestion would include positive words such as, "Please try this supplement, which is expected to have a relaxing effect!" An example of input to the generating AI model would be a prompt message like, "User's emotional state is stressed, high. Age: 35, Gender: Male. Generate suggestion for a B vitamin supplement:"

[0386] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0387] Step 1:

[0388] The device receives health data and emotional state from the user as input. Through a smartphone application, the user can input their age, gender, lifestyle, biometric test results, and select their emotional state. This input data is transmitted in a formatted form via the internet and reaches the server.

[0389] Step 2:

[0390] The server receives data from the terminal as input and performs data preprocessing. This involves formatting the received data, imputing missing values, and converting it into a format usable by machine learning models. Python libraries such as Pandas and NumPy are used for this process.

[0391] Step 3:

[0392] The server takes formatted data as input and performs analysis using a machine learning model. Algorithms using Scikit-learn and TensorFlow analyze the user's health status and suggest suitable nutritional supplements. The output is a list of specific nutritional supplements suggested.

[0393] Step 4:

[0394] The server adjusts the output using an emotion analysis engine based on the analysis results. A generative AI model is used to make linguistic adjustments to ensure the suggestions match the user's emotional state. OpenAI's GPT-3 is used for this, and an example of a generated prompt is: "User's emotional state is stressed, high. Age: 35, Gender: Male. Generate suggestion for vitamin B complex supplements:"

[0395] Step 5:

[0396] The server outputs suggestions, refined using natural language processing technology, to a portable device. Using tools such as the Natural Language Toolkit (NLTK), the suggestions are sent to the device in a user-friendly format. Users can then review these suggestions on their own devices.

[0397] Step 6:

[0398] Users enter feedback on the suggestion via their device. This feedback includes whether they accepted the suggested nutritional supplement and their emotional reaction to the suggestion. This feedback is then formatted again and sent to the server.

[0399] Step 7:

[0400] The server receives user feedback as input and uses it to improve the machine learning model and sentiment analysis engine. The feedback data is incorporated into the model's learning process and becomes data to improve the accuracy of future suggestions.

[0401] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0402] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0403] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0404] [Third Embodiment]

[0405] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0406] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0407] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0408] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0409] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0410] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0411] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0412] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0413] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0414] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0415] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0416] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0417] This invention is based on a system that combines various technologies to offer nutritional supplements optimized for individual users. First, the user enters their personal and health data via a dedicated application or web interface. This includes information on age, gender, lifestyle, and, if necessary, recent biopsy results.

[0418] When a user enters data, the terminal converts this information into the appropriate format and sends it to the server. The server verifies the data received from the user to ensure its quality and integrity. Next, the server analyzes the data using a machine learning model. This model utilizes an extensive medical research database and is trained to identify the most suitable nutritional supplements for each user.

[0419] The suggestions derived from the analysis are provided to the user in the form of explanations in natural language. This makes it easier for the user to understand the background and reasons for the selection of the suggestions. In addition, the server receives feedback from the user and uses this information to continuously improve the machine learning model. As a result, the system can make more accurate suggestions that reflect user feedback.

[0420] As a concrete example, suppose a 35-year-old female user who wishes to become pregnant uses this system and inputs her blood test results and current lifestyle habits. Based on this information, the server suggests combinations of nutrients, including folic acid, and explains the reasons in detail. Furthermore, if the user provides feedback on the suggestions or points of concern, the server collects this information and uses it to improve the model.

[0421] The following describes the processing flow.

[0422] Step 1:

[0423] Users use their devices to launch a dedicated application or web interface and enter their personal and health data. This data includes age, gender, lifestyle, and recent test results.

[0424] Step 2:

[0425] The terminal receives data entered by the user, formats it into a standard format, and then verifies the data's integrity and the presence of missing values. After verification, it sends the data to the server.

[0426] Step 3:

[0427] The server receives data sent from the terminal and performs data quality verification. If inconsistencies or defects are found, it generates an error message and sends it back to the terminal.

[0428] Step 4:

[0429] The server inputs data into a machine learning model and performs analysis based on the user's health status. During this process, it refers to accumulated medical research data to identify the most suitable nutritional supplements.

[0430] Step 5:

[0431] The server describes the suggested nutritional supplements generated based on the analysis results in natural language and creates explanatory text in an easy-to-understand format.

[0432] Step 6:

[0433] The server sends the generated proposal to the terminal and presents it to the user.

[0434] Step 7:

[0435] Users can view proposals on their devices and provide feedback on them. This feedback includes opinions and additional requests regarding the proposals.

[0436] Step 8:

[0437] The device receives feedback from the user and sends it to the server.

[0438] Step 9:

[0439] The server analyzes the feedback and uses it as training data for machine learning models to improve the accuracy of future suggestions.

[0440] (Example 1)

[0441] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0442] When providing personalized nutritional recommendations, it is essential to appropriately utilize diverse biometric and lifestyle information of users to deliver highly accurate recommendations. Furthermore, a system is needed to facilitate understanding of the recommendations, effectively incorporate user feedback, and improve the accuracy of the recommendations.

[0443] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0444] In this invention, the server includes means for receiving the user's biometric and lifestyle information, means for analyzing the received information using a machine learning processing device to generate nutritional suggestions, and means for presenting the generated suggestions to the user using natural language conversion technology. This makes it possible to provide personalized suggestions to the user while improving the accuracy of the suggestions by utilizing feedback.

[0445] "User biometric information" refers to individual-specific physical data, including bioanalysis results and information related to health maintenance.

[0446] "Lifestyle information" refers to data related to the user's daily life, including information on lifestyle habits, diet, exercise, etc.

[0447] A "machine learning processing device" is a computing device that learns from input data, analyzes patterns, and makes suggestions aligned with a specific purpose.

[0448] "Nutrient recommendations" are guidelines that show the combination of necessary nutrients based on the user's information.

[0449] "Natural language conversion technology" is a technology that converts machine-generated information into a natural language format that is easy for humans to understand.

[0450] "Feedback" refers to the evaluations and opinions that users provide regarding suggestions, and contributes to the improvement of the system.

[0451] In this invention, users can input their biometric and lifestyle information using a dedicated application or web interface. This information includes age, gender, weight, activity level, diet, and even the results of biological analyses such as blood tests. The terminal is responsible for formatting the input information into an appropriate format and transmitting it to the server.

[0452] The server uses a highly trained generative AI model to analyze the received data. This AI model has been trained on a large amount of medical research data and can suggest optimal nutrients to the user. The server then uses natural language conversion technology to express the suggested content in a human-readable format and present it to the user.

[0453] As a concrete example, a 35-year-old female user planning a pregnancy can use this system to input her blood test data and current eating habits. The server analyzes this information and suggests that folic acid is particularly important, recommending its intake.

[0454] Furthermore, when users provide feedback on the effectiveness and impression of the suggestions, the server can collect this feedback and use it to improve the machine learning model. This allows the system to continuously improve its accuracy.

[0455] An example of a prompt message given to the generative AI model is, "A 35-year-old woman wants to become pregnant. Please suggest nutrients including folic acid and explain the reasons why."

[0456] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0457] Step 1:

[0458] Users input biometric and lifestyle information via a dedicated application or web interface. This input includes age, gender, weight, activity level, dietary habits, and blood test results. The entered data is stored as digital data in text and numerical formats.

[0459] Step 2:

[0460] The terminal receives information entered by the user and formats the data. This formatting process converts the input data into a standardized format and checks for missing or outlier values. The output of this process is user data converted into an analyzable format.

[0461] Step 3:

[0462] The terminal transfers the formatted data to the server. This data transfer uses a dedicated protocol to ensure secure communication. The server receives this data and prepares it for the next analysis step.

[0463] Step 4:

[0464] The server analyzes the received data using a generating AI model. During this analysis phase, the AI ​​model uses the user's biometric and lifestyle information as input to perform calculations to identify the optimal combination of nutrients. As a result of the analysis, a list of suggested nutrients optimized for the user is output. The AI ​​model is given a prompt such as, "Please suggest the most effective nutrients based on the following user data."

[0465] Step 5:

[0466] The server uses natural language conversion technology to formalize the generated suggestions and presents them to the user in easily understandable language. The output suggestions are meaningful to the user and include explanations of specific nutritional supplements and the reasons behind their recommendations.

[0467] Step 6:

[0468] Users review the proposed content and provide feedback. This feedback includes the usefulness of the proposal and the results of its implementation. User feedback is sent to the server via the device.

[0469] Step 7:

[0470] The server uses the received feedback to improve its machine learning model. Based on the feedback, it adjusts the model's parameters to improve the accuracy of future suggestions. As a result, an improved AI model is generated.

[0471] (Application Example 1)

[0472] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0473] In modern society, there is a growing demand for easily accessible meals optimized for the individual health conditions and lifestyles of each user. However, there is a lack of efficient and reliable means to recommend and quickly deliver nutritious meals tailored to each user. In particular, providing personalized meals based on health information requires a system that analyzes user data and appropriately incorporates feedback.

[0474] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0475] In this invention, the server includes means for receiving user attribute information and health information, means for performing analysis using a machine learning algorithm based on the received information to generate suggestions for highly nutritious meals, and means for processing requests for meals suggested by affiliated restaurants. This makes it possible to quickly suggest and provide personalized, highly nutritious meals to users.

[0476] "User attribute information" refers to individual information about each user, such as age, gender, occupation, and living environment.

[0477] "Health information" refers to information that indicates the user's health status, and includes biological test results, medical history, and current dietary and exercise habits.

[0478] A "machine learning algorithm" is a computational method for analyzing data and discovering patterns, and is used to solve complex problems.

[0479] "Suggesting nutritious meals" involves selecting and recommending meal menus with the optimal nutritional balance based on the user's health information and attribute information.

[0480] A "food and beverage establishment" refers to a partner that provides meals to users, and includes places such as restaurants and food service establishments.

[0481] "Request processing" refers to the procedure for receiving orders from customers and preparing and serving meals accordingly.

[0482] This document describes a mode for carrying out the invention. To realize the system of this invention, a network-connected client terminal, a server system, and an order processing system for affiliated restaurants are required. Users input their attribute information and health information into a specific application using a client terminal (e.g., a smartphone or computer). This application is implemented using a cross-platform development tool such as React Native and securely transmits user data to the server.

[0483] The server uses the Python programming language and machine learning algorithms based on the Scikit-Learn library to analyze the received data. This analysis generates a highly nutritious meal plan optimized for the user. The generated suggestions are presented to the user using natural language processing libraries such as spaCy or NLTK, and explained in an easy-to-understand language.

[0484] When a user accepts a meal suggestion, a request is sent to the order processing system of a partner restaurant. The partner restaurant prepares the meal based on the request and delivers it to the specified time and location. After delivery is complete, user feedback is collected and the information is updated on the server. This allows the machine learning algorithm to continuously improve.

[0485] As a concrete example, a health-conscious user in their 30s inputs information about their exercise habits and preferred foods into the application. Based on this information, the server suggests a meal plan that is high in protein and low in carbohydrates. An example of a prompt text for the generating AI model is, "Suggest a high-protein, low-carbohydrate meal menu." Based on this suggestion, the user can order the menu from a nearby restaurant and have it delivered to their home.

[0486] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0487] Step 1:

[0488] Users open the application using a client device and enter attribute and health information. This data includes age, gender, exercise habits, health status, and dietary preferences. This information is temporarily stored in a database within the application.

[0489] Step 2:

[0490] The terminal converts the input information into JSON format and sends it to the server via a secure protocol (e.g., HTTPS). The server converts the received data into a parseable format and stores it in a database.

[0491] Step 3:

[0492] The server executes machine learning algorithms using Python and Scikit-Learn to analyze the received user data. In this step, it calculates optimal meal suggestions for each individual user based on a dataset of nutritional and health information. The output is a recommended meal menu.

[0493] Step 4:

[0494] The server translates the generated meal suggestions into understandable language using natural language processing techniques. To achieve this, it uses libraries such as spaCy and NLTK to create prompts for the generative AI model. The output is the suggestion details in natural language format.

[0495] Step 5:

[0496] The server sends the proposal details to the client terminal and displays them in the user application. The user can then accept the proposal or specify what needs to be corrected.

[0497] Step 6:

[0498] If the proposal is accepted, the terminal sends an order request to the partner restaurant. The order includes user information and accepted menu details. The accepted order information is then passed to the restaurant's system.

[0499] Step 7:

[0500] After delivery is complete, the user provides feedback through the application. The device sends the feedback to a server where it is recorded. The server uses this feedback to update the machine learning algorithm model and improve accuracy for the next time.

[0501] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0502] This invention is a system that proposes nutritional supplements further optimized for individual users by combining an emotion engine. Users input their personal and health data via a terminal. This includes age, gender, lifestyle, and recent biopsy results. The input data is formatted by the terminal and sent to the server.

[0503] This system uses a machine learning model to analyze data received from terminals, and also incorporates an emotion engine. The emotion engine analyzes the user's emotional state and incorporates it as a factor influencing the data analysis results. This allows for suggestions that take the user's psychological aspects into account.

[0504] The server generates nutritional supplement recommendations based on the analysis. These recommendations are presented to the user via the terminal, written in a user-friendly format using natural language processing technology. The recommendations also reflect the user's emotional state and are adjusted to be emotionally relatable.

[0505] Users can review the presented suggestions and select appropriate nutritional supplements based on that information. Furthermore, they can provide emotional feedback on the suggestions via the emotion engine. This feedback is sent to the server and used to improve the machine learning model and the emotion engine.

[0506] As a concrete example, suppose a salaried worker with a high stress level uses this system. The system analyzes his health data and emotional state and suggests supplements containing nutrients to cope with stress, such as B vitamins. The suggestions include an explanation of why the supplements are effective, and are presented in positive language that takes his emotional state into account.

[0507] Thus, embodiments of the present invention provide a method that comprehensively considers the user's health and emotional aspects and assists in selecting more effective nutritional supplements.

[0508] The following describes the processing flow.

[0509] Step 1:

[0510] Users input their personal and health data via the device. This includes biometric test results and health information from their daily lives. The device also accepts input of the user's emotional state.

[0511] Step 2:

[0512] The terminal converts the input data into a standard format, verifies the data's integrity and validity, and then sends it to the server.

[0513] Step 3:

[0514] The server receives the incoming data and first checks if any data is missing. If there are inconsistencies, it returns an error message to the terminal.

[0515] Step 4:

[0516] The server passes the data to a machine learning model, which then begins an analysis based on the user's health status. This model references a wide range of medical data to suggest appropriate nutritional supplements.

[0517] Step 5:

[0518] The server uses an emotion engine to analyze the user's emotional state. The analysis results are then used to adjust the nutritional supplement recommendations to reflect emotional factors.

[0519] Step 6:

[0520] The server integrates the results of machine learning models and an emotion engine to generate nutritional supplement recommendations. These recommendations are then explained in a user-friendly format using natural language processing techniques.

[0521] Step 7:

[0522] Once the proposal is complete, the server sends the information to the terminal and presents it to the user.

[0523] Step 8:

[0524] Users can view suggestions on their devices to help them with meal choices and health management. The suggestions are personalized based on the user's emotional status.

[0525] Step 9:

[0526] Users input emotional feedback on a proposal into their device and send it to the server. This feedback is used to improve future proposals.

[0527] Step 10:

[0528] The server analyzes user feedback and uses it to improve the emotion engine and machine learning models, thereby increasing the accuracy of future suggestions.

[0529] (Example 2)

[0530] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0531] In modern society, there is a demand for nutritional supplements optimized for each individual user. However, conventional systems have a problem in that they do not take into account the user's emotions or psychological state, resulting in limited acceptance and effectiveness of the recommendations. Therefore, there is a need for individualized recommendations that reflect not only the user's health condition but also their emotional state.

[0532] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0533] This invention includes a server that receives the user's personal data and health data, formats the data, and transmits it to an information processing device; a server that analyzes the received data using a machine learning model and an emotion analysis device to generate personalized nutritional supplement recommendations; and a server that presents the generated recommendations in a language format that takes into account the user's psychological state using natural language processing technology. This makes it possible to propose nutritional supplements that take into account the user's unique emotions and psychological state, thereby increasing the effectiveness and acceptability of the recommendations.

[0534] "User's personal data" refers to attribute information related to an individual, including information such as age, gender, and lifestyle.

[0535] "Health data" refers to information about a user's health status, including biological test results and medical diagnostic data.

[0536] An "information processing device" is a device used for data analysis and computation, and refers to servers and computer systems.

[0537] A "machine learning model" is an algorithmic model that learns specific patterns based on large amounts of data and uses them to perform new data analysis.

[0538] An "emotion analysis device" is a device that analyzes the emotional state of a user and reflects the results in data processing.

[0539] "Natural language processing technology" is a technology that enables computers to understand and generate human language, and to convey information in an easy-to-understand manner.

[0540] "Personalized nutritional supplements" are nutritional supplements that are suggested to users based on their specific health and emotional conditions.

[0541] "Emotional feedback" refers to the emotional reactions and evaluations that users give to suggestions, and is information used to improve the system.

[0542] Users input personal and health data via a terminal. This data includes age, gender, lifestyle, and biometric test results. The terminal formats the input data, encrypts it, and prepares it for transmission to the server. The server receives this data, stores it in a database, and then performs data analysis using machine learning models and sentiment analyzers. The machine learning models analyze the user's health status and identify necessary nutrients. The sentiment analyzer analyzes the user's emotional state and incorporates it as a factor influencing nutritional supplement recommendations. Based on these analysis results, the server uses natural language processing technology to generate nutritional supplement recommendations in a format that takes the user's psychological state into account. The recommendations are presented to the user via the terminal, allowing the user to select appropriate nutritional supplements. Furthermore, users provide emotional feedback on the recommendations via the terminal and send it to the server. This feedback is used to improve the machine learning models and sentiment analyzers.

[0543] Specifically, the server side incorporates AI libraries and natural language processing technologies for data analysis and sentiment analysis. On the terminal side, an application runs to build the user interface, managing data input and the display of suggested content.

[0544] As a concrete example, consider a user who is a salaried worker with a high stress level. This user uses their device to input their fatigue level and stress level from a recent health checkup. Based on this information, the system suggests supplements containing B vitamins. These suggestions are presented in positive language, along with explanations that they help with fatigue recovery and stress reduction. An example of a prompt message sent to the server would be: "A 30-year-old male who works a desk job five days a week and has recently reported feeling stressed. Recent test results show a slight deficiency in B vitamins. Use the emotion engine to generate suggestions for nutritional supplements that can help reduce stress."

[0545] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0546] Step 1:

[0547] The terminal accepts personal and health data input from the user. The data entered by the user includes age, gender, lifestyle, and recent biopsy results. The terminal formats this data and converts it into a format easily analyzable by the information processing device. The output of this step is data in a consistent format.

[0548] Step 2:

[0549] The terminal encrypts the formatted data and sends it to the server. Data transmission is performed using a secure communication protocol, ensuring data confidentiality. The server verifies the data received from the terminal and stores it in a database. The input to this step is the formatted data, and the output is the stored data.

[0550] Step 3:

[0551] The server initiates analysis using a machine learning model based on the stored data. The machine learning model analyzes the user's health status and identifies necessary nutrients, drawing on past data. Furthermore, the server uses an emotion analyzer to detect the user's emotional state. This emotional state is incorporated as a factor influencing the recommendation of nutritional supplements. The output of this step is the analysis results, including necessary nutrients and emotional state.

[0552] Step 4:

[0553] The server combines the results of machine learning models and sentiment analysis, and uses natural language processing techniques to create personalized nutritional supplement recommendations for the user. The generated recommendations are clearly explained, and the content is emotionally resonant, using positive language. The output of this step is a well-customized recommendation.

[0554] Step 5:

[0555] The terminal receives suggestions sent from the server and displays them to the user. The user can review the suggestions and select the corresponding nutritional supplements. Furthermore, the user can send feedback on the suggestions through the terminal. In this step, the input is the suggestions from the server, and the output is the user's feedback.

[0556] Step 6:

[0557] The server receives user feedback and records it in a database. The received feedback is used for the continuous improvement of the machine learning model and sentiment analysis device. The input to this step is user feedback, and the output is information on improvements to the model and device.

[0558] (Application Example 2)

[0559] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0560] Modern consumers seek personalized nutritional supplement recommendations based on their individual health and emotional states, but traditional systems struggle to consider emotional states and therefore cannot provide optimal recommendations. Furthermore, presenting recommendations in a user-friendly format is difficult, hindering the improvement of the purchasing experience.

[0561] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0562] In this invention, the server includes means for acquiring and receiving the user's biometric data and emotional state, means for performing analysis based on the received data using a machine learning model and an emotion analysis engine to generate suggestions for nutritional supplements, and means for presenting the generated suggestions to a portable terminal using natural language processing technology. This makes it possible to generate personalized suggestions that take into account the user's emotional state and present them in a way that is easy for the user to understand.

[0563] A "user" refers to an individual who uses the system to input health data and emotional status and receives personalized suggestions.

[0564] "Biometric data" refers to information that indicates the user's health status, such as age, gender, lifestyle, and recent biopsy results.

[0565] "Emotional state" refers to information that indicates the user's current emotions and psychological state, and is the data analyzed for personalizing suggestions.

[0566] A "machine learning model" refers to a technology that learns from large amounts of data and uses that data to make predictions and perform analyses on new inputs.

[0567] A "sentiment analysis engine" refers to a software configuration that analyzes the user's emotional state and adjusts the suggested content based on that analysis.

[0568] "Natural language processing technology" refers to methodologies and techniques that enable computers to understand and generate human language.

[0569] A "portable device" refers to a device, such as a smartphone or tablet, that is portable and allows the user to directly operate it to receive information.

[0570] "Feedback" refers to the responses and opinions that users give in response to suggestions, and the information used to improve the system.

[0571] This invention is a system that provides recommendations for nutritional supplements optimized based on the health data and emotional state of individual users. This system mainly consists of three elements: a server, a portable terminal, and the user.

[0572] Portable devices, such as smartphones and tablets, allow users to input their health data and emotional state. This includes age, gender, lifestyle, and recent biopsy results. The data entered on the user's portable device is transmitted to a server via the internet.

[0573] The server plays a central role in analyzing the received data. Specifically, it uses machine learning models and sentiment analysis engines based on Python. By utilizing libraries such as Scikit-learn and TensorFlow, it efficiently processes large amounts of data and generates recommendations for nutritional supplements best suited to each individual user. Furthermore, it leverages natural language processing technologies, including Natural Language Toolkit (NLTK) and GPT-3, to generate recommendations in a language format that is easy for users to understand. The recommendations are adjusted according to the user's emotional state, making them more readily accepted.

[0574] Users can receive and review suggestions via devices such as smartphones. They can also provide feedback on the suggestions, which is then sent back to the server to help improve the machine learning models and sentiment analysis engine.

[0575] As a concrete example, if a user inputs the emotion "I'm stressed" on their smartphone, the server will suggest a nutritional supplement containing B vitamins. In this case, the suggestion would include positive words such as, "Please try this supplement, which is expected to have a relaxing effect!" An example of input to the generating AI model would be a prompt message like, "User's emotional state is stressed, high. Age: 35, Gender: Male. Generate suggestion for a B vitamin supplement:"

[0576] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0577] Step 1:

[0578] The device receives health data and emotional state from the user as input. Through a smartphone application, the user can input their age, gender, lifestyle, biometric test results, and select their emotional state. This input data is transmitted in a formatted form via the internet and reaches the server.

[0579] Step 2:

[0580] The server receives data from the terminal as input and performs data preprocessing. This involves formatting the received data, imputing missing values, and converting it into a format usable by machine learning models. Python libraries such as Pandas and NumPy are used for this process.

[0581] Step 3:

[0582] The server takes formatted data as input and performs analysis using a machine learning model. Algorithms using Scikit-learn and TensorFlow analyze the user's health status and suggest suitable nutritional supplements. The output is a list of specific nutritional supplements suggested.

[0583] Step 4:

[0584] The server adjusts the output using an emotion analysis engine based on the analysis results. A generative AI model is used to make linguistic adjustments to ensure the suggestions match the user's emotional state. OpenAI's GPT-3 is used for this, and an example of a generated prompt is: "User's emotional state is stressed, high. Age: 35, Gender: Male. Generate suggestion for vitamin B complex supplements:"

[0585] Step 5:

[0586] The server outputs suggestions, refined using natural language processing technology, to a portable device. Using tools such as the Natural Language Toolkit (NLTK), the suggestions are sent to the device in a user-friendly format. Users can then review these suggestions on their own devices.

[0587] Step 6:

[0588] Users enter feedback on the suggestion via their device. This feedback includes whether they accepted the suggested nutritional supplement and their emotional reaction to the suggestion. This feedback is then formatted again and sent to the server.

[0589] Step 7:

[0590] The server receives user feedback as input and uses it to improve the machine learning model and sentiment analysis engine. The feedback data is incorporated into the model's learning process and becomes data to improve the accuracy of future suggestions.

[0591] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0592] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0593] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0594] [Fourth Embodiment]

[0595] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0596] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0597] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0598] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0599] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0600] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0601] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0602] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0603] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0604] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0605] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0606] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0607] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0608] This invention is based on a system that combines various technologies to offer nutritional supplements optimized for individual users. First, the user enters their personal and health data via a dedicated application or web interface. This includes information on age, gender, lifestyle, and, if necessary, recent biopsy results.

[0609] When a user enters data, the terminal converts this information into the appropriate format and sends it to the server. The server verifies the data received from the user to ensure its quality and integrity. Next, the server analyzes the data using a machine learning model. This model utilizes an extensive medical research database and is trained to identify the most suitable nutritional supplements for each user.

[0610] The suggestions derived from the analysis are provided to the user in the form of explanations in natural language. This makes it easier for the user to understand the background and reasons for the selection of the suggestions. In addition, the server receives feedback from the user and uses this information to continuously improve the machine learning model. As a result, the system can make more accurate suggestions that reflect user feedback.

[0611] As a concrete example, suppose a 35-year-old female user who wishes to become pregnant uses this system and inputs her blood test results and current lifestyle habits. Based on this information, the server suggests combinations of nutrients, including folic acid, and explains the reasons in detail. Furthermore, if the user provides feedback on the suggestions or points of concern, the server collects this information and uses it to improve the model.

[0612] The following describes the processing flow.

[0613] Step 1:

[0614] Users use their devices to launch a dedicated application or web interface and enter their personal and health data. This data includes age, gender, lifestyle, and recent test results.

[0615] Step 2:

[0616] The terminal receives data entered by the user, formats it into a standard format, and then verifies the data's integrity and the presence of missing values. After verification, it sends the data to the server.

[0617] Step 3:

[0618] The server receives data sent from the terminal and performs data quality verification. If inconsistencies or defects are found, it generates an error message and sends it back to the terminal.

[0619] Step 4:

[0620] The server inputs data into a machine learning model and performs analysis based on the user's health status. During this process, it refers to accumulated medical research data to identify the most suitable nutritional supplements.

[0621] Step 5:

[0622] The server describes the suggested nutritional supplements generated based on the analysis results in natural language and creates explanatory text in an easy-to-understand format.

[0623] Step 6:

[0624] The server sends the generated proposal to the terminal and presents it to the user.

[0625] Step 7:

[0626] Users can view proposals on their devices and provide feedback on them. This feedback includes opinions and additional requests regarding the proposals.

[0627] Step 8:

[0628] The device receives feedback from the user and sends it to the server.

[0629] Step 9:

[0630] The server analyzes the feedback and uses it as training data for machine learning models to improve the accuracy of future suggestions.

[0631] (Example 1)

[0632] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0633] When providing personalized nutritional recommendations, it is essential to appropriately utilize diverse biometric and lifestyle information of users to deliver highly accurate recommendations. Furthermore, a system is needed to facilitate understanding of the recommendations, effectively incorporate user feedback, and improve the accuracy of the recommendations.

[0634] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0635] In this invention, the server includes means for receiving the user's biometric and lifestyle information, means for analyzing the received information using a machine learning processing device to generate nutritional suggestions, and means for presenting the generated suggestions to the user using natural language conversion technology. This makes it possible to provide personalized suggestions to the user while improving the accuracy of the suggestions by utilizing feedback.

[0636] "User biometric information" refers to individual-specific physical data, including bioanalysis results and information related to health maintenance.

[0637] "Lifestyle information" refers to data related to the user's daily life, including information on lifestyle habits, diet, exercise, etc.

[0638] A "machine learning processing device" is a computing device that learns from input data, analyzes patterns, and makes suggestions aligned with a specific purpose.

[0639] "Nutrient recommendations" are guidelines that show the combination of necessary nutrients based on the user's information.

[0640] "Natural language conversion technology" is a technology that converts machine-generated information into a natural language format that is easy for humans to understand.

[0641] "Feedback" refers to the evaluations and opinions that users provide regarding suggestions, and contributes to the improvement of the system.

[0642] In this invention, users can input their biometric and lifestyle information using a dedicated application or web interface. This information includes age, gender, weight, activity level, diet, and even the results of biological analyses such as blood tests. The terminal is responsible for formatting the input information into an appropriate format and transmitting it to the server.

[0643] The server uses a highly trained generative AI model to analyze the received data. This AI model has been trained on a large amount of medical research data and can suggest optimal nutrients to the user. The server then uses natural language conversion technology to express the suggested content in a human-readable format and present it to the user.

[0644] As a concrete example, a 35-year-old female user planning a pregnancy can use this system to input her blood test data and current eating habits. The server analyzes this information and suggests that folic acid is particularly important, recommending its intake.

[0645] Furthermore, when users provide feedback on the effectiveness and impression of the suggestions, the server can collect this feedback and use it to improve the machine learning model. This allows the system to continuously improve its accuracy.

[0646] An example of a prompt message given to the generative AI model is, "A 35-year-old woman wants to become pregnant. Please suggest nutrients including folic acid and explain the reasons why."

[0647] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0648] Step 1:

[0649] Users input biometric and lifestyle information via a dedicated application or web interface. This input includes age, gender, weight, activity level, dietary habits, and blood test results. The entered data is stored as digital data in text and numerical formats.

[0650] Step 2:

[0651] The terminal receives information entered by the user and formats the data. This formatting process converts the input data into a standardized format and checks for missing or outlier values. The output of this process is user data converted into an analyzable format.

[0652] Step 3:

[0653] The terminal transfers the formatted data to the server. This data transfer uses a dedicated protocol to ensure secure communication. The server receives this data and prepares it for the next analysis step.

[0654] Step 4:

[0655] The server analyzes the received data using a generating AI model. During this analysis phase, the AI ​​model uses the user's biometric and lifestyle information as input to perform calculations to identify the optimal combination of nutrients. As a result of the analysis, a list of suggested nutrients optimized for the user is output. The AI ​​model is given a prompt such as, "Please suggest the most effective nutrients based on the following user data."

[0656] Step 5:

[0657] The server uses natural language conversion technology to formalize the generated suggestions and presents them to the user in easily understandable language. The output suggestions are meaningful to the user and include explanations of specific nutritional supplements and the reasons behind their recommendations.

[0658] Step 6:

[0659] Users review the proposed content and provide feedback. This feedback includes the usefulness of the proposal and the results of its implementation. User feedback is sent to the server via the device.

[0660] Step 7:

[0661] The server uses the received feedback to improve its machine learning model. Based on the feedback, it adjusts the model's parameters to improve the accuracy of future suggestions. As a result, an improved AI model is generated.

[0662] (Application Example 1)

[0663] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0664] In modern society, there is a growing demand for easily accessible meals optimized for the individual health conditions and lifestyles of each user. However, there is a lack of efficient and reliable means to recommend and quickly deliver nutritious meals tailored to each user. In particular, providing personalized meals based on health information requires a system that analyzes user data and appropriately incorporates feedback.

[0665] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0666] In this invention, the server includes means for receiving user attribute information and health information, means for performing analysis using a machine learning algorithm based on the received information to generate suggestions for highly nutritious meals, and means for processing requests for meals suggested by affiliated restaurants. This makes it possible to quickly suggest and provide personalized, highly nutritious meals to users.

[0667] "User attribute information" refers to individual information about each user, such as age, gender, occupation, and living environment.

[0668] "Health information" refers to information that indicates the user's health status, and includes biological test results, medical history, and current dietary and exercise habits.

[0669] A "machine learning algorithm" is a computational method for analyzing data and discovering patterns, and is used to solve complex problems.

[0670] "Suggesting nutritious meals" involves selecting and recommending meal menus with the optimal nutritional balance based on the user's health information and attribute information.

[0671] A "food and beverage establishment" refers to a partner that provides meals to users, and includes places such as restaurants and food service establishments.

[0672] "Request processing" refers to the procedure for receiving orders from customers and preparing and serving meals accordingly.

[0673] This document describes a mode for carrying out the invention. To realize the system of this invention, a network-connected client terminal, a server system, and an order processing system for affiliated restaurants are required. Users input their attribute information and health information into a specific application using a client terminal (e.g., a smartphone or computer). This application is implemented using a cross-platform development tool such as React Native and securely transmits user data to the server.

[0674] The server uses the Python programming language and machine learning algorithms based on the Scikit-Learn library to analyze the received data. This analysis generates a highly nutritious meal plan optimized for the user. The generated suggestions are presented to the user using natural language processing libraries such as spaCy or NLTK, and explained in an easy-to-understand language.

[0675] When a user accepts a meal suggestion, a request is sent to the order processing system of a partner restaurant. The partner restaurant prepares the meal based on the request and delivers it to the specified time and location. After delivery is complete, user feedback is collected and the information is updated on the server. This allows the machine learning algorithm to continuously improve.

[0676] As a concrete example, a health-conscious user in their 30s inputs information about their exercise habits and preferred foods into the application. Based on this information, the server suggests a meal plan that is high in protein and low in carbohydrates. An example of a prompt text for the generating AI model is, "Suggest a high-protein, low-carbohydrate meal menu." Based on this suggestion, the user can order the menu from a nearby restaurant and have it delivered to their home.

[0677] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0678] Step 1:

[0679] Users open the application using a client device and enter attribute and health information. This data includes age, gender, exercise habits, health status, and dietary preferences. This information is temporarily stored in a database within the application.

[0680] Step 2:

[0681] The terminal converts the input information into JSON format and sends it to the server via a secure protocol (e.g., HTTPS). The server converts the received data into a parseable format and stores it in a database.

[0682] Step 3:

[0683] The server executes machine learning algorithms using Python and Scikit-Learn to analyze the received user data. In this step, it calculates optimal meal suggestions for each individual user based on a dataset of nutritional and health information. The output is a recommended meal menu.

[0684] Step 4:

[0685] The server translates the generated meal suggestions into understandable language using natural language processing techniques. To achieve this, it uses libraries such as spaCy and NLTK to create prompts for the generative AI model. The output is the suggestion details in natural language format.

[0686] Step 5:

[0687] The server sends the proposal details to the client terminal and displays them in the user application. The user can then accept the proposal or specify what needs to be corrected.

[0688] Step 6:

[0689] If the proposal is accepted, the terminal sends an order request to the partner restaurant. The order includes user information and accepted menu details. The accepted order information is then passed to the restaurant's system.

[0690] Step 7:

[0691] After delivery is complete, the user provides feedback through the application. The device sends the feedback to a server where it is recorded. The server uses this feedback to update the machine learning algorithm model and improve accuracy for the next time.

[0692] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0693] This invention is a system that proposes nutritional supplements further optimized for individual users by combining an emotion engine. Users input their personal and health data via a terminal. This includes age, gender, lifestyle, and recent biopsy results. The input data is formatted by the terminal and sent to the server.

[0694] This system uses a machine learning model to analyze data received from terminals, and also incorporates an emotion engine. The emotion engine analyzes the user's emotional state and incorporates it as a factor influencing the data analysis results. This allows for suggestions that take the user's psychological aspects into account.

[0695] The server generates nutritional supplement recommendations based on the analysis. These recommendations are presented to the user via the terminal, written in a user-friendly format using natural language processing technology. The recommendations also reflect the user's emotional state and are adjusted to be emotionally relatable.

[0696] Users can review the presented suggestions and select appropriate nutritional supplements based on that information. Furthermore, they can provide emotional feedback on the suggestions via the emotion engine. This feedback is sent to the server and used to improve the machine learning model and the emotion engine.

[0697] As a concrete example, suppose a salaried worker with a high stress level uses this system. The system analyzes his health data and emotional state and suggests supplements containing nutrients to cope with stress, such as B vitamins. The suggestions include an explanation of why the supplements are effective, and are presented in positive language that takes his emotional state into account.

[0698] Thus, embodiments of the present invention provide a method that comprehensively considers the user's health and emotional aspects and assists in selecting more effective nutritional supplements.

[0699] The following describes the processing flow.

[0700] Step 1:

[0701] Users input their personal and health data via the device. This includes biometric test results and health information from their daily lives. The device also accepts input of the user's emotional state.

[0702] Step 2:

[0703] The terminal converts the input data into a standard format, verifies the data's integrity and validity, and then sends it to the server.

[0704] Step 3:

[0705] The server receives the incoming data and first checks if any data is missing. If there are inconsistencies, it returns an error message to the terminal.

[0706] Step 4:

[0707] The server passes the data to a machine learning model, which then begins an analysis based on the user's health status. This model references a wide range of medical data to suggest appropriate nutritional supplements.

[0708] Step 5:

[0709] The server uses an emotion engine to analyze the user's emotional state. The analysis results are then used to adjust the nutritional supplement recommendations to reflect emotional factors.

[0710] Step 6:

[0711] The server integrates the results of machine learning models and an emotion engine to generate nutritional supplement recommendations. These recommendations are then explained in a user-friendly format using natural language processing techniques.

[0712] Step 7:

[0713] Once the proposal is complete, the server sends the information to the terminal and presents it to the user.

[0714] Step 8:

[0715] Users can view suggestions on their devices to help them with meal choices and health management. The suggestions are personalized based on the user's emotional status.

[0716] Step 9:

[0717] Users input emotional feedback on a proposal into their device and send it to the server. This feedback is used to improve future proposals.

[0718] Step 10:

[0719] The server analyzes user feedback and uses it to improve the emotion engine and machine learning models, thereby increasing the accuracy of future suggestions.

[0720] (Example 2)

[0721] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0722] In modern society, there is a demand for nutritional supplements optimized for each individual user. However, conventional systems have a problem in that they do not take into account the user's emotions or psychological state, resulting in limited acceptance and effectiveness of the recommendations. Therefore, there is a need for individualized recommendations that reflect not only the user's health condition but also their emotional state.

[0723] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0724] This invention includes a server that receives the user's personal data and health data, formats the data, and transmits it to an information processing device; a server that analyzes the received data using a machine learning model and an emotion analysis device to generate personalized nutritional supplement recommendations; and a server that presents the generated recommendations in a language format that takes into account the user's psychological state using natural language processing technology. This makes it possible to propose nutritional supplements that take into account the user's unique emotions and psychological state, thereby increasing the effectiveness and acceptability of the recommendations.

[0725] "User's personal data" refers to attribute information related to an individual, including information such as age, gender, and lifestyle.

[0726] "Health data" refers to information about a user's health status, including biological test results and medical diagnostic data.

[0727] An "information processing device" is a device used for data analysis and computation, and refers to servers and computer systems.

[0728] A "machine learning model" is an algorithmic model that learns specific patterns based on large amounts of data and uses them to perform new data analysis.

[0729] An "emotion analysis device" is a device that analyzes the emotional state of a user and reflects the results in data processing.

[0730] "Natural language processing technology" is a technology that enables computers to understand and generate human language, and to convey information in an easy-to-understand manner.

[0731] "Personalized nutritional supplements" are nutritional supplements that are suggested to users based on their specific health and emotional conditions.

[0732] "Emotional feedback" refers to the emotional reactions and evaluations that users give to suggestions, and is information used to improve the system.

[0733] Users input personal and health data via a terminal. This data includes age, gender, lifestyle, and biometric test results. The terminal formats the input data, encrypts it, and prepares it for transmission to the server. The server receives this data, stores it in a database, and then performs data analysis using machine learning models and sentiment analyzers. The machine learning models analyze the user's health status and identify necessary nutrients. The sentiment analyzer analyzes the user's emotional state and incorporates it as a factor influencing nutritional supplement recommendations. Based on these analysis results, the server uses natural language processing technology to generate nutritional supplement recommendations in a format that takes the user's psychological state into account. The recommendations are presented to the user via the terminal, allowing the user to select appropriate nutritional supplements. Furthermore, users provide emotional feedback on the recommendations via the terminal and send it to the server. This feedback is used to improve the machine learning models and sentiment analyzers.

[0734] Specifically, the server side incorporates AI libraries and natural language processing technologies for data analysis and sentiment analysis. On the terminal side, an application runs to build the user interface, managing data input and the display of suggested content.

[0735] As a concrete example, consider a user who is a salaried worker with a high stress level. This user uses their device to input their fatigue level and stress level from a recent health checkup. Based on this information, the system suggests supplements containing B vitamins. These suggestions are presented in positive language, along with explanations that they help with fatigue recovery and stress reduction. An example of a prompt message sent to the server would be: "A 30-year-old male who works a desk job five days a week and has recently reported feeling stressed. Recent test results show a slight deficiency in B vitamins. Use the emotion engine to generate suggestions for nutritional supplements that can help reduce stress."

[0736] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0737] Step 1:

[0738] The terminal accepts personal and health data input from the user. The data entered by the user includes age, gender, lifestyle, and recent biopsy results. The terminal formats this data and converts it into a format easily analyzable by the information processing device. The output of this step is data in a consistent format.

[0739] Step 2:

[0740] The terminal encrypts the formatted data and sends it to the server. Data transmission is performed using a secure communication protocol, ensuring data confidentiality. The server verifies the data received from the terminal and stores it in a database. The input to this step is the formatted data, and the output is the stored data.

[0741] Step 3:

[0742] The server initiates analysis using a machine learning model based on the stored data. The machine learning model analyzes the user's health status and identifies necessary nutrients, drawing on past data. Furthermore, the server uses an emotion analyzer to detect the user's emotional state. This emotional state is incorporated as a factor influencing the recommendation of nutritional supplements. The output of this step is the analysis results, including necessary nutrients and emotional state.

[0743] Step 4:

[0744] The server combines the results of machine learning models and sentiment analysis, and uses natural language processing techniques to create personalized nutritional supplement recommendations for the user. The generated recommendations are clearly explained, and the content is emotionally resonant, using positive language. The output of this step is a well-customized recommendation.

[0745] Step 5:

[0746] The terminal receives suggestions sent from the server and displays them to the user. The user can review the suggestions and select the corresponding nutritional supplements. Furthermore, the user can send feedback on the suggestions through the terminal. In this step, the input is the suggestions from the server, and the output is the user's feedback.

[0747] Step 6:

[0748] The server receives user feedback and records it in a database. The received feedback is used for the continuous improvement of the machine learning model and sentiment analysis device. The input to this step is user feedback, and the output is information on improvements to the model and device.

[0749] (Application Example 2)

[0750] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0751] Modern consumers seek personalized nutritional supplement recommendations based on their individual health and emotional states, but traditional systems struggle to consider emotional states and therefore cannot provide optimal recommendations. Furthermore, presenting recommendations in a user-friendly format is difficult, hindering the improvement of the purchasing experience.

[0752] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0753] In this invention, the server includes means for acquiring and receiving the user's biometric data and emotional state, means for performing analysis based on the received data using a machine learning model and an emotion analysis engine to generate suggestions for nutritional supplements, and means for presenting the generated suggestions to a portable terminal using natural language processing technology. This makes it possible to generate personalized suggestions that take into account the user's emotional state and present them in a way that is easy for the user to understand.

[0754] A "user" refers to an individual who uses the system to input health data and emotional status and receives personalized suggestions.

[0755] "Biometric data" refers to information that indicates the user's health status, such as age, gender, lifestyle, and recent biopsy results.

[0756] "Emotional state" refers to information that indicates the user's current emotions and psychological state, and is the data analyzed for personalizing suggestions.

[0757] A "machine learning model" refers to a technology that learns from large amounts of data and uses that data to make predictions and perform analyses on new inputs.

[0758] A "sentiment analysis engine" refers to a software configuration that analyzes the user's emotional state and adjusts the suggested content based on that analysis.

[0759] "Natural language processing technology" refers to methodologies and techniques that enable computers to understand and generate human language.

[0760] A "portable device" refers to a device, such as a smartphone or tablet, that is portable and allows the user to directly operate it to receive information.

[0761] "Feedback" refers to the responses and opinions that users give in response to suggestions, and the information used to improve the system.

[0762] This invention is a system that provides recommendations for nutritional supplements optimized based on the health data and emotional state of individual users. This system mainly consists of three elements: a server, a portable terminal, and the user.

[0763] Portable devices, such as smartphones and tablets, allow users to input their health data and emotional state. This includes age, gender, lifestyle, and recent biopsy results. The data entered on the user's portable device is transmitted to a server via the internet.

[0764] The server plays a central role in analyzing the received data. Specifically, it uses machine learning models and sentiment analysis engines based on Python. By utilizing libraries such as Scikit-learn and TensorFlow, it efficiently processes large amounts of data and generates recommendations for nutritional supplements best suited to each individual user. Furthermore, it leverages natural language processing technologies, including Natural Language Toolkit (NLTK) and GPT-3, to generate recommendations in a language format that is easy for users to understand. The recommendations are adjusted according to the user's emotional state, making them more readily accepted.

[0765] Users can receive and review suggestions via devices such as smartphones. They can also provide feedback on the suggestions, which is then sent back to the server to help improve the machine learning models and sentiment analysis engine.

[0766] As a concrete example, if a user inputs the emotion "I'm stressed" on their smartphone, the server will suggest a nutritional supplement containing B vitamins. In this case, the suggestion would include positive words such as, "Please try this supplement, which is expected to have a relaxing effect!" An example of input to the generating AI model would be a prompt message like, "User's emotional state is stressed, high. Age: 35, Gender: Male. Generate suggestion for a B vitamin supplement:"

[0767] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0768] Step 1:

[0769] The device receives health data and emotional state from the user as input. Through a smartphone application, the user can input their age, gender, lifestyle, biometric test results, and select their emotional state. This input data is transmitted in a formatted form via the internet and reaches the server.

[0770] Step 2:

[0771] The server receives data from the terminal as input and performs data preprocessing. This involves formatting the received data, imputing missing values, and converting it into a format usable by machine learning models. Python libraries such as Pandas and NumPy are used for this process.

[0772] Step 3:

[0773] The server takes formatted data as input and performs analysis using a machine learning model. Algorithms using Scikit-learn and TensorFlow analyze the user's health status and suggest suitable nutritional supplements. The output is a list of specific nutritional supplements suggested.

[0774] Step 4:

[0775] The server adjusts the output using an emotion analysis engine based on the analysis results. A generative AI model is used to make linguistic adjustments to ensure the suggestions match the user's emotional state. OpenAI's GPT-3 is used for this, and an example of a generated prompt is: "User's emotional state is stressed, high. Age: 35, Gender: Male. Generate suggestion for vitamin B complex supplements:"

[0776] Step 5:

[0777] The server outputs suggestions, refined using natural language processing technology, to a portable device. Using tools such as the Natural Language Toolkit (NLTK), the suggestions are sent to the device in a user-friendly format. Users can then review these suggestions on their own devices.

[0778] Step 6:

[0779] Users enter feedback on the suggestion via their device. This feedback includes whether they accepted the suggested nutritional supplement and their emotional reaction to the suggestion. This feedback is then formatted again and sent to the server.

[0780] Step 7:

[0781] The server receives user feedback as input and uses it to improve the machine learning model and sentiment analysis engine. The feedback data is incorporated into the model's learning process and becomes data to improve the accuracy of future suggestions.

[0782] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0783] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0784] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0785] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0786] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0787] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0788] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0789] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0790] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0791] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0792] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0793] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0794] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0795] 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.

[0796] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0797] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0798] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0799] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0800] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0801] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0802] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0803] The following is further disclosed regarding the embodiments described above.

[0804] (Claim 1)

[0805] Means for receiving users' personal data and health data,

[0806] A method for generating nutritional supplement suggestions by using a machine learning model to analyze received data,

[0807] A means of presenting the generated proposals to the user using natural language processing technology,

[0808] A means of receiving feedback from users and using it to improve machine learning models,

[0809] A system that includes this.

[0810] (Claim 2)

[0811] The system according to claim 1, which handles data including biological test results as user health data.

[0812] (Claim 3)

[0813] The system according to claim 1, characterized in that it presents information regarding the generated nutritional supplement suggestions in a language that is easy for users to understand.

[0814] "Example 1"

[0815] (Claim 1)

[0816] A means of receiving the user's biometric information and lifestyle information,

[0817] A means of generating nutrient suggestions by performing analysis using a machine learning processing device based on received information,

[0818] A means of presenting the generated proposals to the user using natural language conversion technology,

[0819] A means of receiving responses from users and utilizing them to improve machine learning processing devices,

[0820] Information processing device including

[0821] (Claim 2)

[0822] The information processing device according to claim 1, which handles information including the results of biological analysis as the user's biometric information.

[0823] (Claim 3)

[0824] The information processing device according to claim 1, characterized in that it presents information regarding the generated nutrient suggestions in a language that is easy for the user to understand.

[0825] "Application Example 1"

[0826] (Claim 1)

[0827] A means of receiving user attribute information and health information,

[0828] A method for generating suggestions for highly nutritious meals by using machine learning algorithms to analyze received information,

[0829] A means of presenting the generated proposals to the user using natural language processing technology,

[0830] A means of processing requests for meals proposed by affiliated food and beverage establishments,

[0831] A means of receiving user feedback and using it to improve machine learning algorithms,

[0832] A system that includes this.

[0833] (Claim 2)

[0834] The system according to claim 1, which handles information including biological test results as user health information.

[0835] (Claim 3)

[0836] The system according to claim 1, characterized in that it presents information on the generated nutritious meal suggestions in a format that is easy for users to understand.

[0837] "Example 2 of combining an emotion engine"

[0838] (Claim 1)

[0839] A means for receiving user personal data and health data, formatting the data, and transmitting it to an information processing device,

[0840] A means for generating personalized nutritional supplement recommendations by performing analysis using a machine learning model and an emotion analysis device based on received data,

[0841] A means of presenting the generated suggestions in a language format that takes into account the user's psychological state, using natural language processing technology.

[0842] A means for receiving emotional feedback from users and using it to improve the model of the information processing device and the emotion analysis device,

[0843] A system that includes this.

[0844] (Claim 2)

[0845] The system according to claim 1, which handles data including biological test results as user health data and performs emotional state analysis using an emotion analysis device.

[0846] (Claim 3)

[0847] The system according to claim 1, characterized in that it presents information regarding the generated nutritional supplement suggestions in language that is easy to understand and takes into account the emotional state of the user.

[0848] "Application example 2 when combining with an emotional engine"

[0849] (Claim 1)

[0850] A means of acquiring and receiving the user's biometric data and emotional state,

[0851] A method for generating nutritional supplement recommendations by analyzing received data using a machine learning model and sentiment analysis engine,

[0852] A means of presenting the generated proposals to a portable device using natural language processing technology,

[0853] A means of receiving user feedback and using it to improve machine learning models and sentiment analysis engines,

[0854] A system that includes this.

[0855] (Claim 2)

[0856] The system according to claim 1, which handles biological test results and emotional state data as user health data.

[0857] (Claim 3)

[0858] The system according to claim 1, characterized in that it presents information regarding the generated nutritional supplement suggestions in language that is easy to understand and tailored to the user's emotional state. [Explanation of Symbols]

[0859] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Means for receiving users' personal data and health data, A method for generating nutritional supplement suggestions by using a machine learning model to analyze received data, A means of presenting the generated proposals to the user using natural language processing technology, A means of receiving feedback from users and using it to improve machine learning models, A system that includes this.

2. The system according to claim 1, which handles data including biological test results as user health data.

3. The system according to claim 1, characterized in that it presents information regarding the generated nutritional supplement suggestions in a language that is easy for users to understand.

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