System

Abstract

A system is provided.SOLUTION: A system comprising: means for collecting health data; means for suggesting an ingredient suitable for a user based on the health data; means for selecting the suggested ingredient by the user; means for generating order data of the selected ingredient; means for delivering the generated order data to a retailer, a distributor, or a producer; and means for delivering the ingredient to the user based on the delivered order data.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In recent years, a wide range of food-related issues have arisen, including an ultra-aging society, growing health awareness, and the need to reduce food waste. Furthermore, the logistics industry is facing a growing need to reduce working hours and improve efficiency. It is difficult to solve these issues individually, and a system that can solve them comprehensively is needed. The purpose of this invention is to solve these issues simultaneously, promoting individual health management and efficiency across the industry. [Means for solving the problem]

[0005] The system of the present invention includes a means for collecting health data, a means for suggesting ingredients suitable for a user based on the health data, a means for the user to select the suggested ingredients, a means for generating order data for the selected ingredients, a means for distributing the generated order data to retailers, logistics companies, and producers, and a means for delivering ingredients to the user based on the distributed order data. The system also includes a means for encrypting and transmitting the health data, a means for retailers to manage inventory, a means for logistics companies to optimize delivery routes, and a means for producers to create production plans. This allows for the efficient provision of ingredients suitable for each individual's health condition, reduces food waste, and improves the efficiency and health of the logistics and production industries as a whole.

[0006] "Health data" refers to information about the user's health condition, such as the user's weight, height, calories burned, and allergy information.

[0007] "User" refers to an individual consumer who uses the system to input health data and receive food recommendations and delivery services.

[0008] "Ingredients" refers to food ingredients suggested based on the user's calorie consumption and health status.

[0009] "Order data" refers to data that compiles information about ingredients selected by the user, and is provided to retailers, logistics companies, and producers based on this information.

[0010] "Retailer" refers to a store or company that manages inventory of ingredients provided through the system and provides them to users.

[0011] "Logistics company" refers to a company that is responsible for setting up efficient delivery routes based on order data and delivering ingredients to users.

[0012] "Producers" refers to farmers and food manufacturers who create appropriate production plans based on order data and supply the necessary ingredients.

[0013] The term "system" in this invention refers to a configuration having a series of functions that consistently execute the collection and analysis of health data, food ingredient suggestions, order data generation and distribution, and food ingredient delivery.

[0014] "Encryption" refers to the technology that transforms health data for secure transmission and protects it from unauthorized access.

[0015] "Inventory management" refers to the process by which retailers manage product inventory based on order data and replenish it as needed. [Brief explanation of the drawings]

[0016] [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

[0017] 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.

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

[0019] 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).

[0020] 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.

[0021] 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.

[0022] 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.

[0023] 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."

[0024] [First embodiment]

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

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form 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.

[0031] 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.

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

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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."

[0037] MODE FOR CARRYING OUT THE INVENTION

[0038] This invention is a system that proposes and delivers appropriate food ingredients based on the user's health data, and provides related data to retailers, logistics companies, and producers, thereby achieving personal health management and improving efficiency across the industry. Each component of the system and its function are explained in detail below.

[0039] 1. Health data collection

[0040] User: Enters daily health data such as weight, height, calories burned, and allergy information into a dedicated health app.

[0041] Terminal: The entered health data is encrypted and sent to the server using a secure communication protocol.

[0042] Server: Stores the received health data in a database and associates it with the user ID.

[0043] 2. Generating Ingredient Suggestions

[0044] Server: Analyzes the user's health data stored in the database and evaluates the user's calorie consumption and health condition.

[0045] Server: Runs an algorithm to suggest ingredients suitable for each user and generates an ingredient list.

[0046] Server: Generates a list of suggested ingredients in JSON format and sends it to the user's device.

[0047] 3. User selection and order confirmation

[0048] User: Selects desired ingredients from the list of suggested ingredients, for example, "200g chicken breast" and "100g broccoli."

[0049] Terminal: Sends the user's selection data to the server.

[0050] Server: Stores the received order information in a database and associates it with the order ID.

[0051] 4. Delivery of order data

[0052] Server: Formats order data so that it can be used by retailers, logistics providers, and manufacturers.

[0053] Server: Distributes formatted order data to retailers, logistics companies, and manufacturers.

[0054] Retailer: Checks inventory based on received order data and arranges for replenishment as necessary.

[0055] Logistics company: Plans delivery routes based on received order data.

[0056] Producer: Creates production plans based on received order data.

[0057] 5. Food delivery

[0058] Logistics Agent: Picks up the suggested ingredients from the retailer's store.

[0059] Logistics provider: Ensures efficient delivery based on set delivery routes.

[0060] Logistics company: delivers ingredients to the user's home.

[0061] User: Checks that the received ingredients are correct and notifies the app that receipt is complete.

[0062] Specific examples

[0063] User A weighs 70 kg, is 170 cm tall, and consumes an average of 2,000 kcal per day. His health data also indicates that he tends to be deficient in vitamin D. This data is entered into a healthcare app and sent to a server. The server receives and analyzes the data. The analysis determines that high-protein, low-fat foods and foods rich in vitamin D are suitable for User A.

[0064] The server suggests ingredients such as "200g chicken breast," "100g broccoli," and "50g almonds." User A selects "200g chicken breast" and "100g broccoli" from this list and confirms the order. This data is sent to the server and recorded as order data.

[0065] The order data is then distributed to retailers, logistics companies, and producers. Retailers check inventory, logistics companies plan efficient delivery routes, and producers adjust their production plans based on future demand.

[0066] Finally, the logistics company collects the necessary ingredients and delivers them to User A's home. User A checks the received ingredients and notifies the app that they have been received. This allows User A to continue their healthy eating habits, while also achieving efficiency and health for the entire industry.

[0067] The processing flow will be explained below.

[0068] Specific processing flow of the program

[0069] 1. Health data collection

[0070] Step 1:

[0071] User: Enters daily health data such as weight, height, calories burned, and allergy information into a dedicated health app.

[0072] Step 2:

[0073] Terminal: The entered health data is encrypted and sent to the server using a secure communication protocol.

[0074] Step 3:

[0075] Server: Stores the received health data in a database and associates it with the user ID.

[0076] 2. Generating Ingredient Suggestions

[0077] Step 1:

[0078] Server: Analyzes the user's health data stored in the database and evaluates the user's calorie consumption and health condition.

[0079] Step 2:

[0080] Server: Runs an algorithm to suggest ingredients suitable for each user and generates an ingredient list.

[0081] Step 3:

[0082] Server: Generates a list of suggested ingredients in JSON format and sends it to the user's device.

[0083] 3. User selection and order confirmation

[0084] Step 1:

[0085] User: Selects desired ingredients from the list of suggested ingredients, for example, "200g chicken breast" and "100g broccoli."

[0086] Step 2:

[0087] Terminal: Sends the user's selection data to the server.

[0088] Step 3:

[0089] Server: Stores the received order information in a database and associates it with the order ID.

[0090] 4. Delivery of order data

[0091] Step 1:

[0092] Server: Formats order data so that it can be used by retailers, logistics providers, and manufacturers.

[0093] Step 2:

[0094] Server: Distributes formatted order data to retailers, logistics companies, and manufacturers.

[0095] Step 3:

[0096] Retailer: Checks inventory based on received order data and arranges for replenishment as necessary.

[0097] Step 4:

[0098] Logistics company: Plans delivery routes based on received order data.

[0099] Step 5:

[0100] Producer: Creates production plans based on received order data.

[0101] 5. Food delivery

[0102] Step 1:

[0103] Logistics Agent: Picks up the suggested ingredients from the retailer's store.

[0104] Step 2:

[0105] Logistics provider: Ensures efficient delivery based on set delivery routes.

[0106] Step 3:

[0107] Logistics company: delivers ingredients to the user's home.

[0108] Step 4:

[0109] User: Checks that the received ingredients are correct and notifies the app that receipt is complete.

[0110] Example 1

[0111] 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."

[0112] Conventional health management systems have difficulty recommending appropriate ingredients based on individual users' health data and efficiently delivering those ingredients to users. Furthermore, they do not adequately consider the security of health data or the optimization of inventory management in the delivery process. Therefore, there is a need for improved individual health management and efficiency improvements across the industry.

[0113] 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.

[0114] In this invention, the server includes: means for collecting health data; means for encrypting the health data and transmitting it via a secure communication protocol; means for analyzing the user's calorie consumption and health status based on the health data; means for suggesting ingredients suitable for the user based on the analysis results; means for transmitting the list of suggested ingredients to the user; means for the user to select the suggested ingredients; means for generating order data for the selected ingredients; means for distributing the generated order data to retailers, distributors, and producers; and means for delivering ingredients to the user based on the distributed order data. This enables appropriate ingredients to be suggested based on the user's health data and delivered efficiently. This also improves the security of health data and optimizes inventory management.

[0115] "Health data" refers to information about the user's health condition, such as the user's weight, height, calories burned, and allergy information.

[0116] "Encryption" refers to converting data using a specific algorithm to protect the data from a security perspective, making it difficult for third parties to read.

[0117] A "secure communication protocol" is a communication method used to enhance security during data transmission, and includes HTTPS and TLS.

[0118] "Analysis" means processing and analyzing data to evaluate and diagnose the user's calorie consumption and health condition based on health data.

[0119] "Ingredient suggestions" refers to recommending ingredients suitable for the user based on analyzed health data.

[0120] "Order data" is data that includes information about ingredients selected by the user, and orders are processed based on this data.

[0121] A "retailer" is a business that sells goods to consumers.

[0122] A "logistics company" is a company whose role is to store and deliver goods efficiently.

[0123] A "producer" is a business that is responsible for manufacturing or producing goods.

[0124] "Delivery" refers to the process in which a retailer or a logistics company transports products to a destination based on a user's order.

[0125] MODE FOR CARRYING OUT THE INVENTION

[0126] The present invention is a system that proposes appropriate ingredients based on a user's health data and delivers them to the user, thereby achieving personal health management and improving the efficiency of the entire industry. Specific embodiments for implementing this system are described below.

[0127] System Overview

[0128] This system collects and analyzes the user's health data, suggests ingredients based on the results, distributes order data to retailers, logistics companies, and producers according to the user's selection, and finally delivers the ingredients to the user.

[0129] Hardware and software used

[0130] Hardware: Dedicated healthcare device (smartphone or tablet) used by the user, server (high-performance cloud server)

[0131] Software: Healthcare app, encryption software (AES-256), secure communication protocol (HTTPS), database (PostgreSQL), data analysis tools (Python, Pandas, Scikit-learn), communication API (REST API)

[0132] System details

[0133] 1. Health data collection

[0134] Users enter their daily health data (weight, height, calories burned, allergy information, etc.) into a dedicated healthcare app. This information becomes the basic data for health management.

[0135] The terminal encrypts the entered health data using the AES-256 encryption method and transmits it securely to the server using the HTTPS protocol.

[0136] The server decrypts the received encrypted data and stores it in a database.

[0137] 2. Generating Ingredient Suggestions

[0138] The server uses Python scripts to retrieve and analyze user health data from the database, using the Pandas library and Scikit-learn.

[0139] The server runs machine learning algorithms (such as Random Forest) to suggest suitable ingredients based on the user's calorie consumption and health status (e.g., vitamin D deficiency status).

[0140] The server converts the generated ingredient list into JSON format and sends it back to the user's device via HTTPS.

[0141] 3. User selection and order confirmation

[0142] The user selects the ingredients they want from a list of suggested ingredients through the app.

[0143] The terminal transmits the user's selection data to the server.

[0144] The server stores the received order data in a database and generates and associates an order ID.

[0145] 4. Delivery of order data

[0146] The server converts the order data into CSV format so that it can be used by retailers, logistics companies, and producers, and distributes it to each company using a REST API.

[0147] 5. Food delivery

[0148] The logistics company picks up the suggested ingredients from the retailer's store and delivers them efficiently to the user's home following the planned delivery route.

[0149] The user checks that the received ingredients are correct and is notified of receipt via a dedicated healthcare app.

[0150] Specific examples

[0151] User A weighs 70 kg, is 170 cm tall, consumes an average of 2000 kcal, and tends to be deficient in vitamin D. This health data is entered into a healthcare app and sent to a server. The server receives the data and analyzes it. Based on the analysis results, it is determined that high-protein, low-fat foods and foods rich in vitamin D are suitable for User A.

[0152] The server suggests ingredients such as "200g chicken breast," "100g broccoli," and "50g almonds." User A selects "200g chicken breast" and "100g broccoli" from this list and confirms the order in the app. The server distributes the order data to retailers, logistics companies, and producers. Inventory management, delivery route planning, and production planning are carried out by each company.

[0153] Finally, the logistics company picks up the ingredients and delivers them to User A's home. User A receives the ingredients and is notified of the receipt via the app. This allows User A to maintain a healthy diet and also improves efficiency across the industry.

[0154] Prompt Sentence Examples

[0155] Example prompts for generative AI models:

[0156] "Please explain in natural language the programming process of the system that recommends and delivers appropriate ingredients based on an individual's health data, including the specific hardware, software, and processing details. For example, please provide details on what encryption methods and protocols are used to transmit data, what machine learning algorithms are used to recommend ingredients, and how the order data is distributed to retailers and logistics providers."

[0157] The above is an embodiment of the present invention.

[0158] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0159] Step 1:

[0160] Health data collection

[0161] Users enter health data such as weight, height, calories burned, and allergy information into a dedicated healthcare app, which is then stored on the user's device.

[0162] The terminal receives the entered health data and encrypts it using the AES-256 encryption algorithm, which is then sent to the server via the secure communication protocol HTTPS.

[0163] The server decrypts the received encrypted data using the RSA encryption algorithm, associates it with the user ID, and stores it in the PostgreSQL database. This saves each user's health data in the database.

[0164] Step 2:

[0165] Generating ingredient suggestions

[0166] The server retrieves the user's health data from the database using a Python script, which is then preprocessed using the Pandas library.

[0167] The server then analyzes the user's calorie consumption and health status based on the preprocessed data, applying the Random Forest algorithm using the Scikit-learn library.

[0168] The server generates an ingredient list suitable for each user based on the analysis results. The generated ingredient list is converted into JSON format and sent to the user's device via HTTPS.

[0169] Step 3:

[0170] User selection and order confirmation

[0171] The user opens the app, checks the list of suggested ingredients sent from the server, operates the UI to select ingredients, and confirms the selection.

[0172] The terminal reformats the food ingredient data selected by the user into JSON format and sends it to the server via the HTTPS protocol.

[0173] The server stores the received order data in a database, generates an order ID, and records related data.

[0174] Step 4:

[0175] Order data distribution

[0176] The server formats the order data into a format that can be used by retailers, logistics companies, and manufacturers. Formatting includes converting the data into CSV format and mapping data fields based on each company's API specifications.

[0177] The server then distributes the formatted data to retailers, logistics providers, and producers using a REST API, with different API endpoints used for each provider.

[0178] Based on the received data, the retailer obtains inventory information from the product management system and replenishes inventory as necessary.

[0179] Logistics companies use GIS software to optimize delivery routes based on the order data they receive.

[0180] Producers use the ERP system to adjust their production plans based on the received order data.

[0181] Step 5:

[0182] Food delivery

[0183] The logistics company picks up the suggested ingredients from the retailer's store. For example, it accesses the store's inventory management system and receives "200g of chicken breast" and "100g of broccoli."

[0184] The logistics company will efficiently deliver the ingredients to the user's home according to the optimized delivery route. The delivery status is tracked in real time and the user is notified via a dedicated app.

[0185] The user checks whether the ingredients are correct and notifies the server of the receipt completion through the dedicated healthcare app. Once notified, the server records the receipt completion status in the database.

[0186] The above is the specific operation and input / output flow at each processing step.

[0187] (Application example 1)

[0188] 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."

[0189] Conventional health management systems lack a system that can suggest appropriate ingredients based on users' health data and efficiently manage the entire process from ordering to delivery. Furthermore, users often have to manually enter their health data, leading to problems of inaccuracy and time-consuming data entry. Furthermore, there is insufficient collaboration between retailers, logistics companies, and producers, preventing the industry as a whole from achieving efficiency.

[0190] 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.

[0191] In this invention, the server includes: means for collecting health data; means for suggesting suitable ingredients to a user based on the health data; means for the user to select the suggested ingredients; means for generating order data for the selected ingredients; means for distributing the generated order data to retailers, logistics companies, and producers; means for delivering ingredients to the user based on the distributed order data; a server that executes an algorithm for generating ingredient suggestions based on the health data; and application means for inputting health data from a smartphone. This allows users to easily and accurately input their health data through a smartphone app and receive appropriate ingredient suggestions based on that data, enabling efficient management of the entire food product process from ordering to delivery. Furthermore, collaboration between retailers, logistics companies, and producers can be strengthened, resulting in greater efficiency and soundness in the industry as a whole.

[0192] "Health data" is information that indicates the user's health condition, such as the user's weight, height, calories burned, allergy information, and dietary history.

[0193] "Ingredient suggestions" refers to suggesting ingredients that are best suited to the user's health condition based on collected health data.

[0194] "Server" refers to a computer system that analyzes health data, generates food ingredient suggestions, and manages and distributes order data.

[0195] "Retailer" refers to a company or individual that sells food ingredients and manages inventory.

[0196] "Logistics company" refers to a company or individual that delivers food ingredients.

[0197] "Producers" refers to farmers, food manufacturers, etc. who produce the proposed ingredients.

[0198] "Application" refers to software that allows users to input health data, receive food ingredient suggestions, and place orders via smartphone.

[0199] "Algorithm" refers to the calculation procedure used to analyze health data and suggest appropriate ingredients.

[0200] "Order data" refers to data containing information about ingredients selected by the user, and is distributed to retailers, distributors, and producers.

[0201] The present invention is a system that proposes and delivers appropriate ingredients based on the user's health data and provides related data to various businesses, thereby achieving personal health management and improving the efficiency of the entire industry. Specific embodiments of this system are described below.

[0202] Health data collection and entry

[0203] Users use their smartphones to enter health data such as weight, height, calories burned, and allergy information into a dedicated application. The application was developed using React Native and runs on both iOS and Android platforms.

[0204] Transmission and storage of health data

[0205] The health data entered by the user is encrypted and sent to the server using a secure communication protocol. The server is a Python-based system using Flask, and the received data is stored in a MongoDB database. This data is managed in association with the user ID.

[0206] Generating ingredient suggestions

[0207] The server analyzes the stored health data and runs an algorithm to suggest ingredients suitable for the user. The algorithm used here is based on a generative AI model and recommends optimal ingredients based on the health data. The ingredient list generated by this ingredient suggestion algorithm is sent to the user's device in JSON format.

[0208] User selection and order confirmation

[0209] The user selects the ingredients they want from a list of suggested ingredients through the smartphone app. This selection data is sent back to the server and saved as order data. The order data is managed in association with the user ID.

[0210] Distribution of order data and delivery of ingredients

[0211] The server formats the order data into a format that can be used by retailers, logistics companies, and producers, and distributes it to each of them. Retailers check their inventory and arrange for replenishment as necessary. Logistics companies plan efficient delivery routes and deliver the ingredients to users' homes. Producers adjust their production plans based on future demand.

[0212] Specific examples

[0213] As a concrete example, if User B has health data stating that he weighs 80 kg, is 180 cm tall, and is iron deficient, this data is entered into the application and sent to the server. The server analyzes the data and suggests ingredients such as "200 g of spinach" and "150 g of lean meat." User B selects from this list and confirms the order. This data is stored on the server and distributed as order data to retailers, logistics companies, and producers.

[0214] Prompt Sentence Examples

[0215] An example prompt based on user B's health data is:

[0216] User ID: 12345, Weight: 80kg, Height: 180cm, Allergies: None, Dietary history: Iron deficiency

[0217] As described above, the system of the present invention is capable of consistently providing everything from food ingredient suggestions based on the user's health data to delivery, contributing to personal health management and efficiency improvements across the industry.

[0218] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0219] Step 1:

[0220] A user inputs health data (e.g., weight, height, calories burned, allergy information) into a smartphone app. This data is entered into input fields in the application and stored locally.

[0221] Step 2:

[0222] The device (smartphone app) encrypts the entered health data and sends it to the server using a secure communication protocol (e.g., HTTPS). Here, the entered raw data is encrypted and reaches the server safely.

[0223] Step 3:

[0224] The server receives the encrypted health data, decrypts it, and then stores it in a MongoDB database, where the user ID and health data are associated and stored in the database.

[0225] Step 4:

[0226] The server periodically analyzes the health data stored in the database. The analysis algorithm uses a generative AI model to evaluate the collected health data and suggest the most suitable ingredients for the user. This process takes input data (health data) and outputs an ingredient list (suggested ingredients).

[0227] Step 5:

[0228] The server sends the generated ingredient list in JSON format to a smartphone app, which can then view the ingredients and their quantities.

[0229] Step 6:

[0230] Users can view a list of suggested ingredients on the smartphone app and select the ingredients they want, which are then temporarily stored locally on the device.

[0231] Step 7:

[0232] The terminal transmits the user's selection data to the server, and the server generates order data based on the received selection data. This order data is associated with the user ID and recorded in a database.

[0233] Step 8:

[0234] The server converts the generated order data into the required format and distributes it to retailers, logistics companies, and producers. This distribution process allows retailers to check inventory, logistics companies to plan delivery routes, and producers to adjust production plans.

[0235] Step 9:

[0236] The logistics company picks up the suggested ingredients from the retailer's store and delivers them to the user's home based on an efficient delivery route. This delivery process is updated in real time on the server.

[0237] Step 10:

[0238] The user checks the received ingredients and notifies the smartphone app that the delivery has been completed, which then confirms the delivery was successful throughout the system and updates the user's health data again.

[0239] The above steps complete a series of processes that suggest and deliver appropriate ingredients based on the user's health data and provide related data to the supplier.

[0240] 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.

[0241] MODE FOR CARRYING OUT THE INVENTION

[0242] This invention is a system that proposes and delivers appropriate ingredients based on the user's health and emotional data, and provides related data to retailers, logistics companies, and producers, thereby achieving personal health management and improving efficiency across the industry. Each component of the system and its function are explained in detail below.

[0243] 1. Collecting health and emotional data

[0244] User: Enters daily health data such as weight, height, calories burned, and allergy information into a dedicated healthcare app. User emotional data is also collected through a dedicated emotion recognition engine.

[0245] Device: Health and emotion data are encrypted and sent to a server using a secure communication protocol.

[0246] Server: Stores the received health and emotion data in a database and associates it with the user ID.

[0247] 2. Generating Ingredient Suggestions

[0248] Server: Analyzes the user's health and emotional data stored in the database and evaluates the user's calorie consumption, health condition, and emotional state.

[0249] Server: Runs an algorithm to suggest ingredients suitable for each user and generates an ingredient list. For example, it suggests calorie-balanced ingredients to a user who is feeling stressed.

[0250] Server: Generates a list of suggested ingredients in JSON format and sends it to the user's device.

[0251] 3. User selection and order confirmation

[0252] User: Selects desired ingredients from the list of suggested ingredients, for example, "200g chicken breast" and "100g broccoli."

[0253] Terminal: Sends the user's selection data to the server.

[0254] Server: Stores the received order information in a database and associates it with the order ID.

[0255] 4. Delivery of order data

[0256] Server: Formats order data so that it can be used by retailers, logistics providers, and manufacturers.

[0257] Server: Distributes formatted order data to retailers, logistics companies, and manufacturers.

[0258] Retailer: Checks inventory based on received order data and arranges for replenishment as necessary.

[0259] Logistics company: Plans delivery routes based on received order data.

[0260] Producer: Creates production plans based on received order data.

[0261] 5. Food delivery

[0262] Logistics Agent: Picks up the suggested ingredients from the retailer's store.

[0263] Logistics provider: Ensures efficient delivery based on set delivery routes.

[0264] Logistics company: delivers ingredients to the user's home.

[0265] User: Checks that the received ingredients are correct and notifies the app that receipt is complete.

[0266] Specific examples

[0267] User B weighs 65 kg, is 160 cm tall, and consumes an average of 1,800 kcal per day. Emotional data indicating that User B is under a high level of stress has also been collected. This data is entered into a healthcare app and sent to a server. The server receives and analyzes the data. The analysis determines that low-calorie, nutritionally balanced foods are suitable for User B.

[0268] The server suggests ingredients such as "150g chicken breast," "100g quinoa," and "50g blueberries." User B selects "150g chicken breast" and "50g blueberries" from this list and confirms the order. This data is sent to the server and recorded as order data.

[0269] The order data is then distributed to retailers, logistics companies, and producers. Retailers check inventory, logistics companies plan efficient delivery routes, and producers adjust their production plans based on future demand.

[0270] Finally, the logistics company collects the necessary ingredients and delivers them to User B's home. User B checks the received ingredients and notifies the app that they have been received. This allows User B to maintain a healthy diet that takes into consideration their emotional state, while also achieving efficiency and health for the entire industry.

[0271] The processing flow will be explained below.

[0272] Detailed Description of the Invention - Processing Flow

[0273] 1. Collecting health and emotional data

[0274] Step 1:

[0275] User: Enters daily health data such as weight, height, calories burned, and allergy information into a dedicated health app.

[0276] Step 2:

[0277] On the device: Cameras and sensors are used to collect emotional data from the user's facial expressions and behavior.

[0278] Step 3:

[0279] Device: Collected health and emotional data is encrypted and sent to a server using a secure communication protocol.

[0280] Step 4:

[0281] Server: Stores the received health and emotion data in a database and associates it with each user ID.

[0282] 2. Generating Ingredient Suggestions

[0283] Step 1:

[0284] Server: Analyzes the user's health and emotional data stored in the database and evaluates the user's calorie consumption, health condition, and emotional state.

[0285] Step 2:

[0286] Server: Based on the evaluation results, the server runs an algorithm to suggest ingredients suitable for the user and generates an ingredient list. For example, if the emotion engine determines that the user is feeling stressed, the server will suggest additional ingredients that will help reduce stress.

[0287] Step 3:

[0288] Server: Generates a list of suggested ingredients in JSON format and sends it to the user's device.

[0289] 3. User selection and order confirmation

[0290] Step 1:

[0291] User: Selects desired ingredients from the list of suggested ingredients, for example, "150g chicken breast" and "50g blueberries."

[0292] Step 2:

[0293] Terminal: Sends the user's selection data to the server.

[0294] Step 3:

[0295] Server: Stores the received order information in a database and associates it with the order ID.

[0296] 4. Delivery of order data

[0297] Step 1:

[0298] Server: Formats order data so that it can be used by retailers, logistics providers, and manufacturers.

[0299] Step 2:

[0300] Server: Distributes formatted order data to retailers, logistics companies, and producers via APIs.

[0301] Step 3:

[0302] Retailer: Checks inventory based on received order data and arranges for replenishment as necessary.

[0303] Step 4:

[0304] Logistics company: Plans delivery routes based on received order data.

[0305] Step 5:

[0306] Producer: Creates production plans based on received order data.

[0307] 5. Food delivery

[0308] Step 1:

[0309] Logistics Agent: Picks up the suggested ingredients from the retailer's store.

[0310] Step 2:

[0311] Logistics provider: Executes efficient deliveries based on set delivery routes.

[0312] Step 3:

[0313] Logistics company: delivers ingredients to the user's home.

[0314] Step 4:

[0315] User: Checks that the received ingredients are correct and notifies the app that receipt is complete.

[0316] Example 2

[0317] 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."

[0318] In modern society, individual health management and dietary optimization have become important issues. However, it is not easy for users to select ingredients that suit their own health and emotional state. Furthermore, efficient data sharing and collaboration between retailers, logistics companies, and producers is lacking, leading to inefficiencies across the industry. The present invention aims to solve these issues, improve the efficiency of users' health management, and promote collaboration across the industry.

[0319] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0320] In this invention, the server includes means for collecting health data and emotional data, means for suggesting ingredients suitable for the user based on the health data and emotional data, means for the user to select the suggested ingredients, means for generating order data for the selected ingredients, means for distributing the generated order data to retailers, logistics companies, and producers, means for delivering ingredients to the user based on the distributed order data, and means for the user to notify that delivery of the ingredients has been completed. This allows users to select ingredients optimal for their individual health and emotional states, and enables efficient data sharing and delivery.

[0321] "Health data" refers to data that indicates the user's health condition, such as the user's weight, height, calories burned, and allergy information.

[0322] "Emotion data" is data that represents the stress level and emotional state of the user, and is collected by an emotion recognition engine.

[0323] The "server" is a central processing unit that collects, analyzes, stores, and distributes data, and is a device that is responsible for overseeing each function of this system.

[0324] "Ingredients" means food items suggested and selected for consumption by a User.

[0325] "Suggestion" refers to the act of listing ingredients suitable for the user based on collected health and emotion data.

[0326] "Order data" is data that compiles information about ingredients selected by the user and is used for delivery, inventory management, and production planning.

[0327] A "retailer" is a business that manages inventory to provide ingredients selected by the user.

[0328] A "distributor" is a company that delivers ingredients from retailers to users.

[0329] "Producers" are businesses that produce and supply food ingredients.

[0330] A "communication protocol" is a set of rules and methods for securely sending and receiving data.

[0331] "Encryption" is a transformation operation that protects data from being deciphered by third parties.

[0332] "Notification of receipt completion" is the act of the user receiving the ingredients and notifying the system that they have been received correctly.

[0333] This invention is a system that proposes and delivers appropriate food ingredients based on the user's health and emotional data, and provides related data to retailers, logistics companies, and producers, thereby achieving personal health management and improving efficiency across the industry. Each component of this system and its function are described in detail below.

[0334] Health and emotional data collection

[0335] Using a dedicated healthcare app, users input health data such as weight, height, calories burned, and allergy information. Additionally, data on stress and emotional state is collected through an emotion recognition engine (e.g., Emotion AI), which recognizes facial expressions using the smartphone camera.

[0336] The device encrypts the health data entered by the user and the emotion data obtained by the emotion recognition engine (e.g., using AES (Advanced Encryption Standard)) and sends this encrypted data to the server via the HTTPS protocol.

[0337] The server receives the transmitted data, decrypts it, and stores it in a database (e.g., MySQL) in association with the user ID.

[0338] Generating ingredient suggestions

[0339] The server uses a data analysis engine (e.g., Pandas) to analyze the user's health and emotional data stored in the database. This analysis allows the server to evaluate the user's calorie consumption and emotional state and recommend appropriate ingredients.

[0340] The server runs a suggestion algorithm (e.g., K-means clustering) to generate the most suitable recipe list for each user. For example, if a user is under high stress, the server will suggest recipes that take into account calorie balance (e.g., "150g chicken breast," "100g quinoa," and "50g blueberries").

[0341] The server converts the generated ingredient list into JSON format and sends it to the user's device.

[0342] User selection and order confirmation

[0343] The user can view the suggested ingredients list on the app and select the ingredients they want (e.g., "150g chicken breast" or "50g blueberries").

[0344] The terminal encrypts the food ingredient data selected by the user and transmits it to the server using the HTTPS protocol.

[0345] The server stores the received selection data in a database, assigns an order ID, and manages it.

[0346] Order data distribution

[0347] The server formats the order data stored in the database into a format that can be consumed by retailers, logistics providers, and manufacturers, using CSV format or REST API endpoints.

[0348] The server distributes the formatted order data to retailers, logistics companies, and producers, allowing retailers to check inventory, logistics companies to plan delivery routes, and producers to plan production.

[0349] Food delivery

[0350] The logistics company picks up the suggested ingredients from the retailer's store or warehouse and delivers them efficiently to the user's home based on the planned delivery route.

[0351] The user confirms that the ingredients received are correct and notifies the user of the receipt via a dedicated app.

[0352] Specific examples

[0353] For example, User B weighs 65 kg, is 160 cm tall, consumes an average of 1,800 kcal per day, and emotional data indicates that he or she is highly stressed. This information is entered into a healthcare app and sent to a server. The server analyzes the received data and suggests low-calorie, nutritionally balanced ingredients to User B, such as 150 g of chicken breast, 100 g of quinoa, and 50 g of blueberries. User B selects the ingredients he or she desires from this list, and the order data is sent to the server. The server then distributes this order data to retailers, logistics companies, and producers, who then check inventory and make delivery and production plans. Finally, the logistics company delivers the ingredients to User B's home, and User B notifies them of receipt via the app.

[0354] Prompt Sentence Examples

[0355] "User B's health data is as follows: weight 65 kg, height 160 cm, average daily calorie consumption 1,800 kcal. User B is also under a lot of stress. Please suggest suitable foods for this user."

[0356] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0357] Step 1:

[0358] Users enter their health data, such as weight, height, calories burned, and allergy information, into a dedicated healthcare app. The app also uses an emotion recognition engine to capture their emotional state through the camera. This data is protected using AES encryption and transmitted to the server via HTTPS.

[0359] Input: User's weight, height, calories burned, allergy information, emotional state

[0360] Output: Encrypted health and emotional data

[0361] Step 2:

[0362] The server decrypts the received encrypted data, associates it with the user ID, and stores it in a database (MySQL). This allows for centralized management of each user's health and emotion data.

[0363] Input: Encrypted health and emotional data

[0364] Output: Health and emotion data for each user stored in a database

[0365] Step 3:

[0366] The server analyzes the user's health and emotional data stored in the database. Using a data analysis engine (Pandas), the server evaluates the user's calorie consumption and emotional state and selects appropriate ingredients. For users with high stress levels, the server makes suggestions that take calorie balance into consideration.

[0367] Input: User health and emotional data from a database

[0368] Output: Analyzed user's calorie consumption and emotional state information

[0369] Step 4:

[0370] The server runs a proposed algorithm (K-means clustering) to generate a list of ingredients suitable for each user. For example, it might suggest "150g chicken breast," "100g quinoa," and "50g blueberries." The generated list of ingredients is converted into JSON format and sent to the user's device.

[0371] Input: Analyzed user calorie consumption and emotional state information

[0372] Output: JSON list of ingredients

[0373] Step 5:

[0374] The user checks the suggested ingredients list on the app and selects the ingredients they want, for example, "150g of chicken breast" and "50g of blueberries." The selection data is encrypted and sent to the server.

[0375] Input: User's ingredient selection

[0376] Output: Encrypted selection data

[0377] Step 6:

[0378] The server saves the received selection data in a database and assigns an order ID, which is used to manage the order data.

[0379] Input: Encrypted selection data

[0380] Output: Order data and order ID stored in the database

[0381] Step 7:

[0382] The server converts the order data into a format that can be used by retailers, logistics companies, and producers (CSV or REST API endpoint) and distributes it to each of them. Retailers check inventory, logistics companies plan delivery routes, and producers set production plans.

[0383] Input: Order data and order ID stored in the database

[0384] Output: Order data distributed to retailers, logistics companies, and producers

[0385] Step 8:

[0386] The logistics company will pick up the ingredients selected by the user from the retailer's store or warehouse and deliver them efficiently to the user's home based on the planned delivery route. The user will then receive the ingredients and be notified of the receipt via a dedicated app.

[0387] Input: Retailer inventory check information, delivery route planning information, food pick-up information

[0388] Output: Delivery completion information and receipt completion notification to the user

[0389] (Application example 2)

[0390] 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."

[0391] Conventional food delivery systems do not take into account the user's health or emotional state when recommending ingredients, resulting in little effect on personal health management or stress reduction. Furthermore, the difficulty of tracking delivery status in real time can lead to a decline in user satisfaction. Furthermore, data sharing between retailers, logistics companies, and producers is insufficient, leaving issues with inventory management and delivery efficiency.

[0392] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting health data and emotional data, means for suggesting ingredients suitable for the user based on the health data and emotional data, means for the user to select the suggested ingredients, means for generating order data for the selected ingredients, means for distributing the generated order data to retailers, logistics companies, and producers, means for delivering ingredients to the user based on the distributed order data, and means for tracking delivery status in real time. This enables the suggestion and rapid delivery of ingredients suitable for the user's health and emotional state, thereby realizing personal health management and improving efficiency across the industry.

[0393] Definitions of important words

[0394] "Health data" refers to information about the user's physical condition, including the user's weight, height, calories burned, allergy information, and the like.

[0395] "Emotion data" is information relating to the user's stress level and emotional state.

[0396] "Collection means" refers to devices such as interfaces and sensors for acquiring the user's health data and emotional data.

[0397] The "suggestion means" is a device or system that executes an algorithm to create a list of ingredients suitable for the user based on the collected health data and emotion data.

[0398] The "selection means" refers to an interface or function that allows the user to select desired ingredients from the suggested ingredient list.

[0399] The "order data generation means" is a system for creating order data based on ingredients selected by the user.

[0400] The "distribution means" is a device or system for transmitting the generated order data to retailers, logistics companies, and manufacturers via communication.

[0401] A "delivery means" is a device or system that a retailer or a distributor uses to deliver the selected ingredients to a user.

[0402] The "tracking means" is a device or system for monitoring and notifying the user of the delivery status of ingredients delivered to the user in real time based on order data.

[0403] MODE FOR CARRYING OUT THE INVENTION

[0404] This invention relates to a system that supports users' health management by suggesting, selecting, and delivering ingredients based on the user's health and emotional data. This system includes three main elements: a server, a terminal, and a user, each of which works together to realize specific functions.

[0405] server

[0406] The server performs several functions:

[0407] 1. Data collection and storage: The server collects health data (weight, height, calories burned, allergy information, etc.) and emotional data (stress level, etc.) sent by the user and stores them in a database. This data is encrypted before transmission to ensure data security.

[0408] 2. Data Analysis: The server analyzes the collected data and runs algorithms to assess the user's health and emotional state, which then lists and suggests suitable ingredients for the user.

[0409] 3. Order data generation and distribution: The server generates order data based on the ingredient list selected by the user and distributes it to retailers, logistics companies, and producers.

[0410] 4. Real-time tracking: The server monitors the delivery status in real time and has the function to notify the user. This function allows the user to know the delivery status in real time.

[0411] Terminal

[0412] The terminal functions as a device such as a smartphone.

[0413] 1. Data input: Users input their health and emotional data through a dedicated application. If necessary, the emotion recognition engine automatically collects the user's emotional data.

[0414] 2. Data transmission: The entered data is encrypted and sent to the server using a secure communication protocol.

[0415] 3. Ingredient selection: The user checks the list of suggested ingredients sent from the server and selects the desired ingredients on the application.

[0416] 4. Notifications: The terminal receives notifications from the server and provides the user with real-time updates, especially regarding delivery status.

[0417] User

[0418] The user uses the system to perform the following operations:

[0419] 1. Data entry: Enter daily data such as weight, height, calories burned, allergy information, and emotional state into a dedicated app.

[0420] 2. Ingredient selection: Select the ingredients you want from the suggested options and confirm your order through the application.

[0421] 3. Receipt confirmation: Check the delivered ingredients and notify the customer of receipt via the application.

[0422] Specific examples

[0423] For example, if a user with user ID "user12345" consumes an average of 1,800 kcal per day and is determined to have a high stress level, the server will suggest 150g of chicken breast and 50g of blueberries. The user selects this and confirms the order. The server then distributes the order data to retailers, logistics companies, and producers, and tracks the delivery status in real time. The user can check the delivery status in real time via a smartphone application.

[0424] Prompt Sentence Examples

[0425] Get the user data for user ID "user12345" and suggest recommended foods based on their health and emotional data. Health data includes weight, height, calories burned, and allergy information, and emotional data includes stress level. If this user's stress level is high, suggest low-calorie, nutritionally balanced foods.

[0426] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0427] Program processing steps

[0428] Step 1:

[0429] Entering health and emotional data

[0430] Subject: User

[0431] Specific operation: The user uses a dedicated smartphone application to input their health data (weight, height, calories burned, allergy information, etc.) and emotional data (stress level, etc. via an emotion recognition engine).

[0432] Input: User-entered health and emotional data

[0433] Output: The raw data entered into the app

[0434] Step 2:

[0435] Encryption and transmission of health and emotional data

[0436] Subject: Terminal

[0437] Specific operation: The device encrypts the input health data and emotion data using an encryption algorithm such as AES and sends it to the server using a secure communication protocol (e.g., HTTPS).

[0438] Input: Raw data entered into the app

[0439] Output: Encrypted health and emotion data

[0440] Step 3:

[0441] Receiving and storing data

[0442] Subject: Server

[0443] Specific operation: The server receives the encrypted data sent from the terminal, decrypts it, and then stores it in a secure database.

[0444] Input: Encrypted health and emotion data

[0445] Output: Decoded health and emotion data stored in a database

[0446] Step 4:

[0447] Data analysis and ingredient suggestions

[0448] Subject: Server

[0449] Specific operation: The server analyzes the health and emotional data stored in the database and uses an AI algorithm to generate a list of ingredients suitable for the user.

[0450] Input: Health and emotion data stored in a database

[0451] Output: A list of ingredients suitable for the user

[0452] Step 5:

[0453] Send and display ingredient lists

[0454] Subject: Server and Terminal

[0455] Specific operation: The server sends the generated ingredient list in JSON format to the terminal, and the terminal displays the suggested ingredient list to the user.

[0456] Input: A list of ingredients suitable for the user

[0457] Output: Ingredient list displayed on the device

[0458] Step 6:

[0459] Ingredient selection

[0460] Subject: User

[0461] Specific operation: The user selects the desired ingredients from the list of ingredients displayed on the terminal.

[0462] Input: Ingredient list displayed on the device

[0463] Output: Ingredients selected by the user

[0464] Step 7:

[0465] Generate order data

[0466] Subject: Terminal and Server

[0467] Specific operation: The terminal sends data on ingredients selected by the user to the server, and the server generates order data based on that data.

[0468] Input: Ingredients selected by the user

[0469] Output: Generated order data

[0470] Step 8:

[0471] Order data distribution

[0472] Subject: Server

[0473] Specific operation: The server distributes the generated order data to retailers, logistics companies, and manufacturers.

[0474] Input: Generated order data

[0475] Output: Order data sent to each supplier

[0476] Step 9:

[0477] Food delivery

[0478] Subject: Logistics company

[0479] Specific operation: Based on the received order data, the logistics company picks up the specified ingredients, plans an efficient delivery route, and delivers them to the address specified by the user.

[0480] Input: Order data including shipping instructions

[0481] Output: Food delivered to the user's home

[0482] Step 10:

[0483] Notification of receipt completion

[0484] Subject: User and Device

[0485] Specific operation: The user checks the received ingredients and notifies the application that receipt is complete.

[0486] Input: Delivered ingredients

[0487] Output: Notification of receipt completion

[0488] 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.

[0489] 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.

[0490] 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.

[0491] [Second embodiment]

[0492] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0493] 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.

[0494] 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).

[0495] 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.

[0496] 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.

[0497] 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).

[0498] 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.

[0499] 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.

[0500] 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.

[0501] 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.

[0502] 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.

[0503] 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."

[0504] MODE FOR CARRYING OUT THE INVENTION

[0505] This invention is a system that proposes and delivers appropriate food ingredients based on the user's health data, and provides related data to retailers, logistics companies, and producers, thereby achieving personal health management and improving efficiency across the industry. Each component of the system and its function are explained in detail below.

[0506] 1. Health data collection

[0507] User: Enters daily health data such as weight, height, calories burned, and allergy information into a dedicated health app.

[0508] Terminal: The entered health data is encrypted and sent to the server using a secure communication protocol.

[0509] Server: Stores the received health data in a database and associates it with the user ID.

[0510] 2. Generating Ingredient Suggestions

[0511] Server: Analyzes the user's health data stored in the database and evaluates the user's calorie consumption and health condition.

[0512] Server: Runs an algorithm to suggest ingredients suitable for each user and generates an ingredient list.

[0513] Server: Generates a list of suggested ingredients in JSON format and sends it to the user's device.

[0514] 3. User selection and order confirmation

[0515] User: Selects desired ingredients from the list of suggested ingredients, for example, "200g chicken breast" and "100g broccoli."

[0516] Terminal: Sends the user's selection data to the server.

[0517] Server: Stores the received order information in a database and associates it with the order ID.

[0518] 4. Delivery of order data

[0519] Server: Formats order data so that it can be used by retailers, logistics providers, and manufacturers.

[0520] Server: Distributes formatted order data to retailers, logistics companies, and manufacturers.

[0521] Retailer: Checks inventory based on received order data and arranges for replenishment as necessary.

[0522] Logistics company: Plans delivery routes based on received order data.

[0523] Producer: Creates production plans based on received order data.

[0524] 5. Food delivery

[0525] Logistics Agent: Picks up the suggested ingredients from the retailer's store.

[0526] Logistics provider: Ensures efficient delivery based on set delivery routes.

[0527] Logistics company: delivers ingredients to the user's home.

[0528] User: Checks that the received ingredients are correct and notifies the app that receipt is complete.

[0529] Specific examples

[0530] User A weighs 70 kg, is 170 cm tall, and consumes an average of 2,000 kcal per day. His health data also indicates that he tends to be deficient in vitamin D. This data is entered into a healthcare app and sent to a server. The server receives and analyzes the data. The analysis determines that high-protein, low-fat foods and foods rich in vitamin D are suitable for User A.

[0531] The server suggests ingredients such as "200g chicken breast," "100g broccoli," and "50g almonds." User A selects "200g chicken breast" and "100g broccoli" from this list and confirms the order. This data is sent to the server and recorded as order data.

[0532] The order data is then distributed to retailers, logistics companies, and producers. Retailers check inventory, logistics companies plan efficient delivery routes, and producers adjust their production plans based on future demand.

[0533] Finally, the logistics company collects the necessary ingredients and delivers them to User A's home. User A checks the received ingredients and notifies the app that they have been received. This allows User A to continue their healthy eating habits, while also achieving efficiency and health for the entire industry.

[0534] The processing flow will be explained below.

[0535] Specific processing flow of the program

[0536] 1. Health data collection

[0537] Step 1:

[0538] User: Enters daily health data such as weight, height, calories burned, and allergy information into a dedicated health app.

[0539] Step 2:

[0540] Terminal: The entered health data is encrypted and sent to the server using a secure communication protocol.

[0541] Step 3:

[0542] Server: Stores the received health data in a database and associates it with the user ID.

[0543] 2. Generating Ingredient Suggestions

[0544] Step 1:

[0545] Server: Analyzes the user's health data stored in the database and evaluates the user's calorie consumption and health condition.

[0546] Step 2:

[0547] Server: Runs an algorithm to suggest ingredients suitable for each user and generates an ingredient list.

[0548] Step 3:

[0549] Server: Generates a list of suggested ingredients in JSON format and sends it to the user's device.

[0550] 3. User selection and order confirmation

[0551] Step 1:

[0552] User: Selects desired ingredients from the list of suggested ingredients, for example, "200g chicken breast" and "100g broccoli."

[0553] Step 2:

[0554] Terminal: Sends the user's selection data to the server.

[0555] Step 3:

[0556] Server: Stores the received order information in a database and associates it with the order ID.

[0557] 4. Delivery of order data

[0558] Step 1:

[0559] Server: Formats order data so that it can be used by retailers, logistics providers, and manufacturers.

[0560] Step 2:

[0561] Server: Distributes formatted order data to retailers, logistics companies, and manufacturers.

[0562] Step 3:

[0563] Retailer: Checks inventory based on received order data and arranges for replenishment as necessary.

[0564] Step 4:

[0565] Logistics company: Plans delivery routes based on received order data.

[0566] Step 5:

[0567] Producer: Creates production plans based on received order data.

[0568] 5. Food delivery

[0569] Step 1:

[0570] Logistics Agent: Picks up the suggested ingredients from the retailer's store.

[0571] Step 2:

[0572] Logistics provider: Ensures efficient delivery based on set delivery routes.

[0573] Step 3:

[0574] Logistics company: delivers ingredients to the user's home.

[0575] Step 4:

[0576] User: Checks that the received ingredients are correct and notifies the app that receipt is complete.

[0577] Example 1

[0578] 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."

[0579] Conventional health management systems have difficulty recommending appropriate ingredients based on individual users' health data and efficiently delivering those ingredients to users. Furthermore, they do not adequately consider the security of health data or the optimization of inventory management in the delivery process. Therefore, there is a need for improved individual health management and efficiency improvements across the industry.

[0580] 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.

[0581] In this invention, the server includes: means for collecting health data; means for encrypting the health data and transmitting it via a secure communication protocol; means for analyzing the user's calorie consumption and health status based on the health data; means for suggesting ingredients suitable for the user based on the analysis results; means for transmitting the list of suggested ingredients to the user; means for the user to select the suggested ingredients; means for generating order data for the selected ingredients; means for distributing the generated order data to retailers, distributors, and producers; and means for delivering ingredients to the user based on the distributed order data. This enables appropriate ingredients to be suggested based on the user's health data and delivered efficiently. This also improves the security of health data and optimizes inventory management.

[0582] "Health data" refers to information about the user's health condition, such as the user's weight, height, calories burned, and allergy information.

[0583] "Encryption" refers to converting data using a specific algorithm to protect the data from a security perspective, making it difficult for third parties to read.

[0584] A "secure communication protocol" is a communication method used to enhance security during data transmission, and includes HTTPS and TLS.

[0585] "Analysis" means processing and analyzing data to evaluate and diagnose the user's calorie consumption and health condition based on health data.

[0586] "Ingredient suggestions" refers to recommending ingredients suitable for the user based on analyzed health data.

[0587] "Order data" is data that includes information about ingredients selected by the user, and orders are processed based on this data.

[0588] A "retailer" is a business that sells goods to consumers.

[0589] A "logistics company" is a company whose role is to store and deliver goods efficiently.

[0590] A "producer" is a business that is responsible for manufacturing or producing goods.

[0591] "Delivery" refers to the process in which a retailer or a logistics company transports products to a destination based on a user's order.

[0592] MODE FOR CARRYING OUT THE INVENTION

[0593] The present invention is a system that proposes appropriate ingredients based on a user's health data and delivers them to the user, thereby achieving personal health management and improving the efficiency of the entire industry. Specific embodiments for implementing this system are described below.

[0594] System Overview

[0595] This system collects and analyzes the user's health data, suggests ingredients based on the results, distributes order data to retailers, logistics companies, and producers according to the user's selection, and finally delivers the ingredients to the user.

[0596] Hardware and software used

[0597] Hardware: Dedicated healthcare device (smartphone or tablet) used by the user, server (high-performance cloud server)

[0598] Software: Healthcare app, encryption software (AES-256), secure communication protocol (HTTPS), database (PostgreSQL), data analysis tools (Python, Pandas, Scikit-learn), communication API (REST API)

[0599] System details

[0600] 1. Health data collection

[0601] Users enter their daily health data (weight, height, calories burned, allergy information, etc.) into a dedicated healthcare app. This information becomes the basic data for health management.

[0602] The terminal encrypts the entered health data using the AES-256 encryption method and transmits it securely to the server using the HTTPS protocol.

[0603] The server decrypts the received encrypted data and stores it in a database.

[0604] 2. Generating Ingredient Suggestions

[0605] The server uses Python scripts to retrieve and analyze user health data from the database, using the Pandas library and Scikit-learn.

[0606] The server runs machine learning algorithms (such as Random Forest) to suggest suitable ingredients based on the user's calorie consumption and health status (e.g., vitamin D deficiency status).

[0607] The server converts the generated ingredient list into JSON format and sends it back to the user's device via HTTPS.

[0608] 3. User selection and order confirmation

[0609] The user selects the ingredients they want from a list of suggested ingredients through the app.

[0610] The terminal transmits the user's selection data to the server.

[0611] The server stores the received order data in a database and generates and associates an order ID.

[0612] 4. Delivery of order data

[0613] The server converts the order data into CSV format so that it can be used by retailers, logistics companies, and producers, and distributes it to each company using a REST API.

[0614] 5. Food delivery

[0615] The logistics company picks up the suggested ingredients from the retailer's store and delivers them efficiently to the user's home following the planned delivery route.

[0616] The user checks that the received ingredients are correct and is notified of receipt via a dedicated healthcare app.

[0617] Specific examples

[0618] User A weighs 70 kg, is 170 cm tall, consumes an average of 2000 kcal, and tends to be deficient in vitamin D. This health data is entered into a healthcare app and sent to a server. The server receives the data and analyzes it. Based on the analysis results, it is determined that high-protein, low-fat foods and foods rich in vitamin D are suitable for User A.

[0619] The server suggests ingredients such as "200g chicken breast," "100g broccoli," and "50g almonds." User A selects "200g chicken breast" and "100g broccoli" from this list and confirms the order in the app. The server distributes the order data to retailers, logistics companies, and producers. Inventory management, delivery route planning, and production planning are carried out by each company.

[0620] Finally, the logistics company picks up the ingredients and delivers them to User A's home. User A receives the ingredients and is notified of the receipt via the app. This allows User A to maintain a healthy diet and also improves efficiency across the industry.

[0621] Prompt Sentence Examples

[0622] Example prompts for generative AI models:

[0623] "Please explain in natural language the programming process of the system that recommends and delivers appropriate ingredients based on an individual's health data, including the specific hardware, software, and processing details. For example, please provide details on what encryption methods and protocols are used to transmit data, what machine learning algorithms are used to recommend ingredients, and how the order data is distributed to retailers and logistics providers."

[0624] The above is an embodiment of the present invention.

[0625] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0626] Step 1:

[0627] Health data collection

[0628] Users enter health data such as weight, height, calories burned, and allergy information into a dedicated healthcare app, which is then stored on the user's device.

[0629] The terminal receives the entered health data and encrypts it using the AES-256 encryption algorithm, which is then sent to the server via the secure communication protocol HTTPS.

[0630] The server decrypts the received encrypted data using the RSA encryption algorithm, associates it with the user ID, and stores it in the PostgreSQL database. This saves each user's health data in the database.

[0631] Step 2:

[0632] Generating ingredient suggestions

[0633] The server retrieves the user's health data from the database using a Python script, which is then preprocessed using the Pandas library.

[0634] The server then analyzes the user's calorie consumption and health status based on the preprocessed data, applying the Random Forest algorithm using the Scikit-learn library.

[0635] The server generates an ingredient list suitable for each user based on the analysis results. The generated ingredient list is converted into JSON format and sent to the user's device via HTTPS.

[0636] Step 3:

[0637] User selection and order confirmation

[0638] The user opens the app, checks the list of suggested ingredients sent from the server, operates the UI to select ingredients, and confirms the selection.

[0639] The terminal reformats the food ingredient data selected by the user into JSON format and sends it to the server via the HTTPS protocol.

[0640] The server stores the received order data in a database, generates an order ID, and records related data.

[0641] Step 4:

[0642] Order data distribution

[0643] The server formats the order data into a format that can be used by retailers, logistics companies, and manufacturers. Formatting includes converting the data into CSV format and mapping data fields based on each company's API specifications.

[0644] The server then distributes the formatted data to retailers, logistics providers, and producers using a REST API, with different API endpoints used for each provider.

[0645] Based on the received data, the retailer obtains inventory information from the product management system and replenishes inventory as necessary.

[0646] Logistics companies use GIS software to optimize delivery routes based on the order data they receive.

[0647] Producers use the ERP system to adjust their production plans based on the received order data.

[0648] Step 5:

[0649] Food delivery

[0650] The logistics company picks up the suggested ingredients from the retailer's store. For example, it accesses the store's inventory management system and receives "200g of chicken breast" and "100g of broccoli."

[0651] The logistics company will efficiently deliver the ingredients to the user's home according to the optimized delivery route. The delivery status is tracked in real time and the user is notified via a dedicated app.

[0652] The user checks whether the ingredients are correct and notifies the server of the receipt completion through the dedicated healthcare app. Once notified, the server records the receipt completion status in the database.

[0653] The above is the specific operation and input / output flow at each processing step.

[0654] (Application example 1)

[0655] 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."

[0656] Conventional health management systems lack a system that can suggest appropriate ingredients based on users' health data and efficiently manage the entire process from ordering to delivery. Furthermore, users often have to manually enter their health data, leading to problems of inaccuracy and time-consuming data entry. Furthermore, there is insufficient collaboration between retailers, logistics companies, and producers, preventing the industry as a whole from achieving efficiency.

[0657] 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.

[0658] In this invention, the server includes: means for collecting health data; means for suggesting suitable ingredients to a user based on the health data; means for the user to select the suggested ingredients; means for generating order data for the selected ingredients; means for distributing the generated order data to retailers, logistics companies, and producers; means for delivering ingredients to the user based on the distributed order data; a server that executes an algorithm for generating ingredient suggestions based on the health data; and application means for inputting health data from a smartphone. This allows users to easily and accurately input their health data through a smartphone app and receive appropriate ingredient suggestions based on that data, enabling efficient management of the entire food product process from ordering to delivery. Furthermore, collaboration between retailers, logistics companies, and producers can be strengthened, resulting in greater efficiency and soundness in the industry as a whole.

[0659] "Health data" is information that indicates the user's health condition, such as the user's weight, height, calories burned, allergy information, and dietary history.

[0660] "Ingredient suggestions" refers to suggesting ingredients that are best suited to the user's health condition based on collected health data.

[0661] "Server" refers to a computer system that analyzes health data, generates food ingredient suggestions, and manages and distributes order data.

[0662] "Retailer" refers to a company or individual that sells food ingredients and manages inventory.

[0663] "Logistics company" refers to a company or individual that delivers food ingredients.

[0664] "Producers" refers to farmers, food manufacturers, etc. who produce the proposed ingredients.

[0665] "Application" refers to software that allows users to input health data, receive food ingredient suggestions, and place orders via smartphone.

[0666] "Algorithm" refers to the calculation procedure used to analyze health data and suggest appropriate ingredients.

[0667] "Order data" refers to data containing information about ingredients selected by the user, and is distributed to retailers, distributors, and producers.

[0668] The present invention is a system that proposes and delivers appropriate ingredients based on the user's health data and provides related data to various businesses, thereby achieving personal health management and improving the efficiency of the entire industry. Specific embodiments of this system are described below.

[0669] Health data collection and entry

[0670] Users use their smartphones to enter health data such as weight, height, calories burned, and allergy information into a dedicated application. The application was developed using React Native and runs on both iOS and Android platforms.

[0671] Transmission and storage of health data

[0672] The health data entered by the user is encrypted and sent to the server using a secure communication protocol. The server is a Python-based system using Flask, and the received data is stored in a MongoDB database. This data is managed in association with the user ID.

[0673] Generating ingredient suggestions

[0674] The server analyzes the stored health data and runs an algorithm to suggest ingredients suitable for the user. The algorithm used here is based on a generative AI model and recommends optimal ingredients based on the health data. The ingredient list generated by this ingredient suggestion algorithm is sent to the user's device in JSON format.

[0675] User selection and order confirmation

[0676] The user selects the ingredients they want from a list of suggested ingredients through the smartphone app. This selection data is sent back to the server and saved as order data. The order data is managed in association with the user ID.

[0677] Distribution of order data and delivery of ingredients

[0678] The server formats the order data into a format that can be used by retailers, logistics companies, and producers, and distributes it to each of them. Retailers check their inventory and arrange for replenishment as necessary. Logistics companies plan efficient delivery routes and deliver the ingredients to users' homes. Producers adjust their production plans based on future demand.

[0679] Specific examples

[0680] As a concrete example, if User B has health data stating that he weighs 80 kg, is 180 cm tall, and is iron deficient, this data is entered into the application and sent to the server. The server analyzes the data and suggests ingredients such as "200 g of spinach" and "150 g of lean meat." User B selects from this list and confirms the order. This data is stored on the server and distributed as order data to retailers, logistics companies, and producers.

[0681] Prompt Sentence Examples

[0682] An example prompt based on user B's health data is:

[0683] User ID: 12345, Weight: 80kg, Height: 180cm, Allergies: None, Dietary history: Iron deficiency

[0684] As described above, the system of the present invention is capable of consistently providing everything from food ingredient suggestions based on the user's health data to delivery, contributing to personal health management and efficiency improvements across the industry.

[0685] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0686] Step 1:

[0687] A user inputs health data (e.g., weight, height, calories burned, allergy information) into a smartphone app. This data is entered into input fields in the application and stored locally.

[0688] Step 2:

[0689] The device (smartphone app) encrypts the entered health data and sends it to the server using a secure communication protocol (e.g., HTTPS). Here, the entered raw data is encrypted and reaches the server safely.

[0690] Step 3:

[0691] The server receives the encrypted health data, decrypts it, and then stores it in a MongoDB database, where the user ID and health data are associated and stored in the database.

[0692] Step 4:

[0693] The server periodically analyzes the health data stored in the database. The analysis algorithm uses a generative AI model to evaluate the collected health data and suggest the most suitable ingredients for the user. This process takes input data (health data) and outputs an ingredient list (suggested ingredients).

[0694] Step 5:

[0695] The server sends the generated ingredient list in JSON format to a smartphone app, which can then view the ingredients and their quantities.

[0696] Step 6:

[0697] Users can view a list of suggested ingredients on the smartphone app and select the ingredients they want, which are then temporarily stored locally on the device.

[0698] Step 7:

[0699] The terminal transmits the user's selection data to the server, and the server generates order data based on the received selection data. This order data is associated with the user ID and recorded in a database.

[0700] Step 8:

[0701] The server converts the generated order data into the required format and distributes it to retailers, logistics companies, and producers. This distribution process allows retailers to check inventory, logistics companies to plan delivery routes, and producers to adjust production plans.

[0702] Step 9:

[0703] The logistics company picks up the suggested ingredients from the retailer's store and delivers them to the user's home based on an efficient delivery route. This delivery process is updated in real time on the server.

[0704] Step 10:

[0705] The user checks the received ingredients and notifies the smartphone app that the delivery has been completed, which then confirms the delivery was successful throughout the system and updates the user's health data again.

[0706] The above steps complete a series of processes that suggest and deliver appropriate ingredients based on the user's health data and provide related data to the supplier.

[0707] 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.

[0708] MODE FOR CARRYING OUT THE INVENTION

[0709] This invention is a system that proposes and delivers appropriate ingredients based on the user's health and emotional data, and provides related data to retailers, logistics companies, and producers, thereby achieving personal health management and improving efficiency across the industry. Each component of the system and its function are explained in detail below.

[0710] 1. Collecting health and emotional data

[0711] User: Enters daily health data such as weight, height, calories burned, and allergy information into a dedicated healthcare app. User emotional data is also collected through a dedicated emotion recognition engine.

[0712] Device: Health and emotion data are encrypted and sent to a server using a secure communication protocol.

[0713] Server: Stores the received health and emotion data in a database and associates it with the user ID.

[0714] 2. Generating Ingredient Suggestions

[0715] Server: Analyzes the user's health and emotional data stored in the database and evaluates the user's calorie consumption, health condition, and emotional state.

[0716] Server: Runs an algorithm to suggest ingredients suitable for each user and generates an ingredient list. For example, it suggests calorie-balanced ingredients to a user who is feeling stressed.

[0717] Server: Generates a list of suggested ingredients in JSON format and sends it to the user's device.

[0718] 3. User selection and order confirmation

[0719] User: Selects desired ingredients from the list of suggested ingredients, for example, "200g chicken breast" and "100g broccoli."

[0720] Terminal: Sends the user's selection data to the server.

[0721] Server: Stores the received order information in a database and associates it with the order ID.

[0722] 4. Delivery of order data

[0723] Server: Formats order data so that it can be used by retailers, logistics providers, and manufacturers.

[0724] Server: Distributes formatted order data to retailers, logistics companies, and manufacturers.

[0725] Retailer: Checks inventory based on received order data and arranges for replenishment as necessary.

[0726] Logistics company: Plans delivery routes based on received order data.

[0727] Producer: Creates production plans based on received order data.

[0728] 5. Food delivery

[0729] Logistics Agent: Picks up the suggested ingredients from the retailer's store.

[0730] Logistics provider: Ensures efficient delivery based on set delivery routes.

[0731] Logistics company: delivers ingredients to the user's home.

[0732] User: Checks that the received ingredients are correct and notifies the app that receipt is complete.

[0733] Specific examples

[0734] User B weighs 65 kg, is 160 cm tall, and consumes an average of 1,800 kcal per day. Emotional data indicating that User B is under a high level of stress has also been collected. This data is entered into a healthcare app and sent to a server. The server receives and analyzes the data. The analysis determines that low-calorie, nutritionally balanced foods are suitable for User B.

[0735] The server suggests ingredients such as "150g chicken breast," "100g quinoa," and "50g blueberries." User B selects "150g chicken breast" and "50g blueberries" from this list and confirms the order. This data is sent to the server and recorded as order data.

[0736] The order data is then distributed to retailers, logistics companies, and producers. Retailers check inventory, logistics companies plan efficient delivery routes, and producers adjust their production plans based on future demand.

[0737] Finally, the logistics company collects the necessary ingredients and delivers them to User B's home. User B checks the received ingredients and notifies the app that they have been received. This allows User B to maintain a healthy diet that takes into consideration their emotional state, while also achieving efficiency and health for the entire industry.

[0738] The processing flow will be explained below.

[0739] Detailed Description of the Invention - Processing Flow

[0740] 1. Collecting health and emotional data

[0741] Step 1:

[0742] User: Enters daily health data such as weight, height, calories burned, and allergy information into a dedicated health app.

[0743] Step 2:

[0744] On the device: Cameras and sensors are used to collect emotional data from the user's facial expressions and behavior.

[0745] Step 3:

[0746] Device: Collected health and emotional data is encrypted and sent to a server using a secure communication protocol.

[0747] Step 4:

[0748] Server: Stores the received health and emotion data in a database and associates it with each user ID.

[0749] 2. Generating Ingredient Suggestions

[0750] Step 1:

[0751] Server: Analyzes the user's health and emotional data stored in the database and evaluates the user's calorie consumption, health condition, and emotional state.

[0752] Step 2:

[0753] Server: Based on the evaluation results, the server runs an algorithm to suggest ingredients suitable for the user and generates an ingredient list. For example, if the emotion engine determines that the user is feeling stressed, the server will suggest additional ingredients that will help reduce stress.

[0754] Step 3:

[0755] Server: Generates a list of suggested ingredients in JSON format and sends it to the user's device.

[0756] 3. User selection and order confirmation

[0757] Step 1:

[0758] User: Selects desired ingredients from the list of suggested ingredients, for example, "150g chicken breast" and "50g blueberries."

[0759] Step 2:

[0760] Terminal: Sends the user's selection data to the server.

[0761] Step 3:

[0762] Server: Stores the received order information in a database and associates it with the order ID.

[0763] 4. Delivery of order data

[0764] Step 1:

[0765] Server: Formats order data so that it can be used by retailers, logistics providers, and manufacturers.

[0766] Step 2:

[0767] Server: Distributes formatted order data to retailers, logistics companies, and producers via APIs.

[0768] Step 3:

[0769] Retailer: Checks inventory based on received order data and arranges for replenishment as necessary.

[0770] Step 4:

[0771] Logistics company: Plans delivery routes based on received order data.

[0772] Step 5:

[0773] Producer: Creates production plans based on received order data.

[0774] 5. Food delivery

[0775] Step 1:

[0776] Logistics Agent: Picks up the suggested ingredients from the retailer's store.

[0777] Step 2:

[0778] Logistics provider: Executes efficient deliveries based on set delivery routes.

[0779] Step 3:

[0780] Logistics company: delivers ingredients to the user's home.

[0781] Step 4:

[0782] User: Checks that the received ingredients are correct and notifies the app that receipt is complete.

[0783] Example 2

[0784] 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."

[0785] In modern society, individual health management and dietary optimization have become important issues. However, it is not easy for users to select ingredients that suit their own health and emotional state. Furthermore, efficient data sharing and collaboration between retailers, logistics companies, and producers is lacking, leading to inefficiencies across the industry. The present invention aims to solve these issues, improve the efficiency of users' health management, and promote collaboration across the industry.

[0786] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0787] In this invention, the server includes means for collecting health data and emotional data, means for suggesting ingredients suitable for the user based on the health data and emotional data, means for the user to select the suggested ingredients, means for generating order data for the selected ingredients, means for distributing the generated order data to retailers, logistics companies, and producers, means for delivering ingredients to the user based on the distributed order data, and means for the user to notify that delivery of the ingredients has been completed. This allows users to select ingredients optimal for their individual health and emotional states, and enables efficient data sharing and delivery.

[0788] "Health data" refers to data that indicates the user's health condition, such as the user's weight, height, calories burned, and allergy information.

[0789] "Emotion data" is data that represents the stress level and emotional state of the user, and is collected by an emotion recognition engine.

[0790] The "server" is a central processing unit that collects, analyzes, stores, and distributes data, and is a device that is responsible for overseeing each function of this system.

[0791] "Ingredients" means food items suggested and selected for consumption by a User.

[0792] "Suggestion" refers to the act of listing ingredients suitable for the user based on collected health and emotion data.

[0793] "Order data" is data that compiles information about ingredients selected by the user and is used for delivery, inventory management, and production planning.

[0794] A "retailer" is a business that manages inventory to provide ingredients selected by the user.

[0795] A "distributor" is a company that delivers ingredients from retailers to users.

[0796] "Producers" are businesses that produce and supply food ingredients.

[0797] A "communication protocol" is a set of rules and methods for securely sending and receiving data.

[0798] "Encryption" is a transformation operation that protects data from being deciphered by third parties.

[0799] "Notification of receipt completion" is the act of the user receiving the ingredients and notifying the system that they have been received correctly.

[0800] This invention is a system that proposes and delivers appropriate food ingredients based on the user's health and emotional data, and provides related data to retailers, logistics companies, and producers, thereby achieving personal health management and improving efficiency across the industry. Each component of this system and its function are described in detail below.

[0801] Health and emotional data collection

[0802] Using a dedicated healthcare app, users input health data such as weight, height, calories burned, and allergy information. Additionally, data on stress and emotional state is collected through an emotion recognition engine (e.g., Emotion AI), which recognizes facial expressions using the smartphone camera.

[0803] The device encrypts the health data entered by the user and the emotion data obtained by the emotion recognition engine (e.g., using AES (Advanced Encryption Standard)) and sends this encrypted data to the server via the HTTPS protocol.

[0804] The server receives the transmitted data, decrypts it, and stores it in a database (e.g., MySQL) in association with the user ID.

[0805] Generating ingredient suggestions

[0806] The server uses a data analysis engine (e.g., Pandas) to analyze the user's health and emotional data stored in the database. This analysis allows the server to evaluate the user's calorie consumption and emotional state and recommend appropriate ingredients.

[0807] The server runs a suggestion algorithm (e.g., K-means clustering) to generate the most suitable recipe list for each user. For example, if a user is under high stress, the server will suggest recipes that take into account calorie balance (e.g., "150g chicken breast," "100g quinoa," and "50g blueberries").

[0808] The server converts the generated ingredient list into JSON format and sends it to the user's device.

[0809] User selection and order confirmation

[0810] The user can view the suggested ingredients list on the app and select the ingredients they want (e.g., "150g chicken breast" or "50g blueberries").

[0811] The terminal encrypts the food ingredient data selected by the user and transmits it to the server using the HTTPS protocol.

[0812] The server stores the received selection data in a database, assigns an order ID, and manages it.

[0813] Order data distribution

[0814] The server formats the order data stored in the database into a format that can be consumed by retailers, logistics providers, and manufacturers, using CSV format or REST API endpoints.

[0815] The server distributes the formatted order data to retailers, logistics companies, and producers, allowing retailers to check inventory, logistics companies to plan delivery routes, and producers to plan production.

[0816] Food delivery

[0817] The logistics company picks up the suggested ingredients from the retailer's store or warehouse and delivers them efficiently to the user's home based on the planned delivery route.

[0818] The user confirms that the ingredients received are correct and notifies the user of the receipt via a dedicated app.

[0819] Specific examples

[0820] For example, User B weighs 65 kg, is 160 cm tall, consumes an average of 1,800 kcal per day, and emotional data indicates that he or she is highly stressed. This information is entered into a healthcare app and sent to a server. The server analyzes the received data and suggests low-calorie, nutritionally balanced ingredients to User B, such as 150 g of chicken breast, 100 g of quinoa, and 50 g of blueberries. User B selects the ingredients he or she desires from this list, and the order data is sent to the server. The server then distributes this order data to retailers, logistics companies, and producers, who then check inventory and make delivery and production plans. Finally, the logistics company delivers the ingredients to User B's home, and User B notifies them of receipt via the app.

[0821] Prompt Sentence Examples

[0822] "User B's health data is as follows: weight 65 kg, height 160 cm, average daily calorie consumption 1,800 kcal. User B is also under a lot of stress. Please suggest suitable foods for this user."

[0823] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0824] Step 1:

[0825] Users enter their health data, such as weight, height, calories burned, and allergy information, into a dedicated healthcare app. The app also uses an emotion recognition engine to capture their emotional state through the camera. This data is protected using AES encryption and transmitted to the server via HTTPS.

[0826] Input: User's weight, height, calories burned, allergy information, emotional state

[0827] Output: Encrypted health and emotional data

[0828] Step 2:

[0829] The server decrypts the received encrypted data, associates it with the user ID, and stores it in a database (MySQL). This allows for centralized management of each user's health and emotion data.

[0830] Input: Encrypted health and emotional data

[0831] Output: Health and emotion data for each user stored in a database

[0832] Step 3:

[0833] The server analyzes the user's health and emotional data stored in the database. Using a data analysis engine (Pandas), the server evaluates the user's calorie consumption and emotional state and selects appropriate ingredients. For users with high stress levels, the server makes suggestions that take calorie balance into consideration.

[0834] Input: User health and emotional data from a database

[0835] Output: Analyzed user's calorie consumption and emotional state information

[0836] Step 4:

[0837] The server runs a proposed algorithm (K-means clustering) to generate a list of ingredients suitable for each user. For example, it might suggest "150g chicken breast," "100g quinoa," and "50g blueberries." The generated list of ingredients is converted into JSON format and sent to the user's device.

[0838] Input: Analyzed user calorie consumption and emotional state information

[0839] Output: JSON list of ingredients

[0840] Step 5:

[0841] The user checks the suggested ingredients list on the app and selects the ingredients they want, for example, "150g of chicken breast" and "50g of blueberries." The selection data is encrypted and sent to the server.

[0842] Input: User's ingredient selection

[0843] Output: Encrypted selection data

[0844] Step 6:

[0845] The server saves the received selection data in a database and assigns an order ID, which is used to manage the order data.

[0846] Input: Encrypted selection data

[0847] Output: Order data and order ID stored in the database

[0848] Step 7:

[0849] The server converts the order data into a format that can be used by retailers, logistics companies, and producers (CSV or REST API endpoint) and distributes it to each of them. Retailers check inventory, logistics companies plan delivery routes, and producers set production plans.

[0850] Input: Order data and order ID stored in the database

[0851] Output: Order data distributed to retailers, logistics companies, and producers

[0852] Step 8:

[0853] The logistics company will pick up the ingredients selected by the user from the retailer's store or warehouse and deliver them efficiently to the user's home based on the planned delivery route. The user will then receive the ingredients and be notified of the receipt via a dedicated app.

[0854] Input: Retailer inventory check information, delivery route planning information, food pick-up information

[0855] Output: Delivery completion information and receipt completion notification to the user

[0856] (Application example 2)

[0857] 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."

[0858] Conventional food delivery systems do not take into account the user's health or emotional state when recommending ingredients, resulting in little effect on personal health management or stress reduction. Furthermore, the difficulty of tracking delivery status in real time can lead to a decline in user satisfaction. Furthermore, data sharing between retailers, logistics companies, and producers is insufficient, leaving issues with inventory management and delivery efficiency.

[0859] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting health data and emotional data, means for suggesting ingredients suitable for the user based on the health data and emotional data, means for the user to select the suggested ingredients, means for generating order data for the selected ingredients, means for distributing the generated order data to retailers, logistics companies, and producers, means for delivering ingredients to the user based on the distributed order data, and means for tracking delivery status in real time. This enables the suggestion and rapid delivery of ingredients suitable for the user's health and emotional state, thereby realizing personal health management and improving efficiency across the industry.

[0860] Definitions of important words

[0861] "Health data" refers to information about the user's physical condition, including the user's weight, height, calories burned, allergy information, and the like.

[0862] "Emotion data" is information relating to the user's stress level and emotional state.

[0863] "Collection means" refers to devices such as interfaces and sensors for acquiring the user's health data and emotional data.

[0864] The "suggestion means" is a device or system that executes an algorithm to create a list of ingredients suitable for the user based on the collected health data and emotion data.

[0865] The "selection means" refers to an interface or function that allows the user to select desired ingredients from the suggested ingredient list.

[0866] The "order data generation means" is a system for creating order data based on ingredients selected by the user.

[0867] The "distribution means" is a device or system for transmitting the generated order data to retailers, logistics companies, and manufacturers via communication.

[0868] A "delivery means" is a device or system that a retailer or a distributor uses to deliver the selected ingredients to a user.

[0869] The "tracking means" is a device or system for monitoring and notifying the user of the delivery status of ingredients delivered to the user in real time based on order data.

[0870] MODE FOR CARRYING OUT THE INVENTION

[0871] This invention relates to a system that supports users' health management by suggesting, selecting, and delivering ingredients based on the user's health and emotional data. This system includes three main elements: a server, a terminal, and a user, each of which works together to realize specific functions.

[0872] server

[0873] The server performs several functions:

[0874] 1. Data collection and storage: The server collects health data (weight, height, calories burned, allergy information, etc.) and emotional data (stress level, etc.) sent by the user and stores them in a database. This data is encrypted before transmission to ensure data security.

[0875] 2. Data Analysis: The server analyzes the collected data and runs algorithms to assess the user's health and emotional state, which then lists and suggests suitable ingredients for the user.

[0876] 3. Order data generation and distribution: The server generates order data based on the ingredient list selected by the user and distributes it to retailers, logistics companies, and producers.

[0877] 4. Real-time tracking: The server monitors the delivery status in real time and has the function to notify the user. This function allows the user to know the delivery status in real time.

[0878] Terminal

[0879] The terminal functions as a device such as a smartphone.

[0880] 1. Data input: Users input their health and emotional data through a dedicated application. If necessary, the emotion recognition engine automatically collects the user's emotional data.

[0881] 2. Data transmission: The entered data is encrypted and sent to the server using a secure communication protocol.

[0882] 3. Ingredient selection: The user checks the list of suggested ingredients sent from the server and selects the desired ingredients on the application.

[0883] 4. Notifications: The terminal receives notifications from the server and provides the user with real-time updates, especially regarding delivery status.

[0884] User

[0885] The user uses the system to perform the following operations:

[0886] 1. Data entry: Enter daily data such as weight, height, calories burned, allergy information, and emotional state into a dedicated app.

[0887] 2. Ingredient selection: Select the ingredients you want from the suggested options and confirm your order through the application.

[0888] 3. Receipt confirmation: Check the delivered ingredients and notify the customer of receipt via the application.

[0889] Specific examples

[0890] For example, if a user with user ID "user12345" consumes an average of 1,800 kcal per day and is determined to have a high stress level, the server will suggest 150g of chicken breast and 50g of blueberries. The user selects this and confirms the order. The server then distributes the order data to retailers, logistics companies, and producers, and tracks the delivery status in real time. The user can check the delivery status in real time via a smartphone application.

[0891] Prompt Sentence Examples

[0892] Get the user data for user ID "user12345" and suggest recommended foods based on their health and emotional data. Health data includes weight, height, calories burned, and allergy information, and emotional data includes stress level. If this user's stress level is high, suggest low-calorie, nutritionally balanced foods.

[0893] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0894] Program processing steps

[0895] Step 1:

[0896] Entering health and emotional data

[0897] Subject: User

[0898] Specific operation: The user uses a dedicated smartphone application to input their health data (weight, height, calories burned, allergy information, etc.) and emotional data (stress level, etc. via an emotion recognition engine).

[0899] Input: User-entered health and emotional data

[0900] Output: The raw data entered into the app

[0901] Step 2:

[0902] Encryption and transmission of health and emotional data

[0903] Subject: Terminal

[0904] Specific operation: The device encrypts the input health data and emotion data using an encryption algorithm such as AES and sends it to the server using a secure communication protocol (e.g., HTTPS).

[0905] Input: Raw data entered into the app

[0906] Output: Encrypted health and emotion data

[0907] Step 3:

[0908] Receiving and storing data

[0909] Subject: Server

[0910] Specific operation: The server receives the encrypted data sent from the terminal, decrypts it, and then stores it in a secure database.

[0911] Input: Encrypted health and emotion data

[0912] Output: Decoded health and emotion data stored in a database

[0913] Step 4:

[0914] Data analysis and ingredient suggestions

[0915] Subject: Server

[0916] Specific operation: The server analyzes the health and emotional data stored in the database and uses an AI algorithm to generate a list of ingredients suitable for the user.

[0917] Input: Health and emotion data stored in a database

[0918] Output: A list of ingredients suitable for the user

[0919] Step 5:

[0920] Send and display ingredient lists

[0921] Subject: Server and Terminal

[0922] Specific operation: The server sends the generated ingredient list in JSON format to the terminal, and the terminal displays the suggested ingredient list to the user.

[0923] Input: A list of ingredients suitable for the user

[0924] Output: Ingredient list displayed on the device

[0925] Step 6:

[0926] Ingredient selection

[0927] Subject: User

[0928] Specific operation: The user selects the desired ingredients from the list of ingredients displayed on the terminal.

[0929] Input: Ingredient list displayed on the device

[0930] Output: Ingredients selected by the user

[0931] Step 7:

[0932] Generate order data

[0933] Subject: Terminal and Server

[0934] Specific operation: The terminal sends data on ingredients selected by the user to the server, and the server generates order data based on that data.

[0935] Input: Ingredients selected by the user

[0936] Output: Generated order data

[0937] Step 8:

[0938] Order data distribution

[0939] Subject: Server

[0940] Specific operation: The server distributes the generated order data to retailers, logistics companies, and manufacturers.

[0941] Input: Generated order data

[0942] Output: Order data sent to each supplier

[0943] Step 9:

[0944] Food delivery

[0945] Subject: Logistics company

[0946] Specific operation: Based on the received order data, the logistics company picks up the specified ingredients, plans an efficient delivery route, and delivers them to the address specified by the user.

[0947] Input: Order data including shipping instructions

[0948] Output: Food delivered to the user's home

[0949] Step 10:

[0950] Notification of receipt completion

[0951] Subject: User and Device

[0952] Specific operation: The user checks the received ingredients and notifies the application that receipt is complete.

[0953] Input: Delivered ingredients

[0954] Output: Notification of receipt completion

[0955] 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.

[0956] 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.

[0957] 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.

[0958] [Third embodiment]

[0959] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0960] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0961] 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).

[0962] 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.

[0963] 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.

[0964] 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).

[0965] 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.

[0966] 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.

[0967] 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.

[0968] 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.

[0969] 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.

[0970] 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."

[0971] MODE FOR CARRYING OUT THE INVENTION

[0972] This invention is a system that proposes and delivers appropriate food ingredients based on the user's health data, and provides related data to retailers, logistics companies, and producers, thereby achieving personal health management and improving efficiency across the industry. Each component of the system and its function are explained in detail below.

[0973] 1. Health data collection

[0974] User: Enters daily health data such as weight, height, calories burned, and allergy information into a dedicated health app.

[0975] Terminal: The entered health data is encrypted and sent to the server using a secure communication protocol.

[0976] Server: Stores the received health data in a database and associates it with the user ID.

[0977] 2. Generating Ingredient Suggestions

[0978] Server: Analyzes the user's health data stored in the database and evaluates the user's calorie consumption and health condition.

[0979] Server: Runs an algorithm to suggest ingredients suitable for each user and generates an ingredient list.

[0980] Server: Generates a list of suggested ingredients in JSON format and sends it to the user's device.

[0981] 3. User selection and order confirmation

[0982] User: Selects desired ingredients from the list of suggested ingredients, for example, "200g chicken breast" and "100g broccoli."

[0983] Terminal: Sends the user's selection data to the server.

[0984] Server: Stores the received order information in a database and associates it with the order ID.

[0985] 4. Delivery of order data

[0986] Server: Formats order data so that it can be used by retailers, logistics providers, and manufacturers.

[0987] Server: Distributes formatted order data to retailers, logistics companies, and manufacturers.

[0988] Retailer: Checks inventory based on received order data and arranges for replenishment as necessary.

[0989] Logistics company: Plans delivery routes based on received order data.

[0990] Producer: Creates production plans based on received order data.

[0991] 5. Food delivery

[0992] Logistics Agent: Picks up the suggested ingredients from the retailer's store.

[0993] Logistics provider: Ensures efficient delivery based on set delivery routes.

[0994] Logistics company: delivers ingredients to the user's home.

[0995] User: Checks that the received ingredients are correct and notifies the app that receipt is complete.

[0996] Specific examples

[0997] User A weighs 70 kg, is 170 cm tall, and consumes an average of 2,000 kcal per day. His health data also indicates that he tends to be deficient in vitamin D. This data is entered into a healthcare app and sent to a server. The server receives and analyzes the data. The analysis determines that high-protein, low-fat foods and foods rich in vitamin D are suitable for User A.

[0998] The server suggests ingredients such as "200g chicken breast," "100g broccoli," and "50g almonds." User A selects "200g chicken breast" and "100g broccoli" from this list and confirms the order. This data is sent to the server and recorded as order data.

[0999] The order data is then distributed to retailers, logistics companies, and producers. Retailers check inventory, logistics companies plan efficient delivery routes, and producers adjust their production plans based on future demand.

[1000] Finally, the logistics company collects the necessary ingredients and delivers them to User A's home. User A checks the received ingredients and notifies the app that they have been received. This allows User A to continue their healthy eating habits, while also achieving efficiency and health for the entire industry.

[1001] The processing flow will be explained below.

[1002] Specific processing flow of the program

[1003] 1. Health data collection

[1004] Step 1:

[1005] User: Enters daily health data such as weight, height, calories burned, and allergy information into a dedicated health app.

[1006] Step 2:

[1007] Terminal: The entered health data is encrypted and sent to the server using a secure communication protocol.

[1008] Step 3:

[1009] Server: Stores the received health data in a database and associates it with the user ID.

[1010] 2. Generating Ingredient Suggestions

[1011] Step 1:

[1012] Server: Analyzes the user's health data stored in the database and evaluates the user's calorie consumption and health condition.

[1013] Step 2:

[1014] Server: Runs an algorithm to suggest ingredients suitable for each user and generates an ingredient list.

[1015] Step 3:

[1016] Server: Generates a list of suggested ingredients in JSON format and sends it to the user's device.

[1017] 3. User selection and order confirmation

[1018] Step 1:

[1019] User: Selects desired ingredients from the list of suggested ingredients, for example, "200g chicken breast" and "100g broccoli."

[1020] Step 2:

[1021] Terminal: Sends the user's selection data to the server.

[1022] Step 3:

[1023] Server: Stores the received order information in a database and associates it with the order ID.

[1024] 4. Delivery of order data

[1025] Step 1:

[1026] Server: Formats order data so that it can be used by retailers, logistics providers, and manufacturers.

[1027] Step 2:

[1028] Server: Distributes formatted order data to retailers, logistics companies, and manufacturers.

[1029] Step 3:

[1030] Retailer: Checks inventory based on received order data and arranges for replenishment as necessary.

[1031] Step 4:

[1032] Logistics company: Plans delivery routes based on received order data.

[1033] Step 5:

[1034] Producer: Creates production plans based on received order data.

[1035] 5. Food delivery

[1036] Step 1:

[1037] Logistics Agent: Picks up the suggested ingredients from the retailer's store.

[1038] Step 2:

[1039] Logistics provider: Ensures efficient delivery based on set delivery routes.

[1040] Step 3:

[1041] Logistics company: delivers ingredients to the user's home.

[1042] Step 4:

[1043] User: Checks that the received ingredients are correct and notifies the app that receipt is complete.

[1044] Example 1

[1045] 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."

[1046] Conventional health management systems have difficulty recommending appropriate ingredients based on individual users' health data and efficiently delivering those ingredients to users. Furthermore, they do not adequately consider the security of health data or the optimization of inventory management in the delivery process. Therefore, there is a need for improved individual health management and efficiency improvements across the industry.

[1047] 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.

[1048] In this invention, the server includes: means for collecting health data; means for encrypting the health data and transmitting it via a secure communication protocol; means for analyzing the user's calorie consumption and health status based on the health data; means for suggesting ingredients suitable for the user based on the analysis results; means for transmitting the list of suggested ingredients to the user; means for the user to select the suggested ingredients; means for generating order data for the selected ingredients; means for distributing the generated order data to retailers, distributors, and producers; and means for delivering ingredients to the user based on the distributed order data. This enables appropriate ingredients to be suggested based on the user's health data and delivered efficiently. This also improves the security of health data and optimizes inventory management.

[1049] "Health data" refers to information about the user's health condition, such as the user's weight, height, calories burned, and allergy information.

[1050] "Encryption" refers to converting data using a specific algorithm to protect the data from a security perspective, making it difficult for third parties to read.

[1051] A "secure communication protocol" is a communication method used to enhance security during data transmission, and includes HTTPS and TLS.

[1052] "Analysis" means processing and analyzing data to evaluate and diagnose the user's calorie consumption and health condition based on health data.

[1053] "Ingredient suggestions" refers to recommending ingredients suitable for the user based on analyzed health data.

[1054] "Order data" is data that includes information about ingredients selected by the user, and orders are processed based on this data.

[1055] A "retailer" is a business that sells goods to consumers.

[1056] A "logistics company" is a company whose role is to store and deliver goods efficiently.

[1057] A "producer" is a business that is responsible for manufacturing or producing goods.

[1058] "Delivery" refers to the process in which a retailer or a logistics company transports products to a destination based on a user's order.

[1059] MODE FOR CARRYING OUT THE INVENTION

[1060] The present invention is a system that proposes appropriate ingredients based on a user's health data and delivers them to the user, thereby achieving personal health management and improving the efficiency of the entire industry. Specific embodiments for implementing this system are described below.

[1061] System Overview

[1062] This system collects and analyzes the user's health data, suggests ingredients based on the results, distributes order data to retailers, logistics companies, and producers according to the user's selection, and finally delivers the ingredients to the user.

[1063] Hardware and software used

[1064] Hardware: Dedicated healthcare device (smartphone or tablet) used by the user, server (high-performance cloud server)

[1065] Software: Healthcare app, encryption software (AES-256), secure communication protocol (HTTPS), database (PostgreSQL), data analysis tools (Python, Pandas, Scikit-learn), communication API (REST API)

[1066] System details

[1067] 1. Health data collection

[1068] Users enter their daily health data (weight, height, calories burned, allergy information, etc.) into a dedicated healthcare app. This information becomes the basic data for health management.

[1069] The terminal encrypts the entered health data using the AES-256 encryption method and transmits it securely to the server using the HTTPS protocol.

[1070] The server decrypts the received encrypted data and stores it in a database.

[1071] 2. Generating Ingredient Suggestions

[1072] The server uses Python scripts to retrieve and analyze user health data from the database, using the Pandas library and Scikit-learn.

[1073] The server runs machine learning algorithms (such as Random Forest) to suggest suitable ingredients based on the user's calorie consumption and health status (e.g., vitamin D deficiency status).

[1074] The server converts the generated ingredient list into JSON format and sends it back to the user's device via HTTPS.

[1075] 3. User selection and order confirmation

[1076] The user selects the ingredients they want from a list of suggested ingredients through the app.

[1077] The terminal transmits the user's selection data to the server.

[1078] The server stores the received order data in a database and generates and associates an order ID.

[1079] 4. Delivery of order data

[1080] The server converts the order data into CSV format so that it can be used by retailers, logistics companies, and producers, and distributes it to each company using a REST API.

[1081] 5. Food delivery

[1082] The logistics company picks up the suggested ingredients from the retailer's store and delivers them efficiently to the user's home following the planned delivery route.

[1083] The user checks that the received ingredients are correct and is notified of receipt via a dedicated healthcare app.

[1084] Specific examples

[1085] User A weighs 70 kg, is 170 cm tall, consumes an average of 2000 kcal, and tends to be deficient in vitamin D. This health data is entered into a healthcare app and sent to a server. The server receives the data and analyzes it. Based on the analysis results, it is determined that high-protein, low-fat foods and foods rich in vitamin D are suitable for User A.

[1086] The server suggests ingredients such as "200g chicken breast," "100g broccoli," and "50g almonds." User A selects "200g chicken breast" and "100g broccoli" from this list and confirms the order in the app. The server distributes the order data to retailers, logistics companies, and producers. Inventory management, delivery route planning, and production planning are carried out by each company.

[1087] Finally, the logistics company picks up the ingredients and delivers them to User A's home. User A receives the ingredients and is notified of the receipt via the app. This allows User A to maintain a healthy diet and also improves efficiency across the industry.

[1088] Prompt Sentence Examples

[1089] Example prompts for generative AI models:

[1090] "Please explain in natural language the programming process of the system that recommends and delivers appropriate ingredients based on an individual's health data, including the specific hardware, software, and processing details. For example, please provide details on what encryption methods and protocols are used to transmit data, what machine learning algorithms are used to recommend ingredients, and how the order data is distributed to retailers and logistics providers."

[1091] The above is an embodiment of the present invention.

[1092] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1093] Step 1:

[1094] Health data collection

[1095] Users enter health data such as weight, height, calories burned, and allergy information into a dedicated healthcare app, which is then stored on the user's device.

[1096] The terminal receives the entered health data and encrypts it using the AES-256 encryption algorithm, which is then sent to the server via the secure communication protocol HTTPS.

[1097] The server decrypts the received encrypted data using the RSA encryption algorithm, associates it with the user ID, and stores it in the PostgreSQL database. This saves each user's health data in the database.

[1098] Step 2:

[1099] Generating ingredient suggestions

[1100] The server retrieves the user's health data from the database using a Python script, which is then preprocessed using the Pandas library.

[1101] The server then analyzes the user's calorie consumption and health status based on the preprocessed data, applying the Random Forest algorithm using the Scikit-learn library.

[1102] The server generates an ingredient list suitable for each user based on the analysis results. The generated ingredient list is converted into JSON format and sent to the user's device via HTTPS.

[1103] Step 3:

[1104] User selection and order confirmation

[1105] The user opens the app, checks the list of suggested ingredients sent from the server, operates the UI to select ingredients, and confirms the selection.

[1106] The terminal reformats the food ingredient data selected by the user into JSON format and sends it to the server via the HTTPS protocol.

[1107] The server stores the received order data in a database, generates an order ID, and records related data.

[1108] Step 4:

[1109] Order data distribution

[1110] The server formats the order data into a format that can be used by retailers, logistics companies, and manufacturers. Formatting includes converting the data into CSV format and mapping data fields based on each company's API specifications.

[1111] The server then distributes the formatted data to retailers, logistics providers, and producers using a REST API, with different API endpoints used for each provider.

[1112] Based on the received data, the retailer obtains inventory information from the product management system and replenishes inventory as necessary.

[1113] Logistics companies use GIS software to optimize delivery routes based on the order data they receive.

[1114] Producers use the ERP system to adjust their production plans based on the received order data.

[1115] Step 5:

[1116] Food delivery

[1117] The logistics company picks up the suggested ingredients from the retailer's store. For example, it accesses the store's inventory management system and receives "200g of chicken breast" and "100g of broccoli."

[1118] The logistics company will efficiently deliver the ingredients to the user's home according to the optimized delivery route. The delivery status is tracked in real time and the user is notified via a dedicated app.

[1119] The user checks whether the ingredients are correct and notifies the server of the receipt completion through the dedicated healthcare app. Once notified, the server records the receipt completion status in the database.

[1120] The above is the specific operation and input / output flow at each processing step.

[1121] (Application example 1)

[1122] 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."

[1123] Conventional health management systems lack a system that can suggest appropriate ingredients based on users' health data and efficiently manage the entire process from ordering to delivery. Furthermore, users often have to manually enter their health data, leading to problems of inaccuracy and time-consuming data entry. Furthermore, there is insufficient collaboration between retailers, logistics companies, and producers, preventing the industry as a whole from achieving efficiency.

[1124] 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.

[1125] In this invention, the server includes: means for collecting health data; means for suggesting suitable ingredients to a user based on the health data; means for the user to select the suggested ingredients; means for generating order data for the selected ingredients; means for distributing the generated order data to retailers, logistics companies, and producers; means for delivering ingredients to the user based on the distributed order data; a server that executes an algorithm for generating ingredient suggestions based on the health data; and application means for inputting health data from a smartphone. This allows users to easily and accurately input their health data through a smartphone app and receive appropriate ingredient suggestions based on that data, enabling efficient management of the entire food product process from ordering to delivery. Furthermore, collaboration between retailers, logistics companies, and producers can be strengthened, resulting in greater efficiency and soundness in the industry as a whole.

[1126] "Health data" is information that indicates the user's health condition, such as the user's weight, height, calories burned, allergy information, and dietary history.

[1127] "Ingredient suggestions" refers to suggesting ingredients that are best suited to the user's health condition based on collected health data.

[1128] "Server" refers to a computer system that analyzes health data, generates food ingredient suggestions, and manages and distributes order data.

[1129] "Retailer" refers to a company or individual that sells food ingredients and manages inventory.

[1130] "Logistics company" refers to a company or individual that delivers food ingredients.

[1131] "Producers" refers to farmers, food manufacturers, etc. who produce the proposed ingredients.

[1132] "Application" refers to software that allows users to input health data, receive food ingredient suggestions, and place orders via smartphone.

[1133] "Algorithm" refers to the calculation procedure used to analyze health data and suggest appropriate ingredients.

[1134] "Order data" refers to data containing information about ingredients selected by the user, and is distributed to retailers, distributors, and producers.

[1135] The present invention is a system that proposes and delivers appropriate ingredients based on the user's health data and provides related data to various businesses, thereby achieving personal health management and improving the efficiency of the entire industry. Specific embodiments of this system are described below.

[1136] Health data collection and entry

[1137] Users use their smartphones to enter health data such as weight, height, calories burned, and allergy information into a dedicated application. The application was developed using React Native and runs on both iOS and Android platforms.

[1138] Transmission and storage of health data

[1139] The health data entered by the user is encrypted and sent to the server using a secure communication protocol. The server is a Python-based system using Flask, and the received data is stored in a MongoDB database. This data is managed in association with the user ID.

[1140] Generating ingredient suggestions

[1141] The server analyzes the stored health data and runs an algorithm to suggest ingredients suitable for the user. The algorithm used here is based on a generative AI model and recommends optimal ingredients based on the health data. The ingredient list generated by this ingredient suggestion algorithm is sent to the user's device in JSON format.

[1142] User selection and order confirmation

[1143] The user selects the ingredients they want from a list of suggested ingredients through the smartphone app. This selection data is sent back to the server and saved as order data. The order data is managed in association with the user ID.

[1144] Distribution of order data and delivery of ingredients

[1145] The server formats the order data into a format that can be used by retailers, logistics companies, and producers, and distributes it to each of them. Retailers check their inventory and arrange for replenishment as necessary. Logistics companies plan efficient delivery routes and deliver the ingredients to users' homes. Producers adjust their production plans based on future demand.

[1146] Specific examples

[1147] As a concrete example, if User B has health data stating that he weighs 80 kg, is 180 cm tall, and is iron deficient, this data is entered into the application and sent to the server. The server analyzes the data and suggests ingredients such as "200 g of spinach" and "150 g of lean meat." User B selects from this list and confirms the order. This data is stored on the server and distributed as order data to retailers, logistics companies, and producers.

[1148] Prompt Sentence Examples

[1149] An example prompt based on user B's health data is:

[1150] User ID: 12345, Weight: 80kg, Height: 180cm, Allergies: None, Dietary history: Iron deficiency

[1151] As described above, the system of the present invention is capable of consistently providing everything from food ingredient suggestions based on the user's health data to delivery, contributing to personal health management and efficiency improvements across the industry.

[1152] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1153] Step 1:

[1154] A user inputs health data (e.g., weight, height, calories burned, allergy information) into a smartphone app. This data is entered into input fields in the application and stored locally.

[1155] Step 2:

[1156] The device (smartphone app) encrypts the entered health data and sends it to the server using a secure communication protocol (e.g., HTTPS). Here, the entered raw data is encrypted and reaches the server safely.

[1157] Step 3:

[1158] The server receives the encrypted health data, decrypts it, and then stores it in a MongoDB database, where the user ID and health data are associated and stored in the database.

[1159] Step 4:

[1160] The server periodically analyzes the health data stored in the database. The analysis algorithm uses a generative AI model to evaluate the collected health data and suggest the most suitable ingredients for the user. This process takes input data (health data) and outputs an ingredient list (suggested ingredients).

[1161] Step 5:

[1162] The server sends the generated ingredient list in JSON format to a smartphone app, which can then view the ingredients and their quantities.

[1163] Step 6:

[1164] Users can view a list of suggested ingredients on the smartphone app and select the ingredients they want, which are then temporarily stored locally on the device.

[1165] Step 7:

[1166] The terminal transmits the user's selection data to the server, and the server generates order data based on the received selection data. This order data is associated with the user ID and recorded in a database.

[1167] Step 8:

[1168] The server converts the generated order data into the required format and distributes it to retailers, logistics companies, and producers. This distribution process allows retailers to check inventory, logistics companies to plan delivery routes, and producers to adjust production plans.

[1169] Step 9:

[1170] The logistics company picks up the suggested ingredients from the retailer's store and delivers them to the user's home based on an efficient delivery route. This delivery process is updated in real time on the server.

[1171] Step 10:

[1172] The user checks the received ingredients and notifies the smartphone app that the delivery has been completed, which then confirms the delivery was successful throughout the system and updates the user's health data again.

[1173] The above steps complete a series of processes that suggest and deliver appropriate ingredients based on the user's health data and provide related data to the supplier.

[1174] 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.

[1175] MODE FOR CARRYING OUT THE INVENTION

[1176] This invention is a system that proposes and delivers appropriate ingredients based on the user's health and emotional data, and provides related data to retailers, logistics companies, and producers, thereby achieving personal health management and improving efficiency across the industry. Each component of the system and its function are explained in detail below.

[1177] 1. Collecting health and emotional data

[1178] User: Enters daily health data such as weight, height, calories burned, and allergy information into a dedicated healthcare app. User emotional data is also collected through a dedicated emotion recognition engine.

[1179] Device: Health and emotion data are encrypted and sent to a server using a secure communication protocol.

[1180] Server: Stores the received health and emotion data in a database and associates it with the user ID.

[1181] 2. Generating Ingredient Suggestions

[1182] Server: Analyzes the user's health and emotional data stored in the database and evaluates the user's calorie consumption, health condition, and emotional state.

[1183] Server: Runs an algorithm to suggest ingredients suitable for each user and generates an ingredient list. For example, it suggests calorie-balanced ingredients to a user who is feeling stressed.

[1184] Server: Generates a list of suggested ingredients in JSON format and sends it to the user's device.

[1185] 3. User selection and order confirmation

[1186] User: Selects desired ingredients from the list of suggested ingredients, for example, "200g chicken breast" and "100g broccoli."

[1187] Terminal: Sends the user's selection data to the server.

[1188] Server: Stores the received order information in a database and associates it with the order ID.

[1189] 4. Delivery of order data

[1190] Server: Formats order data so that it can be used by retailers, logistics providers, and manufacturers.

[1191] Server: Distributes formatted order data to retailers, logistics companies, and manufacturers.

[1192] Retailer: Checks inventory based on received order data and arranges for replenishment as necessary.

[1193] Logistics company: Plans delivery routes based on received order data.

[1194] Producer: Creates production plans based on received order data.

[1195] 5. Food delivery

[1196] Logistics Agent: Picks up the suggested ingredients from the retailer's store.

[1197] Logistics provider: Ensures efficient delivery based on set delivery routes.

[1198] Logistics company: delivers ingredients to the user's home.

[1199] User: Checks that the received ingredients are correct and notifies the app that receipt is complete.

[1200] Specific examples

[1201] User B weighs 65 kg, is 160 cm tall, and consumes an average of 1,800 kcal per day. Emotional data indicating that User B is under a high level of stress has also been collected. This data is entered into a healthcare app and sent to a server. The server receives and analyzes the data. The analysis determines that low-calorie, nutritionally balanced foods are suitable for User B.

[1202] The server suggests ingredients such as "150g chicken breast," "100g quinoa," and "50g blueberries." User B selects "150g chicken breast" and "50g blueberries" from this list and confirms the order. This data is sent to the server and recorded as order data.

[1203] The order data is then distributed to retailers, logistics companies, and producers. Retailers check inventory, logistics companies plan efficient delivery routes, and producers adjust their production plans based on future demand.

[1204] Finally, the logistics company collects the necessary ingredients and delivers them to User B's home. User B checks the received ingredients and notifies the app that they have been received. This allows User B to maintain a healthy diet that takes into consideration their emotional state, while also achieving efficiency and health for the entire industry.

[1205] The processing flow will be explained below.

[1206] Detailed Description of the Invention - Processing Flow

[1207] 1. Collecting health and emotional data

[1208] Step 1:

[1209] User: Enters daily health data such as weight, height, calories burned, and allergy information into a dedicated health app.

[1210] Step 2:

[1211] On the device: Cameras and sensors are used to collect emotional data from the user's facial expressions and behavior.

[1212] Step 3:

[1213] Device: Collected health and emotional data is encrypted and sent to a server using a secure communication protocol.

[1214] Step 4:

[1215] Server: Stores the received health and emotion data in a database and associates it with each user ID.

[1216] 2. Generating Ingredient Suggestions

[1217] Step 1:

[1218] Server: Analyzes the user's health and emotional data stored in the database and evaluates the user's calorie consumption, health condition, and emotional state.

[1219] Step 2:

[1220] Server: Based on the evaluation results, the server runs an algorithm to suggest ingredients suitable for the user and generates an ingredient list. For example, if the emotion engine determines that the user is feeling stressed, the server will suggest additional ingredients that will help reduce stress.

[1221] Step 3:

[1222] Server: Generates a list of suggested ingredients in JSON format and sends it to the user's device.

[1223] 3. User selection and order confirmation

[1224] Step 1:

[1225] User: Selects desired ingredients from the list of suggested ingredients, for example, "150g chicken breast" and "50g blueberries."

[1226] Step 2:

[1227] Terminal: Sends the user's selection data to the server.

[1228] Step 3:

[1229] Server: Stores the received order information in a database and associates it with the order ID.

[1230] 4. Delivery of order data

[1231] Step 1:

[1232] Server: Formats order data so that it can be used by retailers, logistics providers, and manufacturers.

[1233] Step 2:

[1234] Server: Distributes formatted order data to retailers, logistics companies, and producers via APIs.

[1235] Step 3:

[1236] Retailer: Checks inventory based on received order data and arranges for replenishment as necessary.

[1237] Step 4:

[1238] Logistics company: Plans delivery routes based on received order data.

[1239] Step 5:

[1240] Producer: Creates production plans based on received order data.

[1241] 5. Food delivery

[1242] Step 1:

[1243] Logistics Agent: Picks up the suggested ingredients from the retailer's store.

[1244] Step 2:

[1245] Logistics provider: Executes efficient deliveries based on set delivery routes.

[1246] Step 3:

[1247] Logistics company: delivers ingredients to the user's home.

[1248] Step 4:

[1249] User: Checks that the received ingredients are correct and notifies the app that receipt is complete.

[1250] Example 2

[1251] 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."

[1252] In modern society, individual health management and dietary optimization have become important issues. However, it is not easy for users to select ingredients that suit their own health and emotional state. Furthermore, efficient data sharing and collaboration between retailers, logistics companies, and producers is lacking, leading to inefficiencies across the industry. The present invention aims to solve these issues, improve the efficiency of users' health management, and promote collaboration across the industry.

[1253] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1254] In this invention, the server includes means for collecting health data and emotional data, means for suggesting ingredients suitable for the user based on the health data and emotional data, means for the user to select the suggested ingredients, means for generating order data for the selected ingredients, means for distributing the generated order data to retailers, logistics companies, and producers, means for delivering ingredients to the user based on the distributed order data, and means for the user to notify that delivery of the ingredients has been completed. This allows users to select ingredients optimal for their individual health and emotional states, and enables efficient data sharing and delivery.

[1255] "Health data" refers to data that indicates the user's health condition, such as the user's weight, height, calories burned, and allergy information.

[1256] "Emotion data" is data that represents the stress level and emotional state of the user, and is collected by an emotion recognition engine.

[1257] The "server" is a central processing unit that collects, analyzes, stores, and distributes data, and is a device that is responsible for overseeing each function of this system.

[1258] "Ingredients" means food items suggested and selected for consumption by a User.

[1259] "Suggestion" refers to the act of listing ingredients suitable for the user based on collected health and emotion data.

[1260] "Order data" is data that compiles information about ingredients selected by the user and is used for delivery, inventory management, and production planning.

[1261] A "retailer" is a business that manages inventory to provide ingredients selected by the user.

[1262] A "distributor" is a company that delivers ingredients from retailers to users.

[1263] "Producers" are businesses that produce and supply food ingredients.

[1264] A "communication protocol" is a set of rules and methods for securely sending and receiving data.

[1265] "Encryption" is a transformation operation that protects data from being deciphered by third parties.

[1266] "Notification of receipt completion" is the act of the user receiving the ingredients and notifying the system that they have been received correctly.

[1267] This invention is a system that proposes and delivers appropriate food ingredients based on the user's health and emotional data, and provides related data to retailers, logistics companies, and producers, thereby achieving personal health management and improving efficiency across the industry. Each component of this system and its function are described in detail below.

[1268] Health and emotional data collection

[1269] Using a dedicated healthcare app, users input health data such as weight, height, calories burned, and allergy information. Additionally, data on stress and emotional state is collected through an emotion recognition engine (e.g., Emotion AI), which recognizes facial expressions using the smartphone camera.

[1270] The device encrypts the health data entered by the user and the emotion data obtained by the emotion recognition engine (e.g., using AES (Advanced Encryption Standard)) and sends this encrypted data to the server via the HTTPS protocol.

[1271] The server receives the transmitted data, decrypts it, and stores it in a database (e.g., MySQL) in association with the user ID.

[1272] Generating ingredient suggestions

[1273] The server uses a data analysis engine (e.g., Pandas) to analyze the user's health and emotional data stored in the database. This analysis allows the server to evaluate the user's calorie consumption and emotional state and recommend appropriate ingredients.

[1274] The server runs a suggestion algorithm (e.g., K-means clustering) to generate the most suitable recipe list for each user. For example, if a user is under high stress, the server will suggest recipes that take into account calorie balance (e.g., "150g chicken breast," "100g quinoa," and "50g blueberries").

[1275] The server converts the generated ingredient list into JSON format and sends it to the user's device.

[1276] User selection and order confirmation

[1277] The user can view the suggested ingredients list on the app and select the ingredients they want (e.g., "150g chicken breast" or "50g blueberries").

[1278] The terminal encrypts the food ingredient data selected by the user and transmits it to the server using the HTTPS protocol.

[1279] The server stores the received selection data in a database, assigns an order ID, and manages it.

[1280] Order data distribution

[1281] The server formats the order data stored in the database into a format that can be consumed by retailers, logistics providers, and manufacturers, using CSV format or REST API endpoints.

[1282] The server distributes the formatted order data to retailers, logistics companies, and producers, allowing retailers to check inventory, logistics companies to plan delivery routes, and producers to plan production.

[1283] Food delivery

[1284] The logistics company picks up the suggested ingredients from the retailer's store or warehouse and delivers them efficiently to the user's home based on the planned delivery route.

[1285] The user confirms that the ingredients received are correct and notifies the user of the receipt via a dedicated app.

[1286] Specific examples

[1287] For example, User B weighs 65 kg, is 160 cm tall, consumes an average of 1,800 kcal per day, and emotional data indicates that he or she is highly stressed. This information is entered into a healthcare app and sent to a server. The server analyzes the received data and suggests low-calorie, nutritionally balanced ingredients to User B, such as 150 g of chicken breast, 100 g of quinoa, and 50 g of blueberries. User B selects the ingredients he or she desires from this list, and the order data is sent to the server. The server then distributes this order data to retailers, logistics companies, and producers, who then check inventory and make delivery and production plans. Finally, the logistics company delivers the ingredients to User B's home, and User B notifies them of receipt via the app.

[1288] Prompt Sentence Examples

[1289] "User B's health data is as follows: weight 65 kg, height 160 cm, average daily calorie consumption 1,800 kcal. User B is also under a lot of stress. Please suggest suitable foods for this user."

[1290] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1291] Step 1:

[1292] Users enter their health data, such as weight, height, calories burned, and allergy information, into a dedicated healthcare app. The app also uses an emotion recognition engine to capture their emotional state through the camera. This data is protected using AES encryption and transmitted to the server via HTTPS.

[1293] Input: User's weight, height, calories burned, allergy information, emotional state

[1294] Output: Encrypted health and emotional data

[1295] Step 2:

[1296] The server decrypts the received encrypted data, associates it with the user ID, and stores it in a database (MySQL). This allows for centralized management of each user's health and emotion data.

[1297] Input: Encrypted health and emotional data

[1298] Output: Health and emotion data for each user stored in a database

[1299] Step 3:

[1300] The server analyzes the user's health and emotional data stored in the database. Using a data analysis engine (Pandas), the server evaluates the user's calorie consumption and emotional state and selects appropriate ingredients. For users with high stress levels, the server makes suggestions that take calorie balance into consideration.

[1301] Input: User health and emotional data from a database

[1302] Output: Analyzed user's calorie consumption and emotional state information

[1303] Step 4:

[1304] The server runs a proposed algorithm (K-means clustering) to generate a list of ingredients suitable for each user. For example, it might suggest "150g chicken breast," "100g quinoa," and "50g blueberries." The generated list of ingredients is converted into JSON format and sent to the user's device.

[1305] Input: Analyzed user calorie consumption and emotional state information

[1306] Output: JSON list of ingredients

[1307] Step 5:

[1308] The user checks the suggested ingredients list on the app and selects the ingredients they want, for example, "150g of chicken breast" and "50g of blueberries." The selection data is encrypted and sent to the server.

[1309] Input: User's ingredient selection

[1310] Output: Encrypted selection data

[1311] Step 6:

[1312] The server saves the received selection data in a database and assigns an order ID, which is used to manage the order data.

[1313] Input: Encrypted selection data

[1314] Output: Order data and order ID stored in the database

[1315] Step 7:

[1316] The server converts the order data into a format that can be used by retailers, logistics companies, and producers (CSV or REST API endpoint) and distributes it to each of them. Retailers check inventory, logistics companies plan delivery routes, and producers set production plans.

[1317] Input: Order data and order ID stored in the database

[1318] Output: Order data distributed to retailers, logistics companies, and producers

[1319] Step 8:

[1320] The logistics company will pick up the ingredients selected by the user from the retailer's store or warehouse and deliver them efficiently to the user's home based on the planned delivery route. The user will then receive the ingredients and be notified of the receipt via a dedicated app.

[1321] Input: Retailer inventory check information, delivery route planning information, food pick-up information

[1322] Output: Delivery completion information and receipt completion notification to the user

[1323] (Application example 2)

[1324] 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."

[1325] Conventional food delivery systems do not take into account the user's health or emotional state when recommending ingredients, resulting in little effect on personal health management or stress reduction. Furthermore, the difficulty of tracking delivery status in real time can lead to a decline in user satisfaction. Furthermore, data sharing between retailers, logistics companies, and producers is insufficient, leaving issues with inventory management and delivery efficiency.

[1326] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting health data and emotional data, means for suggesting ingredients suitable for the user based on the health data and emotional data, means for the user to select the suggested ingredients, means for generating order data for the selected ingredients, means for distributing the generated order data to retailers, logistics companies, and producers, means for delivering ingredients to the user based on the distributed order data, and means for tracking delivery status in real time. This enables the suggestion and rapid delivery of ingredients suitable for the user's health and emotional state, thereby realizing personal health management and improving efficiency across the industry.

[1327] Definitions of important words

[1328] "Health data" refers to information about the user's physical condition, including the user's weight, height, calories burned, allergy information, and the like.

[1329] "Emotion data" is information relating to the user's stress level and emotional state.

[1330] "Collection means" refers to devices such as interfaces and sensors for acquiring the user's health data and emotional data.

[1331] The "suggestion means" is a device or system that executes an algorithm to create a list of ingredients suitable for the user based on the collected health data and emotion data.

[1332] The "selection means" refers to an interface or function that allows the user to select desired ingredients from the suggested ingredient list.

[1333] The "order data generation means" is a system for creating order data based on ingredients selected by the user.

[1334] The "distribution means" is a device or system for transmitting the generated order data to retailers, logistics companies, and manufacturers via communication.

[1335] A "delivery means" is a device or system that a retailer or a distributor uses to deliver the selected ingredients to a user.

[1336] The "tracking means" is a device or system for monitoring and notifying the user of the delivery status of ingredients delivered to the user in real time based on order data.

[1337] MODE FOR CARRYING OUT THE INVENTION

[1338] This invention relates to a system that supports users' health management by suggesting, selecting, and delivering ingredients based on the user's health and emotional data. This system includes three main elements: a server, a terminal, and a user, each of which works together to realize specific functions.

[1339] server

[1340] The server performs several functions:

[1341] 1. Data collection and storage: The server collects health data (weight, height, calories burned, allergy information, etc.) and emotional data (stress level, etc.) sent by the user and stores them in a database. This data is encrypted before transmission to ensure data security.

[1342] 2. Data Analysis: The server analyzes the collected data and runs algorithms to assess the user's health and emotional state, which then lists and suggests suitable ingredients for the user.

[1343] 3. Order data generation and distribution: The server generates order data based on the ingredient list selected by the user and distributes it to retailers, logistics companies, and producers.

[1344] 4. Real-time tracking: The server monitors the delivery status in real time and has the function to notify the user. This function allows the user to know the delivery status in real time.

[1345] Terminal

[1346] The terminal functions as a device such as a smartphone.

[1347] 1. Data input: Users input their health and emotional data through a dedicated application. If necessary, the emotion recognition engine automatically collects the user's emotional data.

[1348] 2. Data transmission: The entered data is encrypted and sent to the server using a secure communication protocol.

[1349] 3. Ingredient selection: The user checks the list of suggested ingredients sent from the server and selects the desired ingredients on the application.

[1350] 4. Notifications: The terminal receives notifications from the server and provides the user with real-time updates, especially regarding delivery status.

[1351] User

[1352] The user uses the system to perform the following operations:

[1353] 1. Data entry: Enter daily data such as weight, height, calories burned, allergy information, and emotional state into a dedicated app.

[1354] 2. Ingredient selection: Select the ingredients you want from the suggested options and confirm your order through the application.

[1355] 3. Receipt confirmation: Check the delivered ingredients and notify the customer of receipt via the application.

[1356] Specific examples

[1357] For example, if a user with user ID "user12345" consumes an average of 1,800 kcal per day and is determined to have a high stress level, the server will suggest 150g of chicken breast and 50g of blueberries. The user selects this and confirms the order. The server then distributes the order data to retailers, logistics companies, and producers, and tracks the delivery status in real time. The user can check the delivery status in real time via a smartphone application.

[1358] Prompt Sentence Examples

[1359] Get the user data for user ID "user12345" and suggest recommended foods based on their health and emotional data. Health data includes weight, height, calories burned, and allergy information, and emotional data includes stress level. If this user's stress level is high, suggest low-calorie, nutritionally balanced foods.

[1360] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1361] Program processing steps

[1362] Step 1:

[1363] Entering health and emotional data

[1364] Subject: User

[1365] Specific operation: The user uses a dedicated smartphone application to input their health data (weight, height, calories burned, allergy information, etc.) and emotional data (stress level, etc. via an emotion recognition engine).

[1366] Input: User-entered health and emotional data

[1367] Output: The raw data entered into the app

[1368] Step 2:

[1369] Encryption and transmission of health and emotional data

[1370] Subject: Terminal

[1371] Specific operation: The device encrypts the input health data and emotion data using an encryption algorithm such as AES and sends it to the server using a secure communication protocol (e.g., HTTPS).

[1372] Input: Raw data entered into the app

[1373] Output: Encrypted health and emotion data

[1374] Step 3:

[1375] Receiving and storing data

[1376] Subject: Server

[1377] Specific operation: The server receives the encrypted data sent from the terminal, decrypts it, and then stores it in a secure database.

[1378] Input: Encrypted health and emotion data

[1379] Output: Decoded health and emotion data stored in a database

[1380] Step 4:

[1381] Data analysis and ingredient suggestions

[1382] Subject: Server

[1383] Specific operation: The server analyzes the health and emotional data stored in the database and uses an AI algorithm to generate a list of ingredients suitable for the user.

[1384] Input: Health and emotion data stored in a database

[1385] Output: A list of ingredients suitable for the user

[1386] Step 5:

[1387] Send and display ingredient lists

[1388] Subject: Server and Terminal

[1389] Specific operation: The server sends the generated ingredient list in JSON format to the terminal, and the terminal displays the suggested ingredient list to the user.

[1390] Input: A list of ingredients suitable for the user

[1391] Output: Ingredient list displayed on the device

[1392] Step 6:

[1393] Ingredient selection

[1394] Subject: User

[1395] Specific operation: The user selects the desired ingredients from the list of ingredients displayed on the terminal.

[1396] Input: Ingredient list displayed on the device

[1397] Output: Ingredients selected by the user

[1398] Step 7:

[1399] Generate order data

[1400] Subject: Terminal and Server

[1401] Specific operation: The terminal sends data on ingredients selected by the user to the server, and the server generates order data based on that data.

[1402] Input: Ingredients selected by the user

[1403] Output: Generated order data

[1404] Step 8:

[1405] Order data distribution

[1406] Subject: Server

[1407] Specific operation: The server distributes the generated order data to retailers, logistics companies, and manufacturers.

[1408] Input: Generated order data

[1409] Output: Order data sent to each supplier

[1410] Step 9:

[1411] Food delivery

[1412] Subject: Logistics company

[1413] Specific operation: Based on the received order data, the logistics company picks up the specified ingredients, plans an efficient delivery route, and delivers them to the address specified by the user.

[1414] Input: Order data including shipping instructions

[1415] Output: Food delivered to the user's home

[1416] Step 10:

[1417] Notification of receipt completion

[1418] Subject: User and Device

[1419] Specific operation: The user checks the received ingredients and notifies the application that receipt is complete.

[1420] Input: Delivered ingredients

[1421] Output: Notification of receipt completion

[1422] 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.

[1423] 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.

[1424] 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.

[1425] [Fourth embodiment]

[1426] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1427] 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.

[1428] 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).

[1429] 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.

[1430] 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.

[1431] 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).

[1432] 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.

[1433] 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.

[1434] 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.

[1435] 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.

[1436] 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.

[1437] 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.

[1438] 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."

[1439] MODE FOR CARRYING OUT THE INVENTION

[1440] This invention is a system that proposes and delivers appropriate food ingredients based on the user's health data, and provides related data to retailers, logistics companies, and producers, thereby achieving personal health management and improving efficiency across the industry. Each component of the system and its function are explained in detail below.

[1441] 1. Health data collection

[1442] User: Enters daily health data such as weight, height, calories burned, and allergy information into a dedicated health app.

[1443] Terminal: The entered health data is encrypted and sent to the server using a secure communication protocol.

[1444] Server: Stores the received health data in a database and associates it with the user ID.

[1445] 2. Generating Ingredient Suggestions

[1446] Server: Analyzes the user's health data stored in the database and evaluates the user's calorie consumption and health condition.

[1447] Server: Runs an algorithm to suggest ingredients suitable for each user and generates an ingredient list.

[1448] Server: Generates a list of suggested ingredients in JSON format and sends it to the user's device.

[1449] 3. User selection and order confirmation

[1450] User: Selects desired ingredients from the list of suggested ingredients, for example, "200g chicken breast" and "100g broccoli."

[1451] Terminal: Sends the user's selection data to the server.

[1452] Server: Stores the received order information in a database and associates it with the order ID.

[1453] 4. Delivery of order data

[1454] Server: Formats order data so that it can be used by retailers, logistics providers, and manufacturers.

[1455] Server: Distributes formatted order data to retailers, logistics companies, and manufacturers.

[1456] Retailer: Checks inventory based on received order data and arranges for replenishment as necessary.

[1457] Logistics company: Plans delivery routes based on received order data.

[1458] Producer: Creates production plans based on received order data.

[1459] 5. Food delivery

[1460] Logistics Agent: Picks up the suggested ingredients from the retailer's store.

[1461] Logistics provider: Ensures efficient delivery based on set delivery routes.

[1462] Logistics company: delivers ingredients to the user's home.

[1463] User: Checks that the received ingredients are correct and notifies the app that receipt is complete.

[1464] Specific examples

[1465] User A weighs 70 kg, is 170 cm tall, and consumes an average of 2,000 kcal per day. His health data also indicates that he tends to be deficient in vitamin D. This data is entered into a healthcare app and sent to a server. The server receives and analyzes the data. The analysis determines that high-protein, low-fat foods and foods rich in vitamin D are suitable for User A.

[1466] The server suggests ingredients such as "200g chicken breast," "100g broccoli," and "50g almonds." User A selects "200g chicken breast" and "100g broccoli" from this list and confirms the order. This data is sent to the server and recorded as order data.

[1467] The order data is then distributed to retailers, logistics companies, and producers. Retailers check inventory, logistics companies plan efficient delivery routes, and producers adjust their production plans based on future demand.

[1468] Finally, the logistics company collects the necessary ingredients and delivers them to User A's home. User A checks the received ingredients and notifies the app that they have been received. This allows User A to continue their healthy eating habits, while also achieving efficiency and health for the entire industry.

[1469] The processing flow will be explained below.

[1470] Specific processing flow of the program

[1471] 1. Health data collection

[1472] Step 1:

[1473] User: Enters daily health data such as weight, height, calories burned, and allergy information into a dedicated health app.

[1474] Step 2:

[1475] Terminal: The entered health data is encrypted and sent to the server using a secure communication protocol.

[1476] Step 3:

[1477] Server: Stores the received health data in a database and associates it with the user ID.

[1478] 2. Generating Ingredient Suggestions

[1479] Step 1:

[1480] Server: Analyzes the user's health data stored in the database and evaluates the user's calorie consumption and health condition.

[1481] Step 2:

[1482] Server: Runs an algorithm to suggest ingredients suitable for each user and generates an ingredient list.

[1483] Step 3:

[1484] Server: Generates a list of suggested ingredients in JSON format and sends it to the user's device.

[1485] 3. User selection and order confirmation

[1486] Step 1:

[1487] User: Selects desired ingredients from the list of suggested ingredients, for example, "200g chicken breast" and "100g broccoli."

[1488] Step 2:

[1489] Terminal: Sends the user's selection data to the server.

[1490] Step 3:

[1491] Server: Stores the received order information in a database and associates it with the order ID.

[1492] 4. Delivery of order data

[1493] Step 1:

[1494] Server: Formats order data so that it can be used by retailers, logistics providers, and manufacturers.

[1495] Step 2:

[1496] Server: Distributes formatted order data to retailers, logistics companies, and manufacturers.

[1497] Step 3:

[1498] Retailer: Checks inventory based on received order data and arranges for replenishment as necessary.

[1499] Step 4:

[1500] Logistics company: Plans delivery routes based on received order data.

[1501] Step 5:

[1502] Producer: Creates production plans based on received order data.

[1503] 5. Food delivery

[1504] Step 1:

[1505] Logistics Agent: Picks up the suggested ingredients from the retailer's store.

[1506] Step 2:

[1507] Logistics provider: Ensures efficient delivery based on set delivery routes.

[1508] Step 3:

[1509] Logistics company: delivers ingredients to the user's home.

[1510] Step 4:

[1511] User: Checks that the received ingredients are correct and notifies the app that receipt is complete.

[1512] Example 1

[1513] 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."

[1514] Conventional health management systems have difficulty recommending appropriate ingredients based on individual users' health data and efficiently delivering those ingredients to users. Furthermore, they do not adequately consider the security of health data or the optimization of inventory management in the delivery process. Therefore, there is a need for improved individual health management and efficiency improvements across the industry.

[1515] 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.

[1516] In this invention, the server includes: means for collecting health data; means for encrypting the health data and transmitting it via a secure communication protocol; means for analyzing the user's calorie consumption and health status based on the health data; means for suggesting ingredients suitable for the user based on the analysis results; means for transmitting the list of suggested ingredients to the user; means for the user to select the suggested ingredients; means for generating order data for the selected ingredients; means for distributing the generated order data to retailers, distributors, and producers; and means for delivering ingredients to the user based on the distributed order data. This enables appropriate ingredients to be suggested based on the user's health data and delivered efficiently. This also improves the security of health data and optimizes inventory management.

[1517] "Health data" refers to information about the user's health condition, such as the user's weight, height, calories burned, and allergy information.

[1518] "Encryption" refers to converting data using a specific algorithm to protect the data from a security perspective, making it difficult for third parties to read.

[1519] A "secure communication protocol" is a communication method used to enhance security during data transmission, and includes HTTPS and TLS.

[1520] "Analysis" means processing and analyzing data to evaluate and diagnose the user's calorie consumption and health condition based on health data.

[1521] "Ingredient suggestions" refers to recommending ingredients suitable for the user based on analyzed health data.

[1522] "Order data" is data that includes information about ingredients selected by the user, and orders are processed based on this data.

[1523] A "retailer" is a business that sells goods to consumers.

[1524] A "logistics company" is a company whose role is to store and deliver goods efficiently.

[1525] A "producer" is a business that is responsible for manufacturing or producing goods.

[1526] "Delivery" refers to the process in which a retailer or a logistics company transports products to a destination based on a user's order.

[1527] MODE FOR CARRYING OUT THE INVENTION

[1528] The present invention is a system that proposes appropriate ingredients based on a user's health data and delivers them to the user, thereby achieving personal health management and improving the efficiency of the entire industry. Specific embodiments for implementing this system are described below.

[1529] System Overview

[1530] This system collects and analyzes the user's health data, suggests ingredients based on the results, distributes order data to retailers, logistics companies, and producers according to the user's selection, and finally delivers the ingredients to the user.

[1531] Hardware and software used

[1532] Hardware: Dedicated healthcare device (smartphone or tablet) used by the user, server (high-performance cloud server)

[1533] Software: Healthcare app, encryption software (AES-256), secure communication protocol (HTTPS), database (PostgreSQL), data analysis tools (Python, Pandas, Scikit-learn), communication API (REST API)

[1534] System details

[1535] 1. Health data collection

[1536] Users enter their daily health data (weight, height, calories burned, allergy information, etc.) into a dedicated healthcare app. This information becomes the basic data for health management.

[1537] The terminal encrypts the entered health data using the AES-256 encryption method and transmits it securely to the server using the HTTPS protocol.

[1538] The server decrypts the received encrypted data and stores it in a database.

[1539] 2. Generating Ingredient Suggestions

[1540] The server uses Python scripts to retrieve and analyze user health data from the database, using the Pandas library and Scikit-learn.

[1541] The server runs machine learning algorithms (such as Random Forest) to suggest suitable ingredients based on the user's calorie consumption and health status (e.g., vitamin D deficiency status).

[1542] The server converts the generated ingredient list into JSON format and sends it back to the user's device via HTTPS.

[1543] 3. User selection and order confirmation

[1544] The user selects the ingredients they want from a list of suggested ingredients through the app.

[1545] The terminal transmits the user's selection data to the server.

[1546] The server stores the received order data in a database and generates and associates an order ID.

[1547] 4. Delivery of order data

[1548] The server converts the order data into CSV format so that it can be used by retailers, logistics companies, and producers, and distributes it to each company using a REST API.

[1549] 5. Food delivery

[1550] The logistics company picks up the suggested ingredients from the retailer's store and delivers them efficiently to the user's home following the planned delivery route.

[1551] The user checks that the received ingredients are correct and is notified of receipt via a dedicated healthcare app.

[1552] Specific examples

[1553] User A weighs 70 kg, is 170 cm tall, consumes an average of 2000 kcal, and tends to be deficient in vitamin D. This health data is entered into a healthcare app and sent to a server. The server receives the data and analyzes it. Based on the analysis results, it is determined that high-protein, low-fat foods and foods rich in vitamin D are suitable for User A.

[1554] The server suggests ingredients such as "200g chicken breast," "100g broccoli," and "50g almonds." User A selects "200g chicken breast" and "100g broccoli" from this list and confirms the order in the app. The server distributes the order data to retailers, logistics companies, and producers. Inventory management, delivery route planning, and production planning are carried out by each company.

[1555] Finally, the logistics company picks up the ingredients and delivers them to User A's home. User A receives the ingredients and is notified of the receipt via the app. This allows User A to maintain a healthy diet and also improves efficiency across the industry.

[1556] Prompt Sentence Examples

[1557] Example prompts for generative AI models:

[1558] "Please explain in natural language the programming process of the system that recommends and delivers appropriate ingredients based on an individual's health data, including the specific hardware, software, and processing details. For example, please provide details on what encryption methods and protocols are used to transmit data, what machine learning algorithms are used to recommend ingredients, and how the order data is distributed to retailers and logistics providers."

[1559] The above is an embodiment of the present invention.

[1560] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1561] Step 1:

[1562] Health data collection

[1563] Users enter health data such as weight, height, calories burned, and allergy information into a dedicated healthcare app, which is then stored on the user's device.

[1564] The terminal receives the entered health data and encrypts it using the AES-256 encryption algorithm, which is then sent to the server via the secure communication protocol HTTPS.

[1565] The server decrypts the received encrypted data using the RSA encryption algorithm, associates it with the user ID, and stores it in the PostgreSQL database. This saves each user's health data in the database.

[1566] Step 2:

[1567] Generating ingredient suggestions

[1568] The server retrieves the user's health data from the database using a Python script, which is then preprocessed using the Pandas library.

[1569] The server then analyzes the user's calorie consumption and health status based on the preprocessed data, applying the Random Forest algorithm using the Scikit-learn library.

[1570] The server generates an ingredient list suitable for each user based on the analysis results. The generated ingredient list is converted into JSON format and sent to the user's device via HTTPS.

[1571] Step 3:

[1572] User selection and order confirmation

[1573] The user opens the app, checks the list of suggested ingredients sent from the server, operates the UI to select ingredients, and confirms the selection.

[1574] The terminal reformats the food ingredient data selected by the user into JSON format and sends it to the server via the HTTPS protocol.

[1575] The server stores the received order data in a database, generates an order ID, and records related data.

[1576] Step 4:

[1577] Order data distribution

[1578] The server formats the order data into a format that can be used by retailers, logistics companies, and manufacturers. Formatting includes converting the data into CSV format and mapping data fields based on each company's API specifications.

[1579] The server then distributes the formatted data to retailers, logistics providers, and producers using a REST API, with different API endpoints used for each provider.

[1580] Based on the received data, the retailer obtains inventory information from the product management system and replenishes inventory as necessary.

[1581] Logistics companies use GIS software to optimize delivery routes based on the order data they receive.

[1582] Producers use the ERP system to adjust their production plans based on the received order data.

[1583] Step 5:

[1584] Food delivery

[1585] The logistics company picks up the suggested ingredients from the retailer's store. For example, it accesses the store's inventory management system and receives "200g of chicken breast" and "100g of broccoli."

[1586] The logistics company will efficiently deliver the ingredients to the user's home according to the optimized delivery route. The delivery status is tracked in real time and the user is notified via a dedicated app.

[1587] The user checks whether the ingredients are correct and notifies the server of the receipt completion through the dedicated healthcare app. Once notified, the server records the receipt completion status in the database.

[1588] The above is the specific operation and input / output flow at each processing step.

[1589] (Application example 1)

[1590] 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."

[1591] Conventional health management systems lack a system that can suggest appropriate ingredients based on users' health data and efficiently manage the entire process from ordering to delivery. Furthermore, users often have to manually enter their health data, leading to problems of inaccuracy and time-consuming data entry. Furthermore, there is insufficient collaboration between retailers, logistics companies, and producers, preventing the industry as a whole from achieving efficiency.

[1592] 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.

[1593] In this invention, the server includes: means for collecting health data; means for suggesting suitable ingredients to a user based on the health data; means for the user to select the suggested ingredients; means for generating order data for the selected ingredients; means for distributing the generated order data to retailers, logistics companies, and producers; means for delivering ingredients to the user based on the distributed order data; a server that executes an algorithm for generating ingredient suggestions based on the health data; and application means for inputting health data from a smartphone. This allows users to easily and accurately input their health data through a smartphone app and receive appropriate ingredient suggestions based on that data, enabling efficient management of the entire food product process from ordering to delivery. Furthermore, collaboration between retailers, logistics companies, and producers can be strengthened, resulting in greater efficiency and soundness in the industry as a whole.

[1594] "Health data" is information that indicates the user's health condition, such as the user's weight, height, calories burned, allergy information, and dietary history.

[1595] "Ingredient suggestions" refers to suggesting ingredients that are best suited to the user's health condition based on collected health data.

[1596] "Server" refers to a computer system that analyzes health data, generates food ingredient suggestions, and manages and distributes order data.

[1597] "Retailer" refers to a company or individual that sells food ingredients and manages inventory.

[1598] "Logistics company" refers to a company or individual that delivers food ingredients.

[1599] "Producers" refers to farmers, food manufacturers, etc. who produce the proposed ingredients.

[1600] "Application" refers to software that allows users to input health data, receive food ingredient suggestions, and place orders via smartphone.

[1601] "Algorithm" refers to the calculation procedure used to analyze health data and suggest appropriate ingredients.

[1602] "Order data" refers to data containing information about ingredients selected by the user, and is distributed to retailers, distributors, and producers.

[1603] The present invention is a system that proposes and delivers appropriate ingredients based on the user's health data and provides related data to various businesses, thereby achieving personal health management and improving the efficiency of the entire industry. Specific embodiments of this system are described below.

[1604] Health data collection and entry

[1605] Users use their smartphones to enter health data such as weight, height, calories burned, and allergy information into a dedicated application. The application was developed using React Native and runs on both iOS and Android platforms.

[1606] Transmission and storage of health data

[1607] The health data entered by the user is encrypted and sent to the server using a secure communication protocol. The server is a Python-based system using Flask, and the received data is stored in a MongoDB database. This data is managed in association with the user ID.

[1608] Generating ingredient suggestions

[1609] The server analyzes the stored health data and runs an algorithm to suggest ingredients suitable for the user. The algorithm used here is based on a generative AI model and recommends optimal ingredients based on the health data. The ingredient list generated by this ingredient suggestion algorithm is sent to the user's device in JSON format.

[1610] User selection and order confirmation

[1611] The user selects the ingredients they want from a list of suggested ingredients through the smartphone app. This selection data is sent back to the server and saved as order data. The order data is managed in association with the user ID.

[1612] Distribution of order data and delivery of ingredients

[1613] The server formats the order data into a format that can be used by retailers, logistics companies, and producers, and distributes it to each of them. Retailers check their inventory and arrange for replenishment as necessary. Logistics companies plan efficient delivery routes and deliver the ingredients to users' homes. Producers adjust their production plans based on future demand.

[1614] Specific examples

[1615] As a concrete example, if User B has health data stating that he weighs 80 kg, is 180 cm tall, and is iron deficient, this data is entered into the application and sent to the server. The server analyzes the data and suggests ingredients such as "200 g of spinach" and "150 g of lean meat." User B selects from this list and confirms the order. This data is stored on the server and distributed as order data to retailers, logistics companies, and producers.

[1616] Prompt Sentence Examples

[1617] An example prompt based on user B's health data is:

[1618] User ID: 12345, Weight: 80kg, Height: 180cm, Allergies: None, Dietary history: Iron deficiency

[1619] As described above, the system of the present invention is capable of consistently providing everything from food ingredient suggestions based on the user's health data to delivery, contributing to personal health management and efficiency improvements across the industry.

[1620] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1621] Step 1:

[1622] A user inputs health data (e.g., weight, height, calories burned, allergy information) into a smartphone app. This data is entered into input fields in the application and stored locally.

[1623] Step 2:

[1624] The device (smartphone app) encrypts the entered health data and sends it to the server using a secure communication protocol (e.g., HTTPS). Here, the entered raw data is encrypted and reaches the server safely.

[1625] Step 3:

[1626] The server receives the encrypted health data, decrypts it, and then stores it in a MongoDB database, where the user ID and health data are associated and stored in the database.

[1627] Step 4:

[1628] The server periodically analyzes the health data stored in the database. The analysis algorithm uses a generative AI model to evaluate the collected health data and suggest the most suitable ingredients for the user. This process takes input data (health data) and outputs an ingredient list (suggested ingredients).

[1629] Step 5:

[1630] The server sends the generated ingredient list in JSON format to a smartphone app, which can then view the ingredients and their quantities.

[1631] Step 6:

[1632] Users can view a list of suggested ingredients on the smartphone app and select the ingredients they want, which are then temporarily stored locally on the device.

[1633] Step 7:

[1634] The terminal transmits the user's selection data to the server, and the server generates order data based on the received selection data. This order data is associated with the user ID and recorded in a database.

[1635] Step 8:

[1636] The server converts the generated order data into the required format and distributes it to retailers, logistics companies, and producers. This distribution process allows retailers to check inventory, logistics companies to plan delivery routes, and producers to adjust production plans.

[1637] Step 9:

[1638] The logistics company picks up the suggested ingredients from the retailer's store and delivers them to the user's home based on an efficient delivery route. This delivery process is updated in real time on the server.

[1639] Step 10:

[1640] The user checks the received ingredients and notifies the smartphone app that the delivery has been completed, which then confirms the delivery was successful throughout the system and updates the user's health data again.

[1641] The above steps complete a series of processes that suggest and deliver appropriate ingredients based on the user's health data and provide related data to the supplier.

[1642] 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.

[1643] MODE FOR CARRYING OUT THE INVENTION

[1644] This invention is a system that proposes and delivers appropriate ingredients based on the user's health and emotional data, and provides related data to retailers, logistics companies, and producers, thereby achieving personal health management and improving efficiency across the industry. Each component of the system and its function are explained in detail below.

[1645] 1. Collecting health and emotional data

[1646] User: Enters daily health data such as weight, height, calories burned, and allergy information into a dedicated healthcare app. User emotional data is also collected through a dedicated emotion recognition engine.

[1647] Device: Health and emotion data are encrypted and sent to a server using a secure communication protocol.

[1648] Server: Stores the received health and emotion data in a database and associates it with the user ID.

[1649] 2. Generating Ingredient Suggestions

[1650] Server: Analyzes the user's health and emotional data stored in the database and evaluates the user's calorie consumption, health condition, and emotional state.

[1651] Server: Runs an algorithm to suggest ingredients suitable for each user and generates an ingredient list. For example, it suggests calorie-balanced ingredients to a user who is feeling stressed.

[1652] Server: Generates a list of suggested ingredients in JSON format and sends it to the user's device.

[1653] 3. User selection and order confirmation

[1654] User: Selects desired ingredients from the list of suggested ingredients, for example, "200g chicken breast" and "100g broccoli."

[1655] Terminal: Sends the user's selection data to the server.

[1656] Server: Stores the received order information in a database and associates it with the order ID.

[1657] 4. Delivery of order data

[1658] Server: Formats order data so that it can be used by retailers, logistics providers, and manufacturers.

[1659] Server: Distributes formatted order data to retailers, logistics companies, and manufacturers.

[1660] Retailer: Checks inventory based on received order data and arranges for replenishment as necessary.

[1661] Logistics company: Plans delivery routes based on received order data.

[1662] Producer: Creates production plans based on received order data.

[1663] 5. Food delivery

[1664] Logistics Agent: Picks up the suggested ingredients from the retailer's store.

[1665] Logistics provider: Ensures efficient delivery based on set delivery routes.

[1666] Logistics company: delivers ingredients to the user's home.

[1667] User: Checks that the received ingredients are correct and notifies the app that receipt is complete.

[1668] Specific examples

[1669] User B weighs 65 kg, is 160 cm tall, and consumes an average of 1,800 kcal per day. Emotional data indicating that User B is under a high level of stress has also been collected. This data is entered into a healthcare app and sent to a server. The server receives and analyzes the data. The analysis determines that low-calorie, nutritionally balanced foods are suitable for User B.

[1670] The server suggests ingredients such as "150g chicken breast," "100g quinoa," and "50g blueberries." User B selects "150g chicken breast" and "50g blueberries" from this list and confirms the order. This data is sent to the server and recorded as order data.

[1671] The order data is then distributed to retailers, logistics companies, and producers. Retailers check inventory, logistics companies plan efficient delivery routes, and producers adjust their production plans based on future demand.

[1672] Finally, the logistics company collects the necessary ingredients and delivers them to User B's home. User B checks the received ingredients and notifies the app that they have been received. This allows User B to maintain a healthy diet that takes into consideration their emotional state, while also achieving efficiency and health for the entire industry.

[1673] The processing flow will be explained below.

[1674] Detailed Description of the Invention - Processing Flow

[1675] 1. Collecting health and emotional data

[1676] Step 1:

[1677] User: Enters daily health data such as weight, height, calories burned, and allergy information into a dedicated health app.

[1678] Step 2:

[1679] On the device: Cameras and sensors are used to collect emotional data from the user's facial expressions and behavior.

[1680] Step 3:

[1681] Device: Collected health and emotional data is encrypted and sent to a server using a secure communication protocol.

[1682] Step 4:

[1683] Server: Stores the received health and emotion data in a database and associates it with each user ID.

[1684] 2. Generating Ingredient Suggestions

[1685] Step 1:

[1686] Server: Analyzes the user's health and emotional data stored in the database and evaluates the user's calorie consumption, health condition, and emotional state.

[1687] Step 2:

[1688] Server: Based on the evaluation results, the server runs an algorithm to suggest ingredients suitable for the user and generates an ingredient list. For example, if the emotion engine determines that the user is feeling stressed, the server will suggest additional ingredients that will help reduce stress.

[1689] Step 3:

[1690] Server: Generates a list of suggested ingredients in JSON format and sends it to the user's device.

[1691] 3. User selection and order confirmation

[1692] Step 1:

[1693] User: Selects desired ingredients from the list of suggested ingredients, for example, "150g chicken breast" and "50g blueberries."

[1694] Step 2:

[1695] Terminal: Sends the user's selection data to the server.

[1696] Step 3:

[1697] Server: Stores the received order information in a database and associates it with the order ID.

[1698] 4. Delivery of order data

[1699] Step 1:

[1700] Server: Formats order data so that it can be used by retailers, logistics providers, and manufacturers.

[1701] Step 2:

[1702] Server: Distributes formatted order data to retailers, logistics companies, and producers via APIs.

[1703] Step 3:

[1704] Retailer: Checks inventory based on received order data and arranges for replenishment as necessary.

[1705] Step 4:

[1706] Logistics company: Plans delivery routes based on received order data.

[1707] Step 5:

[1708] Producer: Creates production plans based on received order data.

[1709] 5. Food delivery

[1710] Step 1:

[1711] Logistics Agent: Picks up the suggested ingredients from the retailer's store.

[1712] Step 2:

[1713] Logistics provider: Executes efficient deliveries based on set delivery routes.

[1714] Step 3:

[1715] Logistics company: delivers ingredients to the user's home.

[1716] Step 4:

[1717] User: Checks that the received ingredients are correct and notifies the app that receipt is complete.

[1718] Example 2

[1719] 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."

[1720] In modern society, individual health management and dietary optimization have become important issues. However, it is not easy for users to select ingredients that suit their own health and emotional state. Furthermore, efficient data sharing and collaboration between retailers, logistics companies, and producers is lacking, leading to inefficiencies across the industry. The present invention aims to solve these issues, improve the efficiency of users' health management, and promote collaboration across the industry.

[1721] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1722] In this invention, the server includes means for collecting health data and emotional data, means for suggesting ingredients suitable for the user based on the health data and emotional data, means for the user to select the suggested ingredients, means for generating order data for the selected ingredients, means for distributing the generated order data to retailers, logistics companies, and producers, means for delivering ingredients to the user based on the distributed order data, and means for the user to notify that delivery of the ingredients has been completed. This allows users to select ingredients optimal for their individual health and emotional states, and enables efficient data sharing and delivery.

[1723] "Health data" refers to data that indicates the user's health condition, such as the user's weight, height, calories burned, and allergy information.

[1724] "Emotion data" is data that represents the stress level and emotional state of the user, and is collected by an emotion recognition engine.

[1725] The "server" is a central processing unit that collects, analyzes, stores, and distributes data, and is a device that is responsible for overseeing each function of this system.

[1726] "Ingredients" means food items suggested and selected for consumption by a User.

[1727] "Suggestion" refers to the act of listing ingredients suitable for the user based on collected health and emotion data.

[1728] "Order data" is data that compiles information about ingredients selected by the user and is used for delivery, inventory management, and production planning.

[1729] A "retailer" is a business that manages inventory to provide ingredients selected by the user.

[1730] A "distributor" is a company that delivers ingredients from retailers to users.

[1731] "Producers" are businesses that produce and supply food ingredients.

[1732] A "communication protocol" is a set of rules and methods for securely sending and receiving data.

[1733] "Encryption" is a transformation operation that protects data from being deciphered by third parties.

[1734] "Notification of receipt completion" is the act of the user receiving the ingredients and notifying the system that they have been received correctly.

[1735] This invention is a system that proposes and delivers appropriate food ingredients based on the user's health and emotional data, and provides related data to retailers, logistics companies, and producers, thereby achieving personal health management and improving efficiency across the industry. Each component of this system and its function are described in detail below.

[1736] Health and emotional data collection

[1737] Using a dedicated healthcare app, users input health data such as weight, height, calories burned, and allergy information. Additionally, data on stress and emotional state is collected through an emotion recognition engine (e.g., Emotion AI), which recognizes facial expressions using the smartphone camera.

[1738] The device encrypts the health data entered by the user and the emotion data obtained by the emotion recognition engine (e.g., using AES (Advanced Encryption Standard)) and sends this encrypted data to the server via the HTTPS protocol.

[1739] The server receives the transmitted data, decrypts it, and stores it in a database (e.g., MySQL) in association with the user ID.

[1740] Generating ingredient suggestions

[1741] The server uses a data analysis engine (e.g., Pandas) to analyze the user's health and emotional data stored in the database. This analysis allows the server to evaluate the user's calorie consumption and emotional state and recommend appropriate ingredients.

[1742] The server runs a suggestion algorithm (e.g., K-means clustering) to generate the most suitable recipe list for each user. For example, if a user is under high stress, the server will suggest recipes that take into account calorie balance (e.g., "150g chicken breast," "100g quinoa," and "50g blueberries").

[1743] The server converts the generated ingredient list into JSON format and sends it to the user's device.

[1744] User selection and order confirmation

[1745] The user can view the suggested ingredients list on the app and select the ingredients they want (e.g., "150g chicken breast" or "50g blueberries").

[1746] The terminal encrypts the food ingredient data selected by the user and transmits it to the server using the HTTPS protocol.

[1747] The server stores the received selection data in a database, assigns an order ID, and manages it.

[1748] Order data distribution

[1749] The server formats the order data stored in the database into a format that can be consumed by retailers, logistics providers, and manufacturers, using CSV format or REST API endpoints.

[1750] The server distributes the formatted order data to retailers, logistics companies, and producers, allowing retailers to check inventory, logistics companies to plan delivery routes, and producers to plan production.

[1751] Food delivery

[1752] The logistics company picks up the suggested ingredients from the retailer's store or warehouse and delivers them efficiently to the user's home based on the planned delivery route.

[1753] The user confirms that the ingredients received are correct and notifies the user of the receipt via a dedicated app.

[1754] Specific examples

[1755] For example, User B weighs 65 kg, is 160 cm tall, consumes an average of 1,800 kcal per day, and emotional data indicates that he or she is highly stressed. This information is entered into a healthcare app and sent to a server. The server analyzes the received data and suggests low-calorie, nutritionally balanced ingredients to User B, such as 150 g of chicken breast, 100 g of quinoa, and 50 g of blueberries. User B selects the ingredients he or she desires from this list, and the order data is sent to the server. The server then distributes this order data to retailers, logistics companies, and producers, who then check inventory and make delivery and production plans. Finally, the logistics company delivers the ingredients to User B's home, and User B notifies them of receipt via the app.

[1756] Prompt Sentence Examples

[1757] "User B's health data is as follows: weight 65 kg, height 160 cm, average daily calorie consumption 1,800 kcal. User B is also under a lot of stress. Please suggest suitable foods for this user."

[1758] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1759] Step 1:

[1760] Users enter their health data, such as weight, height, calories burned, and allergy information, into a dedicated healthcare app. The app also uses an emotion recognition engine to capture their emotional state through the camera. This data is protected using AES encryption and transmitted to the server via HTTPS.

[1761] Input: User's weight, height, calories burned, allergy information, emotional state

[1762] Output: Encrypted health and emotional data

[1763] Step 2:

[1764] The server decrypts the received encrypted data, associates it with the user ID, and stores it in a database (MySQL). This allows for centralized management of each user's health and emotion data.

[1765] Input: Encrypted health and emotional data

[1766] Output: Health and emotion data for each user stored in a database

[1767] Step 3:

[1768] The server analyzes the user's health and emotional data stored in the database. Using a data analysis engine (Pandas), the server evaluates the user's calorie consumption and emotional state and selects appropriate ingredients. For users with high stress levels, the server makes suggestions that take calorie balance into consideration.

[1769] Input: User health and emotional data from a database

[1770] Output: Analyzed user's calorie consumption and emotional state information

[1771] Step 4:

[1772] The server runs a proposed algorithm (K-means clustering) to generate a list of ingredients suitable for each user. For example, it might suggest "150g chicken breast," "100g quinoa," and "50g blueberries." The generated list of ingredients is converted into JSON format and sent to the user's device.

[1773] Input: Analyzed user calorie consumption and emotional state information

[1774] Output: JSON list of ingredients

[1775] Step 5:

[1776] The user checks the suggested ingredients list on the app and selects the ingredients they want, for example, "150g of chicken breast" and "50g of blueberries." The selection data is encrypted and sent to the server.

[1777] Input: User's ingredient selection

[1778] Output: Encrypted selection data

[1779] Step 6:

[1780] The server saves the received selection data in a database and assigns an order ID, which is used to manage the order data.

[1781] Input: Encrypted selection data

[1782] Output: Order data and order ID stored in the database

[1783] Step 7:

[1784] The server converts the order data into a format that can be used by retailers, logistics companies, and producers (CSV or REST API endpoint) and distributes it to each of them. Retailers check inventory, logistics companies plan delivery routes, and producers set production plans.

[1785] Input: Order data and order ID stored in the database

[1786] Output: Order data distributed to retailers, logistics companies, and producers

[1787] Step 8:

[1788] The logistics company will pick up the ingredients selected by the user from the retailer's store or warehouse and deliver them efficiently to the user's home based on the planned delivery route. The user will then receive the ingredients and be notified of the receipt via a dedicated app.

[1789] Input: Retailer inventory check information, delivery route planning information, food pick-up information

[1790] Output: Delivery completion information and receipt completion notification to the user

[1791] (Application example 2)

[1792] 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."

[1793] Conventional food delivery systems do not take into account the user's health or emotional state when recommending ingredients, resulting in little effect on personal health management or stress reduction. Furthermore, the difficulty of tracking delivery status in real time can lead to a decline in user satisfaction. Furthermore, data sharing between retailers, logistics companies, and producers is insufficient, leaving issues with inventory management and delivery efficiency.

[1794] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting health data and emotional data, means for suggesting ingredients suitable for the user based on the health data and emotional data, means for the user to select the suggested ingredients, means for generating order data for the selected ingredients, means for distributing the generated order data to retailers, logistics companies, and producers, means for delivering ingredients to the user based on the distributed order data, and means for tracking delivery status in real time. This enables the suggestion and rapid delivery of ingredients suitable for the user's health and emotional state, thereby realizing personal health management and improving efficiency across the industry.

[1795] Definitions of important words

[1796] "Health data" refers to information about the user's physical condition, including the user's weight, height, calories burned, allergy information, and the like.

[1797] "Emotion data" is information relating to the user's stress level and emotional state.

[1798] "Collection means" refers to devices such as interfaces and sensors for acquiring the user's health data and emotional data.

[1799] The "suggestion means" is a device or system that executes an algorithm to create a list of ingredients suitable for the user based on the collected health data and emotion data.

[1800] The "selection means" refers to an interface or function that allows the user to select desired ingredients from the suggested ingredient list.

[1801] The "order data generation means" is a system for creating order data based on ingredients selected by the user.

[1802] The "distribution means" is a device or system for transmitting the generated order data to retailers, logistics companies, and manufacturers via communication.

[1803] A "delivery means" is a device or system that a retailer or a distributor uses to deliver the selected ingredients to a user.

[1804] The "tracking means" is a device or system for monitoring and notifying the user of the delivery status of ingredients delivered to the user in real time based on order data.

[1805] MODE FOR CARRYING OUT THE INVENTION

[1806] This invention relates to a system that supports users' health management by suggesting, selecting, and delivering ingredients based on the user's health and emotional data. This system includes three main elements: a server, a terminal, and a user, each of which works together to realize specific functions.

[1807] server

[1808] The server performs several functions:

[1809] 1. Data collection and storage: The server collects health data (weight, height, calories burned, allergy information, etc.) and emotional data (stress level, etc.) sent by the user and stores them in a database. This data is encrypted before transmission to ensure data security.

[1810] 2. Data Analysis: The server analyzes the collected data and runs algorithms to assess the user's health and emotional state, which then lists and suggests suitable ingredients for the user.

[1811] 3. Order data generation and distribution: The server generates order data based on the ingredient list selected by the user and distributes it to retailers, logistics companies, and producers.

[1812] 4. Real-time tracking: The server monitors the delivery status in real time and has the function to notify the user. This function allows the user to know the delivery status in real time.

[1813] Terminal

[1814] The terminal functions as a device such as a smartphone.

[1815] 1. Data input: Users input their health and emotional data through a dedicated application. If necessary, the emotion recognition engine automatically collects the user's emotional data.

[1816] 2. Data transmission: The entered data is encrypted and sent to the server using a secure communication protocol.

[1817] 3. Ingredient selection: The user checks the list of suggested ingredients sent from the server and selects the desired ingredients on the application.

[1818] 4. Notifications: The terminal receives notifications from the server and provides the user with real-time updates, especially regarding delivery status.

[1819] User

[1820] The user uses the system to perform the following operations:

[1821] 1. Data entry: Enter daily data such as weight, height, calories burned, allergy information, and emotional state into a dedicated app.

[1822] 2. Ingredient selection: Select the ingredients you want from the suggested options and confirm your order through the application.

[1823] 3. Receipt confirmation: Check the delivered ingredients and notify the customer of receipt via the application.

[1824] Specific examples

[1825] For example, if a user with user ID "user12345" consumes an average of 1,800 kcal per day and is determined to have a high stress level, the server will suggest 150g of chicken breast and 50g of blueberries. The user selects this and confirms the order. The server then distributes the order data to retailers, logistics companies, and producers, and tracks the delivery status in real time. The user can check the delivery status in real time via a smartphone application.

[1826] Prompt Sentence Examples

[1827] Get the user data for user ID "user12345" and suggest recommended foods based on their health and emotional data. Health data includes weight, height, calories burned, and allergy information, and emotional data includes stress level. If this user's stress level is high, suggest low-calorie, nutritionally balanced foods.

[1828] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1829] Program processing steps

[1830] Step 1:

[1831] Entering health and emotional data

[1832] Subject: User

[1833] Specific operation: The user uses a dedicated smartphone application to input their health data (weight, height, calories burned, allergy information, etc.) and emotional data (stress level, etc. via an emotion recognition engine).

[1834] Input: User-entered health and emotional data

[1835] Output: The raw data entered into the app

[1836] Step 2:

[1837] Encryption and transmission of health and emotional data

[1838] Subject: Terminal

[1839] Specific operation: The device encrypts the input health data and emotion data using an encryption algorithm such as AES and sends it to the server using a secure communication protocol (e.g., HTTPS).

[1840] Input: Raw data entered into the app

[1841] Output: Encrypted health and emotion data

[1842] Step 3:

[1843] Receiving and storing data

[1844] Subject: Server

[1845] Specific operation: The server receives the encrypted data sent from the terminal, decrypts it, and then stores it in a secure database.

[1846] Input: Encrypted health and emotion data

[1847] Output: Decoded health and emotion data stored in a database

[1848] Step 4:

[1849] Data analysis and ingredient suggestions

[1850] Subject: Server

[1851] Specific operation: The server analyzes the health and emotional data stored in the database and uses an AI algorithm to generate a list of ingredients suitable for the user.

[1852] Input: Health and emotion data stored in a database

[1853] Output: A list of ingredients suitable for the user

[1854] Step 5:

[1855] Send and display ingredient lists

[1856] Subject: Server and Terminal

[1857] Specific operation: The server sends the generated ingredient list in JSON format to the terminal, and the terminal displays the suggested ingredient list to the user.

[1858] Input: A list of ingredients suitable for the user

[1859] Output: Ingredient list displayed on the device

[1860] Step 6:

[1861] Ingredient selection

[1862] Subject: User

[1863] Specific operation: The user selects the desired ingredients from the list of ingredients displayed on the terminal.

[1864] Input: Ingredient list displayed on the device

[1865] Output: Ingredients selected by the user

[1866] Step 7:

[1867] Generate order data

[1868] Subject: Terminal and Server

[1869] Specific operation: The terminal sends data on ingredients selected by the user to the server, and the server generates order data based on that data.

[1870] Input: Ingredients selected by the user

[1871] Output: Generated order data

[1872] Step 8:

[1873] Order data distribution

[1874] Subject: Server

[1875] Specific operation: The server distributes the generated order data to retailers, logistics companies, and manufacturers.

[1876] Input: Generated order data

[1877] Output: Order data sent to each supplier

[1878] Step 9:

[1879] Food delivery

[1880] Subject: Logistics company

[1881] Specific operation: Based on the received order data, the logistics company picks up the specified ingredients, plans an efficient delivery route, and delivers them to the address specified by the user.

[1882] Input: Order data including shipping instructions

[1883] Output: Food delivered to the user's home

[1884] Step 10:

[1885] Notification of receipt completion

[1886] Subject: User and Device

[1887] Specific operation: The user checks the received ingredients and notifies the application that receipt is complete.

[1888] Input: Delivered ingredients

[1889] Output: Notification of receipt completion

[1890] 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.

[1891] 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.

[1892] 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.

[1893] 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.

[1894] 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.

[1895] 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.

[1896] 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).

[1897] 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.

[1898] 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."

[1899] 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.

[1900] 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).

[1901] 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.

[1902] 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.

[1903] 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.

[1904] 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.

[1905] 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.

[1906] 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.

[1907] 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.

[1908] 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.

[1909] 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.

[1910] 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.

[1911] The following is further disclosed regarding the above embodiment.

[1912] (Claim 1)

[1913] a means of collecting health data;

[1914] A means for suggesting suitable ingredients to a user based on the health data;

[1915] means for a user to select the suggested ingredients;

[1916] means for generating order data for the selected ingredients;

[1917] means for distributing the generated order data to retailers, logistics companies, and manufacturers;

[1918] a means for delivering ingredients to a user based on the distributed order data;

[1919] A system including:

[1920] (Claim 2)

[1921] 10. The system of claim 1, further comprising means for encrypting and transmitting the health data.

[1922] (Claim 3)

[1923] The system of claim 1 further comprising means for a retailer to manage inventory based on the order data.

[1924] (Claim 4)

[1925] The system according to claim 1, further comprising means for a logistics company to optimize a delivery route based on the order data.

[1926] (Claim 5)

[1927] The system according to claim 1, further comprising means for a producer to make a production plan based on the order data.

[1928] "Example 1"

[1929] (Claim 1)

[1930] a means of collecting health data;

[1931] means for encrypting and transmitting said health data via a secure communication protocol;

[1932] means for analyzing the calorie consumption and health condition of the user based on the health data;

[1933] A means for suggesting ingredients suitable for the user based on the analysis results;

[1934] means for transmitting the suggested ingredient list to a user;

[1935] means for a user to select the suggested ingredients;

[1936] means for generating order data for the selected ingredients;

[1937] means for distributing the generated order data to retailers, logistics companies, and manufacturers;

[1938] a means for delivering ingredients to a user based on the distributed order data;

[1939] A system including:

[1940] (Claim 2)

[1941] The system of claim 1 , further comprising: means for performing the ingredient suggestions using a machine learning algorithm.

[1942] (Claim 3)

[1943] The system of claim 1 further comprising means for a retailer to manage inventory based on the order data.

[1944] "Application Example 1"

[1945] (Claim 1)

[1946] a means of collecting health data;

[1947] A means for suggesting suitable ingredients to a user based on the health data;

[1948] means for a user to select the suggested ingredients;

[1949] means for generating order data for the selected ingredients;

[1950] means for distributing the generated order data to retailers, logistics companies, and manufacturers;

[1951] a means for delivering ingredients to a user based on the distributed order data;

[1952] a server that executes an algorithm that generates ingredient recommendations based on the health data;

[1953] an application means for inputting health data from a smartphone;

[1954] A system including:

[1955] (Claim 2)

[1956] 10. The system of claim 1, further comprising means for encrypting and transmitting the health data.

[1957] (Claim 3)

[1958] The system of claim 1 further comprising means for a retailer to manage inventory based on the order data.

[1959] "Example 2: Combining Emotion Engines"

[1960] (Claim 1)

[1961] means for collecting health data and emotional data;

[1962] a means for suggesting ingredients suitable for a user based on the health data and emotion data;

[1963] means for a user to select the suggested ingredients;

[1964] means for generating order data for the selected ingredients;

[1965] means for distributing the generated order data to retailers, logistics companies, and manufacturers;

[1966] a means for delivering ingredients to a user based on the distributed order data;

[1967] The system includes a means for the user to notify that delivery of the ingredients has been completed.

[1968] (Claim 2)

[1969] The system of claim 1, further comprising means for encrypting and transmitting the health data and emotion data.

[1970] (Claim 3)

[1971] 2. The system according to claim 1, further comprising means for allowing a retailer to manage inventory, a logistics company to plan delivery routes, and a manufacturer to make production plans based on the order data.

[1972] "Application example 2 when combining emotion engines"

[1973] (Claim 1)

[1974] a means for collecting health and emotional data;

[1975] means for suggesting ingredients suitable for a user based on the health data and emotion data;

[1976] means for a user to select the suggested ingredients;

[1977] means for generating order data for the selected ingredients;

[1978] means for distributing the generated order data to retailers, logistics companies, and manufacturers;

[1979] a means for delivering ingredients to a user based on the distributed order data;

[1980] Real-time tracking of delivery status,

[1981] A system including:

[1982] (Claim 2)

[1983] 10. The system of claim 1, further comprising means for encrypting and transmitting the health data and emotion data.

[1984] (Claim 3)

[1985] The system of claim 1 further comprising means for a retailer to manage inventory based on the order data. [Explanation of symbols]

[1986] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means of collecting health data; A means for suggesting suitable ingredients to a user based on the health data; means for a user to select the suggested ingredients; means for generating order data for the selected ingredients; means for distributing the generated order data to retailers, logistics companies, and manufacturers; a means for delivering ingredients to a user based on the distributed order data; A system including:

2. The system of claim 1 further comprising means for encrypting and transmitting the health data.

3. 2. The system of claim 1, further comprising means for a retailer to manage inventory based on said order data.

4. The system according to claim 1 , further comprising means for a logistics company to optimize a delivery route based on the order data.

5. 2. The system according to claim 1, further comprising means for a producer to make a production plan based on said order data.

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