System

Abstract

A system is provided.SOLUTION: A system comprising: means for receiving a result of a health examination; means for analyzing the received result to identify required nutrients; means for generating individually optimized ingredients and recipes based on the identified nutrients; means for automatically placing an order with an online supermarket based on a generated ingredient list; and means for calculating a health score of a user and awarding points according to the score.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In modern society, there is an increasing need for regular health checkups, but many people find it difficult to obtain specific guidance on how to improve their health after receiving their health checkup results. This problem leads many people to ignore the results and not take appropriate action, increasing the risk of damaging their health. This hurdle is particularly high for people who cannot understand specific ways to improve their diet and lifestyle habits just by looking at the numbers. Furthermore, with the increase in food purchases via online supermarkets, the process of selecting and ordering ingredients after understanding their own nutritional needs is becoming more and more time-consuming. There is a need for a system that solves these problems and promotes individual health improvement. [Means for solving the problem]

[0005] The present invention provides a means for receiving health checkup results, analyzing the results to identify necessary nutrients, and generating individually optimized ingredients and recipes based on the identified nutrients. Furthermore, the system includes a means for automatically placing orders with an online supermarket based on the generated ingredient list, and a means for calculating the user's health score and awarding points according to the score. This allows users to take specific actions based on the health checkup results and easily obtain ingredients and recipes for improving their health. Furthermore, by periodically updating the health score and awarding points, the system can increase the user's health awareness. This system uses a generative AI model to perform analysis and provide customized dietary advice that takes into account the user's past medical history and the user's parents' medical history.

[0006] A "health checkup" is a periodic health check conducted at a medical institution or other facility, in which an individual's health condition is evaluated through blood tests, physical measurements, etc.

[0007] "Nutrients" are components necessary for maintaining normal bodily functions and promoting growth and development, and include vitamins, minerals, proteins, lipids, carbohydrates, etc.

[0008] A "generative AI model" refers to an algorithm that uses artificial intelligence technology to analyze data and automatically generate recipes and ingredient suggestions tailored to individual needs.

[0009] The "food list" is a list of ingredients needed to supplement the nutrients identified based on the analysis results, and is used for purchasing at the online supermarket.

[0010] An "online supermarket" refers to an online supermarket where you can purchase food and daily necessities via the Internet.

[0011] The "health score" is a numerical value calculated based on a certain standard by evaluating the user's health checkup results and daily health condition, and visually indicates the degree of improvement in the user's health.

[0012] "Points" are incentives given to users in the system according to their improvements in health, and refer to a reward system to increase user motivation. [Brief explanation of the drawings]

[0013] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

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

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

[0016] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

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

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

[0019] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0021] [First embodiment]

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

[0023] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0024] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0026] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0030] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0031] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0032] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0033] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0034] This system utilizes the results of health checkups to suggest optimal ingredients and recipes to users, and allows them to easily place orders through an online supermarket. Furthermore, it calculates the user's health score monthly and awards points according to the score, thereby raising the user's health awareness.

[0035] Overall system configuration

[0036] The system mainly consists of the following components:

[0037] 1. Client terminal (user device)

[0038] 2. Server (including data analysis and generative AI models)

[0039] 3. Online Supermarket API

[0040] Receiving and analyzing health checkup results

[0041] User: Enters health checkup results into a client device (PC or smartphone). For example, blood test results such as LDL cholesterol, HDL cholesterol, triglycerides, and blood glucose levels.

[0042] Terminal: Sends the entered data to the server.

[0043] Server: Analyzes the received data and uses a generative AI model to identify nutrient deficiencies or excesses. For example, it analyzes the data to recommend low-fat foods if LDL cholesterol is high, or low-GI foods if blood sugar levels are high.

[0044] Recipe and ingredient suggestions

[0045] Server: Based on the analysis results, a generative AI model is used to create the optimal recipe for the user, taking into account the user's past medical history and the medical history of their parents to provide more personalized suggestions.

[0046] Terminal: The server generates a recipe and displays the list of ingredients to the user. For example, recipes such as "Simmered Mackerel in Miso" or "Avocado and Quinoa Salad" are displayed.

[0047] Ordering from an online supermarket

[0048] User: Review the suggested recipe and ingredients list and choose to order the ingredients they need from an online supermarket.

[0049] Terminal: Calls the online supermarket's API and automatically processes the process of adding the necessary ingredients to the cart.

[0050] Terminal: A final confirmation screen is displayed to the user to confirm the order details.

[0051] User: Confirms order and purchases ingredients.

[0052] Calculation of health score and awarding points

[0053] Server: Collects regular health checkup results and calculates a monthly health score. The score is calculated based on the degree of improvement, such as whether LDL cholesterol has decreased or blood sugar levels have stabilized.

[0054] Server: Awards points to users based on the calculated score. These points are used to motivate users to improve their health.

[0055] Device: Displays health score and points information to the user, providing feedback for the next health check.

[0056] Specific examples

[0057] 1. User: Enters blood test results into the app (e.g., LDL cholesterol 160 mg / dL, triglycerides 200 mg / dL).

[0058] 2. Terminal: Sends the health check results to the server.

[0059] 3. Server: Analyzes the results and recommends foods rich in omega-3 fatty acids and fiber.

[0060] 4. Server: Generates recipes using mackerel and avocado and sends them to the client device.

[0061] 5. Terminal: Shows the recipe and ingredients list to the user.

[0062] 6. User: Selects to order from the online supermarket.

[0063] 7. Terminal: Add the ingredients you need to your online supermarket cart.

[0064] 8. User: Makes final confirmation and confirms order.

[0065] 9. Server: Calculates monthly health scores and awards points according to improvements.

[0066] 10. Terminal: Displays the user's score and points information, motivating them to take the next health checkup.

[0067] The system allows users to translate their health checkup results into concrete actions, provides easy access to ingredients and recipes that will help improve their health, and encourages ongoing health management through a score and points system.

[0068] The processing flow will be explained below.

[0069] Step 1:

[0070] User: Enters the results of a health check into the client terminal. Specifically, the user enters blood test results (e.g., LDL cholesterol 160 mg / dL, triglycerides 200 mg / dL).

[0071] Step 2:

[0072] Terminal: Sends the entered health check results to the server, which then prepares the data for processing.

[0073] Step 3:

[0074] Server: Stores the received health check results in a database for later analysis.

[0075] Step 4:

[0076] Server: Retrieves health checkup results stored in a database and analyzes them using a generative AI model. Specifically, if LDL cholesterol is high, the analysis recommends a low-fat, high-fiber diet.

[0077] Step 5:

[0078] Server: Based on the analysis results, a generative AI model is used to create individually optimized recipes, such as recipes using ingredients like mackerel and avocado.

[0079] Step 6:

[0080] Server: Sends the created recipe and the list of ingredients to the client device, allowing users to easily access it.

[0081] Step 7:

[0082] Device: The device receives the recipe and ingredient list from the server and displays it to the user, including cooking instructions and nutritional information.

[0083] Step 8:

[0084] User: Checks the suggested recipe and ingredient list and selects to order from the online supermarket. The user confirms the order and proceeds to the next step.

[0085] Step 9:

[0086] Terminal: Calls the online supermarket's API and automatically adds the necessary ingredients to the cart, eliminating the need for the user to manually add them to the cart.

[0087] Step 10:

[0088] Terminal: After the ingredients are added to the cart, the user is presented with a final order confirmation screen where they can finalize their order.

[0089] Step 11:

[0090] User: Checks the order details and presses the button to confirm the order. This completes the ordering process with the online supermarket.

[0091] Step 12:

[0092] Server: Calculates the user's monthly health score based on the results of the health check. The score is calculated based on the degree of improvement, such as whether LDL cholesterol has decreased.

[0093] Step 13:

[0094] Server: Based on the calculated health score, the server awards points to the user. The points are used to increase the user's motivation.

[0095] Step 14:

[0096] Terminal: Displays health score and points information to the user, providing feedback for the next health check.

[0097] These steps allow users to take specific actions based on their health checkup results and aim to improve their health. The system also makes it easy to order ingredients, promoting continuous health management.

[0098] Example 1

[0099] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0100] Conventional health management systems often lack specific feedback and suggestions based on users' health checkup results, leaving users with insufficient means to implement specific health improvements. Furthermore, it is difficult to suggest personalized ingredients and recipes based on individual health conditions, making it difficult for users to easily prepare meals that suit their own health condition. Furthermore, the lack of incentives to continuously promote users' health management makes it difficult to motivate users.

[0101] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0102] In this invention, the server includes a means for receiving health checkup results, a means for analyzing the received results to identify deficient or excessive nutrients, a means for generating individually optimized ingredients and cooking instructions based on the identified nutrients using a generative AI model, a means for automatically placing an order to an e-commerce system based on the generated ingredient list, a means for calculating the user's health score monthly and awarding points according to the score, and a means for transmitting the received health checkup data via a secure communication protocol. This allows for the suggestion and implementation of specific ingredients and recipes based on the individual's health condition, effectively promoting health management and motivation for the user.

[0103] The "means for receiving the results of the medical examination" is a function that allows the server to receive the medical examination data entered by the user.

[0104] "Means for analyzing the received results to identify nutrients that are deficient or in excess" refers to a function that analyzes the received health checkup data using specialized algorithms or AI models to identify nutrients that are deficient or in excess.

[0105] "Means for generating individually optimized ingredients and cooking instructions using a generative AI model" refers to a function that utilizes an AI model (e.g., a generative AI model) to generate optimal ingredients and cooking instructions (recipe) for each user based on identified nutrients.

[0106] "Means for automatically placing orders in an e-commerce system based on the generated ingredient list" refers to a function for automatically ordering ingredients via the API of an online shop or online supermarket based on the generated ingredient list.

[0107] "Means for calculating the user's health score monthly and awarding points according to that score" is a function for calculating the user's health score monthly based on the results of their health checkup and awarding points according to that score.

[0108] "Means for transmitting received medical checkup data using a secure communication protocol" is a function for transmitting the medical checkup data entered by the user to a server using a secure communication protocol (e.g., HTTPS).

[0109] An "electronic commerce system" generally refers to a system that allows products to be purchased online, and specifically includes online supermarkets and online shops.

[0110] A "generative AI model" is an artificial intelligence model that generates a specific output from given input data, such as a text generation model like the GPT series.

[0111] "Identified nutrients" refers to nutrients that are deficient or in excess in the user's health condition, as revealed by the analysis.

[0112] "Cooking instructions" refer to the steps and methods required to prepare a dish, and are generally expressed as a recipe.

[0113] This invention is a system that allows users to order individually optimized ingredients and recipes based on the results of their health checkups through an e-commerce system. Furthermore, it aims to raise users' health awareness by calculating their health score monthly and awarding points based on that score.

[0114] Overall system configuration

[0115] The system mainly consists of the following components:

[0116] 1. Client terminal (user device)

[0117] 2. Server (including data analysis and generative AI models)

[0118] 3. E-commerce system API

[0119] Receiving and analyzing health checkup results

[0120] Users enter their health checkup results using a client terminal such as a PC or smartphone. Specifically, they fill out a form to enter values ​​obtained from blood tests, such as LDL cholesterol, HDL cholesterol, triglycerides, and blood glucose levels. For example, the user might enter data such as LDL cholesterol 160 mg / dL, HDL cholesterol 50 mg / dL, triglycerides 200 mg / dL, and blood glucose 110 mg / dL.

[0121] The device makes an HTTP POST request to send the entered health checkup data to the server, using a secure communication protocol (e.g., HTTPS).

[0122] The server analyzes the received health checkup data. It uses a generative AI model (e.g., GPT-3) to analyze the input numerical data and identify nutrient deficiencies and excesses. Specific examples of prompts used for analysis include the following:

[0123] User's health check data: LDL cholesterol 160 mg / dL, HDL cholesterol 50 mg / dL, triglycerides 200 mg / dL, blood sugar 110 mg / dL.

[0124] Identify nutrient deficiencies and excesses and suggest suitable ingredients and recipes.

[0125] Recipe and ingredient suggestions

[0126] The server uses a generative AI model based on the analysis results to create the optimal recipe for the user. The generated ingredients and cooking instructions take into account information such as the user's past medical history and the medical history of their parents, resulting in a more personalized recommendation.

[0127] The device displays the recipe and ingredient list received from the server to the user. The UI is designed to make it easy for users to browse recipes. For example, recipes such as "Mackerel simmered in miso" and "Avocado and quinoa salad" are displayed.

[0128] Ordering from an online supermarket

[0129] The user reviews the suggested recipe and ingredient list and chooses to order the ingredients they need through the e-commerce system.

[0130] The terminal calls the API of the e-commerce system and automatically adds the suggested ingredients to the cart. A final confirmation screen is displayed, allowing the user to confirm the order details and confirm the order.

[0131] Calculation of health score and awarding points

[0132] The server periodically collects the results of health checkups and calculates the user's monthly health score, which is calculated based on the degree of improvement, such as a reduction in LDL cholesterol and stabilization of blood sugar levels.

[0133] The server awards points to the user based on the health score, which increases the user's motivation to improve their health.

[0134] The device displays the user's health score and points, and provides feedback for the next health check. For example, if the user's health score is 80 and 50 points are awarded, the device displays detailed analysis results and feedback.

[0135] This system makes it easier for users to translate the results of their health checkups into concrete actions, provides convenient access to ingredients and recipes that will help improve their health, and effectively encourages users to continue managing their health through a health score and points system.

[0136] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0137] Step 1:

[0138] User: Enters health checkup results. Specifically, the user uses a client device such as a PC or smartphone to enter the values ​​obtained from a blood test (e.g., LDL cholesterol 160 mg / dL, HDL cholesterol 50 mg / dL, triglycerides 200 mg / dL, blood sugar 110 mg / dL) into an input form.

[0139] Input: Numerical data of health check results.

[0140] Output: Input data of health check results.

[0141] Step 2:

[0142] Terminal: An HTTP POST request is made to send the input data of the health check result to the server. At this time, the data is sent using a secure communication protocol (e.g., HTTPS).

[0143] Input: The entered health check result data and the API endpoint to send it to.

[0144] Output: Health check result sent to the server.

[0145] Step 3:

[0146] Server: Analyzes the received health checkup data. Using a generative AI model (e.g., GPT-3), analyzes the input numerical data and identifies nutrient deficiencies and excesses. Sends prompt text to the generative AI model and obtains the analysis results.

[0147] Input: Medical examination result data. Use the following as an example prompt:

[0148] User's health check data: LDL cholesterol 160 mg / dL, HDL cholesterol 50 mg / dL, triglycerides 200 mg / dL, blood sugar 110 mg / dL.

[0149] Identify nutrient deficiencies and excesses and suggest suitable ingredients and recipes.

[0150] Output: Analysis results identifying nutrient deficiencies and excesses.

[0151] Step 4:

[0152] Server: Based on the analysis results, the server uses a generative AI model to generate the optimal recipe and ingredient list for the user, creating a personalized recipe that takes into account specific past medical history and parental medical history.

[0153] Input: Analysis results (nutrient deficiencies and excess nutrients).

[0154] Output: Individually optimized recipe and ingredient list.

[0155] Step 5:

[0156] Device: The device displays the recipe and ingredient list received from the server to the user. The device displays the recipe in a visually easy-to-understand UI to make it easier for the user to browse the recipe. For example, recipes such as "Mackerel simmered in miso" and "Avocado and quinoa salad" are displayed.

[0157] Input: Recipe and ingredient list sent from the server.

[0158] Output: On-screen display of recipe and ingredients list.

[0159] Step 6:

[0160] User: Review the suggested recipe and ingredient list and choose to order the ingredients they need through the e-commerce system.

[0161] Input: The displayed recipe and ingredient list.

[0162] Output: User's order selection.

[0163] Step 7:

[0164] Terminal: Calls the API of the e-commerce system and automatically adds the suggested ingredients to the cart. A final confirmation screen is displayed, allowing the user to confirm the order and confirm it.

[0165] Input: User order selection and API endpoint of e-commerce system.

[0166] Output: Ingredients added to cart and final confirmation screen.

[0167] Step 8:

[0168] User: Check the order details on the final confirmation screen and confirm the order.

[0169] Input: Information from the final confirmation screen.

[0170] Output: Confirmed order details.

[0171] Step 9:

[0172] Server: Periodically collects health checkup results and calculates the user's monthly health score. The health score is calculated based on the degree of improvement, such as lowering LDL cholesterol and stabilizing blood sugar levels.

[0173] Input: Health checkup result data.

[0174] Output: User's health score.

[0175] Step 10:

[0176] Server: Awards points to users based on their health score, which motivates them to improve their health.

[0177] Input: User's health score.

[0178] Output: Points awarded to the user.

[0179] Step 11:

[0180] Device: Displays the user's health score and points, and provides feedback for the next health check. For example, if the user's health score is 80 and 50 points are awarded, the device displays detailed analysis results and feedback.

[0181] Input: Health score and points information.

[0182] Output: A screen display of the health score and points information presented to the user.

[0183] (Application example 1)

[0184] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0185] In modern society, personal health management is highly valued, and it is recognized that diet in particular has a significant impact on health status. However, there is no system in place to suggest individually optimized ingredients and recipes based on health checkup results, and to easily purchase and order them. As a result, it is difficult for users to choose meals based on their own health status, and there is an issue of ineffective health management. There is also a lack of mechanisms to maintain motivation for health management.

[0186] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0187] In this invention, the server includes a means for receiving health checkup results, a means for analyzing the received results to identify necessary nutrients, and a means for generating individually optimized ingredients and recipes based on the identified nutrients. This allows users to receive recommendations for ingredients and recipes based on the health checkup results and easily order and purchase them through an e-commerce platform or delivery service. Furthermore, by including a means for calculating the user's health score and awarding points according to the score, the server can increase the user's motivation to manage their health.

[0188] A "medical examination" is a medical test conducted to assess an individual's health status.

[0189] Analyzing the "results" means determining the user's health condition and identifying necessary nutrients based on the received health checkup data.

[0190] "Nutrients" are substances such as vitamins, minerals, proteins, carbohydrates, and fats that the body needs to function properly.

[0191] "Ingredients" refers to the food ingredients used to prepare a dish.

[0192] "Recipe" means a list of instructions or ingredients for making a particular dish.

[0193] An "e-commerce platform" is an online system for buying and selling goods and services over the Internet.

[0194] "Automatically placing an order" means that the system automatically processes the product order without the user having to manually enter any information.

[0195] A "health score" is a numerical representation of a user's health condition based on specific criteria.

[0196] "Awarding points" means providing points to users as a reward based on specific actions or conditions.

[0197] A "delivery service" is a service that delivers goods from a specific location to another location.

[0198] This invention is a system that utilizes the user's health checkup results to suggest optimal ingredients and recipes for the user, and then orders them via an e-commerce platform and delivery service.The invention is mainly composed of client terminals such as smartphones, tablets, and PCs, and a server that includes data analysis and generative AI models.

[0199] 1. Receipt and analysis of health examination results

[0200] The server receives health checkup results from the user. The user inputs their health checkup results (e.g., LDL cholesterol, triglycerides, etc.) using an application on their smartphone or PC. The health checkup result data sent from the device is received by the server. The server analyzes the received data and uses a generative AI model to identify nutrients that are lacking or in excess. As a specific example, if LDL cholesterol is high, the server analyzes it to recommend low-fat foods.

[0201] 2. Recipe and ingredient suggestions

[0202] Based on the analysis results, the server uses a generative AI model to generate the optimal recipe for the user, taking into account factors such as past medical history and the medical history of the user's parents. The generated recipe and a list of ingredients are sent to the user's device. For example, recipes such as "grilled salmon" and "oatmeal salad" are displayed.

[0203] 3. Ordering from online supermarkets and delivery services

[0204] When the user checks the suggested recipes and ingredients and selects an order, the server calls the API of the e-commerce platform and automatically adds the necessary ingredients to the cart. Furthermore, in a similar way, it is possible to order food based on the optimized recipes via a delivery service. The user confirms the order details on a final confirmation screen and confirms the order.

[0205] 4. Calculation of health score and awarding points

[0206] The server periodically collects the user's health checkup results and calculates a monthly health score. This score is calculated based on the degree of improvement, such as whether LDL cholesterol has decreased or whether blood sugar levels are stable. Points are awarded to the user based on the calculated score. The point information is displayed on the user's device, providing feedback for the next health checkup.

[0207] Examples of specific examples and prompts

[0208] In an actual usage scenario, a user enters the results of a health check into the app. For example, "LDL cholesterol 160 mg / dL, triglycerides 200 mg / dL." The entered data is sent to the server for analysis. The generative AI model recommends ingredients rich in omega-3 fatty acids and fiber, and the server generates recipes such as "grilled salmon" and "avocado and quinoa salad." The user can then select the procedure to order food based on the provided recipe from a delivery service.

[0209] Example prompt for a generative AI model:

[0210] "The user's health check results show LDL cholesterol of 160 mg / dL and triglycerides of 200 mg / dL. Generate suitable ingredient and recipe suggestions for this user. Also, generate an API request to process the food order based on the results."

[0211] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0212] Step 1:

[0213] Users input the results of their health checkups using an application on their smartphone or PC. This input data includes specific numerical values ​​such as LDL cholesterol and triglycerides. This data is sent from the device to the server. The input data format is specified as a number or a string.

[0214] Step 2:

[0215] The server receives the health checkup result data sent from the device. The received data is stored in a database. The server then starts the analysis process and inputs the received data into the generative AI model. The AI ​​model identifies nutrients that are lacking or in excess based on the input data. The identified nutrients are output as the analysis results.

[0216] Step 3:

[0217] The server generates the optimal recipe for the user based on the analysis results. Using a generative AI model, it also takes into account the user's past medical history and the medical history of their parents. The generated recipe and the list of ingredients are stored in a database and sent to the client device. The generated recipe includes the specific dish name and list of ingredients.

[0218] Step 4:

[0219] The terminal displays the recipe and ingredient list sent from the server to the user. The user checks the displayed recipe and ingredient list. This real-time display allows the user to select the recipe that is best for them.

[0220] Step 5:

[0221] After the user selects a recipe and ingredients, the device automatically calls the API of the e-commerce platform to add the required ingredients to the cart. The API call includes a list of selected ingredients and the user's shipping information.

[0222] Step 6:

[0223] The user checks the final confirmation screen on the terminal to confirm that there are no problems with the order details. If the user's confirmation is received, the order is confirmed and the data is sent to the server. The server then sends the order information to the delivery service.

[0224] Step 7:

[0225] The server periodically collects the user's health checkup results and calculates a monthly health score. The health score is quantified based on the degree of improvement and the maintained health status. The server awards points to the user based on this score. The point information is stored in a database and sent to the client terminal.

[0226] Step 8:

[0227] The device displays the health score and points information to the user, who can then use the displayed information to obtain feedback for their next health checkup, increasing their motivation to manage their health.

[0228] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0229] This invention is a system that utilizes the results of health checkups to not only suggest optimal ingredients and recipes to users, but also provides more personalized health management by combining it with an emotion engine that recognizes the user's emotions. This system calculates the user's health score monthly and awards points according to the score, thereby raising the user's health awareness.

[0230] Overall system configuration

[0231] The system mainly consists of the following components:

[0232] 1. Client terminal (user device)

[0233] 2. Server (including data analysis and generative AI models, emotion engine)

[0234] 3. Online Supermarket API

[0235] Receiving and analyzing health checkup results

[0236] User: Enters health checkup results into a client device (PC or smartphone). For example, blood test results such as LDL cholesterol, HDL cholesterol, triglycerides, and blood glucose levels.

[0237] Terminal: Sends the entered data to the server.

[0238] Server: Stores the received data in a database and analyzes it using a generative AI model. This analysis identifies nutrient deficiencies and excesses and suggests appropriate ingredients and recipes.

[0239] Recipe and ingredient suggestions

[0240] Server: Based on the analysis results, the server uses a generative AI model to create individually optimized recipes, taking into account the user's past medical history and the medical history of their parents. Additionally, the server uses an emotion engine to recognize the user's emotional state and recommend ingredients and recipes that are effective in relieving stress and improving mood.

[0241] Terminal: The server generates a recipe and displays the list of ingredients to the user. For example, recipes such as "Simmered Mackerel in Miso" or "Avocado and Quinoa Salad" are displayed.

[0242] Ordering from an online supermarket

[0243] User: Review the suggested recipe and ingredients list and choose to order from the online supermarket.

[0244] Terminal: Calls the online supermarket's API and automatically adds the necessary ingredients to the cart.

[0245] Terminal: A final confirmation screen is displayed to the user to confirm the order details.

[0246] User: Confirms order and purchases ingredients.

[0247] Calculation of health score and awarding points

[0248] Server: Collects regular health checkup results and calculates a monthly health score. The score is calculated based on the degree of improvement, such as whether LDL cholesterol has decreased or blood sugar levels have stabilized.

[0249] Server: Gives points to users based on their health score, which increases their motivation.

[0250] Device: Displays health score and points information to the user. Provides feedback and messages according to the emotional state recognized by the emotion engine.

[0251] Specific examples

[0252] 1. User: Enters blood test results into the app (e.g., LDL cholesterol 160 mg / dL, triglycerides 200 mg / dL).

[0253] 2. Terminal: Sends the health check results to the server.

[0254] 3. Server: Analyzes the results and recommends foods rich in omega-3 fatty acids and fiber. If the emotion engine detects stress, it will also recommend relaxing drinks like chamomile tea.

[0255] 4. Server: Generates recipes using mackerel and avocado and sends them to the client device.

[0256] 5. Terminal: Shows the recipe and ingredients list to the user.

[0257] 6. User: Selects to order from the online supermarket.

[0258] 7. Terminal: Add the ingredients you need to your online supermarket cart.

[0259] 8. User: Makes final confirmation and confirms order.

[0260] 9. Server: Calculates monthly health scores and awards points according to improvements.

[0261] 10. Terminal: Displays score and point information to the user and provides feedback based on the emotion engine.

[0262] This system allows users to take specific actions based on their health checkup results and aim to improve their health. In addition, the introduction of an emotion engine enables more personalized health management and makes suggestions that take into account the user's psychological stress and motivation.

[0263] The processing flow will be explained below.

[0264] Step 1:

[0265] User: Enters the results of a health check into a client terminal. Input items include blood test results (e.g., LDL cholesterol 160 mg / dL, triglycerides 200 mg / dL) and blood glucose levels.

[0266] Step 2:

[0267] Terminal: Sends the entered health check results to the server, where the data is stored for immediate processing.

[0268] Step 3:

[0269] Server: Stores the results of the health checkup in a database. This data is used for later analysis.

[0270] Step 4:

[0271] Server: The generative AI model retrieves health checkup results stored in a database and analyzes them. This analysis includes identifying specific nutrients, such as recommending a low-fat, high-fiber diet if LDL cholesterol is high.

[0272] Step 5:

[0273] Server: After identifying the necessary nutrients, it uses a generative AI model to create the best recipe for the user, for example, using ingredients like mackerel and avocado.

[0274] Step 6:

[0275] Server: Sends the created recipe and the list of required ingredients in JSON format to the client device, so that the recipe information is ready to be displayed on the device.

[0276] Step 7:

[0277] Terminal: The recipe and ingredient list received from the server are displayed to the user, allowing the user to view the suggested recipes.

[0278] Step 8:

[0279] User: Review the suggested recipe and ingredient list and choose to order from the online supermarket, making it easy for users to purchase the perfect ingredients.

[0280] Step 9:

[0281] Device: Calls the online supermarket's API and automatically adds the necessary ingredients to the cart. In addition, an emotion engine captures the user's emotional state and makes fun comments and suggestions.

[0282] Step 10:

[0283] Terminal: After the ingredients are added to the cart, a final confirmation screen for the order is displayed to the user, where the user can make final confirmation of the order.

[0284] Step 11:

[0285] User: Checks the order details and presses the button to confirm the order. This completes the order to the online supermarket.

[0286] Step 12:

[0287] Server: Calculates the user's monthly health score based on the results of the health check. The score is based on the degree of improvement, such as whether LDL cholesterol has decreased or blood sugar levels have stabilized.

[0288] Step 13:

[0289] Server: Based on the calculated health score, the server awards points to the user. Different points are awarded depending on the degree of health improvement.

[0290] Step 14:

[0291] Device: Displays health score and points information to the user. Adds feedback and encouraging messages depending on the user's emotional state as recognized by the emotion engine.

[0292] In this way, users can take specific actions based on their health checkup results, enabling multifaceted and personalized health management.In addition, the introduction of an emotion engine allows feedback and suggestions to be provided that take into account the user's psychological aspects.

[0293] Example 2

[0294] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0295] In modern society, individual health management is important, but it is difficult for users to properly understand their health checkup results and implement improvement measures. Current systems, which present uniform dietary and lifestyle improvement measures without considering the user's emotional state, have the problem of making it difficult to provide optimal support for each individual user. Furthermore, there is a lack of methods to maintain motivation in order to continuously support users' health improvement.

[0296] 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. In this invention, the server includes a means for receiving health checkup results, a means for analyzing the received results to identify necessary nutrients, a means for generating individually optimized ingredients and recipes based on the identified nutrients, a means for recognizing the user's emotions and adjusting suggestions based on the user's emotional state, a means for automatically placing an order with an e-commerce service based on the generated ingredient list, and a means for calculating the user's health score and awarding points according to the score. This allows the user to properly understand their own health status and implement optimal health improvement measures that take their emotional state into consideration. Furthermore, the monthly updated health score and point awarding help maintain the user's motivation.

[0297] "Physical examination results" are data on the results of a series of medical tests conducted to assess the user's health condition.

[0298] The "receiving means" is a system component that receives data sent from a user via a network, converts it into an appropriate format, and stores it.

[0299] The "means for analyzing and identifying necessary nutrients" is an algorithm or program that determines which nutrients are deficient or in excess based on the received health check results.

[0300] The "means for generating individually optimized ingredients and recipes" refers to a generative AI model or program that provides optimal ingredients and recipes for each user based on the identified nutrients.

[0301] The "means for recognizing the user's emotions and adjusting suggestions based on the emotional state" refers to an emotion engine or analysis program that analyzes the user's emotional state and changes the suggestions made in accordance with that state.

[0302] "Means for automatically placing an order with an e-commerce service based on the generated ingredient list" refers to a system component that uses the generated ingredient list to call the API of an e-commerce service (such as an online supermarket) and automatically processes the order.

[0303] The "means for calculating a user's health score" refers to an algorithm or program that calculates a score for quantitatively evaluating the user's health condition.

[0304] The "means for awarding points" is a system component that awards points to the user as an incentive based on the calculated health score.

[0305] This invention is a system for personalizing health management for users, and is composed of the following components: a client terminal (user device), a server (including a data analysis and generation AI model and an emotion engine), and an e-commerce service API. The specific roles and operations of each component are explained below.

[0306] Receiving and analyzing health checkup results

[0307] User: The user inputs the results of their health checkup into the client terminal. The results of the health checkup include, for example, LDL cholesterol, HDL cholesterol, triglycerides, and blood glucose levels. This information is entered as numerical values ​​into the application's input form.

[0308] Terminal: The terminal sends the health check result data entered by the user to the server. The data is sent using the HTTPS protocol and packaged in JSON format. The sent data is then prepared for processing by the server.

[0309] Server: The server stores the received data in a database, typically a relational database such as MySQL or PostgreSQL. The stored data is then analyzed using a generative AI model. This analysis identifies nutrient deficiencies and excesses.

[0310] Recipe and ingredient suggestions

[0311] Server: The server uses a generative AI model based on the analysis results to create individually optimized recipes. This process also takes into account the user's past medical history and the medical history of their parents. It also uses an emotion engine to recognize the user's emotional state and recommend ingredients and recipes that are effective in relieving stress and improving mood.

[0312] For example, if the system recognizes the user's stress level, it will suggest recipes using chamomile tea, which has a relaxing effect, or mackerel, which is rich in omega-3 fatty acids.

[0313] Ordering from an online supermarket

[0314] User: The user reviews the suggested recipe and ingredient list displayed on the device and selects to order from the online supermarket. The order is placed using the app interface, and the selected ingredients are automatically added to the cart.

[0315] Terminal: The terminal calls the online supermarket's API and automatically adds the required ingredients to the cart. This process uses the REST API and sends the order data via a POST request. The terminal then displays a final confirmation screen for the user to confirm the order.

[0316] User: The user confirms the order and confirms it, completing the food purchase process.

[0317] Calculation of health score and awarding points

[0318] Server: The server periodically collects health checkup results and calculates a monthly health score. The score is calculated based on the degree of improvement, such as whether LDL cholesterol has decreased or whether blood sugar levels are stable. Points are awarded to the user based on the calculated score.

[0319] Terminal: The terminal displays the health score and points to the user. This is done using graphs and numerical displays. In addition, feedback and messages are displayed according to the emotional state recognized by the emotion engine. For example, a message such as "Your health score is 80 points! That's a great achievement" can be displayed to motivate the user.

[0320] Specific examples

[0321] Here is an example of this system:

[0322] 1. User: Enters blood test results into the application's input form and clicks the "Submit" button.

[0323] 2. Terminal: Packages the input data in JSON format and sends it to the server using the HTTPS protocol.

[0324] 3. Server: Stores the received data in a relational database and analyzes it using a generative AI model. As a result of the analysis, it identifies nutrient deficiencies and excesses and generates appropriate ingredients and recipes.

[0325] 4. Server: Combines generative AI models and an emotion engine to generate personalized recipe and ingredient suggestions. For example, if a user's emotion is recognized as stressful, it will suggest ingredients with a relaxing effect (e.g., chamomile tea).

[0326] 5. On the device: Display suggested recipes and ingredient lists in list or card view. For example, "Simmered Mackerel with Miso" or "Avocado and Quinoa Salad" are displayed.

[0327] 6. User: Select the ingredients you want to purchase from the displayed list and click the "Order" button.

[0328] 7. Device: Calls the online supermarket's API to add ingredients to the cart and displays a final confirmation screen. The user checks the details and confirms the order.

[0329] 8. Server: Calculates monthly health score and awards points based on the score.

[0330] 9. Device: Displays health scores and points in graphs and numerical formats. Emotion engine recognizes the user's emotional state and displays feedback and messages to motivate the user.

[0331] This system allows users to take specific actions based on their health checkup results and aim to improve their health. In addition, the introduction of an emotion engine enables more personalized health management and makes suggestions that take into account the user's psychological stress and motivation.

[0332] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0333] Step 1:

[0334] User: Enter the results of the health check into the client terminal.

[0335] Input: Health checkup result data (e.g., LDL cholesterol, HDL cholesterol, triglycerides, blood glucose levels).

[0336] Action: Enter the health check data as numbers into the application's input form and click the "Submit" button.

[0337] Output: Health checkup data entry completed (data saved on the device).

[0338] Step 2:

[0339] Terminal: Sends the entered data to the server.

[0340] Input: Medical examination data.

[0341] What it does: It packages data in JSON format and sends it to the server using the HTTPS protocol.

[0342] Output: Health check data in JSON format is sent to the server.

[0343] Step 3:

[0344] Server: Stores the received data in a database.

[0345] Input: Submitted health check data (JSON format).

[0346] What it does: Stores the received data in a database (e.g. MySQL or PostgreSQL).

[0347] Output: The health check data is stored in a database.

[0348] Step 4:

[0349] Server: Analyzes the stored data using a generative AI model.

[0350] Input: Medical examination data stored in a database.

[0351] How it works: Generative AI models are used to analyze data and identify nutrient deficiencies and excesses.

[0352] Output: Nutrient information identified as a result of the analysis.

[0353] Step 5:

[0354] Server: Utilizes generative AI models to generate individually optimized recipe and ingredient suggestions.

[0355] Input: Analysis results (identified nutrient information).

[0356] How it works: Combines generative AI models and an emotion engine to generate personalized recipes and ingredient lists.

[0357] Output: A suggested recipe and ingredients list.

[0358] Step 6:

[0359] Terminal: Displays the server-generated recipe and ingredient list to the user.

[0360] Input: Recipe and ingredient list sent from the server.

[0361] What it does: Displays a recipe and ingredient list using a list or card view.

[0362] Output: The recipe and ingredients list displayed to the user.

[0363] Step 7:

[0364] User: Review the suggested recipe and ingredients list and choose to order from the online supermarket.

[0365] Input: Recipe and ingredient list displayed on device.

[0366] Action: Select the ingredients you want to purchase and click the "Order" button.

[0367] Output: An order request to the online supermarket.

[0368] Step 8:

[0369] Terminal: Calls the online supermarket's API and automatically adds the necessary ingredients to the cart.

[0370] Input: A list of ingredients selected by the user.

[0371] How it works: Using the REST API, the order data is sent via a POST request and added to the cart.

[0372] Output: Information about ingredients added to the online supermarket cart.

[0373] Step 9:

[0374] Terminal: A final confirmation screen is displayed to the user to confirm the order details.

[0375] Input: Information about ingredients added to cart.

[0376] Behavior: Displays a final confirmation screen, prompting the user for confirmation.

[0377] Output: Order confirmation by user.

[0378] Step 10:

[0379] Server: Calculates monthly health score.

[0380] Input: Routinely collected health examination result data.

[0381] How it works: It uses an algorithm or program to calculate a health score, specifically assessing things like lowering LDL cholesterol and stabilizing blood sugar levels.

[0382] Output: Calculated health score.

[0383] Step 11:

[0384] Server: Awards points based on the calculated health score.

[0385] Input: Health score.

[0386] What it does: Uses the points awarding algorithm to award the appropriate points to the user.

[0387] Output: The user's points are updated.

[0388] Step 12:

[0389] Terminal: Displays health score and points information to the user.

[0390] Input: Health score and updated points information.

[0391] What it does: Displays information using graphical and numerical displays and provides feedback based on an emotion engine.

[0392] Output: Health score and points information displayed to the user, and feedback from the emotion engine.

[0393] (Application example 2)

[0394] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0395] In modern society, there is a demand for systems that suggest specific actions based on individual health conditions and support the implementation of those actions. In particular, it is important to make suggestions for improving daily eating habits using health checkup results, but comprehensive health management that also takes into account the user's emotional state is also necessary. It is also important to have a system that makes it easy to obtain the suggested ingredients and recipes. Furthermore, continuous feedback and incentives are required to maintain the user's motivation to improve their health. There is currently a lack of systems that meet these requirements, so solving this problem is a challenge.

[0396] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0397] In this invention, the server includes means for receiving health checkup results, means for analyzing the received results to identify necessary nutrients, means for generating individually optimized ingredients and recipes based on the identified nutrients, means for automatically placing orders with a food delivery service based on the generated ingredient list, means for calculating the user's health score and awarding points according to the score, and means for detecting the user's emotional state and making recommendations based on the score. This allows the user to receive effective and emotionally sensitive meal suggestions based on the health checkup results and easily purchase the suggested ingredients. Furthermore, by visualizing the user's improvement using the health score and providing incentives through point awarding, motivation for continuous health management can be maintained.

[0398] "Health checkup results" refers to information on blood test items and physical measurements obtained as a result of regular medical examinations and tests that a user undergoes.

[0399] "Nutrients" are components such as vitamins, minerals, proteins, lipids, and carbohydrates that are necessary for maintaining health.

[0400] "Ingredients and recipes" refers to the ingredients used to prepare a meal and the steps and methods for cooking using those ingredients.

[0401] A "food delivery service" is a service that delivers ingredients and dishes selected by the user to their home or designated location.

[0402] "Emotional state" is information that indicates the user's current psychological state, including emotions such as stress, joy, and sadness.

[0403] A "recommendation" is the optimal action or option suggested based on the user's needs and situation.

[0404] A "health score" is a numerical value calculated based on the user's health checkup results and behavioral data, and is an indicator used to evaluate their health status.

[0405] "Points" are rewards and incentives awarded based on health scores and actions, and are used to increase user motivation.

[0406] "Past medical history and parental medical history" refers to historical information about illnesses and health conditions that the user or their parents have experienced in the past.

[0407] The system of this invention is a personalized healthy food delivery app that not only suggests optimal ingredients and recipes to users based on their health checkup results, but also incorporates an emotion engine that recognizes the user's emotions. This system has the following configuration and processing steps.

[0408] Key Components of the System

[0409] 1. Client terminal (user device)

[0410] Smartphone (iOS / Android)

[0411] 2. Server

[0412] Data analysis and generative AI models (Python, TensorFlow)

[0413] Emotion Engine (NLP model, Hugging Face)

[0414] Database (MySQL)

[0415] Net supermarket API integration (REST API)

[0416] Program processing

[0417] 1. User Input

[0418] Users use a smartphone app to input their health checkup results and current emotional state, such as their LDL cholesterol and blood sugar levels from a blood test, and their emotional state, such as "I'm stressed."

[0419] 2. Send to the server

[0420] The data entered from the smartphone is sent to a server, where it is stored in a database and used for analysis.

[0421] 3. Data Analysis

[0422] The server then analyzes the received data using Python scripts and TensorFlow models to identify nutrients and foods necessary for the user's health. For example, if the user's LDL cholesterol is high, it will recommend foods rich in omega-3 fatty acids.

[0423] 4. Emotion Engine

[0424] The server uses an emotion engine to analyze the user's emotion data, and uses an NLP model (e.g., Hugging Face) to suggest foods that have a relaxing effect if the user is feeling stressed.

[0425] 5. Recipe Generation

[0426] Based on the analysis results, a generative AI model (e.g., GPT-3) is used to generate individually optimized recipes. For example, recipes such as "Simmered Mackerel in Miso" or "Avocado and Quinoa Salad" are generated. These recipes are then sent to the client device.

[0427] 6. Food delivery collaboration

[0428] The generated ingredient list is automatically added to the user's cart via the online supermarket's API, and the user then makes a final confirmation on the smartphone app to confirm the order.

[0429] 7. Health Score and Points

[0430] The server periodically calculates a health score based on the results of health checkups and updates the user's score monthly. Points are awarded based on this score, which acts as an incentive to keep the user motivated to improve their health.

[0431] Specific examples

[0432] For example, specific user input and suggestions are given below:

[0433] Example user input: "LDL cholesterol 180 mg / dL, triglycerides 220 mg / dL, emotional state: stressed"

[0434] Example prompts to input to a generative AI model:

[0435] The user's health check results are as follows:

[0436] LDL cholesterol: 180 mg / dL

[0437] Triglycerides: 220 mg / dL

[0438] The user's emotional state is stress. Suggest ingredients and recipes appropriate for the user's state.

[0439] In this way, users can receive effective and emotionally sensitive dietary suggestions based on their health checkup results, and can easily purchase the suggested ingredients. Furthermore, the degree of improvement is visualized through a health score, and points are awarded to maintain motivation for ongoing health management.

[0440] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0441] Step 1:

[0442] Users use a smartphone app to input their health check results and current emotional state, including blood test results and emotional state options, which provides specific data about the user's condition.

[0443] Step 2:

[0444] The terminal sends the data entered by the user to the server. During this process, the data is encrypted and sent, and then securely stored in a database. Here, the input data is stored on the server in preparation for the next analysis step.

[0445] Step 3:

[0446] The server analyzes the received data using Python scripts and TensorFlow models to identify the nutrients and ingredients needed based on the user's health condition. The analysis results are output as a report of the identified nutrients and their deficiency or excess.

[0447] Step 4:

[0448] The server uses an emotion engine to analyze the user's emotion data. It uses an NLP model (e.g., Hugging Face) to generate appropriate recommendations based on the input emotion. For example, if stress is detected, foods with a relaxing effect will be recommended.

[0449] Step 5:

[0450] The server generates individually optimized recipes based on the analysis results using a generative AI model (e.g., GPT-3). The generated recipes are output along with a list of ingredients and sent to the client device.

[0451] Step 6:

[0452] The terminal displays the recipe and ingredient list received from the server to the user, who can then confirm the ingredients based on the suggested list and select a food delivery service.

[0453] Step 7:

[0454] The server then calls the online supermarket's API based on the generated ingredient list and automatically adds the items to the user's cart. At this time, the order details are sent to the online supermarket in encrypted format.

[0455] Step 8:

[0456] The user checks the final confirmation screen on the smartphone app and confirms the order. Once the user confirms the order, the online supermarket prepares the ingredients and delivers them to the specified address.

[0457] Step 9:

[0458] The server periodically calculates a health score based on the results of the health checkup and updates it monthly. Points are awarded to the user's account based on the score, which helps to motivate the user to improve their health.

[0459] Step 10:

[0460] The server displays health scores and points to users, and also utilizes an emotion engine to provide feedback and messages based on their emotional state, motivating users to continue managing their health.

[0461] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0462] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0463] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0464] [Second embodiment]

[0465] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0466] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0467] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0468] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0469] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0470] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0471] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0472] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0473] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0474] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0475] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0476] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0477] This system utilizes the results of health checkups to suggest optimal ingredients and recipes to users, and allows them to easily place orders through an online supermarket. Furthermore, it calculates the user's health score monthly and awards points according to the score, thereby raising the user's health awareness.

[0478] Overall system configuration

[0479] The system mainly consists of the following components:

[0480] 1. Client terminal (user device)

[0481] 2. Server (including data analysis and generative AI models)

[0482] 3. Online Supermarket API

[0483] Receiving and analyzing health checkup results

[0484] User: Enters health checkup results into a client device (PC or smartphone). For example, blood test results such as LDL cholesterol, HDL cholesterol, triglycerides, and blood glucose levels.

[0485] Terminal: Sends the entered data to the server.

[0486] Server: Analyzes the received data and uses a generative AI model to identify nutrient deficiencies or excesses. For example, it analyzes the data to recommend low-fat foods if LDL cholesterol is high, or low-GI foods if blood sugar levels are high.

[0487] Recipe and ingredient suggestions

[0488] Server: Based on the analysis results, a generative AI model is used to create the optimal recipe for the user, taking into account the user's past medical history and the medical history of their parents to provide more personalized suggestions.

[0489] Terminal: The server generates a recipe and displays the list of ingredients to the user. For example, recipes such as "Simmered Mackerel in Miso" or "Avocado and Quinoa Salad" are displayed.

[0490] Ordering from an online supermarket

[0491] User: Review the suggested recipe and ingredients list and choose to order the ingredients they need from an online supermarket.

[0492] Terminal: Calls the online supermarket's API and automatically processes the process of adding the necessary ingredients to the cart.

[0493] Terminal: A final confirmation screen is displayed to the user to confirm the order details.

[0494] User: Confirms order and purchases ingredients.

[0495] Calculation of health score and awarding points

[0496] Server: Collects regular health checkup results and calculates a monthly health score. The score is calculated based on the degree of improvement, such as whether LDL cholesterol has decreased or blood sugar levels have stabilized.

[0497] Server: Awards points to users based on the calculated score. These points are used to motivate users to improve their health.

[0498] Device: Displays health score and points information to the user, providing feedback for the next health check.

[0499] Specific examples

[0500] 1. User: Enters blood test results into the app (e.g., LDL cholesterol 160 mg / dL, triglycerides 200 mg / dL).

[0501] 2. Terminal: Sends the health check results to the server.

[0502] 3. Server: Analyzes the results and recommends foods rich in omega-3 fatty acids and fiber.

[0503] 4. Server: Generates recipes using mackerel and avocado and sends them to the client device.

[0504] 5. Terminal: Shows the recipe and ingredients list to the user.

[0505] 6. User: Selects to order from the online supermarket.

[0506] 7. Terminal: Add the ingredients you need to your online supermarket cart.

[0507] 8. User: Makes final confirmation and confirms order.

[0508] 9. Server: Calculates monthly health scores and awards points according to improvements.

[0509] 10. Terminal: Displays the user's score and points information, motivating them to take the next health checkup.

[0510] The system allows users to translate their health checkup results into concrete actions, provides easy access to ingredients and recipes that will help improve their health, and encourages ongoing health management through a score and points system.

[0511] The processing flow will be explained below.

[0512] Step 1:

[0513] User: Enters the results of a health check into the client terminal. Specifically, the user enters blood test results (e.g., LDL cholesterol 160 mg / dL, triglycerides 200 mg / dL).

[0514] Step 2:

[0515] Terminal: Sends the entered health check results to the server, which then prepares the data for processing.

[0516] Step 3:

[0517] Server: Stores the received health check results in a database for later analysis.

[0518] Step 4:

[0519] Server: Retrieves health checkup results stored in a database and analyzes them using a generative AI model. Specifically, if LDL cholesterol is high, the analysis recommends a low-fat, high-fiber diet.

[0520] Step 5:

[0521] Server: Based on the analysis results, a generative AI model is used to create individually optimized recipes, such as recipes using ingredients like mackerel and avocado.

[0522] Step 6:

[0523] Server: Sends the created recipe and the list of ingredients to the client device, allowing users to easily access it.

[0524] Step 7:

[0525] Device: The device receives the recipe and ingredient list from the server and displays it to the user, including cooking instructions and nutritional information.

[0526] Step 8:

[0527] User: Checks the suggested recipe and ingredient list and selects to order from the online supermarket. The user confirms the order and proceeds to the next step.

[0528] Step 9:

[0529] Terminal: Calls the online supermarket's API and automatically adds the necessary ingredients to the cart, eliminating the need for the user to manually add them to the cart.

[0530] Step 10:

[0531] Terminal: After the ingredients are added to the cart, the user is presented with a final order confirmation screen where they can finalize their order.

[0532] Step 11:

[0533] User: Checks the order details and presses the button to confirm the order. This completes the ordering process with the online supermarket.

[0534] Step 12:

[0535] Server: Calculates the user's monthly health score based on the results of the health check. The score is calculated based on the degree of improvement, such as whether LDL cholesterol has decreased.

[0536] Step 13:

[0537] Server: Based on the calculated health score, the server awards points to the user. The points are used to increase the user's motivation.

[0538] Step 14:

[0539] Terminal: Displays health score and points information to the user, providing feedback for the next health check.

[0540] These steps allow users to take specific actions based on their health checkup results and aim to improve their health. The system also makes it easy to order ingredients, promoting continuous health management.

[0541] Example 1

[0542] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0543] Conventional health management systems often lack specific feedback and suggestions based on users' health checkup results, leaving users with insufficient means to implement specific health improvements. Furthermore, it is difficult to suggest personalized ingredients and recipes based on individual health conditions, making it difficult for users to easily prepare meals that suit their own health condition. Furthermore, the lack of incentives to continuously promote users' health management makes it difficult to motivate users.

[0544] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0545] In this invention, the server includes a means for receiving health checkup results, a means for analyzing the received results to identify deficient or excessive nutrients, a means for generating individually optimized ingredients and cooking instructions based on the identified nutrients using a generative AI model, a means for automatically placing an order to an e-commerce system based on the generated ingredient list, a means for calculating the user's health score monthly and awarding points according to the score, and a means for transmitting the received health checkup data via a secure communication protocol. This allows for the suggestion and implementation of specific ingredients and recipes based on the individual's health condition, effectively promoting health management and motivation for the user.

[0546] The "means for receiving the results of the medical examination" is a function that allows the server to receive the medical examination data entered by the user.

[0547] "Means for analyzing the received results to identify nutrients that are deficient or in excess" refers to a function that analyzes the received health checkup data using specialized algorithms or AI models to identify nutrients that are deficient or in excess.

[0548] "Means for generating individually optimized ingredients and cooking instructions using a generative AI model" refers to a function that utilizes an AI model (e.g., a generative AI model) to generate optimal ingredients and cooking instructions (recipe) for each user based on identified nutrients.

[0549] "Means for automatically placing orders in an e-commerce system based on the generated ingredient list" refers to a function for automatically ordering ingredients via the API of an online shop or online supermarket based on the generated ingredient list.

[0550] "Means for calculating the user's health score monthly and awarding points according to that score" is a function for calculating the user's health score monthly based on the results of their health checkup and awarding points according to that score.

[0551] "Means for transmitting received medical checkup data using a secure communication protocol" is a function for transmitting the medical checkup data entered by the user to a server using a secure communication protocol (e.g., HTTPS).

[0552] An "electronic commerce system" generally refers to a system that allows products to be purchased online, and specifically includes online supermarkets and online shops.

[0553] A "generative AI model" is an artificial intelligence model that generates a specific output from given input data, such as a text generation model like the GPT series.

[0554] "Identified nutrients" refers to nutrients that are deficient or in excess in the user's health condition, as revealed by the analysis.

[0555] "Cooking instructions" refer to the steps and methods required to prepare a dish, and are generally expressed as a recipe.

[0556] This invention is a system that allows users to order individually optimized ingredients and recipes based on the results of their health checkups through an e-commerce system. Furthermore, it aims to raise users' health awareness by calculating their health score monthly and awarding points based on that score.

[0557] Overall system configuration

[0558] The system mainly consists of the following components:

[0559] 1. Client terminal (user device)

[0560] 2. Server (including data analysis and generative AI models)

[0561] 3. E-commerce system API

[0562] Receiving and analyzing health checkup results

[0563] Users enter their health checkup results using a client terminal such as a PC or smartphone. Specifically, they fill out a form to enter values ​​obtained from blood tests, such as LDL cholesterol, HDL cholesterol, triglycerides, and blood glucose levels. For example, the user might enter data such as LDL cholesterol 160 mg / dL, HDL cholesterol 50 mg / dL, triglycerides 200 mg / dL, and blood glucose 110 mg / dL.

[0564] The device makes an HTTP POST request to send the entered health checkup data to the server, using a secure communication protocol (e.g., HTTPS).

[0565] The server analyzes the received health checkup data. It uses a generative AI model (e.g., GPT-3) to analyze the input numerical data and identify nutrient deficiencies and excesses. Specific examples of prompts used for analysis include the following:

[0566] User's health check data: LDL cholesterol 160 mg / dL, HDL cholesterol 50 mg / dL, triglycerides 200 mg / dL, blood sugar 110 mg / dL.

[0567] Identify nutrient deficiencies and excesses and suggest suitable ingredients and recipes.

[0568] Recipe and ingredient suggestions

[0569] The server uses a generative AI model based on the analysis results to create the optimal recipe for the user. The generated ingredients and cooking instructions take into account information such as the user's past medical history and the medical history of their parents, resulting in a more personalized recommendation.

[0570] The device displays the recipe and ingredient list received from the server to the user. The UI is designed to make it easy for users to browse recipes. For example, recipes such as "Mackerel simmered in miso" and "Avocado and quinoa salad" are displayed.

[0571] Ordering from an online supermarket

[0572] The user reviews the suggested recipe and ingredient list and chooses to order the ingredients they need through the e-commerce system.

[0573] The terminal calls the API of the e-commerce system and automatically adds the suggested ingredients to the cart. A final confirmation screen is displayed, allowing the user to confirm the order details and confirm the order.

[0574] Calculation of health score and awarding points

[0575] The server periodically collects the results of health checkups and calculates the user's monthly health score, which is calculated based on the degree of improvement, such as a reduction in LDL cholesterol and stabilization of blood sugar levels.

[0576] The server awards points to the user based on the health score, which increases the user's motivation to improve their health.

[0577] The device displays the user's health score and points, and provides feedback for the next health check. For example, if the user's health score is 80 and 50 points are awarded, the device displays detailed analysis results and feedback.

[0578] This system makes it easier for users to translate the results of their health checkups into concrete actions, provides convenient access to ingredients and recipes that will help improve their health, and effectively encourages users to continue managing their health through a health score and points system.

[0579] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0580] Step 1:

[0581] User: Enters health checkup results. Specifically, the user uses a client device such as a PC or smartphone to enter the values ​​obtained from a blood test (e.g., LDL cholesterol 160 mg / dL, HDL cholesterol 50 mg / dL, triglycerides 200 mg / dL, blood sugar 110 mg / dL) into an input form.

[0582] Input: Numerical data of health check results.

[0583] Output: Input data of health check results.

[0584] Step 2:

[0585] Terminal: An HTTP POST request is made to send the input data of the health check result to the server. At this time, the data is sent using a secure communication protocol (e.g., HTTPS).

[0586] Input: The entered health check result data and the API endpoint to send it to.

[0587] Output: Health check result sent to the server.

[0588] Step 3:

[0589] Server: Analyzes the received health checkup data. Using a generative AI model (e.g., GPT-3), analyzes the input numerical data and identifies nutrient deficiencies and excesses. Sends prompt text to the generative AI model and obtains the analysis results.

[0590] Input: Medical examination result data. Use the following as an example prompt:

[0591] User's health check data: LDL cholesterol 160 mg / dL, HDL cholesterol 50 mg / dL, triglycerides 200 mg / dL, blood sugar 110 mg / dL.

[0592] Identify nutrient deficiencies and excesses and suggest suitable ingredients and recipes.

[0593] Output: Analysis results identifying nutrient deficiencies and excesses.

[0594] Step 4:

[0595] Server: Based on the analysis results, the server uses a generative AI model to generate the optimal recipe and ingredient list for the user, creating a personalized recipe that takes into account specific past medical history and parental medical history.

[0596] Input: Analysis results (nutrient deficiencies and excess nutrients).

[0597] Output: Individually optimized recipe and ingredient list.

[0598] Step 5:

[0599] Device: The device displays the recipe and ingredient list received from the server to the user. The device displays the recipe in a visually easy-to-understand UI to make it easier for the user to browse the recipe. For example, recipes such as "Mackerel simmered in miso" and "Avocado and quinoa salad" are displayed.

[0600] Input: Recipe and ingredient list sent from the server.

[0601] Output: On-screen display of recipe and ingredients list.

[0602] Step 6:

[0603] User: Review the suggested recipe and ingredient list and choose to order the ingredients they need through the e-commerce system.

[0604] Input: The displayed recipe and ingredient list.

[0605] Output: User's order selection.

[0606] Step 7:

[0607] Terminal: Calls the API of the e-commerce system and automatically adds the suggested ingredients to the cart. A final confirmation screen is displayed, allowing the user to confirm the order and confirm it.

[0608] Input: User order selection and API endpoint of e-commerce system.

[0609] Output: Ingredients added to cart and final confirmation screen.

[0610] Step 8:

[0611] User: Check the order details on the final confirmation screen and confirm the order.

[0612] Input: Information from the final confirmation screen.

[0613] Output: Confirmed order details.

[0614] Step 9:

[0615] Server: Periodically collects health checkup results and calculates the user's monthly health score. The health score is calculated based on the degree of improvement, such as lowering LDL cholesterol and stabilizing blood sugar levels.

[0616] Input: Health checkup result data.

[0617] Output: User's health score.

[0618] Step 10:

[0619] Server: Awards points to users based on their health score, which motivates them to improve their health.

[0620] Input: User's health score.

[0621] Output: Points awarded to the user.

[0622] Step 11:

[0623] Device: Displays the user's health score and points, and provides feedback for the next health check. For example, if the user's health score is 80 and 50 points are awarded, the device displays detailed analysis results and feedback.

[0624] Input: Health score and points information.

[0625] Output: A screen display of the health score and points information presented to the user.

[0626] (Application example 1)

[0627] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0628] In modern society, personal health management is highly valued, and it is recognized that diet in particular has a significant impact on health status. However, there is no system in place to suggest individually optimized ingredients and recipes based on health checkup results, and to easily purchase and order them. As a result, it is difficult for users to choose meals based on their own health status, and there is an issue of ineffective health management. There is also a lack of mechanisms to maintain motivation for health management.

[0629] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0630] In this invention, the server includes a means for receiving health checkup results, a means for analyzing the received results to identify necessary nutrients, and a means for generating individually optimized ingredients and recipes based on the identified nutrients. This allows users to receive recommendations for ingredients and recipes based on the health checkup results and easily order and purchase them through an e-commerce platform or delivery service. Furthermore, by including a means for calculating the user's health score and awarding points according to the score, the server can increase the user's motivation to manage their health.

[0631] A "medical examination" is a medical test conducted to assess an individual's health status.

[0632] Analyzing the "results" means determining the user's health condition and identifying necessary nutrients based on the received health checkup data.

[0633] "Nutrients" are substances such as vitamins, minerals, proteins, carbohydrates, and fats that the body needs to function properly.

[0634] "Ingredients" refers to the food ingredients used to prepare a dish.

[0635] "Recipe" means a list of instructions or ingredients for making a particular dish.

[0636] An "e-commerce platform" is an online system for buying and selling goods and services over the Internet.

[0637] "Automatically placing an order" means that the system automatically processes the product order without the user having to manually enter any information.

[0638] A "health score" is a numerical representation of a user's health condition based on specific criteria.

[0639] "Awarding points" means providing points to users as a reward based on specific actions or conditions.

[0640] A "delivery service" is a service that delivers goods from a specific location to another location.

[0641] This invention is a system that utilizes the user's health checkup results to suggest optimal ingredients and recipes for the user, and then orders them via an e-commerce platform and delivery service.The invention is mainly composed of client terminals such as smartphones, tablets, and PCs, and a server that includes data analysis and generative AI models.

[0642] 1. Receipt and analysis of health examination results

[0643] The server receives health checkup results from the user. The user inputs their health checkup results (e.g., LDL cholesterol, triglycerides, etc.) using an application on their smartphone or PC. The health checkup result data sent from the device is received by the server. The server analyzes the received data and uses a generative AI model to identify nutrients that are lacking or in excess. As a specific example, if LDL cholesterol is high, the server analyzes it to recommend low-fat foods.

[0644] 2. Recipe and ingredient suggestions

[0645] Based on the analysis results, the server uses a generative AI model to generate the optimal recipe for the user, taking into account factors such as past medical history and the medical history of the user's parents. The generated recipe and a list of ingredients are sent to the user's device. For example, recipes such as "grilled salmon" and "oatmeal salad" are displayed.

[0646] 3. Ordering from online supermarkets and delivery services

[0647] When the user checks the suggested recipes and ingredients and selects an order, the server calls the API of the e-commerce platform and automatically adds the necessary ingredients to the cart. Furthermore, in a similar way, it is possible to order food based on the optimized recipes via a delivery service. The user confirms the order details on a final confirmation screen and confirms the order.

[0648] 4. Calculation of health score and awarding points

[0649] The server periodically collects the user's health checkup results and calculates a monthly health score. This score is calculated based on the degree of improvement, such as whether LDL cholesterol has decreased or whether blood sugar levels are stable. Points are awarded to the user based on the calculated score. The point information is displayed on the user's device, providing feedback for the next health checkup.

[0650] Examples of specific examples and prompts

[0651] In an actual usage scenario, a user enters the results of a health check into the app. For example, "LDL cholesterol 160 mg / dL, triglycerides 200 mg / dL." The entered data is sent to the server for analysis. The generative AI model recommends ingredients rich in omega-3 fatty acids and fiber, and the server generates recipes such as "grilled salmon" and "avocado and quinoa salad." The user can then select the procedure to order food based on the provided recipe from a delivery service.

[0652] Example prompt for a generative AI model:

[0653] "The user's health check results show LDL cholesterol of 160 mg / dL and triglycerides of 200 mg / dL. Generate suitable ingredient and recipe suggestions for this user. Also, generate an API request to process the food order based on the results."

[0654] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0655] Step 1:

[0656] Users input the results of their health checkups using an application on their smartphone or PC. This input data includes specific numerical values ​​such as LDL cholesterol and triglycerides. This data is sent from the device to the server. The input data format is specified as a number or a string.

[0657] Step 2:

[0658] The server receives the health checkup result data sent from the device. The received data is stored in a database. The server then starts the analysis process and inputs the received data into the generative AI model. The AI ​​model identifies nutrients that are lacking or in excess based on the input data. The identified nutrients are output as the analysis results.

[0659] Step 3:

[0660] The server generates the optimal recipe for the user based on the analysis results. Using a generative AI model, it also takes into account the user's past medical history and the medical history of their parents. The generated recipe and the list of ingredients are stored in a database and sent to the client device. The generated recipe includes the specific dish name and list of ingredients.

[0661] Step 4:

[0662] The terminal displays the recipe and ingredient list sent from the server to the user. The user checks the displayed recipe and ingredient list. This real-time display allows the user to select the recipe that is best for them.

[0663] Step 5:

[0664] After the user selects a recipe and ingredients, the device automatically calls the API of the e-commerce platform to add the required ingredients to the cart. The API call includes a list of selected ingredients and the user's shipping information.

[0665] Step 6:

[0666] The user checks the final confirmation screen on the terminal to confirm that there are no problems with the order details. If the user's confirmation is received, the order is confirmed and the data is sent to the server. The server then sends the order information to the delivery service.

[0667] Step 7:

[0668] The server periodically collects the user's health checkup results and calculates a monthly health score. The health score is quantified based on the degree of improvement and the maintained health status. The server awards points to the user based on this score. The point information is stored in a database and sent to the client terminal.

[0669] Step 8:

[0670] The device displays the health score and points information to the user, who can then use the displayed information to obtain feedback for their next health checkup, increasing their motivation to manage their health.

[0671] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0672] This invention is a system that utilizes the results of health checkups to not only suggest optimal ingredients and recipes to users, but also provides more personalized health management by combining it with an emotion engine that recognizes the user's emotions. This system calculates the user's health score monthly and awards points according to the score, thereby raising the user's health awareness.

[0673] Overall system configuration

[0674] The system mainly consists of the following components:

[0675] 1. Client terminal (user device)

[0676] 2. Server (including data analysis and generative AI models, emotion engine)

[0677] 3. Online Supermarket API

[0678] Receiving and analyzing health checkup results

[0679] User: Enters health checkup results into a client device (PC or smartphone). For example, blood test results such as LDL cholesterol, HDL cholesterol, triglycerides, and blood glucose levels.

[0680] Terminal: Sends the entered data to the server.

[0681] Server: Stores the received data in a database and analyzes it using a generative AI model. This analysis identifies nutrient deficiencies and excesses and suggests appropriate ingredients and recipes.

[0682] Recipe and ingredient suggestions

[0683] Server: Based on the analysis results, the server uses a generative AI model to create individually optimized recipes, taking into account the user's past medical history and the medical history of their parents. Additionally, the server uses an emotion engine to recognize the user's emotional state and recommend ingredients and recipes that are effective in relieving stress and improving mood.

[0684] Terminal: The server generates a recipe and displays the list of ingredients to the user. For example, recipes such as "Simmered Mackerel in Miso" or "Avocado and Quinoa Salad" are displayed.

[0685] Ordering from an online supermarket

[0686] User: Review the suggested recipe and ingredients list and choose to order from the online supermarket.

[0687] Terminal: Calls the online supermarket's API and automatically adds the necessary ingredients to the cart.

[0688] Terminal: A final confirmation screen is displayed to the user to confirm the order details.

[0689] User: Confirms order and purchases ingredients.

[0690] Calculation of health score and awarding points

[0691] Server: Collects regular health checkup results and calculates a monthly health score. The score is calculated based on the degree of improvement, such as whether LDL cholesterol has decreased or blood sugar levels have stabilized.

[0692] Server: Gives points to users based on their health score, which increases their motivation.

[0693] Device: Displays health score and points information to the user. Provides feedback and messages according to the emotional state recognized by the emotion engine.

[0694] Specific examples

[0695] 1. User: Enters blood test results into the app (e.g., LDL cholesterol 160 mg / dL, triglycerides 200 mg / dL).

[0696] 2. Terminal: Sends the health check results to the server.

[0697] 3. Server: Analyzes the results and recommends foods rich in omega-3 fatty acids and fiber. If the emotion engine detects stress, it will also recommend relaxing drinks like chamomile tea.

[0698] 4. Server: Generates recipes using mackerel and avocado and sends them to the client device.

[0699] 5. Terminal: Shows the recipe and ingredients list to the user.

[0700] 6. User: Selects to order from the online supermarket.

[0701] 7. Terminal: Add the ingredients you need to your online supermarket cart.

[0702] 8. User: Makes final confirmation and confirms order.

[0703] 9. Server: Calculates monthly health scores and awards points according to improvements.

[0704] 10. Terminal: Displays score and point information to the user and provides feedback based on the emotion engine.

[0705] This system allows users to take specific actions based on their health checkup results and aim to improve their health. In addition, the introduction of an emotion engine enables more personalized health management and makes suggestions that take into account the user's psychological stress and motivation.

[0706] The processing flow will be explained below.

[0707] Step 1:

[0708] User: Enters the results of a health check into a client terminal. Input items include blood test results (e.g., LDL cholesterol 160 mg / dL, triglycerides 200 mg / dL) and blood glucose levels.

[0709] Step 2:

[0710] Terminal: Sends the entered health check results to the server, where the data is stored for immediate processing.

[0711] Step 3:

[0712] Server: Stores the results of the health checkup in a database. This data is used for later analysis.

[0713] Step 4:

[0714] Server: The generative AI model retrieves health checkup results stored in a database and analyzes them. This analysis includes identifying specific nutrients, such as recommending a low-fat, high-fiber diet if LDL cholesterol is high.

[0715] Step 5:

[0716] Server: After identifying the necessary nutrients, it uses a generative AI model to create the best recipe for the user, for example, using ingredients like mackerel and avocado.

[0717] Step 6:

[0718] Server: Sends the created recipe and the list of required ingredients in JSON format to the client device, so that the recipe information is ready to be displayed on the device.

[0719] Step 7:

[0720] Terminal: The recipe and ingredient list received from the server are displayed to the user, allowing the user to view the suggested recipes.

[0721] Step 8:

[0722] User: Review the suggested recipe and ingredient list and choose to order from the online supermarket, making it easy for users to purchase the perfect ingredients.

[0723] Step 9:

[0724] Device: Calls the online supermarket's API and automatically adds the necessary ingredients to the cart. In addition, an emotion engine captures the user's emotional state and makes fun comments and suggestions.

[0725] Step 10:

[0726] Terminal: After the ingredients are added to the cart, a final confirmation screen for the order is displayed to the user, where the user can make final confirmation of the order.

[0727] Step 11:

[0728] User: Checks the order details and presses the button to confirm the order. This completes the order to the online supermarket.

[0729] Step 12:

[0730] Server: Calculates the user's monthly health score based on the results of the health check. The score is based on the degree of improvement, such as whether LDL cholesterol has decreased or blood sugar levels have stabilized.

[0731] Step 13:

[0732] Server: Based on the calculated health score, the server awards points to the user. Different points are awarded depending on the degree of health improvement.

[0733] Step 14:

[0734] Device: Displays health score and points information to the user. Adds feedback and encouraging messages depending on the user's emotional state as recognized by the emotion engine.

[0735] In this way, users can take specific actions based on their health checkup results, enabling multifaceted and personalized health management.In addition, the introduction of an emotion engine allows feedback and suggestions to be provided that take into account the user's psychological aspects.

[0736] Example 2

[0737] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0738] In modern society, individual health management is important, but it is difficult for users to properly understand their health checkup results and implement improvement measures. Current systems, which present uniform dietary and lifestyle improvement measures without considering the user's emotional state, have the problem of making it difficult to provide optimal support for each individual user. Furthermore, there is a lack of methods to maintain motivation in order to continuously support users' health improvement.

[0739] 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. In this invention, the server includes a means for receiving health checkup results, a means for analyzing the received results to identify necessary nutrients, a means for generating individually optimized ingredients and recipes based on the identified nutrients, a means for recognizing the user's emotions and adjusting suggestions based on the user's emotional state, a means for automatically placing an order with an e-commerce service based on the generated ingredient list, and a means for calculating the user's health score and awarding points according to the score. This allows the user to properly understand their own health status and implement optimal health improvement measures that take their emotional state into consideration. Furthermore, the monthly updated health score and point awarding help maintain the user's motivation.

[0740] "Physical examination results" are data on the results of a series of medical tests conducted to assess the user's health condition.

[0741] The "receiving means" is a system component that receives data sent from a user via a network, converts it into an appropriate format, and stores it.

[0742] The "means for analyzing and identifying necessary nutrients" is an algorithm or program that determines which nutrients are deficient or in excess based on the received health check results.

[0743] The "means for generating individually optimized ingredients and recipes" refers to a generative AI model or program that provides optimal ingredients and recipes for each user based on the identified nutrients.

[0744] The "means for recognizing the user's emotions and adjusting suggestions based on the emotional state" refers to an emotion engine or analysis program that analyzes the user's emotional state and changes the suggestions made in accordance with that state.

[0745] "Means for automatically placing an order with an e-commerce service based on the generated ingredient list" refers to a system component that uses the generated ingredient list to call the API of an e-commerce service (such as an online supermarket) and automatically processes the order.

[0746] The "means for calculating a user's health score" refers to an algorithm or program that calculates a score for quantitatively evaluating the user's health condition.

[0747] The "means for awarding points" is a system component that awards points to the user as an incentive based on the calculated health score.

[0748] This invention is a system for personalizing health management for users, and is composed of the following components: a client terminal (user device), a server (including a data analysis and generation AI model and an emotion engine), and an e-commerce service API. The specific roles and operations of each component are explained below.

[0749] Receiving and analyzing health checkup results

[0750] User: The user inputs the results of their health checkup into the client terminal. The results of the health checkup include, for example, LDL cholesterol, HDL cholesterol, triglycerides, and blood glucose levels. This information is entered as numerical values ​​into the application's input form.

[0751] Terminal: The terminal sends the health check result data entered by the user to the server. The data is sent using the HTTPS protocol and packaged in JSON format. The sent data is then prepared for processing by the server.

[0752] Server: The server stores the received data in a database, typically a relational database such as MySQL or PostgreSQL. The stored data is then analyzed using a generative AI model. This analysis identifies nutrient deficiencies and excesses.

[0753] Recipe and ingredient suggestions

[0754] Server: The server uses a generative AI model based on the analysis results to create individually optimized recipes. This process also takes into account the user's past medical history and the medical history of their parents. It also uses an emotion engine to recognize the user's emotional state and recommend ingredients and recipes that are effective in relieving stress and improving mood.

[0755] For example, if the system recognizes the user's stress level, it will suggest recipes using chamomile tea, which has a relaxing effect, or mackerel, which is rich in omega-3 fatty acids.

[0756] Ordering from an online supermarket

[0757] User: The user reviews the suggested recipe and ingredient list displayed on the device and selects to order from the online supermarket. The order is placed using the app interface, and the selected ingredients are automatically added to the cart.

[0758] Terminal: The terminal calls the online supermarket's API and automatically adds the required ingredients to the cart. This process uses the REST API and sends the order data via a POST request. The terminal then displays a final confirmation screen for the user to confirm the order.

[0759] User: The user confirms the order and confirms it, completing the food purchase process.

[0760] Calculation of health score and awarding points

[0761] Server: The server periodically collects health checkup results and calculates a monthly health score. The score is calculated based on the degree of improvement, such as whether LDL cholesterol has decreased or whether blood sugar levels are stable. Points are awarded to the user based on the calculated score.

[0762] Terminal: The terminal displays the health score and points to the user. This is done using graphs and numerical displays. In addition, feedback and messages are displayed according to the emotional state recognized by the emotion engine. For example, a message such as "Your health score is 80 points! That's a great achievement" can be displayed to motivate the user.

[0763] Specific examples

[0764] Here is an example of this system:

[0765] 1. User: Enters blood test results into the application's input form and clicks the "Submit" button.

[0766] 2. Terminal: Packages the input data in JSON format and sends it to the server using the HTTPS protocol.

[0767] 3. Server: Stores the received data in a relational database and analyzes it using a generative AI model. As a result of the analysis, it identifies nutrient deficiencies and excesses and generates appropriate ingredients and recipes.

[0768] 4. Server: Combines generative AI models and an emotion engine to generate personalized recipe and ingredient suggestions. For example, if a user's emotion is recognized as stressful, it will suggest ingredients with a relaxing effect (e.g., chamomile tea).

[0769] 5. On the device: Display suggested recipes and ingredient lists in list or card view. For example, "Simmered Mackerel with Miso" or "Avocado and Quinoa Salad" are displayed.

[0770] 6. User: Select the ingredients you want to purchase from the displayed list and click the "Order" button.

[0771] 7. Device: Calls the online supermarket's API to add ingredients to the cart and displays a final confirmation screen. The user checks the details and confirms the order.

[0772] 8. Server: Calculates monthly health score and awards points based on the score.

[0773] 9. Device: Displays health scores and points in graphs and numerical formats. Emotion engine recognizes the user's emotional state and displays feedback and messages to motivate the user.

[0774] This system allows users to take specific actions based on their health checkup results and aim to improve their health. In addition, the introduction of an emotion engine enables more personalized health management and makes suggestions that take into account the user's psychological stress and motivation.

[0775] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0776] Step 1:

[0777] User: Enter the results of the health check into the client terminal.

[0778] Input: Health checkup result data (e.g., LDL cholesterol, HDL cholesterol, triglycerides, blood glucose levels).

[0779] Action: Enter the health check data as numbers into the application's input form and click the "Submit" button.

[0780] Output: Health checkup data entry completed (data saved on the device).

[0781] Step 2:

[0782] Terminal: Sends the entered data to the server.

[0783] Input: Medical examination data.

[0784] What it does: It packages data in JSON format and sends it to the server using the HTTPS protocol.

[0785] Output: Health check data in JSON format is sent to the server.

[0786] Step 3:

[0787] Server: Stores the received data in a database.

[0788] Input: Submitted health check data (JSON format).

[0789] What it does: Stores the received data in a database (e.g. MySQL or PostgreSQL).

[0790] Output: The health check data is stored in a database.

[0791] Step 4:

[0792] Server: Analyzes the stored data using a generative AI model.

[0793] Input: Medical examination data stored in a database.

[0794] How it works: Generative AI models are used to analyze data and identify nutrient deficiencies and excesses.

[0795] Output: Nutrient information identified as a result of the analysis.

[0796] Step 5:

[0797] Server: Utilizes generative AI models to generate individually optimized recipe and ingredient suggestions.

[0798] Input: Analysis results (identified nutrient information).

[0799] How it works: Combines generative AI models and an emotion engine to generate personalized recipes and ingredient lists.

[0800] Output: A suggested recipe and ingredients list.

[0801] Step 6:

[0802] Terminal: Displays the server-generated recipe and ingredient list to the user.

[0803] Input: Recipe and ingredient list sent from the server.

[0804] What it does: Displays a recipe and ingredient list using a list or card view.

[0805] Output: The recipe and ingredients list displayed to the user.

[0806] Step 7:

[0807] User: Review the suggested recipe and ingredients list and choose to order from the online supermarket.

[0808] Input: Recipe and ingredient list displayed on device.

[0809] Action: Select the ingredients you want to purchase and click the "Order" button.

[0810] Output: An order request to the online supermarket.

[0811] Step 8:

[0812] Terminal: Calls the online supermarket's API and automatically adds the necessary ingredients to the cart.

[0813] Input: A list of ingredients selected by the user.

[0814] How it works: Using the REST API, the order data is sent via a POST request and added to the cart.

[0815] Output: Information about ingredients added to the online supermarket cart.

[0816] Step 9:

[0817] Terminal: A final confirmation screen is displayed to the user to confirm the order details.

[0818] Input: Information about ingredients added to cart.

[0819] Behavior: Displays a final confirmation screen, prompting the user for confirmation.

[0820] Output: Order confirmation by user.

[0821] Step 10:

[0822] Server: Calculates monthly health score.

[0823] Input: Routinely collected health examination result data.

[0824] How it works: It uses an algorithm or program to calculate a health score, specifically assessing things like lowering LDL cholesterol and stabilizing blood sugar levels.

[0825] Output: Calculated health score.

[0826] Step 11:

[0827] Server: Awards points based on the calculated health score.

[0828] Input: Health score.

[0829] What it does: Uses the points awarding algorithm to award the appropriate points to the user.

[0830] Output: The user's points are updated.

[0831] Step 12:

[0832] Terminal: Displays health score and points information to the user.

[0833] Input: Health score and updated points information.

[0834] What it does: Displays information using graphical and numerical displays and provides feedback based on an emotion engine.

[0835] Output: Health score and points information displayed to the user, and feedback from the emotion engine.

[0836] (Application example 2)

[0837] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0838] In modern society, there is a demand for systems that suggest specific actions based on individual health conditions and support the implementation of those actions. In particular, it is important to make suggestions for improving daily eating habits using health checkup results, but comprehensive health management that also takes into account the user's emotional state is also necessary. It is also important to have a system that makes it easy to obtain the suggested ingredients and recipes. Furthermore, continuous feedback and incentives are required to maintain the user's motivation to improve their health. There is currently a lack of systems that meet these requirements, so solving this problem is a challenge.

[0839] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0840] In this invention, the server includes means for receiving health checkup results, means for analyzing the received results to identify necessary nutrients, means for generating individually optimized ingredients and recipes based on the identified nutrients, means for automatically placing orders with a food delivery service based on the generated ingredient list, means for calculating the user's health score and awarding points according to the score, and means for detecting the user's emotional state and making recommendations based on the score. This allows the user to receive effective and emotionally sensitive meal suggestions based on the health checkup results and easily purchase the suggested ingredients. Furthermore, by visualizing the user's improvement using the health score and providing incentives through point awarding, motivation for continuous health management can be maintained.

[0841] "Health checkup results" refers to information on blood test items and physical measurements obtained as a result of regular medical examinations and tests that a user undergoes.

[0842] "Nutrients" are components such as vitamins, minerals, proteins, lipids, and carbohydrates that are necessary for maintaining health.

[0843] "Ingredients and recipes" refers to the ingredients used to prepare a meal and the steps and methods for cooking using those ingredients.

[0844] A "food delivery service" is a service that delivers ingredients and dishes selected by the user to their home or designated location.

[0845] "Emotional state" is information that indicates the user's current psychological state, including emotions such as stress, joy, and sadness.

[0846] A "recommendation" is the optimal action or option suggested based on the user's needs and situation.

[0847] A "health score" is a numerical value calculated based on the user's health checkup results and behavioral data, and is an indicator used to evaluate their health status.

[0848] "Points" are rewards and incentives awarded based on health scores and actions, and are used to increase user motivation.

[0849] "Past medical history and parental medical history" refers to historical information about illnesses and health conditions that the user or their parents have experienced in the past.

[0850] The system of this invention is a personalized healthy food delivery app that not only suggests optimal ingredients and recipes to users based on their health checkup results, but also incorporates an emotion engine that recognizes the user's emotions. This system has the following configuration and processing steps.

[0851] Key Components of the System

[0852] 1. Client terminal (user device)

[0853] Smartphone (iOS / Android)

[0854] 2. Server

[0855] Data analysis and generative AI models (Python, TensorFlow)

[0856] Emotion Engine (NLP model, Hugging Face)

[0857] Database (MySQL)

[0858] Net supermarket API integration (REST API)

[0859] Program processing

[0860] 1. User Input

[0861] Users use a smartphone app to input their health checkup results and current emotional state, such as their LDL cholesterol and blood sugar levels from a blood test, and their emotional state, such as "I'm stressed."

[0862] 2. Send to the server

[0863] The data entered from the smartphone is sent to a server, where it is stored in a database and used for analysis.

[0864] 3. Data Analysis

[0865] The server then analyzes the received data using Python scripts and TensorFlow models to identify nutrients and foods necessary for the user's health. For example, if the user's LDL cholesterol is high, it will recommend foods rich in omega-3 fatty acids.

[0866] 4. Emotion Engine

[0867] The server uses an emotion engine to analyze the user's emotion data, and uses an NLP model (e.g., Hugging Face) to suggest foods that have a relaxing effect if the user is feeling stressed.

[0868] 5. Recipe Generation

[0869] Based on the analysis results, a generative AI model (e.g., GPT-3) is used to generate individually optimized recipes. For example, recipes such as "Simmered Mackerel in Miso" or "Avocado and Quinoa Salad" are generated. These recipes are then sent to the client device.

[0870] 6. Food delivery collaboration

[0871] The generated ingredient list is automatically added to the user's cart via the online supermarket's API, and the user then makes a final confirmation on the smartphone app to confirm the order.

[0872] 7. Health Score and Points

[0873] The server periodically calculates a health score based on the results of health checkups and updates the user's score monthly. Points are awarded based on this score, which acts as an incentive to keep the user motivated to improve their health.

[0874] Specific examples

[0875] For example, specific user input and suggestions are given below:

[0876] Example user input: "LDL cholesterol 180 mg / dL, triglycerides 220 mg / dL, emotional state: stressed"

[0877] Example prompts to input to a generative AI model:

[0878] The user's health check results are as follows:

[0879] LDL cholesterol: 180 mg / dL

[0880] Triglycerides: 220 mg / dL

[0881] The user's emotional state is stress. Suggest ingredients and recipes appropriate for the user's state.

[0882] In this way, users can receive effective and emotionally sensitive dietary suggestions based on their health checkup results, and can easily purchase the suggested ingredients. Furthermore, the degree of improvement is visualized through a health score, and points are awarded to maintain motivation for ongoing health management.

[0883] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0884] Step 1:

[0885] Users use a smartphone app to input their health check results and current emotional state, including blood test results and emotional state options, which provides specific data about the user's condition.

[0886] Step 2:

[0887] The terminal sends the data entered by the user to the server. During this process, the data is encrypted and sent, and then securely stored in a database. Here, the input data is stored on the server in preparation for the next analysis step.

[0888] Step 3:

[0889] The server analyzes the received data using Python scripts and TensorFlow models to identify the nutrients and ingredients needed based on the user's health condition. The analysis results are output as a report of the identified nutrients and their deficiency or excess.

[0890] Step 4:

[0891] The server uses an emotion engine to analyze the user's emotion data. It uses an NLP model (e.g., Hugging Face) to generate appropriate recommendations based on the input emotion. For example, if stress is detected, foods with a relaxing effect will be recommended.

[0892] Step 5:

[0893] The server generates individually optimized recipes based on the analysis results using a generative AI model (e.g., GPT-3). The generated recipes are output along with a list of ingredients and sent to the client device.

[0894] Step 6:

[0895] The terminal displays the recipe and ingredient list received from the server to the user, who can then confirm the ingredients based on the suggested list and select a food delivery service.

[0896] Step 7:

[0897] The server then calls the online supermarket's API based on the generated ingredient list and automatically adds the items to the user's cart. At this time, the order details are sent to the online supermarket in encrypted format.

[0898] Step 8:

[0899] The user checks the final confirmation screen on the smartphone app and confirms the order. Once the user confirms the order, the online supermarket prepares the ingredients and delivers them to the specified address.

[0900] Step 9:

[0901] The server periodically calculates a health score based on the results of the health checkup and updates it monthly. Points are awarded to the user's account based on the score, which helps to motivate the user to improve their health.

[0902] Step 10:

[0903] The server displays health scores and points to users, and also utilizes an emotion engine to provide feedback and messages based on their emotional state, motivating users to continue managing their health.

[0904] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0905] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0906] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0907] [Third embodiment]

[0908] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0909] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0910] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0911] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0912] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0913] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0914] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0915] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0916] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0917] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0918] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0919] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0920] This system utilizes the results of health checkups to suggest optimal ingredients and recipes to users, and allows them to easily place orders through an online supermarket. Furthermore, it calculates the user's health score monthly and awards points according to the score, thereby raising the user's health awareness.

[0921] Overall system configuration

[0922] The system mainly consists of the following components:

[0923] 1. Client terminal (user device)

[0924] 2. Server (including data analysis and generative AI models)

[0925] 3. Online Supermarket API

[0926] Receiving and analyzing health checkup results

[0927] User: Enters health checkup results into a client device (PC or smartphone). For example, blood test results such as LDL cholesterol, HDL cholesterol, triglycerides, and blood glucose levels.

[0928] Terminal: Sends the entered data to the server.

[0929] Server: Analyzes the received data and uses a generative AI model to identify nutrient deficiencies or excesses. For example, it analyzes the data to recommend low-fat foods if LDL cholesterol is high, or low-GI foods if blood sugar levels are high.

[0930] Recipe and ingredient suggestions

[0931] Server: Based on the analysis results, a generative AI model is used to create the optimal recipe for the user, taking into account the user's past medical history and the medical history of their parents to provide more personalized suggestions.

[0932] Terminal: The server generates a recipe and displays the list of ingredients to the user. For example, recipes such as "Simmered Mackerel in Miso" or "Avocado and Quinoa Salad" are displayed.

[0933] Ordering from an online supermarket

[0934] User: Review the suggested recipe and ingredients list and choose to order the ingredients they need from an online supermarket.

[0935] Terminal: Calls the online supermarket's API and automatically processes the process of adding the necessary ingredients to the cart.

[0936] Terminal: A final confirmation screen is displayed to the user to confirm the order details.

[0937] User: Confirms order and purchases ingredients.

[0938] Calculation of health score and awarding points

[0939] Server: Collects regular health checkup results and calculates a monthly health score. The score is calculated based on the degree of improvement, such as whether LDL cholesterol has decreased or blood sugar levels have stabilized.

[0940] Server: Awards points to users based on the calculated score. These points are used to motivate users to improve their health.

[0941] Device: Displays health score and points information to the user, providing feedback for the next health check.

[0942] Specific examples

[0943] 1. User: Enters blood test results into the app (e.g., LDL cholesterol 160 mg / dL, triglycerides 200 mg / dL).

[0944] 2. Terminal: Sends the health check results to the server.

[0945] 3. Server: Analyzes the results and recommends foods rich in omega-3 fatty acids and fiber.

[0946] 4. Server: Generates recipes using mackerel and avocado and sends them to the client device.

[0947] 5. Terminal: Shows the recipe and ingredients list to the user.

[0948] 6. User: Selects to order from the online supermarket.

[0949] 7. Terminal: Add the ingredients you need to your online supermarket cart.

[0950] 8. User: Makes final confirmation and confirms order.

[0951] 9. Server: Calculates monthly health scores and awards points according to improvements.

[0952] 10. Terminal: Displays the user's score and points information, motivating them to take the next health checkup.

[0953] The system allows users to translate their health checkup results into concrete actions, provides easy access to ingredients and recipes that will help improve their health, and encourages ongoing health management through a score and points system.

[0954] The processing flow will be explained below.

[0955] Step 1:

[0956] User: Enters the results of a health check into the client terminal. Specifically, the user enters blood test results (e.g., LDL cholesterol 160 mg / dL, triglycerides 200 mg / dL).

[0957] Step 2:

[0958] Terminal: Sends the entered health check results to the server, which then prepares the data for processing.

[0959] Step 3:

[0960] Server: Stores the received health check results in a database for later analysis.

[0961] Step 4:

[0962] Server: Retrieves health checkup results stored in a database and analyzes them using a generative AI model. Specifically, if LDL cholesterol is high, the analysis recommends a low-fat, high-fiber diet.

[0963] Step 5:

[0964] Server: Based on the analysis results, a generative AI model is used to create individually optimized recipes, such as recipes using ingredients like mackerel and avocado.

[0965] Step 6:

[0966] Server: Sends the created recipe and the list of ingredients to the client device, allowing users to easily access it.

[0967] Step 7:

[0968] Device: The device receives the recipe and ingredient list from the server and displays it to the user, including cooking instructions and nutritional information.

[0969] Step 8:

[0970] User: Checks the suggested recipe and ingredient list and selects to order from the online supermarket. The user confirms the order and proceeds to the next step.

[0971] Step 9:

[0972] Terminal: Calls the online supermarket's API and automatically adds the necessary ingredients to the cart, eliminating the need for the user to manually add them to the cart.

[0973] Step 10:

[0974] Terminal: After the ingredients are added to the cart, the user is presented with a final order confirmation screen where they can finalize their order.

[0975] Step 11:

[0976] User: Checks the order details and presses the button to confirm the order. This completes the ordering process with the online supermarket.

[0977] Step 12:

[0978] Server: Calculates the user's monthly health score based on the results of the health check. The score is calculated based on the degree of improvement, such as whether LDL cholesterol has decreased.

[0979] Step 13:

[0980] Server: Based on the calculated health score, the server awards points to the user. The points are used to increase the user's motivation.

[0981] Step 14:

[0982] Terminal: Displays health score and points information to the user, providing feedback for the next health check.

[0983] These steps allow users to take specific actions based on their health checkup results and aim to improve their health. The system also makes it easy to order ingredients, promoting continuous health management.

[0984] Example 1

[0985] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0986] Conventional health management systems often lack specific feedback and suggestions based on users' health checkup results, leaving users with insufficient means to implement specific health improvements. Furthermore, it is difficult to suggest personalized ingredients and recipes based on individual health conditions, making it difficult for users to easily prepare meals that suit their own health condition. Furthermore, the lack of incentives to continuously promote users' health management makes it difficult to motivate users.

[0987] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0988] In this invention, the server includes a means for receiving health checkup results, a means for analyzing the received results to identify deficient or excessive nutrients, a means for generating individually optimized ingredients and cooking instructions based on the identified nutrients using a generative AI model, a means for automatically placing an order to an e-commerce system based on the generated ingredient list, a means for calculating the user's health score monthly and awarding points according to the score, and a means for transmitting the received health checkup data via a secure communication protocol. This allows for the suggestion and implementation of specific ingredients and recipes based on the individual's health condition, effectively promoting health management and motivation for the user.

[0989] The "means for receiving the results of the medical examination" is a function that allows the server to receive the medical examination data entered by the user.

[0990] "Means for analyzing the received results to identify nutrients that are deficient or in excess" refers to a function that analyzes the received health checkup data using specialized algorithms or AI models to identify nutrients that are deficient or in excess.

[0991] "Means for generating individually optimized ingredients and cooking instructions using a generative AI model" refers to a function that utilizes an AI model (e.g., a generative AI model) to generate optimal ingredients and cooking instructions (recipe) for each user based on identified nutrients.

[0992] "Means for automatically placing orders in an e-commerce system based on the generated ingredient list" refers to a function for automatically ordering ingredients via the API of an online shop or online supermarket based on the generated ingredient list.

[0993] "Means for calculating the user's health score monthly and awarding points according to that score" is a function for calculating the user's health score monthly based on the results of their health checkup and awarding points according to that score.

[0994] "Means for transmitting received medical checkup data using a secure communication protocol" is a function for transmitting the medical checkup data entered by the user to a server using a secure communication protocol (e.g., HTTPS).

[0995] An "electronic commerce system" generally refers to a system that allows products to be purchased online, and specifically includes online supermarkets and online shops.

[0996] A "generative AI model" is an artificial intelligence model that generates a specific output from given input data, such as a text generation model like the GPT series.

[0997] "Identified nutrients" refers to nutrients that are deficient or in excess in the user's health condition, as revealed by the analysis.

[0998] "Cooking instructions" refer to the steps and methods required to prepare a dish, and are generally expressed as a recipe.

[0999] This invention is a system that allows users to order individually optimized ingredients and recipes based on the results of their health checkups through an e-commerce system. Furthermore, it aims to raise users' health awareness by calculating their health score monthly and awarding points based on that score.

[1000] Overall system configuration

[1001] The system mainly consists of the following components:

[1002] 1. Client terminal (user device)

[1003] 2. Server (including data analysis and generative AI models)

[1004] 3. E-commerce system API

[1005] Receiving and analyzing health checkup results

[1006] Users enter their health checkup results using a client terminal such as a PC or smartphone. Specifically, they fill out a form to enter values ​​obtained from blood tests, such as LDL cholesterol, HDL cholesterol, triglycerides, and blood glucose levels. For example, the user might enter data such as LDL cholesterol 160 mg / dL, HDL cholesterol 50 mg / dL, triglycerides 200 mg / dL, and blood glucose 110 mg / dL.

[1007] The device makes an HTTP POST request to send the entered health checkup data to the server, using a secure communication protocol (e.g., HTTPS).

[1008] The server analyzes the received health checkup data. It uses a generative AI model (e.g., GPT-3) to analyze the input numerical data and identify nutrient deficiencies and excesses. Specific examples of prompts used for analysis include the following:

[1009] User's health check data: LDL cholesterol 160 mg / dL, HDL cholesterol 50 mg / dL, triglycerides 200 mg / dL, blood sugar 110 mg / dL.

[1010] Identify nutrient deficiencies and excesses and suggest suitable ingredients and recipes.

[1011] Recipe and ingredient suggestions

[1012] The server uses a generative AI model based on the analysis results to create the optimal recipe for the user. The generated ingredients and cooking instructions take into account information such as the user's past medical history and the medical history of their parents, resulting in a more personalized recommendation.

[1013] The device displays the recipe and ingredient list received from the server to the user. The UI is designed to make it easy for users to browse recipes. For example, recipes such as "Mackerel simmered in miso" and "Avocado and quinoa salad" are displayed.

[1014] Ordering from an online supermarket

[1015] The user reviews the suggested recipe and ingredient list and chooses to order the ingredients they need through the e-commerce system.

[1016] The terminal calls the API of the e-commerce system and automatically adds the suggested ingredients to the cart. A final confirmation screen is displayed, allowing the user to confirm the order details and confirm the order.

[1017] Calculation of health score and awarding points

[1018] The server periodically collects the results of health checkups and calculates the user's monthly health score, which is calculated based on the degree of improvement, such as a reduction in LDL cholesterol and stabilization of blood sugar levels.

[1019] The server awards points to the user based on the health score, which increases the user's motivation to improve their health.

[1020] The device displays the user's health score and points, and provides feedback for the next health check. For example, if the user's health score is 80 and 50 points are awarded, the device displays detailed analysis results and feedback.

[1021] This system makes it easier for users to translate the results of their health checkups into concrete actions, provides convenient access to ingredients and recipes that will help improve their health, and effectively encourages users to continue managing their health through a health score and points system.

[1022] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1023] Step 1:

[1024] User: Enters health checkup results. Specifically, the user uses a client device such as a PC or smartphone to enter the values ​​obtained from a blood test (e.g., LDL cholesterol 160 mg / dL, HDL cholesterol 50 mg / dL, triglycerides 200 mg / dL, blood sugar 110 mg / dL) into an input form.

[1025] Input: Numerical data of health check results.

[1026] Output: Input data of health check results.

[1027] Step 2:

[1028] Terminal: An HTTP POST request is made to send the input data of the health check result to the server. At this time, the data is sent using a secure communication protocol (e.g., HTTPS).

[1029] Input: The entered health check result data and the API endpoint to send it to.

[1030] Output: Health check result sent to the server.

[1031] Step 3:

[1032] Server: Analyzes the received health checkup data. Using a generative AI model (e.g., GPT-3), analyzes the input numerical data and identifies nutrient deficiencies and excesses. Sends prompt text to the generative AI model and obtains the analysis results.

[1033] Input: Medical examination result data. Use the following as an example prompt:

[1034] User's health check data: LDL cholesterol 160 mg / dL, HDL cholesterol 50 mg / dL, triglycerides 200 mg / dL, blood sugar 110 mg / dL.

[1035] Identify nutrient deficiencies and excesses and suggest suitable ingredients and recipes.

[1036] Output: Analysis results identifying nutrient deficiencies and excesses.

[1037] Step 4:

[1038] Server: Based on the analysis results, the server uses a generative AI model to generate the optimal recipe and ingredient list for the user, creating a personalized recipe that takes into account specific past medical history and parental medical history.

[1039] Input: Analysis results (nutrient deficiencies and excess nutrients).

[1040] Output: Individually optimized recipe and ingredient list.

[1041] Step 5:

[1042] Device: The device displays the recipe and ingredient list received from the server to the user. The device displays the recipe in a visually easy-to-understand UI to make it easier for the user to browse the recipe. For example, recipes such as "Mackerel simmered in miso" and "Avocado and quinoa salad" are displayed.

[1043] Input: Recipe and ingredient list sent from the server.

[1044] Output: On-screen display of recipe and ingredients list.

[1045] Step 6:

[1046] User: Review the suggested recipe and ingredient list and choose to order the ingredients they need through the e-commerce system.

[1047] Input: The displayed recipe and ingredient list.

[1048] Output: User's order selection.

[1049] Step 7:

[1050] Terminal: Calls the API of the e-commerce system and automatically adds the suggested ingredients to the cart. A final confirmation screen is displayed, allowing the user to confirm the order and confirm it.

[1051] Input: User order selection and API endpoint of e-commerce system.

[1052] Output: Ingredients added to cart and final confirmation screen.

[1053] Step 8:

[1054] User: Check the order details on the final confirmation screen and confirm the order.

[1055] Input: Information from the final confirmation screen.

[1056] Output: Confirmed order details.

[1057] Step 9:

[1058] Server: Periodically collects health checkup results and calculates the user's monthly health score. The health score is calculated based on the degree of improvement, such as lowering LDL cholesterol and stabilizing blood sugar levels.

[1059] Input: Health checkup result data.

[1060] Output: User's health score.

[1061] Step 10:

[1062] Server: Awards points to users based on their health score, which motivates them to improve their health.

[1063] Input: User's health score.

[1064] Output: Points awarded to the user.

[1065] Step 11:

[1066] Device: Displays the user's health score and points, and provides feedback for the next health check. For example, if the user's health score is 80 and 50 points are awarded, the device displays detailed analysis results and feedback.

[1067] Input: Health score and points information.

[1068] Output: A screen display of the health score and points information presented to the user.

[1069] (Application example 1)

[1070] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1071] In modern society, personal health management is highly valued, and it is recognized that diet in particular has a significant impact on health status. However, there is no system in place to suggest individually optimized ingredients and recipes based on health checkup results, and to easily purchase and order them. As a result, it is difficult for users to choose meals based on their own health status, and there is an issue of ineffective health management. There is also a lack of mechanisms to maintain motivation for health management.

[1072] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1073] In this invention, the server includes a means for receiving health checkup results, a means for analyzing the received results to identify necessary nutrients, and a means for generating individually optimized ingredients and recipes based on the identified nutrients. This allows users to receive recommendations for ingredients and recipes based on the health checkup results and easily order and purchase them through an e-commerce platform or delivery service. Furthermore, by including a means for calculating the user's health score and awarding points according to the score, the server can increase the user's motivation to manage their health.

[1074] A "medical examination" is a medical test conducted to assess an individual's health status.

[1075] Analyzing the "results" means determining the user's health condition and identifying necessary nutrients based on the received health checkup data.

[1076] "Nutrients" are substances such as vitamins, minerals, proteins, carbohydrates, and fats that the body needs to function properly.

[1077] "Ingredients" refers to the food ingredients used to prepare a dish.

[1078] "Recipe" means a list of instructions or ingredients for making a particular dish.

[1079] An "e-commerce platform" is an online system for buying and selling goods and services over the Internet.

[1080] "Automatically placing an order" means that the system automatically processes the product order without the user having to manually enter any information.

[1081] A "health score" is a numerical representation of a user's health condition based on specific criteria.

[1082] "Awarding points" means providing points to users as a reward based on specific actions or conditions.

[1083] A "delivery service" is a service that delivers goods from a specific location to another location.

[1084] This invention is a system that utilizes the user's health checkup results to suggest optimal ingredients and recipes for the user, and then orders them via an e-commerce platform and delivery service.The invention is mainly composed of client terminals such as smartphones, tablets, and PCs, and a server that includes data analysis and generative AI models.

[1085] 1. Receipt and analysis of health examination results

[1086] The server receives health checkup results from the user. The user inputs their health checkup results (e.g., LDL cholesterol, triglycerides, etc.) using an application on their smartphone or PC. The health checkup result data sent from the device is received by the server. The server analyzes the received data and uses a generative AI model to identify nutrients that are lacking or in excess. As a specific example, if LDL cholesterol is high, the server analyzes it to recommend low-fat foods.

[1087] 2. Recipe and ingredient suggestions

[1088] Based on the analysis results, the server uses a generative AI model to generate the optimal recipe for the user, taking into account factors such as past medical history and the medical history of the user's parents. The generated recipe and a list of ingredients are sent to the user's device. For example, recipes such as "grilled salmon" and "oatmeal salad" are displayed.

[1089] 3. Ordering from online supermarkets and delivery services

[1090] When the user checks the suggested recipes and ingredients and selects an order, the server calls the API of the e-commerce platform and automatically adds the necessary ingredients to the cart. Furthermore, in a similar way, it is possible to order food based on the optimized recipes via a delivery service. The user confirms the order details on a final confirmation screen and confirms the order.

[1091] 4. Calculation of health score and awarding points

[1092] The server periodically collects the user's health checkup results and calculates a monthly health score. This score is calculated based on the degree of improvement, such as whether LDL cholesterol has decreased or whether blood sugar levels are stable. Points are awarded to the user based on the calculated score. The point information is displayed on the user's device, providing feedback for the next health checkup.

[1093] Examples of specific examples and prompts

[1094] In an actual usage scenario, a user enters the results of a health check into the app. For example, "LDL cholesterol 160 mg / dL, triglycerides 200 mg / dL." The entered data is sent to the server for analysis. The generative AI model recommends ingredients rich in omega-3 fatty acids and fiber, and the server generates recipes such as "grilled salmon" and "avocado and quinoa salad." The user can then select the procedure to order food based on the provided recipe from a delivery service.

[1095] Example prompt for a generative AI model:

[1096] "The user's health check results show LDL cholesterol of 160 mg / dL and triglycerides of 200 mg / dL. Generate suitable ingredient and recipe suggestions for this user. Also, generate an API request to process the food order based on the results."

[1097] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1098] Step 1:

[1099] Users input the results of their health checkups using an application on their smartphone or PC. This input data includes specific numerical values ​​such as LDL cholesterol and triglycerides. This data is sent from the device to the server. The input data format is specified as a number or a string.

[1100] Step 2:

[1101] The server receives the health checkup result data sent from the device. The received data is stored in a database. The server then starts the analysis process and inputs the received data into the generative AI model. The AI ​​model identifies nutrients that are lacking or in excess based on the input data. The identified nutrients are output as the analysis results.

[1102] Step 3:

[1103] The server generates the optimal recipe for the user based on the analysis results. Using a generative AI model, it also takes into account the user's past medical history and the medical history of their parents. The generated recipe and the list of ingredients are stored in a database and sent to the client device. The generated recipe includes the specific dish name and list of ingredients.

[1104] Step 4:

[1105] The terminal displays the recipe and ingredient list sent from the server to the user. The user checks the displayed recipe and ingredient list. This real-time display allows the user to select the recipe that is best for them.

[1106] Step 5:

[1107] After the user selects a recipe and ingredients, the device automatically calls the API of the e-commerce platform to add the required ingredients to the cart. The API call includes a list of selected ingredients and the user's shipping information.

[1108] Step 6:

[1109] The user checks the final confirmation screen on the terminal to confirm that there are no problems with the order details. If the user's confirmation is received, the order is confirmed and the data is sent to the server. The server then sends the order information to the delivery service.

[1110] Step 7:

[1111] The server periodically collects the user's health checkup results and calculates a monthly health score. The health score is quantified based on the degree of improvement and the maintained health status. The server awards points to the user based on this score. The point information is stored in a database and sent to the client terminal.

[1112] Step 8:

[1113] The device displays the health score and points information to the user, who can then use the displayed information to obtain feedback for their next health checkup, increasing their motivation to manage their health.

[1114] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1115] This invention is a system that utilizes the results of health checkups to not only suggest optimal ingredients and recipes to users, but also provides more personalized health management by combining it with an emotion engine that recognizes the user's emotions. This system calculates the user's health score monthly and awards points according to the score, thereby raising the user's health awareness.

[1116] Overall system configuration

[1117] The system mainly consists of the following components:

[1118] 1. Client terminal (user device)

[1119] 2. Server (including data analysis and generative AI models, emotion engine)

[1120] 3. Online Supermarket API

[1121] Receiving and analyzing health checkup results

[1122] User: Enters health checkup results into a client device (PC or smartphone). For example, blood test results such as LDL cholesterol, HDL cholesterol, triglycerides, and blood glucose levels.

[1123] Terminal: Sends the entered data to the server.

[1124] Server: Stores the received data in a database and analyzes it using a generative AI model. This analysis identifies nutrient deficiencies and excesses and suggests appropriate ingredients and recipes.

[1125] Recipe and ingredient suggestions

[1126] Server: Based on the analysis results, the server uses a generative AI model to create individually optimized recipes, taking into account the user's past medical history and the medical history of their parents. Additionally, the server uses an emotion engine to recognize the user's emotional state and recommend ingredients and recipes that are effective in relieving stress and improving mood.

[1127] Terminal: The server generates a recipe and displays the list of ingredients to the user. For example, recipes such as "Simmered Mackerel in Miso" or "Avocado and Quinoa Salad" are displayed.

[1128] Ordering from an online supermarket

[1129] User: Review the suggested recipe and ingredients list and choose to order from the online supermarket.

[1130] Terminal: Calls the online supermarket's API and automatically adds the necessary ingredients to the cart.

[1131] Terminal: A final confirmation screen is displayed to the user to confirm the order details.

[1132] User: Confirms order and purchases ingredients.

[1133] Calculation of health score and awarding points

[1134] Server: Collects regular health checkup results and calculates a monthly health score. The score is calculated based on the degree of improvement, such as whether LDL cholesterol has decreased or blood sugar levels have stabilized.

[1135] Server: Gives points to users based on their health score, which increases their motivation.

[1136] Device: Displays health score and points information to the user. Provides feedback and messages according to the emotional state recognized by the emotion engine.

[1137] Specific examples

[1138] 1. User: Enters blood test results into the app (e.g., LDL cholesterol 160 mg / dL, triglycerides 200 mg / dL).

[1139] 2. Terminal: Sends the health check results to the server.

[1140] 3. Server: Analyzes the results and recommends foods rich in omega-3 fatty acids and fiber. If the emotion engine detects stress, it will also recommend relaxing drinks like chamomile tea.

[1141] 4. Server: Generates recipes using mackerel and avocado and sends them to the client device.

[1142] 5. Terminal: Shows the recipe and ingredients list to the user.

[1143] 6. User: Selects to order from the online supermarket.

[1144] 7. Terminal: Add the ingredients you need to your online supermarket cart.

[1145] 8. User: Makes final confirmation and confirms order.

[1146] 9. Server: Calculates monthly health scores and awards points according to improvements.

[1147] 10. Terminal: Displays score and point information to the user and provides feedback based on the emotion engine.

[1148] This system allows users to take specific actions based on their health checkup results and aim to improve their health. In addition, the introduction of an emotion engine enables more personalized health management and makes suggestions that take into account the user's psychological stress and motivation.

[1149] The processing flow will be explained below.

[1150] Step 1:

[1151] User: Enters the results of a health check into a client terminal. Input items include blood test results (e.g., LDL cholesterol 160 mg / dL, triglycerides 200 mg / dL) and blood glucose levels.

[1152] Step 2:

[1153] Terminal: Sends the entered health check results to the server, where the data is stored for immediate processing.

[1154] Step 3:

[1155] Server: Stores the results of the health checkup in a database. This data is used for later analysis.

[1156] Step 4:

[1157] Server: The generative AI model retrieves health checkup results stored in a database and analyzes them. This analysis includes identifying specific nutrients, such as recommending a low-fat, high-fiber diet if LDL cholesterol is high.

[1158] Step 5:

[1159] Server: After identifying the necessary nutrients, it uses a generative AI model to create the best recipe for the user, for example, using ingredients like mackerel and avocado.

[1160] Step 6:

[1161] Server: Sends the created recipe and the list of required ingredients in JSON format to the client device, so that the recipe information is ready to be displayed on the device.

[1162] Step 7:

[1163] Terminal: The recipe and ingredient list received from the server are displayed to the user, allowing the user to view the suggested recipes.

[1164] Step 8:

[1165] User: Review the suggested recipe and ingredient list and choose to order from the online supermarket, making it easy for users to purchase the perfect ingredients.

[1166] Step 9:

[1167] Device: Calls the online supermarket's API and automatically adds the necessary ingredients to the cart. In addition, an emotion engine captures the user's emotional state and makes fun comments and suggestions.

[1168] Step 10:

[1169] Terminal: After the ingredients are added to the cart, a final confirmation screen for the order is displayed to the user, where the user can make final confirmation of the order.

[1170] Step 11:

[1171] User: Checks the order details and presses the button to confirm the order. This completes the order to the online supermarket.

[1172] Step 12:

[1173] Server: Calculates the user's monthly health score based on the results of the health check. The score is based on the degree of improvement, such as whether LDL cholesterol has decreased or blood sugar levels have stabilized.

[1174] Step 13:

[1175] Server: Based on the calculated health score, the server awards points to the user. Different points are awarded depending on the degree of health improvement.

[1176] Step 14:

[1177] Device: Displays health score and points information to the user. Adds feedback and encouraging messages depending on the user's emotional state as recognized by the emotion engine.

[1178] In this way, users can take specific actions based on their health checkup results, enabling multifaceted and personalized health management.In addition, the introduction of an emotion engine allows feedback and suggestions to be provided that take into account the user's psychological aspects.

[1179] Example 2

[1180] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1181] In modern society, individual health management is important, but it is difficult for users to properly understand their health checkup results and implement improvement measures. Current systems, which present uniform dietary and lifestyle improvement measures without considering the user's emotional state, have the problem of making it difficult to provide optimal support for each individual user. Furthermore, there is a lack of methods to maintain motivation in order to continuously support users' health improvement.

[1182] 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. In this invention, the server includes a means for receiving health checkup results, a means for analyzing the received results to identify necessary nutrients, a means for generating individually optimized ingredients and recipes based on the identified nutrients, a means for recognizing the user's emotions and adjusting suggestions based on the user's emotional state, a means for automatically placing an order with an e-commerce service based on the generated ingredient list, and a means for calculating the user's health score and awarding points according to the score. This allows the user to properly understand their own health status and implement optimal health improvement measures that take their emotional state into consideration. Furthermore, the monthly updated health score and point awarding help maintain the user's motivation.

[1183] "Physical examination results" are data on the results of a series of medical tests conducted to assess the user's health condition.

[1184] The "receiving means" is a system component that receives data sent from a user via a network, converts it into an appropriate format, and stores it.

[1185] The "means for analyzing and identifying necessary nutrients" is an algorithm or program that determines which nutrients are deficient or in excess based on the received health check results.

[1186] The "means for generating individually optimized ingredients and recipes" refers to a generative AI model or program that provides optimal ingredients and recipes for each user based on the identified nutrients.

[1187] The "means for recognizing the user's emotions and adjusting suggestions based on the emotional state" refers to an emotion engine or analysis program that analyzes the user's emotional state and changes the suggestions made in accordance with that state.

[1188] "Means for automatically placing an order with an e-commerce service based on the generated ingredient list" refers to a system component that uses the generated ingredient list to call the API of an e-commerce service (such as an online supermarket) and automatically processes the order.

[1189] The "means for calculating a user's health score" refers to an algorithm or program that calculates a score for quantitatively evaluating the user's health condition.

[1190] The "means for awarding points" is a system component that awards points to the user as an incentive based on the calculated health score.

[1191] This invention is a system for personalizing health management for users, and is composed of the following components: a client terminal (user device), a server (including a data analysis and generation AI model and an emotion engine), and an e-commerce service API. The specific roles and operations of each component are explained below.

[1192] Receiving and analyzing health checkup results

[1193] User: The user inputs the results of their health checkup into the client terminal. The results of the health checkup include, for example, LDL cholesterol, HDL cholesterol, triglycerides, and blood glucose levels. This information is entered as numerical values ​​into the application's input form.

[1194] Terminal: The terminal sends the health check result data entered by the user to the server. The data is sent using the HTTPS protocol and packaged in JSON format. The sent data is then prepared for processing by the server.

[1195] Server: The server stores the received data in a database, typically a relational database such as MySQL or PostgreSQL. The stored data is then analyzed using a generative AI model. This analysis identifies nutrient deficiencies and excesses.

[1196] Recipe and ingredient suggestions

[1197] Server: The server uses a generative AI model based on the analysis results to create individually optimized recipes. This process also takes into account the user's past medical history and the medical history of their parents. It also uses an emotion engine to recognize the user's emotional state and recommend ingredients and recipes that are effective in relieving stress and improving mood.

[1198] For example, if the system recognizes the user's stress level, it will suggest recipes using chamomile tea, which has a relaxing effect, or mackerel, which is rich in omega-3 fatty acids.

[1199] Ordering from an online supermarket

[1200] User: The user reviews the suggested recipe and ingredient list displayed on the device and selects to order from the online supermarket. The order is placed using the app interface, and the selected ingredients are automatically added to the cart.

[1201] Terminal: The terminal calls the online supermarket's API and automatically adds the required ingredients to the cart. This process uses the REST API and sends the order data via a POST request. The terminal then displays a final confirmation screen for the user to confirm the order.

[1202] User: The user confirms the order and confirms it, completing the food purchase process.

[1203] Calculation of health score and awarding points

[1204] Server: The server periodically collects health checkup results and calculates a monthly health score. The score is calculated based on the degree of improvement, such as whether LDL cholesterol has decreased or whether blood sugar levels are stable. Points are awarded to the user based on the calculated score.

[1205] Terminal: The terminal displays the health score and points to the user. This is done using graphs and numerical displays. In addition, feedback and messages are displayed according to the emotional state recognized by the emotion engine. For example, a message such as "Your health score is 80 points! That's a great achievement" can be displayed to motivate the user.

[1206] Specific examples

[1207] Here is an example of this system:

[1208] 1. User: Enters blood test results into the application's input form and clicks the "Submit" button.

[1209] 2. Terminal: Packages the input data in JSON format and sends it to the server using the HTTPS protocol.

[1210] 3. Server: Stores the received data in a relational database and analyzes it using a generative AI model. As a result of the analysis, it identifies nutrient deficiencies and excesses and generates appropriate ingredients and recipes.

[1211] 4. Server: Combines generative AI models and an emotion engine to generate personalized recipe and ingredient suggestions. For example, if a user's emotion is recognized as stressful, it will suggest ingredients with a relaxing effect (e.g., chamomile tea).

[1212] 5. On the device: Display suggested recipes and ingredient lists in list or card view. For example, "Simmered Mackerel with Miso" or "Avocado and Quinoa Salad" are displayed.

[1213] 6. User: Select the ingredients you want to purchase from the displayed list and click the "Order" button.

[1214] 7. Device: Calls the online supermarket's API to add ingredients to the cart and displays a final confirmation screen. The user checks the details and confirms the order.

[1215] 8. Server: Calculates monthly health score and awards points based on the score.

[1216] 9. Device: Displays health scores and points in graphs and numerical formats. Emotion engine recognizes the user's emotional state and displays feedback and messages to motivate the user.

[1217] This system allows users to take specific actions based on their health checkup results and aim to improve their health. In addition, the introduction of an emotion engine enables more personalized health management and makes suggestions that take into account the user's psychological stress and motivation.

[1218] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1219] Step 1:

[1220] User: Enter the results of the health check into the client terminal.

[1221] Input: Health checkup result data (e.g., LDL cholesterol, HDL cholesterol, triglycerides, blood glucose levels).

[1222] Action: Enter the health check data as numbers into the application's input form and click the "Submit" button.

[1223] Output: Health checkup data entry completed (data saved on the device).

[1224] Step 2:

[1225] Terminal: Sends the entered data to the server.

[1226] Input: Medical examination data.

[1227] What it does: It packages data in JSON format and sends it to the server using the HTTPS protocol.

[1228] Output: Health check data in JSON format is sent to the server.

[1229] Step 3:

[1230] Server: Stores the received data in a database.

[1231] Input: Submitted health check data (JSON format).

[1232] What it does: Stores the received data in a database (e.g. MySQL or PostgreSQL).

[1233] Output: The health check data is stored in a database.

[1234] Step 4:

[1235] Server: Analyzes the stored data using a generative AI model.

[1236] Input: Medical examination data stored in a database.

[1237] How it works: Generative AI models are used to analyze data and identify nutrient deficiencies and excesses.

[1238] Output: Nutrient information identified as a result of the analysis.

[1239] Step 5:

[1240] Server: Utilizes generative AI models to generate individually optimized recipe and ingredient suggestions.

[1241] Input: Analysis results (identified nutrient information).

[1242] How it works: Combines generative AI models and an emotion engine to generate personalized recipes and ingredient lists.

[1243] Output: A suggested recipe and ingredients list.

[1244] Step 6:

[1245] Terminal: Displays the server-generated recipe and ingredient list to the user.

[1246] Input: Recipe and ingredient list sent from the server.

[1247] What it does: Displays a recipe and ingredient list using a list or card view.

[1248] Output: The recipe and ingredients list displayed to the user.

[1249] Step 7:

[1250] User: Review the suggested recipe and ingredients list and choose to order from the online supermarket.

[1251] Input: Recipe and ingredient list displayed on device.

[1252] Action: Select the ingredients you want to purchase and click the "Order" button.

[1253] Output: An order request to the online supermarket.

[1254] Step 8:

[1255] Terminal: Calls the online supermarket's API and automatically adds the necessary ingredients to the cart.

[1256] Input: A list of ingredients selected by the user.

[1257] How it works: Using the REST API, the order data is sent via a POST request and added to the cart.

[1258] Output: Information about ingredients added to the online supermarket cart.

[1259] Step 9:

[1260] Terminal: A final confirmation screen is displayed to the user to confirm the order details.

[1261] Input: Information about ingredients added to cart.

[1262] Behavior: Displays a final confirmation screen, prompting the user for confirmation.

[1263] Output: Order confirmation by user.

[1264] Step 10:

[1265] Server: Calculates monthly health score.

[1266] Input: Routinely collected health examination result data.

[1267] How it works: It uses an algorithm or program to calculate a health score, specifically assessing things like lowering LDL cholesterol and stabilizing blood sugar levels.

[1268] Output: Calculated health score.

[1269] Step 11:

[1270] Server: Awards points based on the calculated health score.

[1271] Input: Health score.

[1272] What it does: Uses the points awarding algorithm to award the appropriate points to the user.

[1273] Output: The user's points are updated.

[1274] Step 12:

[1275] Terminal: Displays health score and points information to the user.

[1276] Input: Health score and updated points information.

[1277] What it does: Displays information using graphical and numerical displays and provides feedback based on an emotion engine.

[1278] Output: Health score and points information displayed to the user, and feedback from the emotion engine.

[1279] (Application example 2)

[1280] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1281] In modern society, there is a demand for systems that suggest specific actions based on individual health conditions and support the implementation of those actions. In particular, it is important to make suggestions for improving daily eating habits using health checkup results, but comprehensive health management that also takes into account the user's emotional state is also necessary. It is also important to have a system that makes it easy to obtain the suggested ingredients and recipes. Furthermore, continuous feedback and incentives are required to maintain the user's motivation to improve their health. There is currently a lack of systems that meet these requirements, so solving this problem is a challenge.

[1282] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1283] In this invention, the server includes means for receiving health checkup results, means for analyzing the received results to identify necessary nutrients, means for generating individually optimized ingredients and recipes based on the identified nutrients, means for automatically placing orders with a food delivery service based on the generated ingredient list, means for calculating the user's health score and awarding points according to the score, and means for detecting the user's emotional state and making recommendations based on the score. This allows the user to receive effective and emotionally sensitive meal suggestions based on the health checkup results and easily purchase the suggested ingredients. Furthermore, by visualizing the user's improvement using the health score and providing incentives through point awarding, motivation for continuous health management can be maintained.

[1284] "Health checkup results" refers to information on blood test items and physical measurements obtained as a result of regular medical examinations and tests that a user undergoes.

[1285] "Nutrients" are components such as vitamins, minerals, proteins, lipids, and carbohydrates that are necessary for maintaining health.

[1286] "Ingredients and recipes" refers to the ingredients used to prepare a meal and the steps and methods for cooking using those ingredients.

[1287] A "food delivery service" is a service that delivers ingredients and dishes selected by the user to their home or designated location.

[1288] "Emotional state" is information that indicates the user's current psychological state, including emotions such as stress, joy, and sadness.

[1289] A "recommendation" is the optimal action or option suggested based on the user's needs and situation.

[1290] A "health score" is a numerical value calculated based on the user's health checkup results and behavioral data, and is an indicator used to evaluate their health status.

[1291] "Points" are rewards and incentives awarded based on health scores and actions, and are used to increase user motivation.

[1292] "Past medical history and parental medical history" refers to historical information about illnesses and health conditions that the user or their parents have experienced in the past.

[1293] The system of this invention is a personalized healthy food delivery app that not only suggests optimal ingredients and recipes to users based on their health checkup results, but also incorporates an emotion engine that recognizes the user's emotions. This system has the following configuration and processing steps.

[1294] Key Components of the System

[1295] 1. Client terminal (user device)

[1296] Smartphone (iOS / Android)

[1297] 2. Server

[1298] Data analysis and generative AI models (Python, TensorFlow)

[1299] Emotion Engine (NLP model, Hugging Face)

[1300] Database (MySQL)

[1301] Net supermarket API integration (REST API)

[1302] Program processing

[1303] 1. User Input

[1304] Users use a smartphone app to input their health checkup results and current emotional state, such as their LDL cholesterol and blood sugar levels from a blood test, and their emotional state, such as "I'm stressed."

[1305] 2. Send to the server

[1306] The data entered from the smartphone is sent to a server, where it is stored in a database and used for analysis.

[1307] 3. Data Analysis

[1308] The server then analyzes the received data using Python scripts and TensorFlow models to identify nutrients and foods necessary for the user's health. For example, if the user's LDL cholesterol is high, it will recommend foods rich in omega-3 fatty acids.

[1309] 4. Emotion Engine

[1310] The server uses an emotion engine to analyze the user's emotion data, and uses an NLP model (e.g., Hugging Face) to suggest foods that have a relaxing effect if the user is feeling stressed.

[1311] 5. Recipe Generation

[1312] Based on the analysis results, a generative AI model (e.g., GPT-3) is used to generate individually optimized recipes. For example, recipes such as "Simmered Mackerel in Miso" or "Avocado and Quinoa Salad" are generated. These recipes are then sent to the client device.

[1313] 6. Food delivery collaboration

[1314] The generated ingredient list is automatically added to the user's cart via the online supermarket's API, and the user then makes a final confirmation on the smartphone app to confirm the order.

[1315] 7. Health Score and Points

[1316] The server periodically calculates a health score based on the results of health checkups and updates the user's score monthly. Points are awarded based on this score, which acts as an incentive to keep the user motivated to improve their health.

[1317] Specific examples

[1318] For example, specific user input and suggestions are given below:

[1319] Example user input: "LDL cholesterol 180 mg / dL, triglycerides 220 mg / dL, emotional state: stressed"

[1320] Example prompts to input to a generative AI model:

[1321] The user's health check results are as follows:

[1322] LDL cholesterol: 180 mg / dL

[1323] Triglycerides: 220 mg / dL

[1324] The user's emotional state is stress. Suggest ingredients and recipes appropriate for the user's state.

[1325] In this way, users can receive effective and emotionally sensitive dietary suggestions based on their health checkup results, and can easily purchase the suggested ingredients. Furthermore, the degree of improvement is visualized through a health score, and points are awarded to maintain motivation for ongoing health management.

[1326] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1327] Step 1:

[1328] Users use a smartphone app to input their health check results and current emotional state, including blood test results and emotional state options, which provides specific data about the user's condition.

[1329] Step 2:

[1330] The terminal sends the data entered by the user to the server. During this process, the data is encrypted and sent, and then securely stored in a database. Here, the input data is stored on the server in preparation for the next analysis step.

[1331] Step 3:

[1332] The server analyzes the received data using Python scripts and TensorFlow models to identify the nutrients and ingredients needed based on the user's health condition. The analysis results are output as a report of the identified nutrients and their deficiency or excess.

[1333] Step 4:

[1334] The server uses an emotion engine to analyze the user's emotion data. It uses an NLP model (e.g., Hugging Face) to generate appropriate recommendations based on the input emotion. For example, if stress is detected, foods with a relaxing effect will be recommended.

[1335] Step 5:

[1336] The server generates individually optimized recipes based on the analysis results using a generative AI model (e.g., GPT-3). The generated recipes are output along with a list of ingredients and sent to the client device.

[1337] Step 6:

[1338] The terminal displays the recipe and ingredient list received from the server to the user, who can then confirm the ingredients based on the suggested list and select a food delivery service.

[1339] Step 7:

[1340] The server then calls the online supermarket's API based on the generated ingredient list and automatically adds the items to the user's cart. At this time, the order details are sent to the online supermarket in encrypted format.

[1341] Step 8:

[1342] The user checks the final confirmation screen on the smartphone app and confirms the order. Once the user confirms the order, the online supermarket prepares the ingredients and delivers them to the specified address.

[1343] Step 9:

[1344] The server periodically calculates a health score based on the results of the health checkup and updates it monthly. Points are awarded to the user's account based on the score, which helps to motivate the user to improve their health.

[1345] Step 10:

[1346] The server displays health scores and points to users, and also utilizes an emotion engine to provide feedback and messages based on their emotional state, motivating users to continue managing their health.

[1347] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1348] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1349] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1350] [Fourth embodiment]

[1351] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1352] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1353] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1354] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1355] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1356] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1357] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1358] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1359] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1360] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1361] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1362] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1363] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1364] This system utilizes the results of health checkups to suggest optimal ingredients and recipes to users, and allows them to easily place orders through an online supermarket. Furthermore, it calculates the user's health score monthly and awards points according to the score, thereby raising the user's health awareness.

[1365] Overall system configuration

[1366] The system mainly consists of the following components:

[1367] 1. Client terminal (user device)

[1368] 2. Server (including data analysis and generative AI models)

[1369] 3. Online Supermarket API

[1370] Receiving and analyzing health checkup results

[1371] User: Enters health checkup results into a client device (PC or smartphone). For example, blood test results such as LDL cholesterol, HDL cholesterol, triglycerides, and blood glucose levels.

[1372] Terminal: Sends the entered data to the server.

[1373] Server: Analyzes the received data and uses a generative AI model to identify nutrient deficiencies or excesses. For example, it analyzes the data to recommend low-fat foods if LDL cholesterol is high, or low-GI foods if blood sugar levels are high.

[1374] Recipe and ingredient suggestions

[1375] Server: Based on the analysis results, a generative AI model is used to create the optimal recipe for the user, taking into account the user's past medical history and the medical history of their parents to provide more personalized suggestions.

[1376] Terminal: The server generates a recipe and displays the list of ingredients to the user. For example, recipes such as "Simmered Mackerel in Miso" or "Avocado and Quinoa Salad" are displayed.

[1377] Ordering from an online supermarket

[1378] User: Review the suggested recipe and ingredients list and choose to order the ingredients they need from an online supermarket.

[1379] Terminal: Calls the online supermarket's API and automatically processes the process of adding the necessary ingredients to the cart.

[1380] Terminal: A final confirmation screen is displayed to the user to confirm the order details.

[1381] User: Confirms order and purchases ingredients.

[1382] Calculation of health score and awarding points

[1383] Server: Collects regular health checkup results and calculates a monthly health score. The score is calculated based on the degree of improvement, such as whether LDL cholesterol has decreased or blood sugar levels have stabilized.

[1384] Server: Awards points to users based on the calculated score. These points are used to motivate users to improve their health.

[1385] Device: Displays health score and points information to the user, providing feedback for the next health check.

[1386] Specific examples

[1387] 1. User: Enters blood test results into the app (e.g., LDL cholesterol 160 mg / dL, triglycerides 200 mg / dL).

[1388] 2. Terminal: Sends the health check results to the server.

[1389] 3. Server: Analyzes the results and recommends foods rich in omega-3 fatty acids and fiber.

[1390] 4. Server: Generates recipes using mackerel and avocado and sends them to the client device.

[1391] 5. Terminal: Shows the recipe and ingredients list to the user.

[1392] 6. User: Selects to order from the online supermarket.

[1393] 7. Terminal: Add the ingredients you need to your online supermarket cart.

[1394] 8. User: Makes final confirmation and confirms order.

[1395] 9. Server: Calculates monthly health scores and awards points according to improvements.

[1396] 10. Terminal: Displays the user's score and points information, motivating them to take the next health checkup.

[1397] The system allows users to translate their health checkup results into concrete actions, provides easy access to ingredients and recipes that will help improve their health, and encourages ongoing health management through a score and points system.

[1398] The processing flow will be explained below.

[1399] Step 1:

[1400] User: Enters the results of a health check into the client terminal. Specifically, the user enters blood test results (e.g., LDL cholesterol 160 mg / dL, triglycerides 200 mg / dL).

[1401] Step 2:

[1402] Terminal: Sends the entered health check results to the server, which then prepares the data for processing.

[1403] Step 3:

[1404] Server: Stores the received health check results in a database for later analysis.

[1405] Step 4:

[1406] Server: Retrieves health checkup results stored in a database and analyzes them using a generative AI model. Specifically, if LDL cholesterol is high, the analysis recommends a low-fat, high-fiber diet.

[1407] Step 5:

[1408] Server: Based on the analysis results, a generative AI model is used to create individually optimized recipes, such as recipes using ingredients like mackerel and avocado.

[1409] Step 6:

[1410] Server: Sends the created recipe and the list of ingredients to the client device, allowing users to easily access it.

[1411] Step 7:

[1412] Device: The device receives the recipe and ingredient list from the server and displays it to the user, including cooking instructions and nutritional information.

[1413] Step 8:

[1414] User: Checks the suggested recipe and ingredient list and selects to order from the online supermarket. The user confirms the order and proceeds to the next step.

[1415] Step 9:

[1416] Terminal: Calls the online supermarket's API and automatically adds the necessary ingredients to the cart, eliminating the need for the user to manually add them to the cart.

[1417] Step 10:

[1418] Terminal: After the ingredients are added to the cart, the user is presented with a final order confirmation screen where they can finalize their order.

[1419] Step 11:

[1420] User: Checks the order details and presses the button to confirm the order. This completes the ordering process with the online supermarket.

[1421] Step 12:

[1422] Server: Calculates the user's monthly health score based on the results of the health check. The score is calculated based on the degree of improvement, such as whether LDL cholesterol has decreased.

[1423] Step 13:

[1424] Server: Based on the calculated health score, the server awards points to the user. The points are used to increase the user's motivation.

[1425] Step 14:

[1426] Terminal: Displays health score and points information to the user, providing feedback for the next health check.

[1427] These steps allow users to take specific actions based on their health checkup results and aim to improve their health. The system also makes it easy to order ingredients, promoting continuous health management.

[1428] Example 1

[1429] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1430] Conventional health management systems often lack specific feedback and suggestions based on users' health checkup results, leaving users with insufficient means to implement specific health improvements. Furthermore, it is difficult to suggest personalized ingredients and recipes based on individual health conditions, making it difficult for users to easily prepare meals that suit their own health condition. Furthermore, the lack of incentives to continuously promote users' health management makes it difficult to motivate users.

[1431] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1432] In this invention, the server includes a means for receiving health checkup results, a means for analyzing the received results to identify deficient or excessive nutrients, a means for generating individually optimized ingredients and cooking instructions based on the identified nutrients using a generative AI model, a means for automatically placing an order to an e-commerce system based on the generated ingredient list, a means for calculating the user's health score monthly and awarding points according to the score, and a means for transmitting the received health checkup data via a secure communication protocol. This allows for the suggestion and implementation of specific ingredients and recipes based on the individual's health condition, effectively promoting health management and motivation for the user.

[1433] The "means for receiving the results of the medical examination" is a function that allows the server to receive the medical examination data entered by the user.

[1434] "Means for analyzing the received results to identify nutrients that are deficient or in excess" refers to a function that analyzes the received health checkup data using specialized algorithms or AI models to identify nutrients that are deficient or in excess.

[1435] "Means for generating individually optimized ingredients and cooking instructions using a generative AI model" refers to a function that utilizes an AI model (e.g., a generative AI model) to generate optimal ingredients and cooking instructions (recipe) for each user based on identified nutrients.

[1436] "Means for automatically placing orders in an e-commerce system based on the generated ingredient list" refers to a function for automatically ordering ingredients via the API of an online shop or online supermarket based on the generated ingredient list.

[1437] "Means for calculating the user's health score monthly and awarding points according to that score" is a function for calculating the user's health score monthly based on the results of their health checkup and awarding points according to that score.

[1438] "Means for transmitting received medical checkup data using a secure communication protocol" is a function for transmitting the medical checkup data entered by the user to a server using a secure communication protocol (e.g., HTTPS).

[1439] An "electronic commerce system" generally refers to a system that allows products to be purchased online, and specifically includes online supermarkets and online shops.

[1440] A "generative AI model" is an artificial intelligence model that generates a specific output from given input data, such as a text generation model like the GPT series.

[1441] "Identified nutrients" refers to nutrients that are deficient or in excess in the user's health condition, as revealed by the analysis.

[1442] "Cooking instructions" refer to the steps and methods required to prepare a dish, and are generally expressed as a recipe.

[1443] This invention is a system that allows users to order individually optimized ingredients and recipes based on the results of their health checkups through an e-commerce system. Furthermore, it aims to raise users' health awareness by calculating their health score monthly and awarding points based on that score.

[1444] Overall system configuration

[1445] The system mainly consists of the following components:

[1446] 1. Client terminal (user device)

[1447] 2. Server (including data analysis and generative AI models)

[1448] 3. E-commerce system API

[1449] Receiving and analyzing health checkup results

[1450] Users enter their health checkup results using a client terminal such as a PC or smartphone. Specifically, they fill out a form to enter values ​​obtained from blood tests, such as LDL cholesterol, HDL cholesterol, triglycerides, and blood glucose levels. For example, the user might enter data such as LDL cholesterol 160 mg / dL, HDL cholesterol 50 mg / dL, triglycerides 200 mg / dL, and blood glucose 110 mg / dL.

[1451] The device makes an HTTP POST request to send the entered health checkup data to the server, using a secure communication protocol (e.g., HTTPS).

[1452] The server analyzes the received health checkup data. It uses a generative AI model (e.g., GPT-3) to analyze the input numerical data and identify nutrient deficiencies and excesses. Specific examples of prompts used for analysis include the following:

[1453] User's health check data: LDL cholesterol 160 mg / dL, HDL cholesterol 50 mg / dL, triglycerides 200 mg / dL, blood sugar 110 mg / dL.

[1454] Identify nutrient deficiencies and excesses and suggest suitable ingredients and recipes.

[1455] Recipe and ingredient suggestions

[1456] The server uses a generative AI model based on the analysis results to create the optimal recipe for the user. The generated ingredients and cooking instructions take into account information such as the user's past medical history and the medical history of their parents, resulting in a more personalized recommendation.

[1457] The device displays the recipe and ingredient list received from the server to the user. The UI is designed to make it easy for users to browse recipes. For example, recipes such as "Mackerel simmered in miso" and "Avocado and quinoa salad" are displayed.

[1458] Ordering from an online supermarket

[1459] The user reviews the suggested recipe and ingredient list and chooses to order the ingredients they need through the e-commerce system.

[1460] The terminal calls the API of the e-commerce system and automatically adds the suggested ingredients to the cart. A final confirmation screen is displayed, allowing the user to confirm the order details and confirm the order.

[1461] Calculation of health score and awarding points

[1462] The server periodically collects the results of health checkups and calculates the user's monthly health score, which is calculated based on the degree of improvement, such as a reduction in LDL cholesterol and stabilization of blood sugar levels.

[1463] The server awards points to the user based on the health score, which increases the user's motivation to improve their health.

[1464] The device displays the user's health score and points, and provides feedback for the next health check. For example, if the user's health score is 80 and 50 points are awarded, the device displays detailed analysis results and feedback.

[1465] This system makes it easier for users to translate the results of their health checkups into concrete actions, provides convenient access to ingredients and recipes that will help improve their health, and effectively encourages users to continue managing their health through a health score and points system.

[1466] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1467] Step 1:

[1468] User: Enters health checkup results. Specifically, the user uses a client device such as a PC or smartphone to enter the values ​​obtained from a blood test (e.g., LDL cholesterol 160 mg / dL, HDL cholesterol 50 mg / dL, triglycerides 200 mg / dL, blood sugar 110 mg / dL) into an input form.

[1469] Input: Numerical data of health check results.

[1470] Output: Input data of health check results.

[1471] Step 2:

[1472] Terminal: An HTTP POST request is made to send the input data of the health check result to the server. At this time, the data is sent using a secure communication protocol (e.g., HTTPS).

[1473] Input: The entered health check result data and the API endpoint to send it to.

[1474] Output: Health check result sent to the server.

[1475] Step 3:

[1476] Server: Analyzes the received health checkup data. Using a generative AI model (e.g., GPT-3), analyzes the input numerical data and identifies nutrient deficiencies and excesses. Sends prompt text to the generative AI model and obtains the analysis results.

[1477] Input: Medical examination result data. Use the following as an example prompt:

[1478] User's health check data: LDL cholesterol 160 mg / dL, HDL cholesterol 50 mg / dL, triglycerides 200 mg / dL, blood sugar 110 mg / dL.

[1479] Identify nutrient deficiencies and excesses and suggest suitable ingredients and recipes.

[1480] Output: Analysis results identifying nutrient deficiencies and excesses.

[1481] Step 4:

[1482] Server: Based on the analysis results, the server uses a generative AI model to generate the optimal recipe and ingredient list for the user, creating a personalized recipe that takes into account specific past medical history and parental medical history.

[1483] Input: Analysis results (nutrient deficiencies and excess nutrients).

[1484] Output: Individually optimized recipe and ingredient list.

[1485] Step 5:

[1486] Device: The device displays the recipe and ingredient list received from the server to the user. The device displays the recipe in a visually easy-to-understand UI to make it easier for the user to browse the recipe. For example, recipes such as "Mackerel simmered in miso" and "Avocado and quinoa salad" are displayed.

[1487] Input: Recipe and ingredient list sent from the server.

[1488] Output: On-screen display of recipe and ingredients list.

[1489] Step 6:

[1490] User: Review the suggested recipe and ingredient list and choose to order the ingredients they need through the e-commerce system.

[1491] Input: The displayed recipe and ingredient list.

[1492] Output: User's order selection.

[1493] Step 7:

[1494] Terminal: Calls the API of the e-commerce system and automatically adds the suggested ingredients to the cart. A final confirmation screen is displayed, allowing the user to confirm the order and confirm it.

[1495] Input: User order selection and API endpoint of e-commerce system.

[1496] Output: Ingredients added to cart and final confirmation screen.

[1497] Step 8:

[1498] User: Check the order details on the final confirmation screen and confirm the order.

[1499] Input: Information from the final confirmation screen.

[1500] Output: Confirmed order details.

[1501] Step 9:

[1502] Server: Periodically collects health checkup results and calculates the user's monthly health score. The health score is calculated based on the degree of improvement, such as lowering LDL cholesterol and stabilizing blood sugar levels.

[1503] Input: Health checkup result data.

[1504] Output: User's health score.

[1505] Step 10:

[1506] Server: Awards points to users based on their health score, which motivates them to improve their health.

[1507] Input: User's health score.

[1508] Output: Points awarded to the user.

[1509] Step 11:

[1510] Device: Displays the user's health score and points, and provides feedback for the next health check. For example, if the user's health score is 80 and 50 points are awarded, the device displays detailed analysis results and feedback.

[1511] Input: Health score and points information.

[1512] Output: A screen display of the health score and points information presented to the user.

[1513] (Application example 1)

[1514] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1515] In modern society, personal health management is highly valued, and it is recognized that diet in particular has a significant impact on health status. However, there is no system in place to suggest individually optimized ingredients and recipes based on health checkup results, and to easily purchase and order them. As a result, it is difficult for users to choose meals based on their own health status, and there is an issue of ineffective health management. There is also a lack of mechanisms to maintain motivation for health management.

[1516] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1517] In this invention, the server includes a means for receiving health checkup results, a means for analyzing the received results to identify necessary nutrients, and a means for generating individually optimized ingredients and recipes based on the identified nutrients. This allows users to receive recommendations for ingredients and recipes based on the health checkup results and easily order and purchase them through an e-commerce platform or delivery service. Furthermore, by including a means for calculating the user's health score and awarding points according to the score, the server can increase the user's motivation to manage their health.

[1518] A "medical examination" is a medical test conducted to assess an individual's health status.

[1519] Analyzing the "results" means determining the user's health condition and identifying necessary nutrients based on the received health checkup data.

[1520] "Nutrients" are substances such as vitamins, minerals, proteins, carbohydrates, and fats that the body needs to function properly.

[1521] "Ingredients" refers to the food ingredients used to prepare a dish.

[1522] "Recipe" means a list of instructions or ingredients for making a particular dish.

[1523] An "e-commerce platform" is an online system for buying and selling goods and services over the Internet.

[1524] "Automatically placing an order" means that the system automatically processes the product order without the user having to manually enter any information.

[1525] A "health score" is a numerical representation of a user's health condition based on specific criteria.

[1526] "Awarding points" means providing points to users as a reward based on specific actions or conditions.

[1527] A "delivery service" is a service that delivers goods from a specific location to another location.

[1528] This invention is a system that utilizes the user's health checkup results to suggest optimal ingredients and recipes for the user, and then orders them via an e-commerce platform and delivery service.The invention is mainly composed of client terminals such as smartphones, tablets, and PCs, and a server that includes data analysis and generative AI models.

[1529] 1. Receipt and analysis of health examination results

[1530] The server receives health checkup results from the user. The user inputs their health checkup results (e.g., LDL cholesterol, triglycerides, etc.) using an application on their smartphone or PC. The health checkup result data sent from the device is received by the server. The server analyzes the received data and uses a generative AI model to identify nutrients that are lacking or in excess. As a specific example, if LDL cholesterol is high, the server analyzes it to recommend low-fat foods.

[1531] 2. Recipe and ingredient suggestions

[1532] Based on the analysis results, the server uses a generative AI model to generate the optimal recipe for the user, taking into account factors such as past medical history and the medical history of the user's parents. The generated recipe and a list of ingredients are sent to the user's device. For example, recipes such as "grilled salmon" and "oatmeal salad" are displayed.

[1533] 3. Ordering from online supermarkets and delivery services

[1534] When the user checks the suggested recipes and ingredients and selects an order, the server calls the API of the e-commerce platform and automatically adds the necessary ingredients to the cart. Furthermore, in a similar way, it is possible to order food based on the optimized recipes via a delivery service. The user confirms the order details on a final confirmation screen and confirms the order.

[1535] 4. Calculation of health score and awarding points

[1536] The server periodically collects the user's health checkup results and calculates a monthly health score. This score is calculated based on the degree of improvement, such as whether LDL cholesterol has decreased or whether blood sugar levels are stable. Points are awarded to the user based on the calculated score. The point information is displayed on the user's device, providing feedback for the next health checkup.

[1537] Examples of specific examples and prompts

[1538] In an actual usage scenario, a user enters the results of a health check into the app. For example, "LDL cholesterol 160 mg / dL, triglycerides 200 mg / dL." The entered data is sent to the server for analysis. The generative AI model recommends ingredients rich in omega-3 fatty acids and fiber, and the server generates recipes such as "grilled salmon" and "avocado and quinoa salad." The user can then select the procedure to order food based on the provided recipe from a delivery service.

[1539] Example prompt for a generative AI model:

[1540] "The user's health check results show LDL cholesterol of 160 mg / dL and triglycerides of 200 mg / dL. Generate suitable ingredient and recipe suggestions for this user. Also, generate an API request to process the food order based on the results."

[1541] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1542] Step 1:

[1543] Users input the results of their health checkups using an application on their smartphone or PC. This input data includes specific numerical values ​​such as LDL cholesterol and triglycerides. This data is sent from the device to the server. The input data format is specified as a number or a string.

[1544] Step 2:

[1545] The server receives the health checkup result data sent from the device. The received data is stored in a database. The server then starts the analysis process and inputs the received data into the generative AI model. The AI ​​model identifies nutrients that are lacking or in excess based on the input data. The identified nutrients are output as the analysis results.

[1546] Step 3:

[1547] The server generates the optimal recipe for the user based on the analysis results. Using a generative AI model, it also takes into account the user's past medical history and the medical history of their parents. The generated recipe and the list of ingredients are stored in a database and sent to the client device. The generated recipe includes the specific dish name and list of ingredients.

[1548] Step 4:

[1549] The terminal displays the recipe and ingredient list sent from the server to the user. The user checks the displayed recipe and ingredient list. This real-time display allows the user to select the recipe that is best for them.

[1550] Step 5:

[1551] After the user selects a recipe and ingredients, the device automatically calls the API of the e-commerce platform to add the required ingredients to the cart. The API call includes a list of selected ingredients and the user's shipping information.

[1552] Step 6:

[1553] The user checks the final confirmation screen on the terminal to confirm that there are no problems with the order details. If the user's confirmation is received, the order is confirmed and the data is sent to the server. The server then sends the order information to the delivery service.

[1554] Step 7:

[1555] The server periodically collects the user's health checkup results and calculates a monthly health score. The health score is quantified based on the degree of improvement and the maintained health status. The server awards points to the user based on this score. The point information is stored in a database and sent to the client terminal.

[1556] Step 8:

[1557] The device displays the health score and points information to the user, who can then use the displayed information to obtain feedback for their next health checkup, increasing their motivation to manage their health.

[1558] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1559] This invention is a system that utilizes the results of health checkups to not only suggest optimal ingredients and recipes to users, but also provides more personalized health management by combining it with an emotion engine that recognizes the user's emotions. This system calculates the user's health score monthly and awards points according to the score, thereby raising the user's health awareness.

[1560] Overall system configuration

[1561] The system mainly consists of the following components:

[1562] 1. Client terminal (user device)

[1563] 2. Server (including data analysis and generative AI models, emotion engine)

[1564] 3. Online Supermarket API

[1565] Receiving and analyzing health checkup results

[1566] User: Enters health checkup results into a client device (PC or smartphone). For example, blood test results such as LDL cholesterol, HDL cholesterol, triglycerides, and blood glucose levels.

[1567] Terminal: Sends the entered data to the server.

[1568] Server: Stores the received data in a database and analyzes it using a generative AI model. This analysis identifies nutrient deficiencies and excesses and suggests appropriate ingredients and recipes.

[1569] Recipe and ingredient suggestions

[1570] Server: Based on the analysis results, the server uses a generative AI model to create individually optimized recipes, taking into account the user's past medical history and the medical history of their parents. Additionally, the server uses an emotion engine to recognize the user's emotional state and recommend ingredients and recipes that are effective in relieving stress and improving mood.

[1571] Terminal: The server generates a recipe and displays the list of ingredients to the user. For example, recipes such as "Simmered Mackerel in Miso" or "Avocado and Quinoa Salad" are displayed.

[1572] Ordering from an online supermarket

[1573] User: Review the suggested recipe and ingredients list and choose to order from the online supermarket.

[1574] Terminal: Calls the online supermarket's API and automatically adds the necessary ingredients to the cart.

[1575] Terminal: A final confirmation screen is displayed to the user to confirm the order details.

[1576] User: Confirms order and purchases ingredients.

[1577] Calculation of health score and awarding points

[1578] Server: Collects regular health checkup results and calculates a monthly health score. The score is calculated based on the degree of improvement, such as whether LDL cholesterol has decreased or blood sugar levels have stabilized.

[1579] Server: Gives points to users based on their health score, which increases their motivation.

[1580] Device: Displays health score and points information to the user. Provides feedback and messages according to the emotional state recognized by the emotion engine.

[1581] Specific examples

[1582] 1. User: Enters blood test results into the app (e.g., LDL cholesterol 160 mg / dL, triglycerides 200 mg / dL).

[1583] 2. Terminal: Sends the health check results to the server.

[1584] 3. Server: Analyzes the results and recommends foods rich in omega-3 fatty acids and fiber. If the emotion engine detects stress, it will also recommend relaxing drinks like chamomile tea.

[1585] 4. Server: Generates recipes using mackerel and avocado and sends them to the client device.

[1586] 5. Terminal: Shows the recipe and ingredients list to the user.

[1587] 6. User: Selects to order from the online supermarket.

[1588] 7. Terminal: Add the ingredients you need to your online supermarket cart.

[1589] 8. User: Makes final confirmation and confirms order.

[1590] 9. Server: Calculates monthly health scores and awards points according to improvements.

[1591] 10. Terminal: Displays score and point information to the user and provides feedback based on the emotion engine.

[1592] This system allows users to take specific actions based on their health checkup results and aim to improve their health. In addition, the introduction of an emotion engine enables more personalized health management and makes suggestions that take into account the user's psychological stress and motivation.

[1593] The processing flow will be explained below.

[1594] Step 1:

[1595] User: Enters the results of a health check into a client terminal. Input items include blood test results (e.g., LDL cholesterol 160 mg / dL, triglycerides 200 mg / dL) and blood glucose levels.

[1596] Step 2:

[1597] Terminal: Sends the entered health check results to the server, where the data is stored for immediate processing.

[1598] Step 3:

[1599] Server: Stores the results of the health checkup in a database. This data is used for later analysis.

[1600] Step 4:

[1601] Server: The generative AI model retrieves health checkup results stored in a database and analyzes them. This analysis includes identifying specific nutrients, such as recommending a low-fat, high-fiber diet if LDL cholesterol is high.

[1602] Step 5:

[1603] Server: After identifying the necessary nutrients, it uses a generative AI model to create the best recipe for the user, for example, using ingredients like mackerel and avocado.

[1604] Step 6:

[1605] Server: Sends the created recipe and the list of required ingredients in JSON format to the client device, so that the recipe information is ready to be displayed on the device.

[1606] Step 7:

[1607] Terminal: The recipe and ingredient list received from the server are displayed to the user, allowing the user to view the suggested recipes.

[1608] Step 8:

[1609] User: Review the suggested recipe and ingredient list and choose to order from the online supermarket, making it easy for users to purchase the perfect ingredients.

[1610] Step 9:

[1611] Device: Calls the online supermarket's API and automatically adds the necessary ingredients to the cart. In addition, an emotion engine captures the user's emotional state and makes fun comments and suggestions.

[1612] Step 10:

[1613] Terminal: After the ingredients are added to the cart, a final confirmation screen for the order is displayed to the user, where the user can make final confirmation of the order.

[1614] Step 11:

[1615] User: Checks the order details and presses the button to confirm the order. This completes the order to the online supermarket.

[1616] Step 12:

[1617] Server: Calculates the user's monthly health score based on the results of the health check. The score is based on the degree of improvement, such as whether LDL cholesterol has decreased or blood sugar levels have stabilized.

[1618] Step 13:

[1619] Server: Based on the calculated health score, the server awards points to the user. Different points are awarded depending on the degree of health improvement.

[1620] Step 14:

[1621] Device: Displays health score and points information to the user. Adds feedback and encouraging messages depending on the user's emotional state as recognized by the emotion engine.

[1622] In this way, users can take specific actions based on their health checkup results, enabling multifaceted and personalized health management.In addition, the introduction of an emotion engine allows feedback and suggestions to be provided that take into account the user's psychological aspects.

[1623] Example 2

[1624] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1625] In modern society, individual health management is important, but it is difficult for users to properly understand their health checkup results and implement improvement measures. Current systems, which present uniform dietary and lifestyle improvement measures without considering the user's emotional state, have the problem of making it difficult to provide optimal support for each individual user. Furthermore, there is a lack of methods to maintain motivation in order to continuously support users' health improvement.

[1626] 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. In this invention, the server includes a means for receiving health checkup results, a means for analyzing the received results to identify necessary nutrients, a means for generating individually optimized ingredients and recipes based on the identified nutrients, a means for recognizing the user's emotions and adjusting suggestions based on the user's emotional state, a means for automatically placing an order with an e-commerce service based on the generated ingredient list, and a means for calculating the user's health score and awarding points according to the score. This allows the user to properly understand their own health status and implement optimal health improvement measures that take their emotional state into consideration. Furthermore, the monthly updated health score and point awarding help maintain the user's motivation.

[1627] "Physical examination results" are data on the results of a series of medical tests conducted to assess the user's health condition.

[1628] The "receiving means" is a system component that receives data sent from a user via a network, converts it into an appropriate format, and stores it.

[1629] The "means for analyzing and identifying necessary nutrients" is an algorithm or program that determines which nutrients are deficient or in excess based on the received health check results.

[1630] The "means for generating individually optimized ingredients and recipes" refers to a generative AI model or program that provides optimal ingredients and recipes for each user based on the identified nutrients.

[1631] The "means for recognizing the user's emotions and adjusting suggestions based on the emotional state" refers to an emotion engine or analysis program that analyzes the user's emotional state and changes the suggestions made in accordance with that state.

[1632] "Means for automatically placing an order with an e-commerce service based on the generated ingredient list" refers to a system component that uses the generated ingredient list to call the API of an e-commerce service (such as an online supermarket) and automatically processes the order.

[1633] The "means for calculating a user's health score" refers to an algorithm or program that calculates a score for quantitatively evaluating the user's health condition.

[1634] The "means for awarding points" is a system component that awards points to the user as an incentive based on the calculated health score.

[1635] This invention is a system for personalizing health management for users, and is composed of the following components: a client terminal (user device), a server (including a data analysis and generation AI model and an emotion engine), and an e-commerce service API. The specific roles and operations of each component are explained below.

[1636] Receiving and analyzing health checkup results

[1637] User: The user inputs the results of their health checkup into the client terminal. The results of the health checkup include, for example, LDL cholesterol, HDL cholesterol, triglycerides, and blood glucose levels. This information is entered as numerical values ​​into the application's input form.

[1638] Terminal: The terminal sends the health check result data entered by the user to the server. The data is sent using the HTTPS protocol and packaged in JSON format. The sent data is then prepared for processing by the server.

[1639] Server: The server stores the received data in a database, typically a relational database such as MySQL or PostgreSQL. The stored data is then analyzed using a generative AI model. This analysis identifies nutrient deficiencies and excesses.

[1640] Recipe and ingredient suggestions

[1641] Server: The server uses a generative AI model based on the analysis results to create individually optimized recipes. This process also takes into account the user's past medical history and the medical history of their parents. It also uses an emotion engine to recognize the user's emotional state and recommend ingredients and recipes that are effective in relieving stress and improving mood.

[1642] For example, if the system recognizes the user's stress level, it will suggest recipes using chamomile tea, which has a relaxing effect, or mackerel, which is rich in omega-3 fatty acids.

[1643] Ordering from an online supermarket

[1644] User: The user reviews the suggested recipe and ingredient list displayed on the device and selects to order from the online supermarket. The order is placed using the app interface, and the selected ingredients are automatically added to the cart.

[1645] Terminal: The terminal calls the online supermarket's API and automatically adds the required ingredients to the cart. This process uses the REST API and sends the order data via a POST request. The terminal then displays a final confirmation screen for the user to confirm the order.

[1646] User: The user confirms the order and confirms it, completing the food purchase process.

[1647] Calculation of health score and awarding points

[1648] Server: The server periodically collects health checkup results and calculates a monthly health score. The score is calculated based on the degree of improvement, such as whether LDL cholesterol has decreased or whether blood sugar levels are stable. Points are awarded to the user based on the calculated score.

[1649] Terminal: The terminal displays the health score and points to the user. This is done using graphs and numerical displays. In addition, feedback and messages are displayed according to the emotional state recognized by the emotion engine. For example, a message such as "Your health score is 80 points! That's a great achievement" can be displayed to motivate the user.

[1650] Specific examples

[1651] Here is an example of this system:

[1652] 1. User: Enters blood test results into the application's input form and clicks the "Submit" button.

[1653] 2. Terminal: Packages the input data in JSON format and sends it to the server using the HTTPS protocol.

[1654] 3. Server: Stores the received data in a relational database and analyzes it using a generative AI model. As a result of the analysis, it identifies nutrient deficiencies and excesses and generates appropriate ingredients and recipes.

[1655] 4. Server: Combines generative AI models and an emotion engine to generate personalized recipe and ingredient suggestions. For example, if a user's emotion is recognized as stressful, it will suggest ingredients with a relaxing effect (e.g., chamomile tea).

[1656] 5. On the device: Display suggested recipes and ingredient lists in list or card view. For example, "Simmered Mackerel with Miso" or "Avocado and Quinoa Salad" are displayed.

[1657] 6. User: Select the ingredients you want to purchase from the displayed list and click the "Order" button.

[1658] 7. Device: Calls the online supermarket's API to add ingredients to the cart and displays a final confirmation screen. The user checks the details and confirms the order.

[1659] 8. Server: Calculates monthly health score and awards points based on the score.

[1660] 9. Device: Displays health scores and points in graphs and numerical formats. Emotion engine recognizes the user's emotional state and displays feedback and messages to motivate the user.

[1661] This system allows users to take specific actions based on their health checkup results and aim to improve their health. In addition, the introduction of an emotion engine enables more personalized health management and makes suggestions that take into account the user's psychological stress and motivation.

[1662] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1663] Step 1:

[1664] User: Enter the results of the health check into the client terminal.

[1665] Input: Health checkup result data (e.g., LDL cholesterol, HDL cholesterol, triglycerides, blood glucose levels).

[1666] Action: Enter the health check data as numbers into the application's input form and click the "Submit" button.

[1667] Output: Health checkup data entry completed (data saved on the device).

[1668] Step 2:

[1669] Terminal: Sends the entered data to the server.

[1670] Input: Medical examination data.

[1671] What it does: It packages data in JSON format and sends it to the server using the HTTPS protocol.

[1672] Output: Health check data in JSON format is sent to the server.

[1673] Step 3:

[1674] Server: Stores the received data in a database.

[1675] Input: Submitted health check data (JSON format).

[1676] What it does: Stores the received data in a database (e.g. MySQL or PostgreSQL).

[1677] Output: The health check data is stored in a database.

[1678] Step 4:

[1679] Server: Analyzes the stored data using a generative AI model.

[1680] Input: Medical examination data stored in a database.

[1681] How it works: Generative AI models are used to analyze data and identify nutrient deficiencies and excesses.

[1682] Output: Nutrient information identified as a result of the analysis.

[1683] Step 5:

[1684] Server: Utilizes generative AI models to generate individually optimized recipe and ingredient suggestions.

[1685] Input: Analysis results (identified nutrient information).

[1686] How it works: Combines generative AI models and an emotion engine to generate personalized recipes and ingredient lists.

[1687] Output: A suggested recipe and ingredients list.

[1688] Step 6:

[1689] Terminal: Displays the server-generated recipe and ingredient list to the user.

[1690] Input: Recipe and ingredient list sent from the server.

[1691] What it does: Displays a recipe and ingredient list using a list or card view.

[1692] Output: The recipe and ingredients list displayed to the user.

[1693] Step 7:

[1694] User: Review the suggested recipe and ingredients list and choose to order from the online supermarket.

[1695] Input: Recipe and ingredient list displayed on device.

[1696] Action: Select the ingredients you want to purchase and click the "Order" button.

[1697] Output: An order request to the online supermarket.

[1698] Step 8:

[1699] Terminal: Calls the online supermarket's API and automatically adds the necessary ingredients to the cart.

[1700] Input: A list of ingredients selected by the user.

[1701] How it works: Using the REST API, the order data is sent via a POST request and added to the cart.

[1702] Output: Information about ingredients added to the online supermarket cart.

[1703] Step 9:

[1704] Terminal: A final confirmation screen is displayed to the user to confirm the order details.

[1705] Input: Information about ingredients added to cart.

[1706] Behavior: Displays a final confirmation screen, prompting the user for confirmation.

[1707] Output: Order confirmation by user.

[1708] Step 10:

[1709] Server: Calculates monthly health score.

[1710] Input: Routinely collected health examination result data.

[1711] How it works: It uses an algorithm or program to calculate a health score, specifically assessing things like lowering LDL cholesterol and stabilizing blood sugar levels.

[1712] Output: Calculated health score.

[1713] Step 11:

[1714] Server: Awards points based on the calculated health score.

[1715] Input: Health score.

[1716] What it does: Uses the points awarding algorithm to award the appropriate points to the user.

[1717] Output: The user's points are updated.

[1718] Step 12:

[1719] Terminal: Displays health score and points information to the user.

[1720] Input: Health score and updated points information.

[1721] What it does: Displays information using graphical and numerical displays and provides feedback based on an emotion engine.

[1722] Output: Health score and points information displayed to the user, and feedback from the emotion engine.

[1723] (Application example 2)

[1724] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1725] In modern society, there is a demand for systems that suggest specific actions based on individual health conditions and support the implementation of those actions. In particular, it is important to make suggestions for improving daily eating habits using health checkup results, but comprehensive health management that also takes into account the user's emotional state is also necessary. It is also important to have a system that makes it easy to obtain the suggested ingredients and recipes. Furthermore, continuous feedback and incentives are required to maintain the user's motivation to improve their health. There is currently a lack of systems that meet these requirements, so solving this problem is a challenge.

[1726] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1727] In this invention, the server includes means for receiving health checkup results, means for analyzing the received results to identify necessary nutrients, means for generating individually optimized ingredients and recipes based on the identified nutrients, means for automatically placing orders with a food delivery service based on the generated ingredient list, means for calculating the user's health score and awarding points according to the score, and means for detecting the user's emotional state and making recommendations based on the score. This allows the user to receive effective and emotionally sensitive meal suggestions based on the health checkup results and easily purchase the suggested ingredients. Furthermore, by visualizing the user's improvement using the health score and providing incentives through point awarding, motivation for continuous health management can be maintained.

[1728] "Health checkup results" refers to information on blood test items and physical measurements obtained as a result of regular medical examinations and tests that a user undergoes.

[1729] "Nutrients" are components such as vitamins, minerals, proteins, lipids, and carbohydrates that are necessary for maintaining health.

[1730] "Ingredients and recipes" refers to the ingredients used to prepare a meal and the steps and methods for cooking using those ingredients.

[1731] A "food delivery service" is a service that delivers ingredients and dishes selected by the user to their home or designated location.

[1732] "Emotional state" is information that indicates the user's current psychological state, including emotions such as stress, joy, and sadness.

[1733] A "recommendation" is the optimal action or option suggested based on the user's needs and situation.

[1734] A "health score" is a numerical value calculated based on the user's health checkup results and behavioral data, and is an indicator used to evaluate their health status.

[1735] "Points" are rewards and incentives awarded based on health scores and actions, and are used to increase user motivation.

[1736] "Past medical history and parental medical history" refers to historical information about illnesses and health conditions that the user or their parents have experienced in the past.

[1737] The system of this invention is a personalized healthy food delivery app that not only suggests optimal ingredients and recipes to users based on their health checkup results, but also incorporates an emotion engine that recognizes the user's emotions. This system has the following configuration and processing steps.

[1738] Key Components of the System

[1739] 1. Client terminal (user device)

[1740] Smartphone (iOS / Android)

[1741] 2. Server

[1742] Data analysis and generative AI models (Python, TensorFlow)

[1743] Emotion Engine (NLP model, Hugging Face)

[1744] Database (MySQL)

[1745] Net supermarket API integration (REST API)

[1746] Program processing

[1747] 1. User Input

[1748] Users use a smartphone app to input their health checkup results and current emotional state, such as their LDL cholesterol and blood sugar levels from a blood test, and their emotional state, such as "I'm stressed."

[1749] 2. Send to the server

[1750] The data entered from the smartphone is sent to a server, where it is stored in a database and used for analysis.

[1751] 3. Data Analysis

[1752] The server then analyzes the received data using Python scripts and TensorFlow models to identify nutrients and foods necessary for the user's health. For example, if the user's LDL cholesterol is high, it will recommend foods rich in omega-3 fatty acids.

[1753] 4. Emotion Engine

[1754] The server uses an emotion engine to analyze the user's emotion data, and uses an NLP model (e.g., Hugging Face) to suggest foods that have a relaxing effect if the user is feeling stressed.

[1755] 5. Recipe Generation

[1756] Based on the analysis results, a generative AI model (e.g., GPT-3) is used to generate individually optimized recipes. For example, recipes such as "Simmered Mackerel in Miso" or "Avocado and Quinoa Salad" are generated. These recipes are then sent to the client device.

[1757] 6. Food delivery collaboration

[1758] The generated ingredient list is automatically added to the user's cart via the online supermarket's API, and the user then makes a final confirmation on the smartphone app to confirm the order.

[1759] 7. Health Score and Points

[1760] The server periodically calculates a health score based on the results of health checkups and updates the user's score monthly. Points are awarded based on this score, which acts as an incentive to keep the user motivated to improve their health.

[1761] Specific examples

[1762] For example, specific user input and suggestions are given below:

[1763] Example user input: "LDL cholesterol 180 mg / dL, triglycerides 220 mg / dL, emotional state: stressed"

[1764] Example prompts to input to a generative AI model:

[1765] The user's health check results are as follows:

[1766] LDL cholesterol: 180 mg / dL

[1767] Triglycerides: 220 mg / dL

[1768] The user's emotional state is stress. Suggest ingredients and recipes appropriate for the user's state.

[1769] In this way, users can receive effective and emotionally sensitive dietary suggestions based on their health checkup results, and can easily purchase the suggested ingredients. Furthermore, the degree of improvement is visualized through a health score, and points are awarded to maintain motivation for ongoing health management.

[1770] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1771] Step 1:

[1772] Users use a smartphone app to input their health check results and current emotional state, including blood test results and emotional state options, which provides specific data about the user's condition.

[1773] Step 2:

[1774] The terminal sends the data entered by the user to the server. During this process, the data is encrypted and sent, and then securely stored in a database. Here, the input data is stored on the server in preparation for the next analysis step.

[1775] Step 3:

[1776] The server analyzes the received data using Python scripts and TensorFlow models to identify the nutrients and ingredients needed based on the user's health condition. The analysis results are output as a report of the identified nutrients and their deficiency or excess.

[1777] Step 4:

[1778] The server uses an emotion engine to analyze the user's emotion data. It uses an NLP model (e.g., Hugging Face) to generate appropriate recommendations based on the input emotion. For example, if stress is detected, foods with a relaxing effect will be recommended.

[1779] Step 5:

[1780] The server generates individually optimized recipes based on the analysis results using a generative AI model (e.g., GPT-3). The generated recipes are output along with a list of ingredients and sent to the client device.

[1781] Step 6:

[1782] The terminal displays the recipe and ingredient list received from the server to the user, who can then confirm the ingredients based on the suggested list and select a food delivery service.

[1783] Step 7:

[1784] The server then calls the online supermarket's API based on the generated ingredient list and automatically adds the items to the user's cart. At this time, the order details are sent to the online supermarket in encrypted format.

[1785] Step 8:

[1786] The user checks the final confirmation screen on the smartphone app and confirms the order. Once the user confirms the order, the online supermarket prepares the ingredients and delivers them to the specified address.

[1787] Step 9:

[1788] The server periodically calculates a health score based on the results of the health checkup and updates it monthly. Points are awarded to the user's account based on the score, which helps to motivate the user to improve their health.

[1789] Step 10:

[1790] The server displays health scores and points to users, and also utilizes an emotion engine to provide feedback and messages based on their emotional state, motivating users to continue managing their health.

[1791] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1792] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1793] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1794] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1795] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1796] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1797] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1798] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1799] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1800] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1801] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1802] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1803] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1804] 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.

[1805] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1806] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1807] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1808] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1809] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1810] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1811] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1812] The following is further disclosed regarding the above embodiment.

[1813] (Claim 1)

[1814] means for receiving the results of the health examination;

[1815] means for analyzing the received results to identify nutrient needs;

[1816] a means for generating individually optimized ingredients and recipes based on the identified nutrients;

[1817] A method for automatically placing orders with online supermarkets based on the generated ingredient list;

[1818] A means for calculating a health score of a user and awarding points according to the score;

[1819] A system including:

[1820] (Claim 2)

[1821] 10. The system of claim 1, further comprising means for taking into account past medical history and parental medical history in generating the recipe based on the identified nutrients.

[1822] (Claim 3)

[1823] 10. The system of claim 1, further comprising means for updating the health score monthly and motivating the user based on score fluctuations.

[1824] "Example 1"

[1825] (Claim 1)

[1826] means for receiving the results of the health examination;

[1827] means for analyzing the received results to identify nutrient deficiencies or excess nutrients;

[1828] a means for generating individually optimized ingredients and cooking instructions based on the identified nutrients using a generative AI model; and

[1829] a means for automatically placing an order in an electronic commerce system based on the generated ingredient list;

[1830] A means for calculating a user's health score monthly and awarding points according to the score;

[1831] means for transmitting the received health checkup data using a secure communication protocol;

[1832] A system including:

[1833] (Claim 2)

[1834] 10. The system of claim 1, further comprising means for taking into account past medical history and parental medical history in generating the recipe based on the identified nutrients.

[1835] (Claim 3)

[1836] 10. The system of claim 1, further comprising means for updating the health score monthly and motivating the user based on score fluctuations.

[1837] "Application Example 1"

[1838] (Claim 1)

[1839] means for receiving the results of the health examination;

[1840] means for analyzing the received results to identify nutrient needs;

[1841] a means for generating individually optimized ingredients and recipes based on the identified nutrients;

[1842] a means for automatically placing an order on an e-commerce platform based on the generated ingredient list;

[1843] A means for calculating a health score of a user and awarding points according to the score;

[1844] A means of ordering food from a delivery service based on the optimized recipe;

[1845] A system including:

[1846] (Claim 2)

[1847] 10. The system of claim 1, further comprising means for taking into account past medical history and parental medical history in generating the recipe based on the identified nutrients.

[1848] (Claim 3)

[1849] 10. The system of claim 1, further comprising means for updating the health score monthly and motivating the user based on score fluctuations.

[1850] "Example 2: Combining Emotion Engines"

[1851] (Claim 1)

[1852] means for receiving the results of the health examination;

[1853] means for analyzing the received results to identify nutrient needs;

[1854] a means for generating individually optimized ingredients and recipes based on the identified nutrients;

[1855] means for recognizing a user's emotions and tailoring suggestions based on the user's emotional state;

[1856] a means for automatically placing an order with an e-commerce service based on the generated ingredient list;

[1857] A means for calculating a health score of a user and awarding points according to the score;

[1858] A system including:

[1859] (Claim 2)

[1860] 10. The system of claim 1, further comprising means for taking into account past medical history and parental medical history in generating the recipe based on the identified nutrients.

[1861] (Claim 3)

[1862] 10. The system of claim 1, further comprising means for updating the health score monthly and motivating the user based on score fluctuations.

[1863] "Application example 2 when combining emotion engines"

[1864] (Claim 1)

[1865] means for receiving the results of the health examination;

[1866] means for analyzing the received results to identify nutrient needs;

[1867] a means for generating individually optimized ingredients and recipes based on the identified nutrients;

[1868] A means for automatically placing an order with a food delivery service based on the generated ingredient list;

[1869] A means for calculating a health score of a user and awarding points according to the score;

[1870] a means for detecting a user's emotional state and making recommendations based thereon;

[1871] A system including:

[1872] (Claim 2)

[1873] 10. The system of claim 1, further comprising means for taking into account past medical history and parental medical history in generating the recipe based on the identified nutrients.

[1874] (Claim 3)

[1875] 10. The system of claim 1, further comprising means for updating the health score monthly and motivating the user based on score fluctuations. [Explanation of symbols]

[1876] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for receiving the results of the health examination; means for analyzing the received results to identify nutrient needs; a means for generating individually optimized ingredients and recipes based on the identified nutrients; A method for automatically placing orders with online supermarkets based on the generated ingredient list; A means for calculating a health score of a user and awarding points according to the score; A system including:

2. The system of claim 1 , further comprising means for taking into account past medical history and parental medical history in generating the recipe based on the identified nutrients.

3. The system of claim 1 , further comprising means for updating the health score monthly and motivating the user based on score fluctuations.

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