system

Abstract

We provide the system. [Solution] A means for collecting the user's basic information and meal records, Based on the collected data, a means of analyzing nutrient intake and calculating the necessary nutrients and calories, A means of generating a well-balanced menu based on the analysis results and proposing it to the user, A means of sharing the generated menu and recipe information with nearby cooking facilities, A means of cooking meals based on shared recipes and delivering them to the user's home, A means of collecting user feedback and incorporating it into service improvements, A system that includes this.

Description

Technical Field

[0004] , , ,

[0005] , , , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern society, there is a problem that it is difficult for the elderly to obtain a nutritionally balanced diet. In particular, for the elderly living in mountainous areas or areas with inconvenient access to purchase necessary foods, it is very difficult to appropriately intake the nutrients required in a day. Also, many elderly people do not have the time or knowledge to review their own diet. Due to such a situation, there is a risk of an increase in nutritional deficiencies and health problems among the elderly. Therefore, in order to solve these problems, it is necessary to develop a system that enables the elderly to easily enjoy a nutritionally balanced diet.

Means for Solving the Problems

[0006] A "user" is someone who provides information and benefits from the service in order to receive nutritionally balanced meals, such as an elderly person or their caregiver who uses the system.

[0007] "Basic information" refers to data related to an individual's health status and nutritional management, such as the user's age, gender, height, weight, allergy information, and food preferences.

[0008] A "meal diary" is information that records the contents of the food a user consumes on a daily basis.

[0009] "Means of collection" refers to methods such as mobile applications and web forms used to obtain basic information and meal records from users and store them in a database.

[0010] "Generative artificial intelligence" refers to algorithms and software that analyze collected data, calculate appropriate nutritional balance, and generate meal plans.

[0011] "Nutrient intake status" refers to information that shows the amount and types of various nutrients that the user has consumed through their diet.

[0012] "Means of analysis" refers to the process and technology of evaluating the user's health status based on collected data and calculating the necessary nutrients and calories.

[0013] "Methods for generating menus" refers to methods or systems that devise combinations of dishes to satisfy the user's nutritional needs based on analysis results.

[0014] "Means of suggestion" refers to methods and systems for notifying users of generated menus and providing them with their contents.

[0015] A "cooking facility" refers to a kitchen or restaurant located near the user's residence, where meals are prepared based on the suggested menu.

[0016] "Means of sharing" refers to methods and systems for transmitting generated menu and recipe information to cooking facilities so that staff can review and use it.

[0017] "Means of cooking" refers to the methods and utensils used to prepare a dish based on shared recipe information.

[0018] "Delivery method" refers to the method or means of transporting cooked food to the user's home.

[0019] "Feedback" refers to the act of users providing evaluations and comments about the services or dishes they have received.

[0020] "Service improvement" refers to the process and methods of improving systems and services based on collected user feedback to increase user satisfaction. [Brief explanation of the drawing]

[0021] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1 [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1 [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Embodiments for Carrying Out the Invention

[0022] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0023] First, let's explain the terminology used in the following explanation.

[0024] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), and APU (Accelerated Processing Unit).

[0025] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0026] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0027] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0028] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0029] [First Embodiment]

[0030] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0031] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0032] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0033] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0034] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0035] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0036] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0037] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0038] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0039] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0040] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0041] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0042] 1. Data Acquisition Module

[0043] User:

[0044] Users enter personal information (age, gender, height, weight, allergy information, dietary preferences, etc.) using a mobile application or web form. They also record their daily meals, and this allows for the collection of dietary records.

[0045] 2. Dietary Habits Analysis Module

[0046] server:

[0047] The server stores the data collected from users in a database. Next, generative artificial intelligence within the server is used to analyze this data and gain a detailed understanding of the user's nutrient intake. For example, analysis of data from a woman in her 70s, Ms. B, reveals that her daily calorie requirement is 1600 kcal and that she is deficient in B vitamins.

[0048] 3. Menu Creation Module

[0049] server:

[0050] Based on the analysis results, the server generates a well-balanced menu. In this process, a generative artificial intelligence suggests the optimal combination of ingredients and dishes. For example, for user B, it might create a menu including stir-fried chicken liver (rich in B vitamins), blanched spinach, and fruit yogurt.

[0051] Specific example:

[0052] Based on User B's daily calorie needs and nutrient deficiencies, we propose the following meal plan: "Breakfast: Fruit yogurt, Lunch: Stir-fried chicken liver and blanched spinach, Dinner: Grilled fish and steamed vegetables."

[0053] 4. Locally shared modules

[0054] server:

[0055] The generated menu and its detailed recipe information are shared with the cooking facility closest to the user's area. For example, the server sends the recipe information to "Nearby Cooking Facility X" located in the area where user B lives.

[0056] 5. Cooking and Delivery Module

[0057] Terminal (Cooking Staff):

[0058] Based on the shared recipe information, the cooking staff prepares the dishes for the menu. Specifically, they check the recipe sent from the server, prepare the necessary ingredients, and begin cooking.

[0059] Terminal (Delivery Staff):

[0060] The delivery staff will properly package the cooked meals and deliver them to user B's home. For example, stir-fried chicken liver and blanched spinach for lunch will be delivered at lunchtime, and grilled fish and steamed vegetables for dinner will be delivered at dinnertime.

[0061] 6. Feedback Module

[0062] User:

[0063] User B receives the delivered food, finishes eating, and then submits feedback through the application. They fill in their evaluation of the taste, quantity, and quality, and then submit it.

[0064] server:

[0065] The server collects feedback from user B and uses it to create future menus and improve the service. For example, if user B provides feedback that "the stir-fried chicken liver was a little too salty," the seasoning will be adjusted next time.

[0066] As described above, this system enables elderly people to continuously receive nutritionally balanced meals. By collecting basic user information and meal records, analyzing this information using generative artificial intelligence, generating nutritionally balanced menus, cooking at a cooking facility, delivery, and collecting and incorporating feedback, the system supports the user's health.

[0067] The following describes the processing flow.

[0068] Step 1:

[0069] User:

[0070] Users log in to a mobile application or web form and enter personal information (age, gender, height, weight, allergy information, dietary preferences, etc.). They also record their daily meals. For example, user C enters the toast and coffee they ate for breakfast.

[0071] Step 2:

[0072] server:

[0073] The server stores personal information and meal records collected from users in a database. The stored data is appropriately formatted for analysis. For example, user C's personal information and data on bread and coffee are stored in the database.

[0074] Step 3:

[0075] server:

[0076] The generative artificial intelligence on the server retrieves the user's meal records and basic information from the database and performs analysis. This calculates the user's nutrient intake. For example, it identifies that user C's daily calorie requirement is 2000 kcal and that their calcium intake is insufficient.

[0077] Step 4:

[0078] server:

[0079] Based on the analysis results, the server generates a nutritionally balanced menu. Generative artificial intelligence suggests the optimal combination of ingredients and dishes. For example, for user C, it creates a menu that includes "grilled salmon, broccoli salad, and soy milk smoothie," which are rich in calcium.

[0080] Step 5:

[0081] server:

[0082] The generated menu and its detailed recipe information are shared with the cooking facility closest to the user's area. For example, recipe information for grilled salmon, broccoli salad, and soy milk smoothie is sent to "Nearby Cooking Facility Y" in the area where user C lives.

[0083] Step 6:

[0084] Terminal (Cooking Staff):

[0085] Based on shared recipe information, the cooking staff prepares the dishes for the menu. For example, they check the recipe, gather the necessary ingredients, and begin cooking. After cooking is complete, they properly package the dishes.

[0086] Step 7:

[0087] Terminal (Delivery Staff):

[0088] A delivery staff member picks up the prepared food and delivers it to the user's home. For example, grilled salmon, broccoli salad, and a soy milk smoothie are delivered to user C's home.

[0089] Step 8:

[0090] User:

[0091] The user receives the delivered food and actually eats it. For example, user C enjoys grilled salmon and broccoli salad for dinner.

[0092] Step 9:

[0093] User:

[0094] After their meal, users enter their ratings and feedback through the application. For example, user C might submit feedback stating, "The grilled salmon was very tasty, but there was a little too much salad dressing."

[0095] Step 10:

[0096] server:

[0097] The server collects user feedback and uses it to create future menus and improve the service. For example, based on user C's feedback, the amount of salad dressing will be adjusted next time.

[0098] Through these steps, the system enables elderly people to easily receive nutritionally balanced meals.

[0099] (Example 1)

[0100] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0101] To address the current challenges that make it difficult for the elderly to consistently receive nutritionally balanced meals, there is a need for a system that can accurately understand users' nutrient intake, generate appropriate menus based on that information, and efficiently manage the cooking and delivery processes.

[0102] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0103] In this invention, the server includes means for collecting the user's basic information and meal records; means for storing the collected data and linking it to a user ID; means for analyzing the nutrient intake status based on the stored data and calculating the necessary nutrients and calories; means for generating a well-balanced menu using generative artificial intelligence based on the analysis results and proposing it to the user; means for sharing the generated menu and recipe information with cooking facilities near the user; means for cooking meals based on the shared recipes and delivering them to the user's home; and means for collecting feedback from the user and reflecting it in creating future menus and improving the service. This makes it possible for elderly people to continuously receive nutritionally balanced meals and support their health.

[0104] "User basic information" refers to data about an individual user's basic health and lifestyle habits, such as age, gender, height, weight, allergy information, and food preferences.

[0105] A "meal diary" is data that users record about their daily meals, including specific food names, quantities consumed, and meal times.

[0106] "Means of collection" refers to technical methods or devices for obtaining data entered by users, such as through mobile applications or web forms.

[0107] "Means of preservation" refers to technologies for storing and managing collected data in data storage such as databases.

[0108] A "User ID" is a unique identifier assigned to each user and is used to distinguish a user's data from that of other users.

[0109] "Methods for analyzing nutrient intake" refer to technologies that, based on collected data, identify the amount and types of nutrients a user is consuming and analyze any deficiencies or excesses in necessary nutrients or calories.

[0110] "Generative artificial intelligence" is an artificial intelligence technology that uses advanced algorithms such as machine learning and deep learning to analyze data, make predictions, and provide optimal suggestions.

[0111] "Methods for generating menus" refers to technologies that create well-balanced meal plans based on analysis results and propose them to users.

[0112] A "cooking facility" refers to a facility located near the user that performs cooking, and includes restaurants and catering services.

[0113] "Recipe information" refers to information such as specific cooking methods, necessary ingredients, and cooking procedures based on a menu.

[0114] "Means of cooking" refer to the techniques and equipment used to actually prepare a meal based on shared recipe information.

[0115] "Delivery methods" refer to the methods and technologies used to deliver cooked food to the user's home.

[0116] "Means of collecting feedback" refers to the technologies and methods used to obtain ratings and comments from users.

[0117] "Means of reflecting feedback in service improvement" refers to methods used to create future menus and improve service quality based on the collected feedback.

[0118] This invention relates to a system for elderly people to continuously receive nutritionally balanced meals. This system collects basic information and meal records from the user, analyzes nutrient intake based on this information, and generates an appropriate menu. Furthermore, the generated menu and recipe information are shared with cooking facilities, the cooked meals are delivered to the user's home, and feedback is collected to help create future menus and improve the service.

[0119] This system uses the following hardware and software.

[0120] User:

[0121] Users enter personal information (age, gender, height, weight, allergy information, food preferences, etc.) and daily meal details using a mobile application or web form. Specifically, they use devices such as smartphones or PCs. An example of information a user might enter is "Height 165cm, weight 60kg, likes chicken."

[0122] server:

[0123] The server receives data sent by the user and stores it in a database. This database can be a common relational database system (e.g., PostgreSQL). Next, generative artificial intelligence (e.g., GPT-3®) is used to analyze the collected data and identify the user's nutrient intake. For example, analysis of data from a female user in her 70s might reveal that her daily calorie requirement is 1600 kcal and that she is deficient in B vitamins.

[0124] Based on the analysis results, the server uses generative artificial intelligence to generate a well-balanced menu and propose it to the user. For example, it might suggest: "Breakfast: Fruit yogurt, Lunch: Stir-fried chicken liver and blanched spinach, Dinner: Grilled fish and steamed vegetables." An example of a prompt message would be: "Based on data collected from a female user in her 70s, please propose a well-balanced menu that matches the dietary analysis results showing a deficiency in B vitamins."

[0125] The generated menu and its detailed recipe information are shared with the cooking facility closest to the user's area. The server uses the SMTP protocol to send emails to the cooking facility, providing the recipe information. Alternatively, data can be shared via a dedicated API.

[0126] Terminal (Cooking Staff):

[0127] Cooking staff check recipe information sent from the server on their terminals (PCs or tablets) and prepare the necessary ingredients. Specifically, they check the inventory of necessary ingredients in real time and place uncooked ingredients on the cooking counter. Cooking staff cook the ingredients using cooking utensils such as frying pans and ovens while checking the recipe instructions. For example, they might sauté chicken liver for the specified time and boil and cool spinach.

[0128] Terminal (Delivery Staff):

[0129] Delivery staff carefully package the cooked meals and deliver them to the user's home. Using GPS devices, delivery staff ensure the meals arrive precisely at the designated time. Specifically, they carefully pack the food into packaging materials and head towards the user's address while monitoring traffic conditions in real time.

[0130] User:

[0131] After receiving their delivered food and finishing their meal, users submit feedback through the application. They rate the taste, quantity, and quality, and then submit the feedback. For example, one piece of feedback might be, "The stir-fried chicken liver was a little too salty."

[0132] server:

[0133] The server collects user feedback and uses it to create future menus and improve the service. For example, based on feedback such as "the stir-fried chicken liver was a little too salty," the seasoning will be adjusted next time.

[0134] This will provide a system that supports users' health and enables elderly people to continuously receive nutritionally balanced meals.

[0135] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0136] Step 1: Data Collection

[0137] User:

[0138] Users open a mobile application or web form and enter their personal information and daily meal details. Specifically, they use a smartphone or PC to fill in the required fields in the form.

[0139] Input: Personal information entered by the user (age, gender, height, weight, allergy information, food preferences, etc.) and meal details.

[0140] Output: Dataset of user's basic information and meal records

[0141] Step 2: Save Data

[0142] server:

[0143] The server receives data sent by the user and stores it in the database. The database uses a relational database system (e.g., PostgreSQL). The data is associated with the user ID upon storage.

[0144] Input: User's basic information and meal record dataset

[0145] Output: User's basic information and meal records stored in the database

[0146] Step 3: Data Analysis

[0147] server:

[0148] The generative artificial intelligence model (e.g., GPT-3) on the server analyzes the stored data to identify the user's nutrient intake. For example, it calculates the daily calorie requirement and any deficient nutrients.

[0149] Input: User's basic information and meal records stored in the database.

[0150] Output: User's nutrient intake status and specific analysis results

[0151] Step 4: Menu Generation

[0152] server:

[0153] Based on the analysis results, the server uses generative artificial intelligence to generate a well-balanced menu. For example, it might suggest "Breakfast: Fruit yogurt, Lunch: Stir-fried chicken liver and blanched spinach, Dinner: Grilled fish and steamed vegetables."

[0154] Input: User's nutrient intake status and analysis results

[0155] Output: A well-balanced menu and detailed recipe information

[0156] Step 5: Local Sharing

[0157] server:

[0158] The generated menu and recipe information is shared with the cooking facility closest to the user's area. Data is transmitted via SMTP protocol or a dedicated API.

[0159] Input: A well-balanced menu and detailed recipe information

[0160] Output: Recipe information sent to the cooking facility

[0161] Step 6: Preparation for cooking

[0162] Terminal (Cooking Staff):

[0163] Cooking staff check recipe information sent from the server on their devices (PCs or tablets) and prepare the necessary ingredients. They also check the inventory of necessary ingredients in real time and place them on the cooking counter.

[0164] Input: Recipe information sent to the cooking facility

[0165] Output: Prepared ingredients

[0166] Step 7: Cooking

[0167] Terminal (Cooking Staff):

[0168] The cooking staff begins cooking using the prepared ingredients. They use cooking utensils such as frying pans and ovens and cook according to the recipe.

[0169] Input: Prepared ingredients

[0170] Output: Finished dish

[0171] Step 8: Delivery

[0172] Terminal (Delivery Staff):

[0173] Delivery staff properly package the cooked meals and deliver them to the user's home. They use GPS to monitor traffic conditions and deliver at the specified time.

[0174] Input: Finished dish

[0175] Output: Food delivered to the user's home

[0176] Step 9: Gathering Feedback

[0177] User:

[0178] After receiving and eating their delivered food, users submit feedback through the application. They rate the taste, quantity, and quality of the food and then submit it.

[0179] Input: Post-use evaluation of delivered food

[0180] Output: Feedback data

[0181] Step 10: Feedback Analysis

[0182] server:

[0183] The server collects user feedback and uses it to create future menus and improve the service. Based on the feedback, it inputs the next prompt into the generative artificial intelligence and makes adjustments.

[0184] Input: Feedback data

[0185] Output: Improved menu planning and service quality

[0186] In this way, by performing data processing and calculations using specific inputs and outputs, as well as generating AI models and prompts at each step, a system is realized that supports the health of the elderly while maintaining nutritional balance.

[0187] (Application Example 1)

[0188] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal." For elderly people and users who require nutritional management, ensuring a balanced diet on a daily basis can be difficult. Furthermore, continuously providing menus that consider nutritional balance is time-consuming, making continuous health management difficult. Therefore, based on the user's personal information and meal records, support will be provided to help them lead a safe and healthy diet.

[0189] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means: The server includes means for collecting the user's basic information and meal records; means for analyzing the intake status of nutrients based on the collected data and calculating the necessary nutrients and calories; means for generating a well-balanced menu based on the analysis results and proposing it to the user; means for sharing the generated menu and recipe information with cooking facilities near the user; means for cooking based on the shared recipes and delivering them to the user's home; means for collecting feedback from the user and reflecting it in improving the service; and means for managing the user's data through a smartphone application, generating menus using generative artificial intelligence, and sharing the results with cooking facilities.

[0190] A "user" refers to anyone who uses the system to input personal information or meal records.

[0191] "Basic information" refers to personal data such as the user's age, gender, height, weight, allergy information, and food preferences.

[0192] A "food diary" refers to the contents of the food a user consumes on a daily basis.

[0193] "Data" refers to basic information and meal records collected from users.

[0194] "Nutrients" refer to components necessary for nutritional balance, such as proteins, vitamins, and minerals.

[0195] "Analysis" refers to the process of evaluating nutrient intake based on collected data.

[0196] "Calories" refer to the amount of energy contained in food.

[0197] A "menu" refers to a meal plan that is designed with a balance of nutrients in mind.

[0198] "Generative artificial intelligence" refers to algorithms and models that generate nutritionally balanced meal plans based on user data.

[0199] A "cooking facility" refers to a place where food is prepared based on the generated menu and recipe information.

[0200] "Feedback" refers to the information that users send back to the system, evaluating the dishes they are served.

[0201] "Service improvement" refers to improving menu creation, cooking, and delivery services for future orders based on the collected feedback information.

[0202] A "smartphone application" refers to a mobile app that allows users to input basic information and meal records, and receive analysis results and meal plan suggestions.

[0203] Modes for carrying out the invention

[0204] System Overview

[0205] This invention is a food delivery system that provides users with nutritionally balanced meals. Users input basic information and meal records through a smartphone application, and based on that data, a meal plan is generated, prepared at the nearest cooking facility to the user, and delivered by delivery staff. User feedback is used to improve the service.

[0206] Specific Embodiments

[0207] 1. Collection of user data

[0208] Users input basic information such as age, gender, height, weight, allergy information, and dietary preferences through a smartphone application. Their daily meals are also recorded. This data is sent to a server and stored in a database.

[0209] 2. Analysis of Nutrients

[0210] The server analyzes the collected basic information and meal records and uses a generative AI model to understand the user's nutrient intake. This analysis can identify which nutrients the user needs to consume and in what quantities.

[0211] 3. Menu generation

[0212] The generation AI model generates optimal menus based on the analysis results. The generated menus include food items with a well-balanced combination of nutrients. For example, for a user who is deficient in a specific vitamin, dishes using ingredients rich in that vitamin will be suggested.

[0213] 4. Data Sharing

[0214] The generated menu and its detailed recipe information are shared with the nearest cooking facility in the user's area. The cooking facility then prepares the dishes based on the received recipe information.

[0215] 5. Food preparation and delivery

[0216] The kitchen staff prepares the meals according to the generated recipes and packages them appropriately. Then, delivery staff deliver the meals to the user's address. This ensures that users receive fresh, nutritionally balanced meals tailored to their time of day.

[0217] 6. Gathering feedback and improving services

[0218] After receiving and eating their delivered food, users provide feedback through a smartphone application. They input ratings regarding taste, quantity, and quality. This feedback is sent to the server and used to create future menus and improve the service.

[0219] Hardware and software to be used

[0220] Hardware: Users use smartphones, while kitchen staff and delivery personnel use tablets or PCs. Servers are used for data storage and analysis.

[0221] Software: A smartphone application is used for inputting basic user information and meal records. SQLite is used for the database, and Flask is used for the web application's backend. The OpenAI® API is used for the generative AI model.

[0222] Examples of prompt statements

[0223] The following is an example of a prompt to input into a generative AI model:

[0224] User information:

[0225] Age: 70, Gender: Female, Height: 160cm, Weight: 55kg.

[0226] Allergens: Shrimp, nuts.

[0227] Meal plan: Breakfast: Toast and coffee. Lunch: Chicken salad. Dinner: Braised fish and stir-fried vegetables.

[0228] Please provide a nutritionally balanced menu.

[0229] Example output from a generative AI model for this:

[0230] Breakfast: Fruit yogurt

[0231] Lunch: Stir-fried chicken liver, blanched spinach

[0232] Dinner: Grilled fish and steamed vegetables

[0233] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0234] Step 1: Collect basic user information and meal records.

[0235] Users enter basic information (age, gender, height, weight, allergy information, and dietary preferences) using a smartphone application. They also enter their daily meal details. The entered information is sent to a server and stored in an SQLite database. Specifically, the text-based input data is converted into a database format.

[0236] Input: User's basic information and meal records

[0237] Output: User information and meal records stored in the database

[0238] Step 2: Nutrient Analysis

[0239] The server analyzes nutrient intake based on user information and meal records stored in the database. This analysis uses OpenAI's generative AI model. Specifically, user information and meal records are input to the generative AI model as prompts, and data processing is performed to identify deficient nutrients based on the analysis results obtained.

[0240] Input: User information and meal records in the database

[0241] Output: Analysis results (such as deficient nutrients and required calories)

[0242] Step 3: Menu Generation

[0243] The server uses the OpenAI API to generate a nutritionally balanced menu based on the nutrient analysis results. It inputs prompts into the AI ​​model to obtain the optimal menu for the user. The generated menu includes specific food items and their recipes.

[0244] Input: Nutrient analysis results and prompt text

[0245] Output: A well-balanced menu and its recipe information

[0246] Step 4: Sharing menu information

[0247] The server sends the generated menu and recipe information to the nearest cooking facility in the user's area. The cooking facility then begins preparing the food based on the received recipe information. This step involves transmitting data using communication means.

[0248] Input: Menu and recipe information

[0249] Output: Sending recipe information to cooking facilities

[0250] Step 5: Cooking and delivering the food

[0251] The kitchen staff prepares the dishes based on the received recipe information. The finished dishes are properly packaged and delivered to the user's address by delivery staff. This step includes the actions required for cooking and delivery.

[0252] Input: Recipe information received by the cooking facility

[0253] Output: Cooked food delivered to the user's address.

[0254] Step 6: Gathering feedback and improving the service

[0255] After users have eaten the food they received, they provide feedback through a smartphone application. This feedback is sent to the server and used to generate future menus and improve the service. The server analyzes this feedback and incorporates it into the next menu generation.

[0256] Input: User feedback

[0257] Output: Improved service and menu generation process

[0258] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0259] 1. Data Acquisition Module

[0260] User:

[0261] Users log in to a mobile application or web form and enter personal information (age, gender, height, weight, allergy information, dietary preferences, etc.) and details of their daily meals. For example, user D might record scrambled eggs and toast for breakfast as part of their meal details.

[0262] 2. Dietary Habits Analysis Module

[0263] server:

[0264] The server stores collected personal information and meal records in a database. Based on the stored data, generative artificial intelligence is used to analyze the user's nutrient intake and calculate the necessary nutrients and calories. For example, analyzing user D's data reveals that their daily calorie requirement is 1800 kcal and that they are deficient in vitamin D.

[0265] 3. Emotion Recognition Module

[0266] User:

[0267] The user uses the application to perform voice input and facial recognition. For example, user D launches the application and inputs "Today's meal was very delicious" by voice.

[0268] server:

[0269] The server analyzes voice input and facial recognition data to extract user emotion data. For example, it extracts the emotion "satisfied" from user D's voice input.

[0270] 4. Menu Creation Module

[0271] server:

[0272] The server generates a well-balanced menu based on analysis results and emotional data. Generative artificial intelligence suggests the optimal combination of ingredients and dishes. For example, it suggests a menu to user D that includes dishes rich in vitamin D, such as "grilled salmon, spinach salad, and soy milk smoothie."

[0273] 5. Locally shared modules

[0274] server:

[0275] The generated menu and detailed recipe information are shared with the cooking facility closest to the user's area. For example, recipe information for grilled salmon, spinach salad, and soy milk smoothie is sent to "Nearby Cooking Facility Z" in the area where user D lives.

[0276] 6. Cooking and Delivery Module

[0277] Terminal (Cooking Staff):

[0278] Based on shared recipe information, the cooking staff prepares the dishes for the menu. For example, they check the recipe, gather the necessary ingredients, and begin cooking. After cooking is complete, they properly package the dishes.

[0279] Terminal (Delivery Staff):

[0280] The delivered cooked meal is received by the delivery staff and delivered to the user's home. For example, deliver grilled salmon, spinach salad, and soy milk smoothie to User D's home.

[0281] 7. Feedback Module

[0282] User:

[0283] After User D receives the delivered cooked meal and finishes eating, they input evaluations and feedback through the application. For example, input an evaluation such as "The grilled salmon was very delicious, but there was too much dressing on the salad."

[0284] Server:

[0285] The server collects feedback from the user and uses it, along with sentiment data, for the next menu creation and service improvement. For example, based on User D's feedback, adjust the amount of dressing on the salad for the next time, and further utilize the sentiment data to reflect it in creating a more satisfying menu.

[0286] Through the above steps, this system enables the elderly to easily receive nutritionally balanced meals and provide a more satisfying service using sentiment data. It collects the user's basic information and meal records, generates an optimal menu based on the analysis results and sentiment data, shares it with the cooking facility, and is a system that is continuously improved through the process of cooking, delivery, and feedback collection.

[0287] The following explains the processing flow.

[0288] Step 1:

[0289] User:

[0290] Users log in to a mobile application or web form and enter personal information (age, gender, height, weight, allergy information, food preferences, etc.). They also record their daily meals. For example, user D records the scrambled eggs and toast they ate for breakfast.

[0291] Step 2:

[0292] server:

[0293] The server stores personal information and meal records collected from users in a database and formats them appropriately for analysis. User D's personal information and meal record (scrambled eggs and toast) are stored in the database.

[0294] Step 3:

[0295] server:

[0296] Using generative artificial intelligence on the server, the system retrieves users' meal records and basic information from a database and analyzes their nutrient intake. This allows it to identify their daily calorie needs and any nutrient deficiencies. For example, it might determine that user D needs 1800 kcal and is deficient in vitamin D.

[0297] Step 4:

[0298] User:

[0299] The user performs voice input and facial recognition through a mobile application. User D voice-inputs, "Today's meal was very delicious," and the app recognizes the user's facial expression using the camera.

[0300] Step 5:

[0301] server:

[0302] The server analyzes voice input and facial recognition data to extract the user's emotion data. For example, it recognizes the emotion of "satisfaction" from the voice of user D and "smiling face" from the facial expression.

[0303] Step 6:

[0304] Server:

[0305] Based on the analysis results and emotion data, the server generates a balanced menu. The generative artificial intelligence proposes the optimal combination of ingredients and dishes. For example, it proposes a menu of "grilled salmon, spinach salad, soy milk smoothie" rich in vitamin D to user D.

[0306] Step 7:

[0307] Server:

[0308] Share the generated menu and detailed recipe information with the cooking facility closest to the user's living area. For example, send the recipe information of grilled salmon, spinach salad, and soy milk smoothie to "Neighborhood Cooking Facility Z" in the area where user D lives.

[0309] Step 8:

[0310] Terminal (cooking staff):

[0311] Based on the shared recipe information, the cooking staff prepares the dishes on the menu. They check the recipe, gather the necessary ingredients, and start cooking. After cooking, they properly package the dishes.

[0312] Step 9:

[0313] Terminal (delivery staff):

[0314] The delivery staff picks up the cooked dishes and delivers them to the user's home. For example, deliver grilled salmon, spinach salad, and soy milk smoothie to user D's home.

[0315] Step 10:

[0316] User:

[0317] User D receives the delivered food and enjoys the meal. For example, for dinner, they might have grilled salmon and a spinach salad.

[0318] Step 11:

[0319] User:

[0320] After the meal, users enter ratings and feedback through the application. For example, they might enter a rating such as, "The grilled salmon was very tasty, but there was too much salad dressing."

[0321] Step 12:

[0322] server:

[0323] The server collects user feedback to help create future menus and improve the service. Based on user D's feedback, the amount of salad dressing will be adjusted next time, and emotional data will be used to generate a more satisfying menu.

[0324] Through the steps described above, this system allows elderly individuals to easily receive nutritionally balanced meals and provides a more satisfying service by utilizing emotional data. The system is continuously improved through a process that collects basic user information and meal records, generates optimal menus based on analysis results and emotional data, shares them with cooking facilities, handles cooking and delivery, and collects feedback.

[0325] (Example 2)

[0326] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0327] In modern times, providing easily accessible, nutritionally balanced meals for the elderly and those with difficulty managing their diets is a crucial challenge. However, systems that analyze individual nutrients and calories, and provide menus that take into account individual meal content and emotional states, are extremely limited. Therefore, to address these challenges, there is a need to develop a system that collects individual dietary and emotional data, proposes optimal menus based on this data, and provides consistent support from cooking and delivery to feedback.

[0328] The identification processing performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for collecting the user's basic information and meal records; means for analyzing the intake status of nutrients based on the collected data and calculating the necessary nutrients and calories; means for collecting and analyzing the user's emotional data; means for generating a well-balanced menu based on the analysis results and emotional data and proposing it to the user; means for sharing the generated menu and recipe information with cooking facilities near the user; means for cooking the food based on the shared recipe and delivering it to the user's home; and means for collecting feedback from the user and reflecting it in improving the service. This makes it possible to provide menus based on individual nutritional management and emotional state, enabling the realization of a more satisfying service.

[0329] "User basic information" refers to personal data including the user's age, gender, height, weight, allergy information, and food preferences.

[0330] "Meal records" refer to information that users input and record about their daily meals through applications or web forms.

[0331] "Means of collection" refers to systems and applications that receive data input from users and store it in a database.

[0332] "Methods for analyzing nutrient intake and calculating necessary nutrients and calories" refers to algorithms or programs that analyze a user's nutritional intake based on collected meal records and calculate the necessary nutrients and calorie amounts.

[0333] "Emotional data" refers to information indicating the user's emotional state (e.g., satisfaction, dissatisfaction, joy) collected through voice input and facial recognition.

[0334] "Means for collecting and analyzing emotional data" refers to software and algorithms that collect emotional data through user voice input or facial recognition and then analyze that data.

[0335] "A means of generating and proposing well-balanced menus to users" refers to a generative artificial intelligence system that generates and proposes combinations of ingredients and dishes suitable for the user based on the results of nutrient analysis and emotional data.

[0336] "Means of sharing recipe information with cooking facilities near the user" refers to systems or networks that transmit generated menus and detailed recipe information to cooking facilities near the user's residential area.

[0337] "Means of preparing food and delivering it to the user's home" refers to the means by which a cooking facility prepares food based on shared recipe information and delivers it to the user's home through a delivery service.

[0338] "Means for collecting feedback and using it to improve services" refers to a system that collects ratings and opinions provided by users through the application and uses them to improve the quality of future services and menus.

[0339] This invention is a system that proposes nutritionally balanced meals based on the user's basic information, daily meal records, and emotional data, and then prepares and delivers those meals. A specific embodiment of this system is shown below.

[0340] 1. Data Collection

[0341] User:

[0342] Users log in to a mobile application or web form and enter personal information (age, gender, height, weight, allergy information, dietary preferences, etc.). They also enter details of their daily meals into the application. For example, user D might record the scrambled eggs and toast they ate for breakfast. The application used should ideally have a user-friendly interface.

[0343] 2. Data storage

[0344] server:

[0345] The server stores personal information and meal records submitted by users in a database. Specifically, it utilizes database management systems such as MySQL® or PostgreSQL. The stored data is later used for analysis.

[0346] 3. Nutritional Analysis

[0347] server:

[0348] Based on the stored data, the server uses generative artificial intelligence (e.g., OpenAI GPT-4®) to analyze the user's nutrient intake and calculate the necessary nutrients and calories. For example, it might identify that user D's daily calorie requirement is 1800 kcal and that they are deficient in vitamin D.

[0349] 4. Collection and Analysis of Sentimental Data

[0350] User:

[0351] The user uses the application to perform voice input and facial recognition. For example, they might voice input such as, "Today's meal was very delicious."

[0352] server:

[0353] The server uses speech recognition software (e.g., Google® Speech-to-Text API) and facial expression analysis algorithms (e.g., Microsoft® Face API) to analyze emotional data and extract emotions such as satisfaction levels. The analysis results are stored in a database and used in the next step.

[0354] 5. Menu generation

[0355] server:

[0356] Based on nutritional analysis results and emotional data, the server uses generative artificial intelligence to generate a well-balanced menu. For example, it might suggest a menu rich in vitamin D, such as "grilled salmon, spinach salad, and soy milk smoothie." This menu information is then sent to the cooking facility in the next step.

[0357] 6. Recipe Sharing

[0358] server:

[0359] The server shares the generated menu and detailed recipe information with cooking facilities near the user's location. For example, it sends menu information for grilled salmon, spinach salad, and soy milk smoothie to "nearby cooking facilities" in the user D's area.

[0360] 7. Cooking and Delivery

[0361] Terminal (Cooking Staff):

[0362] The cooking staff prepares the dishes for the menu based on shared recipe information. For example, they gather the necessary ingredients and begin cooking, and once cooking is complete, they properly package the dishes.

[0363] Terminal (Delivery Staff):

[0364] Delivery staff pick up the prepared food and deliver it to the user's home. For example, they might deliver "grilled salmon, spinach salad, and a soy milk smoothie" to user D's home.

[0365] 8. Gathering Feedback

[0366] User:

[0367] User D receives the delivered food, finishes eating, and then enters a rating and feedback through the application. For example, they might enter a rating such as, "The grilled salmon was very tasty, but there was too much salad dressing."

[0368] server:

[0369] The server collects user feedback and uses it, along with sentiment data, to create future menus and improve the service.

[0370] Specific example:

[0371] User D records the scrambled eggs and toast they ate for breakfast using the app.

[0372] The server analyzes daily nutrient intake based on recorded meal data and identifies vitamin D deficiency.

[0373] User D inputs "Today's meal was very delicious" via voice input, and the server extracts the emotion of "satisfaction."

[0374] The server generates a menu of "grilled salmon, spinach salad, and soy milk smoothie" and shares it with the cooking facility.

[0375] Cooking staff prepare the meals based on the menu, and delivery staff deliver the meals to user D.

[0376] User D enters feedback via the app after the meal, and the server adjusts the next menu based on that data.

[0377] Examples of prompts for a generative AI model:

[0378] "Based on the scrambled eggs and toast that User D ate for breakfast, please suggest a well-balanced menu that is rich in vitamin D."

[0379] Therefore, this system allows elderly people and those who have difficulty managing their diet to easily receive nutritionally balanced meals, and also enables the provision of more satisfying services by utilizing emotional data.

[0380] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0381] Step 1: Data Entry

[0382] User:

[0383] Input: Users enter personal information (age, gender, height, weight, allergy information, dietary preferences, etc.) through a mobile application or web form.

[0384] Operation: The user accesses the application and enters the necessary personal information into the input form. They also enter details of their daily meals. For example, user D records the scrambled eggs and toast they ate for breakfast.

[0385] Output: The entered personal information and meal record data are formatted within the application and ready for transmission.

[0386] Step 2: Save Data

[0387] server:

[0388] Input: Personal information and meal record data submitted by the user.

[0389] Operation: The server receives data and saves it to a database such as MySQL or PostgreSQL. It performs data integrity checks and notifies the user if errors occur.

[0390] Output: User personal information and meal record data stored in the database.

[0391] Step 3: Nutritional Analysis

[0392] server:

[0393] Input: User meal record data stored in the database.

[0394] Operation: Using generative artificial intelligence (e.g., OpenAI GPT-4), the system analyzes stored meal record data to determine the user's nutrient intake. Specifically, it calculates the daily calorie requirement and any deficient nutrients based on user D's diet.

[0395] Output: The analysis results provide data on calorie intake and deficient nutrients (e.g., vitamin D). This analysis result is also stored in a database and used for subsequent processing.

[0396] Step 4: Collecting emotional data

[0397] User:

[0398] Input: Users input emotional data into the application using voice input or facial recognition.

[0399] Operation: For example, user D inputs "Today's meal was very delicious" via voice. The application formats the voice and facial expression data and prepares it for transmission to the server.

[0400] Output: The formatted audio data and facial expression data are sent to the server.

[0401] Step 5: Emotion Analysis

[0402] server:

[0403] Input: Voice data and facial expression data sent by the user.

[0404] Operation: Uses speech recognition software (e.g., Google Speech-to-Text API) and facial expression analysis algorithms (e.g., Microsoft Face API) to analyze emotional data and extract emotions such as satisfaction levels.

[0405] Output: Extracted sentiment data (e.g., "satisfied") is obtained. This sentiment data is also stored in the database and used in the next step.

[0406] Step 6: Menu Generation

[0407] server:

[0408] Input: Nutritional analysis results and emotional data.

[0409] Operation: Based on nutritional analysis results and emotional data, it uses generative artificial intelligence to generate a well-balanced menu. For example, it might suggest "grilled salmon, spinach salad, and soy milk smoothie," which are rich in vitamin D.

[0410] Output: The proposed menu information is generated and ready to be sent to the kitchen facility. This information is also stored in the database.

[0411] Step 7: Recipe Sharing

[0412] server:

[0413] Input: Generated menu and detailed recipe information.

[0414] Operation: The server transmits data to cooking facilities near the user. Communication is conducted via the internet or a dedicated network.

[0415] Output: Menu and recipe information sent to the cooking facility.

[0416] Step 8: Cooking

[0417] Terminal (Cooking Staff):

[0418] Input: Menu and recipe information received by the cooking facility.

[0419] Action: Based on this information, the kitchen staff gathers the necessary ingredients and begins cooking. For example, they might prepare grilled salmon, spinach salad, and soy milk smoothies.

[0420] Output: Cooking is complete and the food is properly packaged.

[0421] Step 9: Delivery

[0422] Terminal (Delivery Staff):

[0423] Input: Package of the finished dish.

[0424] Operation: Delivery staff receive the food and prepare it for delivery. They deliver the food to the user's home at the specified time. For example, deliver grilled salmon, spinach salad, and a soy milk smoothie to user D's home.

[0425] Output: Food delivered to the user's home.

[0426] Step 10: Gathering Feedback

[0427] User:

[0428] Input: Ratings and opinions about the delivered food.

[0429] Operation: After user D finishes their meal, they enter their rating and feedback through the application. For example, they might enter the opinion, "The grilled salmon was very tasty, but there was too much salad dressing."

[0430] Output: The input feedback is sent to the server.

[0431] server:

[0432] Input: Feedback submitted by the user.

[0433] Operation: Collects feedback and stores it in a database. Uses the feedback data to create future menus and improve services.

[0434] Output: Feedback information useful for improvement is accumulated in the database, contributing to service enhancement.

[0435] (Application Example 2)

[0436] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0437] In modern society, many users lead busy lives and lack the time to plan and prepare balanced meals. Furthermore, accurately understanding each individual's nutrient intake and generating optimal menus to supplement any deficiencies is challenging. Moreover, menu suggestions and service improvements that consider user satisfaction and emotions regarding meals are insufficient. Therefore, there is a need for a system that allows users to easily obtain balanced meals while also increasing their satisfaction with those meals.

[0438] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0439] In this invention, the server includes means for collecting the user's basic information and meal records; means for analyzing the user's nutrient intake based on the collected data and calculating the necessary nutrients and calories; means for generating a well-balanced menu based on the analysis results and emotional data and proposing it to the user; means for sharing the generated menu and recipe information with cooking facilities near the user; means for cooking the meal based on the shared recipe and delivering it to the user's home; means for extracting the user's emotional data through voice input and facial recognition; and means for collecting user feedback and emotional data and reflecting it in improving the service. As a result, users can easily obtain well-balanced meals tailored to their individual nutritional needs and increase their satisfaction with those meals.

[0440] "User basic information" refers to personal data such as age, gender, weight, height, allergy information, and food preferences.

[0441] A "meal diary" is data in which a user specifically records the contents of their daily meals.

[0442] "Nutrient intake status" refers to the amount and balance of each nutrient that the user is consuming through their diet.

[0443] "Required nutrients and calories" refers to the specific nutrients and calories that a user needs to maintain their health and achieve their goals.

[0444] "Analysis results" refer to the results regarding nutrient intake and nutrient deficiencies derived by a generative artificial intelligence system based on the user's basic information and dietary records that have been collected.

[0445] "Emotional data" refers to data such as satisfaction levels and moods extracted through user voice input and facial recognition.

[0446] A "menu" refers to a specific set of dishes or meal combinations suggested to the user.

[0447] "Recipe information" refers to detailed instructions that include how to prepare a dish and a list of necessary ingredients.

[0448] A "cooking facility" is a facility that prepares meals based on menus suggested to users.

[0449] "Feedback" refers to data that users input and submit regarding the food or service they receive, including their evaluations and impressions.

[0450] "Service improvement" means improving future menu suggestions and service content based on collected feedback and sentiment data.

[0451] This invention is a system that collects basic user information and meal records, and generates and proposes well-balanced menus based on the analysis results and emotional data. The aim of this system is to enable users to effectively manage their daily meals and increase their satisfaction with them.

[0452] 1. Data Acquisition Module

[0453] User:

[0454] Users use a mobile application to input personal information (age, gender, height, weight, allergy information, food preferences, etc.) and details of their daily meals. For example, a user might record the scrambled eggs and toast they ate for breakfast.

[0455] 2. Dietary Habits Analysis Module

[0456] server:

[0457] The server stores collected personal information and meal records in a database. Based on the stored data, generative artificial intelligence is used to analyze the user's nutrient intake and calculate the necessary nutrients and calories. For example, by analyzing the user's data, it might identify that the user needs 1800 kcal per day and is deficient in vitamin D.

[0458] 3. Emotion Recognition Module

[0459] User:

[0460] The user uses the application for voice input and facial recognition. For example, the user launches the application and voice-inputs, "Today's meal was very delicious."

[0461] server:

[0462] The server analyzes voice input and facial recognition data to extract user emotion data. For example, it might extract the emotion of "satisfaction" from the user's voice input.

[0463] 4. Menu Creation Module

[0464] server:

[0465] The server generates a well-balanced menu based on analysis results and emotional data. Generative artificial intelligence suggests the optimal combination of ingredients and dishes. For example, it might suggest a menu to the user that includes dishes rich in vitamin D, such as "grilled salmon, spinach salad, and soy milk smoothie."

[0466] 5. Locally shared modules

[0467] server:

[0468] The generated menu and detailed recipe information are shared with the cooking facility closest to the user's area. For example, recipes for grilled salmon, spinach salad, and soy milk smoothie are sent to cooking facilities in the user's area.

[0469] 6. Cooking and Delivery Module

[0470] Terminal (Cooking Staff):

[0471] Based on shared recipe information, the cooking staff prepares the dishes for the menu. For example, they check the recipe, gather the necessary ingredients, and begin cooking. After cooking is complete, they properly package the dishes.

[0472] Terminal (Delivery Staff):

[0473] Cooked food is picked up by delivery staff and delivered to the user's home. For example, grilled salmon, spinach salad, and a soy milk smoothie can be delivered to the user's home.

[0474] 7. Feedback Module

[0475] User:

[0476] After receiving their delivered food and finishing their meal, users enter ratings and feedback through the application. For example, they might write, "The grilled salmon was very tasty, but there was too much salad dressing."

[0477] server:

[0478] The server collects user feedback and uses it, along with sentiment data, to create future menus and improve the service. For example, based on user feedback, the amount of salad dressing might be adjusted next time, and sentiment data might be used to create more satisfying menus.

[0479] The hardware used includes common smartphones (ANDROID® / iOS), servers, and terminals for cooking and delivery staff. Software includes Android Studio and iOS development tools for mobile application development, Flask for the server side, SQLite for the database, and Tensorflow® for AI models.

[0480] As a concrete example, the following prompt sentences are used in generative AI models:

[0481] User data:

[0482] Age: 30

[0483] Gender: Male

[0484] Weight: 70kg

[0485] Height: 175cm

[0486] Dietary restrictions: None

[0487] Recent meals: Breakfast - Oatmeal with fruits, Lunch - Chicken salad, Dinner - Grilled salmon with vegetables

[0488] User feeling about today's meals: Satisfied

[0489] Generate a balanced menu for the next day considering the user's health needs and preferences.

[0490] In this way, the system allows individual users to easily enjoy nutritionally balanced meals and achieve highly satisfying and smart meal management.

[0491] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0492] Step 1:

[0493] Users enter personal information and meal records.

[0494] Input: Users use a smartphone application to enter personal information such as age, gender, height, weight, allergy information, and dietary preferences, as well as details of their daily meals.

[0495] Data processing and calculation: The application checks the input data and organizes it according to the format.

[0496] Output: Organized personal information and meal records are sent to the server and stored in the database.

[0497] Specific operation: The user enters the scrambled eggs and toast they ate for breakfast, and the application organizes that information and sends it to the server.

[0498] Step 2:

[0499] The server performs dietary analysis.

[0500] Input: User's personal information and meal records stored in the database.

[0501] Data processing and calculation: The server uses generative artificial intelligence to analyze the user's nutrient intake and calculate the necessary nutrients and calories.

[0502] Output: The calculated analysis results are generated.

[0503] Specific operation: The server analyzes the user's data and identifies that their daily calorie requirement is 1800 kcal and that they are deficient in vitamin D.

[0504] Step 3:

[0505] Users input emotional data.

[0506] Input: The user launches the application and provides emotional data through voice input or facial recognition.

[0507] Data Processing and Calculation: The application performs preprocessing to analyze voice input and facial recognition data.

[0508] Output: Pre-processed emotion data is sent to the server.

[0509] Specific operation: The user voice-inputs "Today's meal was very delicious," and this information is pre-processed before being sent to the server.

[0510] Step 4:

[0511] The server analyzes the emotional data.

[0512] Input: Sentiment data submitted by the user.

[0513] Data processing and calculation: The server uses generative artificial intelligence to analyze emotional data and identify user satisfaction and emotions.

[0514] Output: Emotional data extracted as an analysis result.

[0515] Specific operation: The server extracts the emotion of "satisfaction" from the voice input.

[0516] Step 5:

[0517] The server generates the menu.

[0518] Input: Analysis results and sentiment data.

[0519] Data processing and calculation: The server uses generative artificial intelligence to calculate the optimal combination of ingredients and dishes.

[0520] Output: A well-balanced menu is generated.

[0521] Specific action: A menu is proposed that includes dishes rich in vitamin D, such as "grilled salmon, spinach salad, and soy milk smoothie."

[0522] Step 6:

[0523] The server shares recipe information with the cooking facilities.

[0524] Input: Generated menu and detailed recipe information.

[0525] Data processing and calculation: The server converts menu and recipe information into a specific format and sends it to the nearest cooking facility.

[0526] Output: Shared data for the cooking facility to receive.

[0527] Specific action: Send recipe information for "grilled salmon, spinach salad, and soy milk smoothie" to a cooking facility in the user's area.

[0528] Step 7:

[0529] The cooking facility prepares the food.

[0530] Input: Recipe information shared from the server.

[0531] Data processing and calculation: Cooking staff gather the necessary ingredients based on the recipe and begin cooking.

[0532] Output: Cooked food.

[0533] Specific tasks: Cooking staff will review the recipes and prepare and properly package the grilled salmon, spinach salad, and soy milk smoothie.

[0534] Step 8:

[0535] Delivery staff deliver the food.

[0536] Input: Dishes prepared in a cooking facility.

[0537] Data processing and calculation: Delivery staff pick up the food and deliver it to the user's home.

[0538] Output: Meals delivered to the user's home.

[0539] Specific actions: A delivery staff member delivers grilled salmon, spinach salad, and a soy milk smoothie to the user's home.

[0540] Step 9:

[0541] Users enter feedback

[0542] Input: Your evaluation or comments on the food you received.

[0543] Data processing and calculation: The application collects the input feedback and sends it to the server.

[0544] Output: Collected feedback data.

[0545] Specific operation: The user enters a review stating, "The grilled salmon was very tasty, but there was too much salad dressing," and this information is sent to the server.

[0546] Step 10:

[0547] The server analyzes the feedback and uses it to improve the service.

[0548] Input: User feedback and sentiment data.

[0549] Data processing and calculation: Generative artificial intelligence is used to analyze feedback and utilize it for creating future menus and improving services.

[0550] Output: Improved menu suggestions and service details.

[0551] Specific operation: Based on user feedback, the server adjusts the amount of salad dressing in the next menu.

[0552] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0553] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0554] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0555] [Second Embodiment]

[0556] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0557] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0558] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0559] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0560] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0561] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0562] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0563] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0564] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0565] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0566] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0567] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0568] 1. Data Acquisition Module

[0569] User:

[0570] Users enter personal information (age, gender, height, weight, allergy information, dietary preferences, etc.) using a mobile application or web form. They also record their daily meals, and this allows for the collection of dietary records.

[0571] 2. Dietary Habits Analysis Module

[0572] server:

[0573] The server stores the data collected from users in a database. Next, generative artificial intelligence within the server is used to analyze this data and gain a detailed understanding of the user's nutrient intake. For example, analysis of data from a woman in her 70s, Ms. B, reveals that her daily calorie requirement is 1600 kcal and that she is deficient in B vitamins.

[0574] 3. Menu Creation Module

[0575] server:

[0576] Based on the analysis results, the server generates a well-balanced menu. In this process, a generative artificial intelligence suggests the optimal combination of ingredients and dishes. For example, for user B, it might create a menu including stir-fried chicken liver (rich in B vitamins), blanched spinach, and fruit yogurt.

[0577] Specific example:

[0578] Based on User B's daily calorie needs and nutrient deficiencies, we propose the following meal plan: "Breakfast: Fruit yogurt, Lunch: Stir-fried chicken liver and blanched spinach, Dinner: Grilled fish and steamed vegetables."

[0579] 4. Locally shared modules

[0580] server:

[0581] The generated menu and its detailed recipe information are shared with the cooking facility closest to the user's area. For example, the server sends the recipe information to "Nearby Cooking Facility X" located in the area where user B lives.

[0582] 5. Cooking and Delivery Module

[0583] Terminal (Cooking Staff):

[0584] Based on the shared recipe information, the cooking staff prepares the dishes for the menu. Specifically, they check the recipe sent from the server, prepare the necessary ingredients, and begin cooking.

[0585] Terminal (Delivery Staff):

[0586] The delivery staff will properly package the cooked meals and deliver them to user B's home. For example, stir-fried chicken liver and blanched spinach for lunch will be delivered at lunchtime, and grilled fish and steamed vegetables for dinner will be delivered at dinnertime.

[0587] 6. Feedback Module

[0588] User:

[0589] User B receives the delivered food, finishes eating, and then submits feedback through the application. They fill in their evaluation of the taste, quantity, and quality, and then submit it.

[0590] server:

[0591] The server collects feedback from user B and uses it to create future menus and improve the service. For example, if user B provides feedback that "the stir-fried chicken liver was a little too salty," the seasoning will be adjusted next time.

[0592] As described above, this system enables elderly people to continuously receive nutritionally balanced meals. By collecting basic user information and meal records, analyzing this information using generative artificial intelligence, generating nutritionally balanced menus, cooking at a cooking facility, delivery, and collecting and incorporating feedback, the system supports the user's health.

[0593] The following describes the processing flow.

[0594] Step 1:

[0595] User:

[0596] Users log in to a mobile application or web form and enter personal information (age, gender, height, weight, allergy information, dietary preferences, etc.). They also record their daily meals. For example, user C enters the toast and coffee they ate for breakfast.

[0597] Step 2:

[0598] server:

[0599] The server stores personal information and meal records collected from users in a database. The stored data is appropriately formatted for analysis. For example, user C's personal information and data on bread and coffee are stored in the database.

[0600] Step 3:

[0601] server:

[0602] The generative artificial intelligence on the server retrieves the user's meal records and basic information from the database and performs analysis. This calculates the user's nutrient intake. For example, it identifies that user C's daily calorie requirement is 2000 kcal and that their calcium intake is insufficient.

[0603] Step 4:

[0604] server:

[0605] Based on the analysis results, the server generates a nutritionally balanced menu. Generative artificial intelligence suggests the optimal combination of ingredients and dishes. For example, for user C, it creates a menu that includes "grilled salmon, broccoli salad, and soy milk smoothie," which are rich in calcium.

[0606] Step 5:

[0607] server:

[0608] The generated menu and its detailed recipe information are shared with the cooking facility closest to the user's area. For example, recipe information for grilled salmon, broccoli salad, and soy milk smoothie is sent to "Nearby Cooking Facility Y" in the area where user C lives.

[0609] Step 6:

[0610] Terminal (Cooking Staff):

[0611] Based on shared recipe information, the cooking staff prepares the dishes for the menu. For example, they check the recipe, gather the necessary ingredients, and begin cooking. After cooking is complete, they properly package the dishes.

[0612] Step 7:

[0613] Terminal (Delivery Staff):

[0614] A delivery staff member picks up the prepared food and delivers it to the user's home. For example, grilled salmon, broccoli salad, and a soy milk smoothie are delivered to user C's home.

[0615] Step 8:

[0616] User:

[0617] The user receives the delivered food and actually eats it. For example, user C enjoys grilled salmon and broccoli salad for dinner.

[0618] Step 9:

[0619] User:

[0620] After their meal, users enter their ratings and feedback through the application. For example, user C might submit feedback stating, "The grilled salmon was very tasty, but there was a little too much salad dressing."

[0621] Step 10:

[0622] server:

[0623] The server collects user feedback and uses it to create future menus and improve the service. For example, based on user C's feedback, the amount of salad dressing will be adjusted next time.

[0624] Through these steps, the system enables elderly people to easily receive nutritionally balanced meals.

[0625] (Example 1)

[0626] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0627] To address the current challenges that make it difficult for the elderly to consistently receive nutritionally balanced meals, there is a need for a system that can accurately understand users' nutrient intake, generate appropriate menus based on that information, and efficiently manage the cooking and delivery processes.

[0628] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0629] In this invention, the server includes means for collecting the user's basic information and meal records; means for storing the collected data and linking it to a user ID; means for analyzing the nutrient intake status based on the stored data and calculating the necessary nutrients and calories; means for generating a well-balanced menu using generative artificial intelligence based on the analysis results and proposing it to the user; means for sharing the generated menu and recipe information with cooking facilities near the user; means for cooking meals based on the shared recipes and delivering them to the user's home; and means for collecting feedback from the user and reflecting it in creating future menus and improving the service. This makes it possible for elderly people to continuously receive nutritionally balanced meals and support their health.

[0630] "User basic information" refers to data about an individual user's basic health and lifestyle habits, such as age, gender, height, weight, allergy information, and food preferences.

[0631] A "meal diary" is data that users record about their daily meals, including specific food names, quantities consumed, and meal times.

[0632] "Means of collection" refers to technical methods or devices for obtaining data entered by users, such as through mobile applications or web forms.

[0633] "Means of preservation" refers to technologies for storing and managing collected data in data storage such as databases.

[0634] A "User ID" is a unique identifier assigned to each user and is used to distinguish a user's data from that of other users.

[0635] "Methods for analyzing nutrient intake" refer to technologies that, based on collected data, identify the amount and types of nutrients a user is consuming and analyze any deficiencies or excesses in necessary nutrients or calories.

[0636] "Generative artificial intelligence" is an artificial intelligence technology that uses advanced algorithms such as machine learning and deep learning to analyze data, make predictions, and provide optimal suggestions.

[0637] "Methods for generating menus" refers to technologies that create well-balanced meal plans based on analysis results and propose them to users.

[0638] A "cooking facility" refers to a facility located near the user that performs cooking, and includes restaurants and catering services.

[0639] "Recipe information" refers to information such as specific cooking methods, necessary ingredients, and cooking procedures based on a menu.

[0640] "Means of cooking" refer to the techniques and equipment used to actually prepare a meal based on shared recipe information.

[0641] "Delivery methods" refer to the methods and technologies used to deliver cooked food to the user's home.

[0642] "Means of collecting feedback" refers to the technologies and methods used to obtain ratings and comments from users.

[0643] "Means of reflecting feedback in service improvement" refers to methods used to create future menus and improve service quality based on the collected feedback.

[0644] This invention relates to a system for elderly people to continuously receive nutritionally balanced meals. This system collects basic information and meal records from the user, analyzes nutrient intake based on this information, and generates an appropriate menu. Furthermore, the generated menu and recipe information are shared with cooking facilities, the cooked meals are delivered to the user's home, and feedback is collected to help create future menus and improve the service.

[0645] This system uses the following hardware and software.

[0646] User:

[0647] Users enter personal information (age, gender, height, weight, allergy information, food preferences, etc.) and daily meal details using a mobile application or web form. Specifically, they use devices such as smartphones or PCs. An example of information a user might enter is "Height 165cm, weight 60kg, likes chicken."

[0648] server:

[0649] The server receives data sent by the user and stores it in a database. This database can be a common relational database system (e.g., PostgreSQL). Next, generative artificial intelligence (e.g., GPT-3) is used to analyze the collected data and identify the user's nutrient intake. For example, analysis of data from a female user in her 70s might reveal that her daily calorie requirement is 1600 kcal and that she is deficient in B vitamins.

[0650] Based on the analysis results, the server uses generative artificial intelligence to generate a well-balanced menu and propose it to the user. For example, it might suggest: "Breakfast: Fruit yogurt, Lunch: Stir-fried chicken liver and blanched spinach, Dinner: Grilled fish and steamed vegetables." An example of a prompt message would be: "Based on data collected from a female user in her 70s, please propose a well-balanced menu that matches the dietary analysis results showing a deficiency in B vitamins."

[0651] The generated menu and its detailed recipe information are shared with the cooking facility closest to the user's area. The server uses the SMTP protocol to send emails to the cooking facility, providing the recipe information. Alternatively, data can be shared via a dedicated API.

[0652] Terminal (Cooking Staff):

[0653] Cooking staff check recipe information sent from the server on their terminals (PCs or tablets) and prepare the necessary ingredients. Specifically, they check the inventory of necessary ingredients in real time and place uncooked ingredients on the cooking counter. Cooking staff cook the ingredients using cooking utensils such as frying pans and ovens while checking the recipe instructions. For example, they might sauté chicken liver for the specified time and boil and cool spinach.

[0654] Terminal (Delivery Staff):

[0655] Delivery staff carefully package the cooked meals and deliver them to the user's home. Using GPS devices, delivery staff ensure the meals arrive precisely at the designated time. Specifically, they carefully pack the food into packaging materials and head towards the user's address while monitoring traffic conditions in real time.

[0656] User:

[0657] After receiving their delivered food and finishing their meal, users submit feedback through the application. They rate the taste, quantity, and quality, and then submit the feedback. For example, one piece of feedback might be, "The stir-fried chicken liver was a little too salty."

[0658] server:

[0659] The server collects user feedback and uses it to create future menus and improve the service. For example, based on feedback such as "the stir-fried chicken liver was a little too salty," the seasoning will be adjusted next time.

[0660] This will provide a system that supports users' health and enables elderly people to continuously receive nutritionally balanced meals.

[0661] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0662] Step 1: Data Collection

[0663] User:

[0664] Users open a mobile application or web form and enter their personal information and daily meal details. Specifically, they use a smartphone or PC to fill in the required fields in the form.

[0665] Input: Personal information entered by the user (age, gender, height, weight, allergy information, food preferences, etc.) and meal details.

[0666] Output: Dataset of user's basic information and meal records

[0667] Step 2: Save Data

[0668] server:

[0669] The server receives data sent by the user and stores it in the database. The database uses a relational database system (e.g., PostgreSQL). The data is associated with the user ID upon storage.

[0670] Input: User's basic information and meal record dataset

[0671] Output: User's basic information and meal records stored in the database

[0672] Step 3: Data Analysis

[0673] server:

[0674] The generative artificial intelligence model (e.g., GPT-3) on the server analyzes the stored data to identify the user's nutrient intake. For example, it calculates the daily calorie requirement and any deficient nutrients.

[0675] Input: User's basic information and meal records stored in the database.

[0676] Output: User's nutrient intake status and specific analysis results

[0677] Step 4: Menu Generation

[0678] server:

[0679] Based on the analysis results, the server uses generative artificial intelligence to generate a well-balanced menu. For example, it might suggest "Breakfast: Fruit yogurt, Lunch: Stir-fried chicken liver and blanched spinach, Dinner: Grilled fish and steamed vegetables."

[0680] Input: User's nutrient intake status and analysis results

[0681] Output: A well-balanced menu and detailed recipe information

[0682] Step 5: Local Sharing

[0683] server:

[0684] The generated menu and recipe information is shared with the cooking facility closest to the user's area. Data is transmitted via SMTP protocol or a dedicated API.

[0685] Input: A well-balanced menu and detailed recipe information

[0686] Output: Recipe information sent to the cooking facility

[0687] Step 6: Preparation for cooking

[0688] Terminal (Cooking Staff):

[0689] Cooking staff check recipe information sent from the server on their devices (PCs or tablets) and prepare the necessary ingredients. They also check the inventory of necessary ingredients in real time and place them on the cooking counter.

[0690] Input: Recipe information sent to the cooking facility

[0691] Output: Prepared ingredients

[0692] Step 7: Cooking

[0693] Terminal (Cooking Staff):

[0694] The cooking staff begins cooking using the prepared ingredients. They use cooking utensils such as frying pans and ovens and cook according to the recipe.

[0695] Input: Prepared ingredients

[0696] Output: Finished dish

[0697] Step 8: Delivery

[0698] Terminal (Delivery Staff):

[0699] Delivery staff properly package the cooked meals and deliver them to the user's home. They use GPS to monitor traffic conditions and deliver at the specified time.

[0700] Input: Finished dish

[0701] Output: Food delivered to the user's home

[0702] Step 9: Gathering Feedback

[0703] User:

[0704] After receiving and eating their delivered food, users submit feedback through the application. They rate the taste, quantity, and quality of the food and then submit it.

[0705] Input: Post-use evaluation of delivered food

[0706] Output: Feedback data

[0707] Step 10: Feedback Analysis

[0708] server:

[0709] The server collects user feedback and uses it to create future menus and improve the service. Based on the feedback, it inputs the next prompt into the generative artificial intelligence and makes adjustments.

[0710] Input: Feedback data

[0711] Output: Improved menu planning and service quality

[0712] In this way, by performing data processing and calculations using specific inputs and outputs, as well as generating AI models and prompts at each step, a system is realized that supports the health of the elderly while maintaining nutritional balance.

[0713] (Application Example 1)

[0714] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal." For elderly people and users who require nutritional management, ensuring a balanced diet on a daily basis can be difficult. Furthermore, continuously providing menus that take nutritional balance into consideration is time-consuming, making continuous health management difficult. Therefore, based on the user's personal information and meal records, support will be provided to help them lead a safe and healthy diet.

[0715] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means: The server includes means for collecting the user's basic information and meal records; means for analyzing the intake status of nutrients based on the collected data and calculating the necessary nutrients and calories; means for generating a well-balanced menu based on the analysis results and proposing it to the user; means for sharing the generated menu and recipe information with cooking facilities near the user; means for cooking based on the shared recipes and delivering them to the user's home; means for collecting feedback from the user and reflecting it in improving the service; and means for managing the user's data through a smartphone application, generating menus using generative artificial intelligence, and sharing the results with cooking facilities.

[0716] A "user" refers to anyone who uses the system to input personal information or meal records.

[0717] "Basic information" refers to personal data such as the user's age, gender, height, weight, allergy information, and food preferences.

[0718] A "food diary" refers to the contents of the food a user consumes on a daily basis.

[0719] "Data" refers to basic information and meal records collected from users.

[0720] "Nutrients" refer to components necessary for nutritional balance, such as proteins, vitamins, and minerals.

[0721] "Analysis" refers to the process of evaluating nutrient intake based on collected data.

[0722] "Calories" refer to the amount of energy contained in food.

[0723] A "menu" refers to a meal plan that is designed with a balance of nutrients in mind.

[0724] "Generative artificial intelligence" refers to algorithms and models that generate nutritionally balanced meal plans based on user data.

[0725] A "cooking facility" refers to a place where food is prepared based on the generated menu and recipe information.

[0726] "Feedback" refers to the information that users send back to the system, evaluating the dishes they are served.

[0727] "Service improvement" refers to improving menu creation, cooking, and delivery services for future orders based on the collected feedback information.

[0728] A "smartphone application" refers to a mobile app that allows users to input basic information and meal records, and receive analysis results and meal plan suggestions.

[0729] Modes for carrying out the invention

[0730] System Overview

[0731] This invention is a food delivery system that provides users with nutritionally balanced meals. Users input basic information and meal records through a smartphone application, and based on that data, a meal plan is generated, prepared at the nearest cooking facility to the user, and delivered by delivery staff. User feedback is used to improve the service.

[0732] Specific Embodiments

[0733] 1. Collection of user data

[0734] Users input basic information such as age, gender, height, weight, allergy information, and dietary preferences through a smartphone application. Their daily meals are also recorded. This data is sent to a server and stored in a database.

[0735] 2. Analysis of Nutrients

[0736] The server analyzes the collected basic information and meal records and uses a generative AI model to understand the user's nutrient intake. This analysis can identify which nutrients the user needs to consume and in what quantities.

[0737] 3. Menu generation

[0738] The generation AI model generates optimal menus based on the analysis results. The generated menus include food items with a well-balanced combination of nutrients. For example, for a user who is deficient in a specific vitamin, dishes using ingredients rich in that vitamin will be suggested.

[0739] 4. Data Sharing

[0740] The generated menu and its detailed recipe information are shared with the nearest cooking facility in the user's area. The cooking facility then prepares the dishes based on the received recipe information.

[0741] 5. Food preparation and delivery

[0742] The kitchen staff prepares the meals according to the generated recipes and packages them appropriately. Then, delivery staff deliver the meals to the user's address. This ensures that users receive fresh, nutritionally balanced meals tailored to their time of day.

[0743] 6. Gathering feedback and improving services

[0744] After receiving and eating their delivered food, users provide feedback through a smartphone application. They input ratings regarding taste, quantity, and quality. This feedback is sent to the server and used to create future menus and improve the service.

[0745] Hardware and software to be used

[0746] Hardware: Users use smartphones, while kitchen staff and delivery personnel use tablets or PCs. Servers are used for data storage and analysis.

[0747] Software: A smartphone application is used for inputting basic user information and meal records. SQLite is used for the database, and Flask is used for the backend of the web application. The OpenAI API is used for generative AI models.

[0748] Examples of prompt statements

[0749] The following is an example of a prompt to input into a generative AI model:

[0750] User information:

[0751] Age: 70, Gender: Female, Height: 160cm, Weight: 55kg.

[0752] Allergens: Shrimp, nuts.

[0753] Meal plan: Breakfast: Toast and coffee. Lunch: Chicken salad. Dinner: Braised fish and stir-fried vegetables.

[0754] Please provide a nutritionally balanced menu.

[0755] Example output from a generative AI model for this:

[0756] Breakfast: Fruit yogurt

[0757] Lunch: Stir-fried chicken liver, blanched spinach

[0758] Dinner: Grilled fish and steamed vegetables

[0759] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0760] Step 1: Collect basic user information and meal records.

[0761] Users enter basic information (age, gender, height, weight, allergy information, and dietary preferences) using a smartphone application. They also enter their daily meal details. The entered information is sent to a server and stored in an SQLite database. Specifically, the text-based input data is converted into a database format.

[0762] Input: User's basic information and meal records

[0763] Output: User information and meal records stored in the database

[0764] Step 2: Nutrient Analysis

[0765] The server analyzes nutrient intake based on user information and meal records stored in the database. This analysis uses OpenAI's generative AI model. Specifically, user information and meal records are input to the generative AI model as prompts, and data processing is performed to identify deficient nutrients based on the analysis results obtained.

[0766] Input: User information and meal records in the database

[0767] Output: Analysis results (such as deficient nutrients and required calories)

[0768] Step 3: Menu Generation

[0769] The server uses the OpenAI API to generate a nutritionally balanced menu based on the nutrient analysis results. It inputs prompts into the AI ​​model to obtain the optimal menu for the user. The generated menu includes specific food items and their recipes.

[0770] Input: Nutrient analysis results and prompt text

[0771] Output: A well-balanced menu and its recipe information

[0772] Step 4: Sharing menu information

[0773] The server sends the generated menu and recipe information to the nearest cooking facility in the user's area. The cooking facility then begins preparing the food based on the received recipe information. This step involves transmitting data using communication means.

[0774] Input: Menu and recipe information

[0775] Output: Sending recipe information to cooking facilities

[0776] Step 5: Cooking and delivering the food

[0777] The kitchen staff prepares the dishes based on the received recipe information. The finished dishes are properly packaged and delivered to the user's address by delivery staff. This step includes the actions required for cooking and delivery.

[0778] Input: Recipe information received by the cooking facility

[0779] Output: Cooked food delivered to the user's address.

[0780] Step 6: Gathering feedback and improving the service

[0781] After users have eaten the food they received, they provide feedback through a smartphone application. This feedback is sent to the server and used to generate future menus and improve the service. The server analyzes this feedback and incorporates it into the next menu generation.

[0782] Input: User feedback

[0783] Output: Improved service and menu generation process

[0784] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0785] 1. Data Acquisition Module

[0786] User:

[0787] Users log in to a mobile application or web form and enter personal information (age, gender, height, weight, allergy information, dietary preferences, etc.) and details of their daily meals. For example, user D might record scrambled eggs and toast for breakfast as part of their meal details.

[0788] 2. Dietary Habits Analysis Module

[0789] server:

[0790] The server stores collected personal information and meal records in a database. Based on the stored data, generative artificial intelligence is used to analyze the user's nutrient intake and calculate the necessary nutrients and calories. For example, analyzing user D's data reveals that their daily calorie requirement is 1800 kcal and that they are deficient in vitamin D.

[0791] 3. Emotion Recognition Module

[0792] User:

[0793] The user uses the application to perform voice input and facial recognition. For example, user D launches the application and inputs "Today's meal was very delicious" by voice.

[0794] server:

[0795] The server analyzes voice input and facial recognition data to extract user emotion data. For example, it extracts the emotion "satisfied" from user D's voice input.

[0796] 4. Menu Creation Module

[0797] server:

[0798] The server generates a well-balanced menu based on analysis results and emotional data. Generative artificial intelligence suggests the optimal combination of ingredients and dishes. For example, it suggests a menu to user D that includes dishes rich in vitamin D, such as "grilled salmon, spinach salad, and soy milk smoothie."

[0799] 5. Locally shared modules

[0800] server:

[0801] The generated menu and detailed recipe information are shared with the cooking facility closest to the user's area. For example, recipe information for grilled salmon, spinach salad, and soy milk smoothie is sent to "Nearby Cooking Facility Z" in the area where user D lives.

[0802] 6. Cooking and Delivery Module

[0803] Terminal (Cooking Staff):

[0804] Based on shared recipe information, the cooking staff prepares the dishes for the menu. For example, they check the recipe, gather the necessary ingredients, and begin cooking. After cooking is complete, they properly package the dishes.

[0805] Terminal (Delivery Staff):

[0806] A delivery staff member picks up the prepared food and delivers it to the user's home. For example, grilled salmon, spinach salad, and a soy milk smoothie are delivered to user D's home.

[0807] 7. Feedback Module

[0808] User:

[0809] User D receives the delivered food, finishes eating, and then enters a rating and feedback through the application. For example, they might enter a rating such as, "The grilled salmon was very tasty, but there was too much salad dressing."

[0810] server:

[0811] The server collects user feedback and uses it, along with sentiment data, to create future menus and improve the service. For example, based on user D's feedback, the amount of salad dressing will be adjusted next time, and the sentiment data will be used to create more satisfying menus.

[0812] Through the steps described above, this system allows elderly individuals to easily receive nutritionally balanced meals and provides a more satisfying service by utilizing emotional data. The system is continuously improved through a process that collects basic user information and meal records, generates optimal menus based on analysis results and emotional data, shares them with cooking facilities, handles cooking and delivery, and collects feedback.

[0813] The following describes the processing flow.

[0814] Step 1:

[0815] User:

[0816] Users log in to a mobile application or web form and enter personal information (age, gender, height, weight, allergy information, food preferences, etc.). They also record their daily meals. For example, user D records the scrambled eggs and toast they ate for breakfast.

[0817] Step 2:

[0818] server:

[0819] The server stores personal information and meal records collected from users in a database and formats them appropriately for analysis. User D's personal information and meal record (scrambled eggs and toast) are stored in the database.

[0820] Step 3:

[0821] server:

[0822] Using generative artificial intelligence on the server, the system retrieves users' meal records and basic information from a database and analyzes their nutrient intake. This allows it to identify their daily calorie needs and any nutrient deficiencies. For example, it might determine that user D needs 1800 kcal and is deficient in vitamin D.

[0823] Step 4:

[0824] User:

[0825] The user performs voice input and facial recognition through a mobile application. User D voice-inputs, "Today's meal was very delicious," and the app recognizes the user's facial expression using the camera.

[0826] Step 5:

[0827] server:

[0828] The server analyzes voice input and facial recognition data to extract user emotion data. For example, it recognizes the emotion of "satisfaction" from user D's voice and "smile" from their facial expression.

[0829] Step 6:

[0830] server:

[0831] Based on the analysis results and emotional data, the server generates a well-balanced menu. Generative artificial intelligence suggests the optimal combination of ingredients and dishes. For example, it might suggest a menu to user D consisting of "grilled salmon, spinach salad, and soy milk smoothie" as a dish rich in vitamin D.

[0832] Step 7:

[0833] server:

[0834] The generated menu and detailed recipe information are shared with the cooking facility closest to the user's area. For example, recipe information for grilled salmon, spinach salad, and soy milk smoothie is sent to "Nearby Cooking Facility Z" in the area where user D lives.

[0835] Step 8:

[0836] Terminal (Cooking Staff):

[0837] Based on the shared recipe information, the cooking staff prepares the dishes for the menu. They check the recipe, gather the necessary ingredients, and begin cooking. After cooking is complete, they properly package the food.

[0838] Step 9:

[0839] Terminal (Delivery Staff):

[0840] A delivery staff member picks up the prepared food and delivers it to the user's home. For example, grilled salmon, spinach salad, and a soy milk smoothie are delivered to user D's home.

[0841] Step 10:

[0842] User:

[0843] User D receives the delivered food and enjoys the meal. For example, for dinner, they might have grilled salmon and a spinach salad.

[0844] Step 11:

[0845] User:

[0846] After the meal, users enter ratings and feedback through the application. For example, they might enter a rating such as, "The grilled salmon was very tasty, but there was too much salad dressing."

[0847] Step 12:

[0848] server:

[0849] The server collects user feedback to help create future menus and improve the service. Based on user D's feedback, the amount of salad dressing will be adjusted next time, and emotional data will be used to generate a more satisfying menu.

[0850] Through the steps described above, this system allows elderly individuals to easily receive nutritionally balanced meals and provides a more satisfying service by utilizing emotional data. The system is continuously improved through a process that collects basic user information and meal records, generates optimal menus based on analysis results and emotional data, shares them with cooking facilities, handles cooking and delivery, and collects feedback.

[0851] (Example 2)

[0852] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0853] In modern times, providing easily accessible, nutritionally balanced meals for the elderly and those with difficulty managing their diets is a crucial challenge. However, systems that analyze individual nutrients and calories, and provide menus that take into account individual meal content and emotional states, are extremely limited. Therefore, to address these challenges, there is a need to develop a system that collects individual dietary and emotional data, proposes optimal menus based on this data, and provides consistent support from cooking and delivery to feedback.

[0854] The identification processing performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for collecting the user's basic information and meal records; means for analyzing the intake status of nutrients based on the collected data and calculating the necessary nutrients and calories; means for collecting and analyzing the user's emotional data; means for generating a well-balanced menu based on the analysis results and emotional data and proposing it to the user; means for sharing the generated menu and recipe information with cooking facilities near the user; means for cooking the food based on the shared recipe and delivering it to the user's home; and means for collecting feedback from the user and reflecting it in improving the service. This makes it possible to provide menus based on individual nutritional management and emotional state, enabling the realization of a more satisfying service.

[0855] "User basic information" refers to personal data including the user's age, gender, height, weight, allergy information, and food preferences.

[0856] "Meal records" refer to information that users input and record about their daily meals through applications or web forms.

[0857] "Means of collection" refers to systems and applications that receive data input from users and store it in a database.

[0858] "Methods for analyzing nutrient intake and calculating necessary nutrients and calories" refers to algorithms or programs that analyze a user's nutritional intake based on collected meal records and calculate the necessary nutrients and calorie amounts.

[0859] "Emotional data" refers to information indicating the user's emotional state (e.g., satisfaction, dissatisfaction, joy) collected through voice input and facial recognition.

[0860] "Means for collecting and analyzing emotional data" refers to software and algorithms that collect emotional data through user voice input or facial recognition and then analyze that data.

[0861] "A means of generating and proposing well-balanced menus to users" refers to a generative artificial intelligence system that generates and proposes combinations of ingredients and dishes suitable for the user based on the results of nutrient analysis and emotional data.

[0862] "Means of sharing recipe information with cooking facilities near the user" refers to systems or networks that transmit generated menus and detailed recipe information to cooking facilities near the user's residential area.

[0863] "Means of preparing food and delivering it to the user's home" refers to the means by which a cooking facility prepares food based on shared recipe information and delivers it to the user's home through a delivery service.

[0864] "Means for collecting feedback and using it to improve services" refers to a system that collects ratings and opinions provided by users through the application and uses them to improve the quality of future services and menus.

[0865] This invention is a system that proposes nutritionally balanced meals based on the user's basic information, daily meal records, and emotional data, and then prepares and delivers those meals. A specific embodiment of this system is shown below.

[0866] 1. Data Collection

[0867] User:

[0868] Users log in to a mobile application or web form and enter personal information (age, gender, height, weight, allergy information, dietary preferences, etc.). They also enter details of their daily meals into the application. For example, user D might record the scrambled eggs and toast they ate for breakfast. The application used should ideally have a user-friendly interface.

[0869] 2. Data storage

[0870] server:

[0871] The server stores personal information and meal records submitted by users in a database. Specifically, it uses database management systems such as MySQL or PostgreSQL. The stored data is later used for analysis.

[0872] 3. Nutritional Analysis

[0873] server:

[0874] Based on the stored data, the server uses generative artificial intelligence (e.g., OpenAI GPT-4) to analyze the user's nutrient intake and calculate the necessary nutrients and calories. For example, it might identify that user D's daily calorie requirement is 1800 kcal and that they are deficient in vitamin D.

[0875] 4. Collection and Analysis of Sentimental Data

[0876] User:

[0877] The user uses the application to perform voice input and facial recognition. For example, they might voice input such as, "Today's meal was very delicious."

[0878] server:

[0879] The server uses speech recognition software (e.g., Google Speech-to-Text API) and facial expression analysis algorithms (e.g., Microsoft Face API) to analyze emotional data and extract emotions such as satisfaction levels. The analysis results are stored in a database and used in the next step.

[0880] 5. Menu generation

[0881] server:

[0882] Based on nutritional analysis results and emotional data, the server uses generative artificial intelligence to generate a well-balanced menu. For example, it might suggest a menu rich in vitamin D, such as "grilled salmon, spinach salad, and soy milk smoothie." This menu information is then sent to the cooking facility in the next step.

[0883] 6. Recipe Sharing

[0884] server:

[0885] The server shares the generated menu and detailed recipe information with cooking facilities near the user's location. For example, it sends menu information for grilled salmon, spinach salad, and soy milk smoothie to "nearby cooking facilities" in the user D's area.

[0886] 7. Cooking and Delivery

[0887] Terminal (Cooking Staff):

[0888] The cooking staff prepares the dishes for the menu based on shared recipe information. For example, they gather the necessary ingredients and begin cooking, and once cooking is complete, they properly package the dishes.

[0889] Terminal (Delivery Staff):

[0890] Delivery staff pick up the prepared food and deliver it to the user's home. For example, they might deliver "grilled salmon, spinach salad, and a soy milk smoothie" to user D's home.

[0891] 8. Gathering Feedback

[0892] User:

[0893] User D receives the delivered food, finishes eating, and then enters a rating and feedback through the application. For example, they might enter a rating such as, "The grilled salmon was very tasty, but there was too much salad dressing."

[0894] server:

[0895] The server collects user feedback and uses it, along with sentiment data, to create future menus and improve the service.

[0896] Specific example:

[0897] User D records the scrambled eggs and toast they ate for breakfast using the app.

[0898] The server analyzes daily nutrient intake based on recorded meal data and identifies vitamin D deficiency.

[0899] User D inputs "Today's meal was very delicious" via voice input, and the server extracts the emotion of "satisfaction."

[0900] The server generates a menu of "grilled salmon, spinach salad, and soy milk smoothie" and shares it with the cooking facility.

[0901] Cooking staff prepare the meals based on the menu, and delivery staff deliver the meals to user D.

[0902] User D enters feedback via the app after the meal, and the server adjusts the next menu based on that data.

[0903] Examples of prompts for a generative AI model:

[0904] "Based on the scrambled eggs and toast that User D ate for breakfast, please suggest a well-balanced menu that is rich in vitamin D."

[0905] Therefore, this system allows elderly people and those who have difficulty managing their diet to easily receive nutritionally balanced meals, and also enables the provision of more satisfying services by utilizing emotional data.

[0906] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0907] Step 1: Data Entry

[0908] User:

[0909] Input: Users enter personal information (age, gender, height, weight, allergy information, dietary preferences, etc.) through a mobile application or web form.

[0910] Operation: The user accesses the application and enters the necessary personal information into the input form. They also enter details of their daily meals. For example, user D records the scrambled eggs and toast they ate for breakfast.

[0911] Output: The entered personal information and meal record data are formatted within the application and ready for transmission.

[0912] Step 2: Save Data

[0913] server:

[0914] Input: Personal information and meal record data submitted by the user.

[0915] Operation: The server receives data and saves it to a database such as MySQL or PostgreSQL. It performs data integrity checks and notifies the user if errors occur.

[0916] Output: User personal information and meal record data stored in the database.

[0917] Step 3: Nutritional Analysis

[0918] server:

[0919] Input: User meal record data stored in the database.

[0920] Operation: Using generative artificial intelligence (e.g., OpenAI GPT-4), the system analyzes stored meal record data to determine the user's nutrient intake. Specifically, it calculates the daily calorie requirement and any deficient nutrients based on user D's diet.

[0921] Output: The analysis results provide data on calorie intake and deficient nutrients (e.g., vitamin D). This analysis result is also stored in a database and used for subsequent processing.

[0922] Step 4: Collecting emotional data

[0923] User:

[0924] Input: Users input emotional data into the application using voice input or facial recognition.

[0925] Operation: For example, user D inputs "Today's meal was very delicious" via voice. The application formats the voice and facial expression data and prepares it for transmission to the server.

[0926] Output: The formatted audio data and facial expression data are sent to the server.

[0927] Step 5: Emotion Analysis

[0928] server:

[0929] Input: Voice data and facial expression data sent by the user.

[0930] Operation: Uses speech recognition software (e.g., Google Speech-to-Text API) and facial expression analysis algorithms (e.g., Microsoft Face API) to analyze emotional data and extract emotions such as satisfaction levels.

[0931] Output: Extracted sentiment data (e.g., "satisfied") is obtained. This sentiment data is also stored in the database and used in the next step.

[0932] Step 6: Menu Generation

[0933] server:

[0934] Input: Nutritional analysis results and emotional data.

[0935] Operation: Based on nutritional analysis results and emotional data, it uses generative artificial intelligence to generate a well-balanced menu. For example, it might suggest "grilled salmon, spinach salad, and soy milk smoothie," which are rich in vitamin D.

[0936] Output: The proposed menu information is generated and ready to be sent to the kitchen facility. This information is also stored in the database.

[0937] Step 7: Recipe Sharing

[0938] server:

[0939] Input: Generated menu and detailed recipe information.

[0940] Operation: The server transmits data to cooking facilities near the user. Communication is conducted via the internet or a dedicated network.

[0941] Output: Menu and recipe information sent to the cooking facility.

[0942] Step 8: Cooking

[0943] Terminal (Cooking Staff):

[0944] Input: Menu and recipe information received by the cooking facility.

[0945] Action: Based on this information, the kitchen staff gathers the necessary ingredients and begins cooking. For example, they might prepare grilled salmon, spinach salad, and soy milk smoothies.

[0946] Output: Cooking is complete and the food is properly packaged.

[0947] Step 9: Delivery

[0948] Terminal (Delivery Staff):

[0949] Input: Package of the finished dish.

[0950] Operation: Delivery staff receive the food and prepare it for delivery. They deliver the food to the user's home at the specified time. For example, deliver grilled salmon, spinach salad, and a soy milk smoothie to user D's home.

[0951] Output: Food delivered to the user's home.

[0952] Step 10: Gathering Feedback

[0953] User:

[0954] Input: Ratings and opinions about the delivered food.

[0955] Operation: After user D finishes their meal, they enter their rating and feedback through the application. For example, they might enter the opinion, "The grilled salmon was very tasty, but there was too much salad dressing."

[0956] Output: The input feedback is sent to the server.

[0957] server:

[0958] Input: Feedback submitted by the user.

[0959] Operation: Collects feedback and stores it in a database. Uses the feedback data to create future menus and improve services.

[0960] Output: Feedback information useful for improvement is accumulated in the database, contributing to service enhancement.

[0961] (Application Example 2)

[0962] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0963] In modern society, many users lead busy lives and lack the time to plan and prepare balanced meals. Furthermore, accurately understanding each individual's nutrient intake and generating optimal menus to supplement any deficiencies is challenging. Moreover, menu suggestions and service improvements that consider user satisfaction and emotions regarding meals are insufficient. Therefore, there is a need for a system that allows users to easily obtain balanced meals while also increasing their satisfaction with those meals.

[0964] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0965] In this invention, the server includes means for collecting the user's basic information and meal records; means for analyzing the user's nutrient intake based on the collected data and calculating the necessary nutrients and calories; means for generating a well-balanced menu based on the analysis results and emotional data and proposing it to the user; means for sharing the generated menu and recipe information with cooking facilities near the user; means for cooking the meal based on the shared recipe and delivering it to the user's home; means for extracting the user's emotional data through voice input and facial recognition; and means for collecting user feedback and emotional data and reflecting it in improving the service. As a result, users can easily obtain well-balanced meals tailored to their individual nutritional needs and increase their satisfaction with those meals.

[0966] "User basic information" refers to personal data such as age, gender, weight, height, allergy information, and food preferences.

[0967] A "meal diary" is data in which a user specifically records the contents of their daily meals.

[0968] "Nutrient intake status" refers to the amount and balance of each nutrient that the user is consuming through their diet.

[0969] "Required nutrients and calories" refers to the specific nutrients and calories that a user needs to maintain their health and achieve their goals.

[0970] "Analysis results" refer to the results regarding nutrient intake and nutrient deficiencies derived by a generative artificial intelligence system based on the user's basic information and dietary records that have been collected.

[0971] "Emotional data" refers to data such as satisfaction levels and moods extracted through user voice input and facial recognition.

[0972] A "menu" refers to a specific set of dishes or meal combinations suggested to the user.

[0973] "Recipe information" refers to detailed instructions that include how to prepare a dish and a list of necessary ingredients.

[0974] A "cooking facility" is a facility that prepares meals based on menus suggested to users.

[0975] "Feedback" refers to data that users input and submit regarding the food or service they receive, including their evaluations and impressions.

[0976] "Service improvement" means improving future menu suggestions and service content based on collected feedback and sentiment data.

[0977] This invention is a system that collects basic user information and meal records, and generates and proposes well-balanced menus based on the analysis results and emotional data. The aim of this system is to enable users to effectively manage their daily meals and increase their satisfaction with them.

[0978] 1. Data Acquisition Module

[0979] User:

[0980] Users use a mobile application to input personal information (age, gender, height, weight, allergy information, food preferences, etc.) and details of their daily meals. For example, a user might record the scrambled eggs and toast they ate for breakfast.

[0981] 2. Dietary Habits Analysis Module

[0982] server:

[0983] The server stores collected personal information and meal records in a database. Based on the stored data, generative artificial intelligence is used to analyze the user's nutrient intake and calculate the necessary nutrients and calories. For example, by analyzing the user's data, it might identify that the user needs 1800 kcal per day and is deficient in vitamin D.

[0984] 3. Emotion Recognition Module

[0985] User:

[0986] The user uses the application for voice input and facial recognition. For example, the user launches the application and voice-inputs, "Today's meal was very delicious."

[0987] server:

[0988] The server analyzes voice input and facial recognition data to extract user emotion data. For example, it might extract the emotion of "satisfaction" from the user's voice input.

[0989] 4. Menu Creation Module

[0990] server:

[0991] The server generates a well-balanced menu based on analysis results and emotional data. Generative artificial intelligence suggests the optimal combination of ingredients and dishes. For example, it might suggest a menu to the user that includes dishes rich in vitamin D, such as "grilled salmon, spinach salad, and soy milk smoothie."

[0992] 5. Locally shared modules

[0993] server:

[0994] The generated menu and detailed recipe information are shared with the cooking facility closest to the user's area. For example, recipes for grilled salmon, spinach salad, and soy milk smoothie are sent to cooking facilities in the user's area.

[0995] 6. Cooking and Delivery Module

[0996] Terminal (Cooking Staff):

[0997] Based on shared recipe information, the cooking staff prepares the dishes for the menu. For example, they check the recipe, gather the necessary ingredients, and begin cooking. After cooking is complete, they properly package the dishes.

[0998] Terminal (Delivery Staff):

[0999] Cooked food is picked up by delivery staff and delivered to the user's home. For example, grilled salmon, spinach salad, and a soy milk smoothie can be delivered to the user's home.

[1000] 7. Feedback Module

[1001] User:

[1002] After receiving their delivered food and finishing their meal, users enter ratings and feedback through the application. For example, they might write, "The grilled salmon was very tasty, but there was too much salad dressing."

[1003] server:

[1004] The server collects user feedback and uses it, along with sentiment data, to create future menus and improve the service. For example, based on user feedback, the amount of salad dressing might be adjusted next time, and sentiment data might be used to create more satisfying menus.

[1005] The hardware used includes common smartphones (Android / iOS), servers, and terminals for cooking and delivery staff. Software includes Android Studio and iOS development tools for mobile applications, Flask for the server side, SQLite for the database, and TensorFlow for AI models.

[1006] As a concrete example, the following prompt sentences are used in generative AI models:

[1007] User data:

[1008] Age: 30

[1009] Gender: Male

[1010] Weight: 70kg

[1011] Height: 175cm

[1012] Dietary restrictions: None

[1013] Recent meals: Breakfast - Oatmeal with fruits, Lunch - Chicken salad, Dinner - Grilled salmon with vegetables

[1014] User feeling about today's meals: Satisfied

[1015] Generate a balanced menu for the next day considering the user's health needs and preferences.

[1016] In this way, the system allows individual users to easily enjoy nutritionally balanced meals and achieve highly satisfying and smart meal management.

[1017] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1018] Step 1:

[1019] Users enter personal information and meal records.

[1020] Input: Users use a smartphone application to enter personal information such as age, gender, height, weight, allergy information, and dietary preferences, as well as details of their daily meals.

[1021] Data processing and calculation: The application checks the input data and organizes it according to the format.

[1022] Output: Organized personal information and meal records are sent to the server and stored in the database.

[1023] Specific operation: The user enters the scrambled eggs and toast they ate for breakfast, and the application organizes that information and sends it to the server.

[1024] Step 2:

[1025] The server performs dietary analysis.

[1026] Input: User's personal information and meal records stored in the database.

[1027] Data processing and calculation: The server uses generative artificial intelligence to analyze the user's nutrient intake and calculate the necessary nutrients and calories.

[1028] Output: The calculated analysis results are generated.

[1029] Specific operation: The server analyzes the user's data and identifies that their daily calorie requirement is 1800 kcal and that they are deficient in vitamin D.

[1030] Step 3:

[1031] Users input emotional data.

[1032] Input: The user launches the application and provides emotional data through voice input or facial recognition.

[1033] Data Processing and Calculation: The application performs preprocessing to analyze voice input and facial recognition data.

[1034] Output: Pre-processed emotion data is sent to the server.

[1035] Specific operation: The user voice-inputs "Today's meal was very delicious," and this information is pre-processed before being sent to the server.

[1036] Step 4:

[1037] The server analyzes the emotional data.

[1038] Input: Sentiment data submitted by the user.

[1039] Data processing and calculation: The server uses generative artificial intelligence to analyze emotional data and identify user satisfaction and emotions.

[1040] Output: Emotional data extracted as an analysis result.

[1041] Specific operation: The server extracts the emotion of "satisfaction" from the voice input.

[1042] Step 5:

[1043] The server generates the menu.

[1044] Input: Analysis results and sentiment data.

[1045] Data processing and calculation: The server uses generative artificial intelligence to calculate the optimal combination of ingredients and dishes.

[1046] Output: A well-balanced menu is generated.

[1047] Specific action: A menu is proposed that includes dishes rich in vitamin D, such as "grilled salmon, spinach salad, and soy milk smoothie."

[1048] Step 6:

[1049] The server shares recipe information with the cooking facilities.

[1050] Input: Generated menu and detailed recipe information.

[1051] Data processing and calculation: The server converts menu and recipe information into a specific format and sends it to the nearest cooking facility.

[1052] Output: Shared data for the cooking facility to receive.

[1053] Specific action: Send recipe information for "grilled salmon, spinach salad, and soy milk smoothie" to a cooking facility in the user's area.

[1054] Step 7:

[1055] The cooking facility prepares the food.

[1056] Input: Recipe information shared from the server.

[1057] Data processing and calculation: Cooking staff gather the necessary ingredients based on the recipe and begin cooking.

[1058] Output: Cooked food.

[1059] Specific tasks: Cooking staff will review the recipes and prepare and properly package the grilled salmon, spinach salad, and soy milk smoothie.

[1060] Step 8:

[1061] Delivery staff deliver the food.

[1062] Input: Dishes prepared in a cooking facility.

[1063] Data processing and calculation: Delivery staff pick up the food and deliver it to the user's home.

[1064] Output: Meals delivered to the user's home.

[1065] Specific actions: A delivery staff member delivers grilled salmon, spinach salad, and a soy milk smoothie to the user's home.

[1066] Step 9:

[1067] Users enter feedback

[1068] Input: Your evaluation or comments on the food you received.

[1069] Data processing and calculation: The application collects the input feedback and sends it to the server.

[1070] Output: Collected feedback data.

[1071] Specific operation: The user enters a review stating, "The grilled salmon was very tasty, but there was too much salad dressing," and this information is sent to the server.

[1072] Step 10:

[1073] The server analyzes the feedback and uses it to improve the service.

[1074] Input: User feedback and sentiment data.

[1075] Data processing and calculation: Generative artificial intelligence is used to analyze feedback and utilize it for creating future menus and improving services.

[1076] Output: Improved menu suggestions and service details.

[1077] Specific operation: Based on user feedback, the server adjusts the amount of salad dressing in the next menu.

[1078] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1079] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1080] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[1081] [Third Embodiment]

[1082] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[1083] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1084] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1085] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[1086] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[1087] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[1088] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[1089] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[1090] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1091] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1092] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[1093] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[1094] 1. Data Acquisition Module

[1095] User:

[1096] Users enter personal information (age, gender, height, weight, allergy information, dietary preferences, etc.) using a mobile application or web form. They also record their daily meals, and this allows for the collection of dietary records.

[1097] 2. Dietary Habits Analysis Module

[1098] server:

[1099] The server stores the data collected from users in a database. Next, generative artificial intelligence within the server is used to analyze this data and gain a detailed understanding of the user's nutrient intake. For example, analysis of data from a woman in her 70s, Ms. B, reveals that her daily calorie requirement is 1600 kcal and that she is deficient in B vitamins.

[1100] 3. Menu Creation Module

[1101] server:

[1102] Based on the analysis results, the server generates a well-balanced menu. In this process, a generative artificial intelligence suggests the optimal combination of ingredients and dishes. For example, for user B, it might create a menu including stir-fried chicken liver (rich in B vitamins), blanched spinach, and fruit yogurt.

[1103] Specific example:

[1104] Based on User B's daily calorie needs and nutrient deficiencies, we propose the following meal plan: "Breakfast: Fruit yogurt, Lunch: Stir-fried chicken liver and blanched spinach, Dinner: Grilled fish and steamed vegetables."

[1105] 4. Locally shared modules

[1106] server:

[1107] The generated menu and its detailed recipe information are shared with the cooking facility closest to the user's area. For example, the server sends the recipe information to "Nearby Cooking Facility X" located in the area where user B lives.

[1108] 5. Cooking and Delivery Module

[1109] Terminal (Cooking Staff):

[1110] Based on the shared recipe information, the cooking staff prepares the dishes for the menu. Specifically, they check the recipe sent from the server, prepare the necessary ingredients, and begin cooking.

[1111] Terminal (Delivery Staff):

[1112] The delivery staff will properly package the cooked meals and deliver them to user B's home. For example, stir-fried chicken liver and blanched spinach for lunch will be delivered at lunchtime, and grilled fish and steamed vegetables for dinner will be delivered at dinnertime.

[1113] 6. Feedback Module

[1114] User:

[1115] User B receives the delivered food, finishes eating, and then submits feedback through the application. They fill in their evaluation of the taste, quantity, and quality, and then submit it.

[1116] server:

[1117] The server collects feedback from user B and uses it to create future menus and improve the service. For example, if user B provides feedback that "the stir-fried chicken liver was a little too salty," the seasoning will be adjusted next time.

[1118] As described above, this system enables elderly people to continuously receive nutritionally balanced meals. By collecting basic user information and meal records, analyzing this information using generative artificial intelligence, generating nutritionally balanced menus, cooking at a cooking facility, delivery, and collecting and incorporating feedback, the system supports the user's health.

[1119] The following describes the processing flow.

[1120] Step 1:

[1121] User:

[1122] Users log in to a mobile application or web form and enter personal information (age, gender, height, weight, allergy information, dietary preferences, etc.). They also record their daily meals. For example, user C enters the toast and coffee they ate for breakfast.

[1123] Step 2:

[1124] server:

[1125] The server stores personal information and meal records collected from users in a database. The stored data is appropriately formatted for analysis. For example, user C's personal information and data on bread and coffee are stored in the database.

[1126] Step 3:

[1127] server:

[1128] The generative artificial intelligence on the server retrieves the user's meal records and basic information from the database and performs analysis. This calculates the user's nutrient intake. For example, it identifies that user C's daily calorie requirement is 2000 kcal and that their calcium intake is insufficient.

[1129] Step 4:

[1130] server:

[1131] Based on the analysis results, the server generates a nutritionally balanced menu. Generative artificial intelligence suggests the optimal combination of ingredients and dishes. For example, for user C, it creates a menu that includes "grilled salmon, broccoli salad, and soy milk smoothie," which are rich in calcium.

[1132] Step 5:

[1133] server:

[1134] The generated menu and its detailed recipe information are shared with the cooking facility closest to the user's area. For example, recipe information for grilled salmon, broccoli salad, and soy milk smoothie is sent to "Nearby Cooking Facility Y" in the area where user C lives.

[1135] Step 6:

[1136] Terminal (Cooking Staff):

[1137] Based on shared recipe information, the cooking staff prepares the dishes for the menu. For example, they check the recipe, gather the necessary ingredients, and begin cooking. After cooking is complete, they properly package the dishes.

[1138] Step 7:

[1139] Terminal (Delivery Staff):

[1140] A delivery staff member picks up the prepared food and delivers it to the user's home. For example, grilled salmon, broccoli salad, and a soy milk smoothie are delivered to user C's home.

[1141] Step 8:

[1142] User:

[1143] The user receives the delivered food and actually eats it. For example, user C enjoys grilled salmon and broccoli salad for dinner.

[1144] Step 9:

[1145] User:

[1146] After their meal, users enter their ratings and feedback through the application. For example, user C might submit feedback stating, "The grilled salmon was very tasty, but there was a little too much salad dressing."

[1147] Step 10:

[1148] server:

[1149] The server collects user feedback and uses it to create future menus and improve the service. For example, based on user C's feedback, the amount of salad dressing will be adjusted next time.

[1150] Through these steps, the system enables elderly people to easily receive nutritionally balanced meals.

[1151] (Example 1)

[1152] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1153] To address the current challenges that make it difficult for the elderly to consistently receive nutritionally balanced meals, there is a need for a system that can accurately understand users' nutrient intake, generate appropriate menus based on that information, and efficiently manage the cooking and delivery processes.

[1154] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1155] In this invention, the server includes means for collecting the user's basic information and meal records; means for storing the collected data and linking it to a user ID; means for analyzing the nutrient intake status based on the stored data and calculating the necessary nutrients and calories; means for generating a well-balanced menu using generative artificial intelligence based on the analysis results and proposing it to the user; means for sharing the generated menu and recipe information with cooking facilities near the user; means for cooking meals based on the shared recipes and delivering them to the user's home; and means for collecting feedback from the user and reflecting it in creating future menus and improving the service. This makes it possible for elderly people to continuously receive nutritionally balanced meals and support their health.

[1156] "User basic information" refers to data about an individual user's basic health and lifestyle habits, such as age, gender, height, weight, allergy information, and food preferences.

[1157] A "meal diary" is data that users record about their daily meals, including specific food names, quantities consumed, and meal times.

[1158] "Means of collection" refers to technical methods or devices for obtaining data entered by users, such as through mobile applications or web forms.

[1159] "Means of preservation" refers to technologies for storing and managing collected data in data storage such as databases.

[1160] A "User ID" is a unique identifier assigned to each user and is used to distinguish a user's data from that of other users.

[1161] "Methods for analyzing nutrient intake" refer to technologies that, based on collected data, identify the amount and types of nutrients a user is consuming and analyze any deficiencies or excesses in necessary nutrients or calories.

[1162] "Generative artificial intelligence" is an artificial intelligence technology that uses advanced algorithms such as machine learning and deep learning to analyze data, make predictions, and provide optimal suggestions.

[1163] "Methods for generating menus" refers to technologies that create well-balanced meal plans based on analysis results and propose them to users.

[1164] A "cooking facility" refers to a facility located near the user that performs cooking, and includes restaurants and catering services.

[1165] "Recipe information" refers to information such as specific cooking methods, necessary ingredients, and cooking procedures based on a menu.

[1166] "Means of cooking" refer to the techniques and equipment used to actually prepare a meal based on shared recipe information.

[1167] "Delivery methods" refer to the methods and technologies used to deliver cooked food to the user's home.

[1168] "Means of collecting feedback" refers to the technologies and methods used to obtain ratings and comments from users.

[1169] "Means of reflecting feedback in service improvement" refers to methods used to create future menus and improve service quality based on the collected feedback.

[1170] This invention relates to a system for elderly people to continuously receive nutritionally balanced meals. This system collects basic information and meal records from the user, analyzes nutrient intake based on this information, and generates an appropriate menu. Furthermore, the generated menu and recipe information are shared with cooking facilities, the cooked meals are delivered to the user's home, and feedback is collected to help create future menus and improve the service.

[1171] This system uses the following hardware and software.

[1172] User:

[1173] Users enter personal information (age, gender, height, weight, allergy information, food preferences, etc.) and daily meal details using a mobile application or web form. Specifically, they use devices such as smartphones or PCs. An example of information a user might enter is "Height 165cm, weight 60kg, likes chicken."

[1174] server:

[1175] The server receives data sent by the user and stores it in a database. This database can be a common relational database system (e.g., PostgreSQL). Next, generative artificial intelligence (e.g., GPT-3) is used to analyze the collected data and identify the user's nutrient intake. For example, analysis of data from a female user in her 70s might reveal that her daily calorie requirement is 1600 kcal and that she is deficient in B vitamins.

[1176] Based on the analysis results, the server uses generative artificial intelligence to generate a well-balanced menu and propose it to the user. For example, it might suggest: "Breakfast: Fruit yogurt, Lunch: Stir-fried chicken liver and blanched spinach, Dinner: Grilled fish and steamed vegetables." An example of a prompt message would be: "Based on data collected from a female user in her 70s, please propose a well-balanced menu that matches the dietary analysis results showing a deficiency in B vitamins."

[1177] The generated menu and its detailed recipe information are shared with the cooking facility closest to the user's area. The server uses the SMTP protocol to send emails to the cooking facility, providing the recipe information. Alternatively, data can be shared via a dedicated API.

[1178] Terminal (Cooking Staff):

[1179] Cooking staff check recipe information sent from the server on their terminals (PCs or tablets) and prepare the necessary ingredients. Specifically, they check the inventory of necessary ingredients in real time and place uncooked ingredients on the cooking counter. Cooking staff cook the ingredients using cooking utensils such as frying pans and ovens while checking the recipe instructions. For example, they might sauté chicken liver for the specified time and boil and cool spinach.

[1180] Terminal (Delivery Staff):

[1181] Delivery staff carefully package the cooked meals and deliver them to the user's home. Using GPS devices, delivery staff ensure the meals arrive precisely at the designated time. Specifically, they carefully pack the food into packaging materials and head towards the user's address while monitoring traffic conditions in real time.

[1182] User:

[1183] After receiving their delivered food and finishing their meal, users submit feedback through the application. They rate the taste, quantity, and quality, and then submit the feedback. For example, one piece of feedback might be, "The stir-fried chicken liver was a little too salty."

[1184] server:

[1185] The server collects user feedback and uses it to create future menus and improve the service. For example, based on feedback such as "the stir-fried chicken liver was a little too salty," the seasoning will be adjusted next time.

[1186] This will provide a system that supports users' health and enables elderly people to continuously receive nutritionally balanced meals.

[1187] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1188] Step 1: Data Collection

[1189] User:

[1190] Users open a mobile application or web form and enter their personal information and daily meal details. Specifically, they use a smartphone or PC to fill in the required fields in the form.

[1191] Input: Personal information entered by the user (age, gender, height, weight, allergy information, food preferences, etc.) and meal details.

[1192] Output: Dataset of user's basic information and meal records

[1193] Step 2: Save Data

[1194] server:

[1195] The server receives data sent by the user and stores it in the database. The database uses a relational database system (e.g., PostgreSQL). The data is associated with the user ID upon storage.

[1196] Input: User's basic information and meal record dataset

[1197] Output: User's basic information and meal records stored in the database

[1198] Step 3: Data Analysis

[1199] server:

[1200] The generative artificial intelligence model (e.g., GPT-3) on the server analyzes the stored data to identify the user's nutrient intake. For example, it calculates the daily calorie requirement and any deficient nutrients.

[1201] Input: User's basic information and meal records stored in the database.

[1202] Output: User's nutrient intake status and specific analysis results

[1203] Step 4: Menu Generation

[1204] server:

[1205] Based on the analysis results, the server uses generative artificial intelligence to generate a well-balanced menu. For example, it might suggest "Breakfast: Fruit yogurt, Lunch: Stir-fried chicken liver and blanched spinach, Dinner: Grilled fish and steamed vegetables."

[1206] Input: User's nutrient intake status and analysis results

[1207] Output: A well-balanced menu and detailed recipe information

[1208] Step 5: Local Sharing

[1209] server:

[1210] The generated menu and recipe information is shared with the cooking facility closest to the user's area. Data is transmitted via SMTP protocol or a dedicated API.

[1211] Input: A well-balanced menu and detailed recipe information

[1212] Output: Recipe information sent to the cooking facility

[1213] Step 6: Preparation for cooking

[1214] Terminal (Cooking Staff):

[1215] Cooking staff check recipe information sent from the server on their devices (PCs or tablets) and prepare the necessary ingredients. They also check the inventory of necessary ingredients in real time and place them on the cooking counter.

[1216] Input: Recipe information sent to the cooking facility

[1217] Output: Prepared ingredients

[1218] Step 7: Cooking

[1219] Terminal (Cooking Staff):

[1220] The cooking staff begins cooking using the prepared ingredients. They use cooking utensils such as frying pans and ovens and cook according to the recipe.

[1221] Input: Prepared ingredients

[1222] Output: Finished dish

[1223] Step 8: Delivery

[1224] Terminal (Delivery Staff):

[1225] Delivery staff properly package the cooked meals and deliver them to the user's home. They use GPS to monitor traffic conditions and deliver at the specified time.

[1226] Input: Finished dish

[1227] Output: Food delivered to the user's home

[1228] Step 9: Gathering Feedback

[1229] User:

[1230] After receiving and eating their delivered food, users submit feedback through the application. They rate the taste, quantity, and quality of the food and then submit it.

[1231] Input: Post-use evaluation of delivered food

[1232] Output: Feedback data

[1233] Step 10: Feedback Analysis

[1234] server:

[1235] The server collects user feedback and uses it to create future menus and improve the service. Based on the feedback, it inputs the next prompt into the generative artificial intelligence and makes adjustments.

[1236] Input: Feedback data

[1237] Output: Improved menu planning and service quality

[1238] In this way, by performing data processing and calculations using specific inputs and outputs, as well as generating AI models and prompts at each step, a system is realized that supports the health of the elderly while maintaining nutritional balance.

[1239] (Application Example 1)

[1240] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset terminal 314 will be referred to as the "terminal." For elderly people and users who require nutritional management, ensuring a balanced diet on a daily basis can be difficult. Furthermore, continuously providing menus that consider nutritional balance is time-consuming, making continuous health management difficult. Therefore, based on the user's personal information and meal records, support will be provided to help them lead a safe and healthy diet.

[1241] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means: The server includes means for collecting the user's basic information and meal records; means for analyzing the intake status of nutrients based on the collected data and calculating the necessary nutrients and calories; means for generating a well-balanced menu based on the analysis results and proposing it to the user; means for sharing the generated menu and recipe information with cooking facilities near the user; means for cooking based on the shared recipes and delivering them to the user's home; means for collecting feedback from the user and reflecting it in improving the service; and means for managing the user's data through a smartphone application, generating menus using generative artificial intelligence, and sharing the results with cooking facilities.

[1242] A "user" refers to anyone who uses the system to input personal information or meal records.

[1243] "Basic information" refers to personal data such as the user's age, gender, height, weight, allergy information, and food preferences.

[1244] A "food diary" refers to the contents of the food a user consumes on a daily basis.

[1245] "Data" refers to basic information and meal records collected from users.

[1246] "Nutrients" refer to components necessary for nutritional balance, such as proteins, vitamins, and minerals.

[1247] "Analysis" refers to the process of evaluating nutrient intake based on collected data.

[1248] "Calories" refer to the amount of energy contained in food.

[1249] A "menu" refers to a meal plan that is designed with a balance of nutrients in mind.

[1250] "Generative artificial intelligence" refers to algorithms and models that generate nutritionally balanced meal plans based on user data.

[1251] A "cooking facility" refers to a place where food is prepared based on the generated menu and recipe information.

[1252] "Feedback" refers to the information that users send back to the system, evaluating the dishes they are served.

[1253] "Service improvement" refers to improving menu creation, cooking, and delivery services for future orders based on the collected feedback information.

[1254] A "smartphone application" refers to a mobile app that allows users to input basic information and meal records, and receive analysis results and meal plan suggestions.

[1255] Modes for carrying out the invention

[1256] System Overview

[1257] This invention is a food delivery system that provides users with nutritionally balanced meals. Users input basic information and meal records through a smartphone application, and based on that data, a meal plan is generated, prepared at the nearest cooking facility to the user, and delivered by delivery staff. User feedback is used to improve the service.

[1258] Specific Embodiments

[1259] 1. Collection of user data

[1260] Users input basic information such as age, gender, height, weight, allergy information, and dietary preferences through a smartphone application. Their daily meals are also recorded. This data is sent to a server and stored in a database.

[1261] 2. Analysis of Nutrients

[1262] The server analyzes the collected basic information and meal records and uses a generative AI model to understand the user's nutrient intake. This analysis can identify which nutrients the user needs to consume and in what quantities.

[1263] 3. Menu generation

[1264] The generation AI model generates optimal menus based on the analysis results. The generated menus include food items with a well-balanced combination of nutrients. For example, for a user who is deficient in a specific vitamin, dishes using ingredients rich in that vitamin will be suggested.

[1265] 4. Data Sharing

[1266] The generated menu and its detailed recipe information are shared with the nearest cooking facility in the user's area. The cooking facility then prepares the dishes based on the received recipe information.

[1267] 5. Food preparation and delivery

[1268] The kitchen staff prepares the meals according to the generated recipes and packages them appropriately. Then, delivery staff deliver the meals to the user's address. This ensures that users receive fresh, nutritionally balanced meals tailored to their time of day.

[1269] 6. Gathering feedback and improving services

[1270] After receiving and eating their delivered food, users provide feedback through a smartphone application. They input ratings regarding taste, quantity, and quality. This feedback is sent to the server and used to create future menus and improve the service.

[1271] Hardware and software to be used

[1272] Hardware: Users use smartphones, while kitchen staff and delivery personnel use tablets or PCs. Servers are used for data storage and analysis.

[1273] Software: A smartphone application is used for inputting basic user information and meal records. SQLite is used for the database, and Flask is used for the backend of the web application. The OpenAI API is used for generative AI models.

[1274] Examples of prompt statements

[1275] The following is an example of a prompt to input into a generative AI model:

[1276] User information:

[1277] Age: 70, Gender: Female, Height: 160cm, Weight: 55kg.

[1278] Allergens: Shrimp, nuts.

[1279] Meal plan: Breakfast: Toast and coffee. Lunch: Chicken salad. Dinner: Braised fish and stir-fried vegetables.

[1280] Please provide a nutritionally balanced menu.

[1281] Example output from a generative AI model for this:

[1282] Breakfast: Fruit yogurt

[1283] Lunch: Stir-fried chicken liver, blanched spinach

[1284] Dinner: Grilled fish and steamed vegetables

[1285] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1286] Step 1: Collect basic user information and meal records.

[1287] Users enter basic information (age, gender, height, weight, allergy information, and dietary preferences) using a smartphone application. They also enter their daily meal details. The entered information is sent to a server and stored in an SQLite database. Specifically, the text-based input data is converted into a database format.

[1288] Input: User's basic information and meal records

[1289] Output: User information and meal records stored in the database

[1290] Step 2: Nutrient Analysis

[1291] The server analyzes nutrient intake based on user information and meal records stored in the database. This analysis uses OpenAI's generative AI model. Specifically, user information and meal records are input to the generative AI model as prompts, and data processing is performed to identify deficient nutrients based on the analysis results obtained.

[1292] Input: User information and meal records in the database

[1293] Output: Analysis results (such as deficient nutrients and required calories)

[1294] Step 3: Menu Generation

[1295] The server uses the OpenAI API to generate a nutritionally balanced menu based on the nutrient analysis results. It inputs prompts into the AI ​​model to obtain the optimal menu for the user. The generated menu includes specific food items and their recipes.

[1296] Input: Nutrient analysis results and prompt text

[1297] Output: A well-balanced menu and its recipe information

[1298] Step 4: Sharing menu information

[1299] The server sends the generated menu and recipe information to the nearest cooking facility in the user's area. The cooking facility then begins preparing the food based on the received recipe information. This step involves transmitting data using communication means.

[1300] Input: Menu and recipe information

[1301] Output: Sending recipe information to cooking facilities

[1302] Step 5: Cooking and delivering the food

[1303] The kitchen staff prepares the dishes based on the received recipe information. The finished dishes are properly packaged and delivered to the user's address by delivery staff. This step includes the actions required for cooking and delivery.

[1304] Input: Recipe information received by the cooking facility

[1305] Output: Cooked food delivered to the user's address.

[1306] Step 6: Gathering feedback and improving the service

[1307] After users have eaten the food they received, they provide feedback through a smartphone application. This feedback is sent to the server and used to generate future menus and improve the service. The server analyzes this feedback and incorporates it into the next menu generation.

[1308] Input: User feedback

[1309] Output: Improved service and menu generation process

[1310] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1311] 1. Data Acquisition Module

[1312] User:

[1313] Users log in to a mobile application or web form and enter personal information (age, gender, height, weight, allergy information, dietary preferences, etc.) and details of their daily meals. For example, user D might record scrambled eggs and toast for breakfast as part of their meal details.

[1314] 2. Dietary Habits Analysis Module

[1315] server:

[1316] The server stores collected personal information and meal records in a database. Based on the stored data, generative artificial intelligence is used to analyze the user's nutrient intake and calculate the necessary nutrients and calories. For example, analyzing user D's data reveals that their daily calorie requirement is 1800 kcal and that they are deficient in vitamin D.

[1317] 3. Emotion Recognition Module

[1318] User:

[1319] The user uses the application to perform voice input and facial recognition. For example, user D launches the application and inputs "Today's meal was very delicious" by voice.

[1320] server:

[1321] The server analyzes voice input and facial recognition data to extract user emotion data. For example, it extracts the emotion "satisfied" from user D's voice input.

[1322] 4. Menu Creation Module

[1323] server:

[1324] The server generates a well-balanced menu based on analysis results and emotional data. Generative artificial intelligence suggests the optimal combination of ingredients and dishes. For example, it suggests a menu to user D that includes dishes rich in vitamin D, such as "grilled salmon, spinach salad, and soy milk smoothie."

[1325] 5. Locally shared modules

[1326] server:

[1327] The generated menu and detailed recipe information are shared with the cooking facility closest to the user's area. For example, recipe information for grilled salmon, spinach salad, and soy milk smoothie is sent to "Nearby Cooking Facility Z" in the area where user D lives.

[1328] 6. Cooking and Delivery Module

[1329] Terminal (Cooking Staff):

[1330] Based on shared recipe information, the cooking staff prepares the dishes for the menu. For example, they check the recipe, gather the necessary ingredients, and begin cooking. After cooking is complete, they properly package the dishes.

[1331] Terminal (Delivery Staff):

[1332] A delivery staff member picks up the prepared food and delivers it to the user's home. For example, grilled salmon, spinach salad, and a soy milk smoothie are delivered to user D's home.

[1333] 7. Feedback Module

[1334] User:

[1335] User D receives the delivered food, finishes eating, and then enters a rating and feedback through the application. For example, they might enter a rating such as, "The grilled salmon was very tasty, but there was too much salad dressing."

[1336] server:

[1337] The server collects user feedback and uses it, along with sentiment data, to create future menus and improve the service. For example, based on user D's feedback, the amount of salad dressing will be adjusted next time, and the sentiment data will be used to create more satisfying menus.

[1338] Through the steps described above, this system allows elderly individuals to easily receive nutritionally balanced meals and provides a more satisfying service by utilizing emotional data. The system is continuously improved through a process that collects basic user information and meal records, generates optimal menus based on analysis results and emotional data, shares them with cooking facilities, handles cooking and delivery, and collects feedback.

[1339] The following describes the processing flow.

[1340] Step 1:

[1341] User:

[1342] Users log in to a mobile application or web form and enter personal information (age, gender, height, weight, allergy information, food preferences, etc.). They also record their daily meals. For example, user D records the scrambled eggs and toast they ate for breakfast.

[1343] Step 2:

[1344] server:

[1345] The server stores personal information and meal records collected from users in a database and formats them appropriately for analysis. User D's personal information and meal record (scrambled eggs and toast) are stored in the database.

[1346] Step 3:

[1347] server:

[1348] Using generative artificial intelligence on the server, the system retrieves users' meal records and basic information from a database and analyzes their nutrient intake. This allows it to identify their daily calorie needs and any nutrient deficiencies. For example, it might determine that user D needs 1800 kcal and is deficient in vitamin D.

[1349] Step 4:

[1350] User:

[1351] The user performs voice input and facial recognition through a mobile application. User D voice-inputs, "Today's meal was very delicious," and the app recognizes the user's facial expression using the camera.

[1352] Step 5:

[1353] server:

[1354] The server analyzes voice input and facial recognition data to extract user emotion data. For example, it recognizes the emotion of "satisfaction" from user D's voice and "smile" from their facial expression.

[1355] Step 6:

[1356] server:

[1357] Based on the analysis results and emotional data, the server generates a well-balanced menu. Generative artificial intelligence suggests the optimal combination of ingredients and dishes. For example, it might suggest a menu to user D consisting of "grilled salmon, spinach salad, and soy milk smoothie" as a dish rich in vitamin D.

[1358] Step 7:

[1359] server:

[1360] The generated menu and detailed recipe information are shared with the cooking facility closest to the user's area. For example, recipe information for grilled salmon, spinach salad, and soy milk smoothie is sent to "Nearby Cooking Facility Z" in the area where user D lives.

[1361] Step 8:

[1362] Terminal (Cooking Staff):

[1363] Based on the shared recipe information, the cooking staff prepares the dishes for the menu. They check the recipe, gather the necessary ingredients, and begin cooking. After cooking is complete, they properly package the food.

[1364] Step 9:

[1365] Terminal (Delivery Staff):

[1366] A delivery staff member picks up the prepared food and delivers it to the user's home. For example, grilled salmon, spinach salad, and a soy milk smoothie are delivered to user D's home.

[1367] Step 10:

[1368] User:

[1369] User D receives the delivered food and enjoys the meal. For example, for dinner, they might have grilled salmon and a spinach salad.

[1370] Step 11:

[1371] User:

[1372] After the meal, users enter ratings and feedback through the application. For example, they might enter a rating such as, "The grilled salmon was very tasty, but there was too much salad dressing."

[1373] Step 12:

[1374] server:

[1375] The server collects user feedback to help create future menus and improve the service. Based on user D's feedback, the amount of salad dressing will be adjusted next time, and emotional data will be used to generate a more satisfying menu.

[1376] Through the steps described above, this system allows elderly individuals to easily receive nutritionally balanced meals and provides a more satisfying service by utilizing emotional data. The system is continuously improved through a process that collects basic user information and meal records, generates optimal menus based on analysis results and emotional data, shares them with cooking facilities, handles cooking and delivery, and collects feedback.

[1377] (Example 2)

[1378] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1379] In modern times, providing easily accessible, nutritionally balanced meals for the elderly and those with difficulty managing their diets is a crucial challenge. However, systems that analyze individual nutrients and calories, and provide menus that take into account individual meal content and emotional states, are extremely limited. Therefore, to address these challenges, there is a need to develop a system that collects individual dietary and emotional data, proposes optimal menus based on this data, and provides consistent support from cooking and delivery to feedback.

[1380] The identification processing performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for collecting the user's basic information and meal records; means for analyzing the intake status of nutrients based on the collected data and calculating the necessary nutrients and calories; means for collecting and analyzing the user's emotional data; means for generating a well-balanced menu based on the analysis results and emotional data and proposing it to the user; means for sharing the generated menu and recipe information with cooking facilities near the user; means for cooking the food based on the shared recipe and delivering it to the user's home; and means for collecting feedback from the user and reflecting it in improving the service. This makes it possible to provide menus based on individual nutritional management and emotional state, enabling the realization of a more satisfying service.

[1381] "User basic information" refers to personal data including the user's age, gender, height, weight, allergy information, and food preferences.

[1382] "Meal records" refer to information that users input and record about their daily meals through applications or web forms.

[1383] "Means of collection" refers to systems and applications that receive data input from users and store it in a database.

[1384] "Methods for analyzing nutrient intake and calculating necessary nutrients and calories" refers to algorithms or programs that analyze a user's nutritional intake based on collected meal records and calculate the necessary nutrients and calorie amounts.

[1385] "Emotional data" refers to information indicating the user's emotional state (e.g., satisfaction, dissatisfaction, joy) collected through voice input and facial recognition.

[1386] "Means for collecting and analyzing emotional data" refers to software and algorithms that collect emotional data through user voice input or facial recognition and then analyze that data.

[1387] "A means of generating and proposing well-balanced menus to users" refers to a generative artificial intelligence system that generates and proposes combinations of ingredients and dishes suitable for the user based on the results of nutrient analysis and emotional data.

[1388] "Means of sharing recipe information with cooking facilities near the user" refers to systems or networks that transmit generated menus and detailed recipe information to cooking facilities near the user's residential area.

[1389] "Means of preparing food and delivering it to the user's home" refers to the means by which a cooking facility prepares food based on shared recipe information and delivers it to the user's home through a delivery service.

[1390] "Means for collecting feedback and using it to improve services" refers to a system that collects ratings and opinions provided by users through the application and uses them to improve the quality of future services and menus.

[1391] This invention is a system that proposes nutritionally balanced meals based on the user's basic information, daily meal records, and emotional data, and then prepares and delivers those meals. A specific embodiment of this system is shown below.

[1392] 1. Data Collection

[1393] User:

[1394] Users log in to a mobile application or web form and enter personal information (age, gender, height, weight, allergy information, dietary preferences, etc.). They also enter details of their daily meals into the application. For example, user D might record the scrambled eggs and toast they ate for breakfast. The application used should ideally have a user-friendly interface.

[1395] 2. Data storage

[1396] server:

[1397] The server stores personal information and meal records submitted by users in a database. Specifically, it uses database management systems such as MySQL or PostgreSQL. The stored data is later used for analysis.

[1398] 3. Nutritional Analysis

[1399] server:

[1400] Based on the stored data, the server uses generative artificial intelligence (e.g., OpenAI GPT-4) to analyze the user's nutrient intake and calculate the necessary nutrients and calories. For example, it might identify that user D's daily calorie requirement is 1800 kcal and that they are deficient in vitamin D.

[1401] 4. Collection and Analysis of Sentimental Data

[1402] User:

[1403] The user uses the application to perform voice input and facial recognition. For example, they might voice input such as, "Today's meal was very delicious."

[1404] server:

[1405] The server uses speech recognition software (e.g., Google Speech-to-Text API) and facial expression analysis algorithms (e.g., Microsoft Face API) to analyze emotional data and extract emotions such as satisfaction levels. The analysis results are stored in a database and used in the next step.

[1406] 5. Menu generation

[1407] server:

[1408] Based on nutritional analysis results and emotional data, the server uses generative artificial intelligence to generate a well-balanced menu. For example, it might suggest a menu rich in vitamin D, such as "grilled salmon, spinach salad, and soy milk smoothie." This menu information is then sent to the cooking facility in the next step.

[1409] 6. Recipe Sharing

[1410] server:

[1411] The server shares the generated menu and detailed recipe information with cooking facilities near the user's location. For example, it sends menu information for grilled salmon, spinach salad, and soy milk smoothie to "nearby cooking facilities" in the user D's area.

[1412] 7. Cooking and Delivery

[1413] Terminal (Cooking Staff):

[1414] The cooking staff prepares the dishes for the menu based on shared recipe information. For example, they gather the necessary ingredients and begin cooking, and once cooking is complete, they properly package the dishes.

[1415] Terminal (Delivery Staff):

[1416] Delivery staff pick up the prepared food and deliver it to the user's home. For example, they might deliver "grilled salmon, spinach salad, and a soy milk smoothie" to user D's home.

[1417] 8. Gathering Feedback

[1418] User:

[1419] User D receives the delivered food, finishes eating, and then enters a rating and feedback through the application. For example, they might enter a rating such as, "The grilled salmon was very tasty, but there was too much salad dressing."

[1420] server:

[1421] The server collects user feedback and uses it, along with sentiment data, to create future menus and improve the service.

[1422] Specific example:

[1423] User D records the scrambled eggs and toast they ate for breakfast using the app.

[1424] The server analyzes daily nutrient intake based on recorded meal data and identifies vitamin D deficiency.

[1425] User D inputs "Today's meal was very delicious" via voice input, and the server extracts the emotion of "satisfaction."

[1426] The server generates a menu of "grilled salmon, spinach salad, and soy milk smoothie" and shares it with the cooking facility.

[1427] Cooking staff prepare the meals based on the menu, and delivery staff deliver the meals to user D.

[1428] User D enters feedback via the app after the meal, and the server adjusts the next menu based on that data.

[1429] Examples of prompts for a generative AI model:

[1430] "Based on the scrambled eggs and toast that User D ate for breakfast, please suggest a well-balanced menu that is rich in vitamin D."

[1431] Therefore, this system allows elderly people and those who have difficulty managing their diet to easily receive nutritionally balanced meals, and also enables the provision of more satisfying services by utilizing emotional data.

[1432] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1433] Step 1: Data Entry

[1434] User:

[1435] Input: Users enter personal information (age, gender, height, weight, allergy information, dietary preferences, etc.) through a mobile application or web form.

[1436] Operation: The user accesses the application and enters the necessary personal information into the input form. They also enter details of their daily meals. For example, user D records the scrambled eggs and toast they ate for breakfast.

[1437] Output: The entered personal information and meal record data are formatted within the application and ready for transmission.

[1438] Step 2: Save Data

[1439] server:

[1440] Input: Personal information and meal record data submitted by the user.

[1441] Operation: The server receives data and saves it to a database such as MySQL or PostgreSQL. It performs data integrity checks and notifies the user if errors occur.

[1442] Output: User personal information and meal record data stored in the database.

[1443] Step 3: Nutritional Analysis

[1444] server:

[1445] Input: User meal record data stored in the database.

[1446] Operation: Using generative artificial intelligence (e.g., OpenAI GPT-4), the system analyzes stored meal record data to determine the user's nutrient intake. Specifically, it calculates the daily calorie requirement and any deficient nutrients based on user D's diet.

[1447] Output: The analysis results provide data on calorie intake and deficient nutrients (e.g., vitamin D). This analysis result is also stored in a database and used for subsequent processing.

[1448] Step 4: Collecting emotional data

[1449] User:

[1450] Input: Users input emotional data into the application using voice input or facial recognition.

[1451] Operation: For example, user D inputs "Today's meal was very delicious" via voice. The application formats the voice and facial expression data and prepares it for transmission to the server.

[1452] Output: The formatted audio data and facial expression data are sent to the server.

[1453] Step 5: Emotion Analysis

[1454] server:

[1455] Input: Voice data and facial expression data sent by the user.

[1456] Operation: Uses speech recognition software (e.g., Google Speech-to-Text API) and facial expression analysis algorithms (e.g., Microsoft Face API) to analyze emotional data and extract emotions such as satisfaction levels.

[1457] Output: Extracted sentiment data (e.g., "satisfied") is obtained. This sentiment data is also stored in the database and used in the next step.

[1458] Step 6: Menu Generation

[1459] server:

[1460] Input: Nutritional analysis results and emotional data.

[1461] Operation: Based on nutritional analysis results and emotional data, it uses generative artificial intelligence to generate a well-balanced menu. For example, it might suggest "grilled salmon, spinach salad, and soy milk smoothie," which are rich in vitamin D.

[1462] Output: The proposed menu information is generated and ready to be sent to the kitchen facility. This information is also stored in the database.

[1463] Step 7: Recipe Sharing

[1464] server:

[1465] Input: Generated menu and detailed recipe information.

[1466] Operation: The server transmits data to cooking facilities near the user. Communication is conducted via the internet or a dedicated network.

[1467] Output: Menu and recipe information sent to the cooking facility.

[1468] Step 8: Cooking

[1469] Terminal (Cooking Staff):

[1470] Input: Menu and recipe information received by the cooking facility.

[1471] Action: Based on this information, the kitchen staff gathers the necessary ingredients and begins cooking. For example, they might prepare grilled salmon, spinach salad, and soy milk smoothies.

[1472] Output: Cooking is complete and the food is properly packaged.

[1473] Step 9: Delivery

[1474] Terminal (Delivery Staff):

[1475] Input: Package of the finished dish.

[1476] Operation: Delivery staff receive the food and prepare it for delivery. They deliver the food to the user's home at the specified time. For example, deliver grilled salmon, spinach salad, and a soy milk smoothie to user D's home.

[1477] Output: Food delivered to the user's home.

[1478] Step 10: Gathering Feedback

[1479] User:

[1480] Input: Ratings and opinions about the delivered food.

[1481] Operation: After user D finishes their meal, they enter their rating and feedback through the application. For example, they might enter the opinion, "The grilled salmon was very tasty, but there was too much salad dressing."

[1482] Output: The input feedback is sent to the server.

[1483] server:

[1484] Input: Feedback submitted by the user.

[1485] Operation: Collects feedback and stores it in a database. Uses the feedback data to create future menus and improve services.

[1486] Output: Feedback information useful for improvement is accumulated in the database, contributing to service enhancement.

[1487] (Application Example 2)

[1488] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1489] In modern society, many users lead busy lives and lack the time to plan and prepare balanced meals. Furthermore, accurately understanding each individual's nutrient intake and generating optimal menus to supplement any deficiencies is challenging. Moreover, menu suggestions and service improvements that consider user satisfaction and emotions regarding meals are insufficient. Therefore, there is a need for a system that allows users to easily obtain balanced meals while also increasing their satisfaction with those meals.

[1490] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[1491] In this invention, the server includes means for collecting the user's basic information and meal records; means for analyzing the user's nutrient intake based on the collected data and calculating the necessary nutrients and calories; means for generating a well-balanced menu based on the analysis results and emotional data and proposing it to the user; means for sharing the generated menu and recipe information with cooking facilities near the user; means for cooking the meal based on the shared recipe and delivering it to the user's home; means for extracting the user's emotional data through voice input and facial recognition; and means for collecting user feedback and emotional data and reflecting it in improving the service. As a result, users can easily obtain well-balanced meals tailored to their individual nutritional needs and increase their satisfaction with those meals.

[1492] "User basic information" refers to personal data such as age, gender, weight, height, allergy information, and food preferences.

[1493] A "meal diary" is data in which a user specifically records the contents of their daily meals.

[1494] "Nutrient intake status" refers to the amount and balance of each nutrient that the user is consuming through their diet.

[1495] "Required nutrients and calories" refers to the specific nutrients and calories that a user needs to maintain their health and achieve their goals.

[1496] "Analysis results" refer to the results regarding nutrient intake and nutrient deficiencies derived by a generative artificial intelligence system based on the user's basic information and dietary records that have been collected.

[1497] "Emotional data" refers to data such as satisfaction levels and moods extracted through user voice input and facial recognition.

[1498] A "menu" refers to a specific set of dishes or meal combinations suggested to the user.

[1499] "Recipe information" refers to detailed instructions that include how to prepare a dish and a list of necessary ingredients.

[1500] A "cooking facility" is a facility that prepares meals based on menus suggested to users.

[1501] "Feedback" refers to data that users input and submit regarding the food or service they receive, including their evaluations and impressions.

[1502] "Service improvement" means improving future menu suggestions and service content based on collected feedback and sentiment data.

[1503] This invention is a system that collects basic user information and meal records, and generates and proposes well-balanced menus based on the analysis results and emotional data. The aim of this system is to enable users to effectively manage their daily meals and increase their satisfaction with them.

[1504] 1. Data Acquisition Module

[1505] User:

[1506] Users use a mobile application to input personal information (age, gender, height, weight, allergy information, food preferences, etc.) and details of their daily meals. For example, a user might record the scrambled eggs and toast they ate for breakfast.

[1507] 2. Dietary Habits Analysis Module

[1508] server:

[1509] The server stores collected personal information and meal records in a database. Based on the stored data, generative artificial intelligence is used to analyze the user's nutrient intake and calculate the necessary nutrients and calories. For example, by analyzing the user's data, it might identify that the user needs 1800 kcal per day and is deficient in vitamin D.

[1510] 3. Emotion Recognition Module

[1511] User:

[1512] The user uses the application for voice input and facial recognition. For example, the user launches the application and voice-inputs, "Today's meal was very delicious."

[1513] server:

[1514] The server analyzes voice input and facial recognition data to extract user emotion data. For example, it might extract the emotion of "satisfaction" from the user's voice input.

[1515] 4. Menu Creation Module

[1516] server:

[1517] The server generates a well-balanced menu based on analysis results and emotional data. Generative artificial intelligence suggests the optimal combination of ingredients and dishes. For example, it might suggest a menu to the user that includes dishes rich in vitamin D, such as "grilled salmon, spinach salad, and soy milk smoothie."

[1518] 5. Locally shared modules

[1519] server:

[1520] The generated menu and detailed recipe information are shared with the cooking facility closest to the user's area. For example, recipes for grilled salmon, spinach salad, and soy milk smoothie are sent to cooking facilities in the user's area.

[1521] 6. Cooking and Delivery Module

[1522] Terminal (Cooking Staff):

[1523] Based on shared recipe information, the cooking staff prepares the dishes for the menu. For example, they check the recipe, gather the necessary ingredients, and begin cooking. After cooking is complete, they properly package the dishes.

[1524] Terminal (Delivery Staff):

[1525] Cooked food is picked up by delivery staff and delivered to the user's home. For example, grilled salmon, spinach salad, and a soy milk smoothie can be delivered to the user's home.

[1526] 7. Feedback Module

[1527] User:

[1528] After receiving their delivered food and finishing their meal, users enter ratings and feedback through the application. For example, they might write, "The grilled salmon was very tasty, but there was too much salad dressing."

[1529] server:

[1530] The server collects user feedback and uses it, along with sentiment data, to create future menus and improve the service. For example, based on user feedback, the amount of salad dressing might be adjusted next time, and sentiment data might be used to create more satisfying menus.

[1531] The hardware used includes common smartphones (Android / iOS), servers, and terminals for cooking and delivery staff. Software includes Android Studio and iOS development tools for mobile applications, Flask for the server side, SQLite for the database, and TensorFlow for AI models.

[1532] As a concrete example, the following prompt sentences are used in generative AI models:

[1533] User data:

[1534] Age: 30

[1535] Gender: Male

[1536] Weight: 70kg

[1537] Height: 175cm

[1538] Dietary restrictions: None

[1539] Recent meals: Breakfast - Oatmeal with fruits, Lunch - Chicken salad, Dinner - Grilled salmon with vegetables

[1540] User feeling about today's meals: Satisfied

[1541] Generate a balanced menu for the next day considering the user's health needs and preferences.

[1542] In this way, the system allows individual users to easily enjoy nutritionally balanced meals and achieve highly satisfying and smart meal management.

[1543] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1544] Step 1:

[1545] Users enter personal information and meal records.

[1546] Input: Users use a smartphone application to enter personal information such as age, gender, height, weight, allergy information, and dietary preferences, as well as details of their daily meals.

[1547] Data processing and calculation: The application checks the input data and organizes it according to the format.

[1548] Output: Organized personal information and meal records are sent to the server and stored in the database.

[1549] Specific operation: The user enters the scrambled eggs and toast they ate for breakfast, and the application organizes that information and sends it to the server.

[1550] Step 2:

[1551] The server performs dietary analysis.

[1552] Input: User's personal information and meal records stored in the database.

[1553] Data processing and calculation: The server uses generative artificial intelligence to analyze the user's nutrient intake and calculate the necessary nutrients and calories.

[1554] Output: The calculated analysis results are generated.

[1555] Specific operation: The server analyzes the user's data and identifies that their daily calorie requirement is 1800 kcal and that they are deficient in vitamin D.

[1556] Step 3:

[1557] Users input emotional data.

[1558] Input: The user launches the application and provides emotional data through voice input or facial recognition.

[1559] Data Processing and Calculation: The application performs preprocessing to analyze voice input and facial recognition data.

[1560] Output: Pre-processed emotion data is sent to the server.

[1561] Specific operation: The user voice-inputs "Today's meal was very delicious," and this information is pre-processed before being sent to the server.

[1562] Step 4:

[1563] The server analyzes the emotional data.

[1564] Input: Sentiment data submitted by the user.

[1565] Data processing and calculation: The server uses generative artificial intelligence to analyze emotional data and identify user satisfaction and emotions.

[1566] Output: Emotional data extracted as an analysis result.

[1567] Specific operation: The server extracts the emotion of "satisfaction" from the voice input.

[1568] Step 5:

[1569] The server generates the menu.

[1570] Input: Analysis results and sentiment data.

[1571] Data processing and calculation: The server uses generative artificial intelligence to calculate the optimal combination of ingredients and dishes.

[1572] Output: A well-balanced menu is generated.

[1573] Specific action: A menu is proposed that includes dishes rich in vitamin D, such as "grilled salmon, spinach salad, and soy milk smoothie."

[1574] Step 6:

[1575] The server shares recipe information with the cooking facilities.

[1576] Input: Generated menu and detailed recipe information.

[1577] Data processing and calculation: The server converts menu and recipe information into a specific format and sends it to the nearest cooking facility.

[1578] Output: Shared data for the cooking facility to receive.

[1579] Specific action: Send recipe information for "grilled salmon, spinach salad, and soy milk smoothie" to a cooking facility in the user's area.

[1580] Step 7:

[1581] The cooking facility prepares the food.

[1582] Input: Recipe information shared from the server.

[1583] Data processing and calculation: Cooking staff gather the necessary ingredients based on the recipe and begin cooking.

[1584] Output: Cooked food.

[1585] Specific tasks: Cooking staff will review the recipes and prepare and properly package the grilled salmon, spinach salad, and soy milk smoothie.

[1586] Step 8:

[1587] Delivery staff deliver the food.

[1588] Input: Dishes prepared in a cooking facility.

[1589] Data processing and calculation: Delivery staff pick up the food and deliver it to the user's home.

[1590] Output: Meals delivered to the user's home.

[1591] Specific actions: A delivery staff member delivers grilled salmon, spinach salad, and a soy milk smoothie to the user's home.

[1592] Step 9:

[1593] Users enter feedback

[1594] Input: Your evaluation or comments on the food you received.

[1595] Data processing and calculation: The application collects the input feedback and sends it to the server.

[1596] Output: Collected feedback data.

[1597] Specific operation: The user enters a review stating, "The grilled salmon was very tasty, but there was too much salad dressing," and this information is sent to the server.

[1598] Step 10:

[1599] The server analyzes the feedback and uses it to improve the service.

[1600] Input: User feedback and sentiment data.

[1601] Data processing and calculation: Generative artificial intelligence is used to analyze feedback and utilize it for creating future menus and improving services.

[1602] Output: Improved menu suggestions and service details.

[1603] Specific operation: Based on user feedback, the server adjusts the amount of salad dressing in the next menu.

[1604] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1605] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1606] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[1607] [Fourth Embodiment]

[1608] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1609] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1610] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1611] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[1612] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[1613] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[1614] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[1615] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[1616] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[1617] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1618] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1619] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[1620] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1621] 1. Data Acquisition Module

[1622] User:

[1623] Users enter personal information (age, gender, height, weight, allergy information, dietary preferences, etc.) using a mobile application or web form. They also record their daily meals, and this allows for the collection of dietary records.

[1624] 2. Dietary Habits Analysis Module

[1625] server:

[1626] The server stores the data collected from users in a database. Next, generative artificial intelligence within the server is used to analyze this data and gain a detailed understanding of the user's nutrient intake. For example, analysis of data from a woman in her 70s, Ms. B, reveals that her daily calorie requirement is 1600 kcal and that she is deficient in B vitamins.

[1627] 3. Menu Creation Module

[1628] server:

[1629] Based on the analysis results, the server generates a well-balanced menu. In this process, a generative artificial intelligence suggests the optimal combination of ingredients and dishes. For example, for user B, it might create a menu including stir-fried chicken liver (rich in B vitamins), blanched spinach, and fruit yogurt.

[1630] Specific example:

[1631] Based on User B's daily calorie needs and nutrient deficiencies, we propose the following meal plan: "Breakfast: Fruit yogurt, Lunch: Stir-fried chicken liver and blanched spinach, Dinner: Grilled fish and steamed vegetables."

[1632] 4. Locally shared modules

[1633] server:

[1634] The generated menu and its detailed recipe information are shared with the cooking facility closest to the user's area. For example, the server sends the recipe information to "Nearby Cooking Facility X" located in the area where user B lives.

[1635] 5. Cooking and Delivery Module

[1636] Terminal (Cooking Staff):

[1637] Based on the shared recipe information, the cooking staff prepares the dishes for the menu. Specifically, they check the recipe sent from the server, prepare the necessary ingredients, and begin cooking.

[1638] Terminal (Delivery Staff):

[1639] The delivery staff will properly package the cooked meals and deliver them to user B's home. For example, stir-fried chicken liver and blanched spinach for lunch will be delivered at lunchtime, and grilled fish and steamed vegetables for dinner will be delivered at dinnertime.

[1640] 6. Feedback Module

[1641] User:

[1642] User B receives the delivered food, finishes eating, and then submits feedback through the application. They fill in their evaluation of the taste, quantity, and quality, and then submit it.

[1643] server:

[1644] The server collects feedback from user B and uses it to create future menus and improve the service. For example, if user B provides feedback that "the stir-fried chicken liver was a little too salty," the seasoning will be adjusted next time.

[1645] As described above, this system enables elderly people to continuously receive nutritionally balanced meals. By collecting basic user information and meal records, analyzing this information using generative artificial intelligence, generating nutritionally balanced menus, cooking at a cooking facility, delivery, and collecting and incorporating feedback, the system supports the user's health.

[1646] The following describes the processing flow.

[1647] Step 1:

[1648] User:

[1649] Users log in to a mobile application or web form and enter personal information (age, gender, height, weight, allergy information, dietary preferences, etc.). They also record their daily meals. For example, user C enters the toast and coffee they ate for breakfast.

[1650] Step 2:

[1651] server:

[1652] The server stores personal information and meal records collected from users in a database. The stored data is appropriately formatted for analysis. For example, user C's personal information and data on bread and coffee are stored in the database.

[1653] Step 3:

[1654] server:

[1655] The generative artificial intelligence on the server retrieves the user's meal records and basic information from the database and performs analysis. This calculates the user's nutrient intake. For example, it identifies that user C's daily calorie requirement is 2000 kcal and that their calcium intake is insufficient.

[1656] Step 4:

[1657] server:

[1658] Based on the analysis results, the server generates a nutritionally balanced menu. Generative artificial intelligence suggests the optimal combination of ingredients and dishes. For example, for user C, it creates a menu that includes "grilled salmon, broccoli salad, and soy milk smoothie," which are rich in calcium.

[1659] Step 5:

[1660] server:

[1661] The generated menu and its detailed recipe information are shared with the cooking facility closest to the user's area. For example, recipe information for grilled salmon, broccoli salad, and soy milk smoothie is sent to "Nearby Cooking Facility Y" in the area where user C lives.

[1662] Step 6:

[1663] Terminal (Cooking Staff):

[1664] Based on shared recipe information, the cooking staff prepares the dishes for the menu. For example, they check the recipe, gather the necessary ingredients, and begin cooking. After cooking is complete, they properly package the dishes.

[1665] Step 7:

[1666] Terminal (Delivery Staff):

[1667] A delivery staff member picks up the prepared food and delivers it to the user's home. For example, grilled salmon, broccoli salad, and a soy milk smoothie are delivered to user C's home.

[1668] Step 8:

[1669] User:

[1670] The user receives the delivered food and actually eats it. For example, user C enjoys grilled salmon and broccoli salad for dinner.

[1671] Step 9:

[1672] User:

[1673] After their meal, users enter their ratings and feedback through the application. For example, user C might submit feedback stating, "The grilled salmon was very tasty, but there was a little too much salad dressing."

[1674] Step 10:

[1675] server:

[1676] The server collects user feedback and uses it to create future menus and improve the service. For example, based on user C's feedback, the amount of salad dressing will be adjusted next time.

[1677] Through these steps, the system enables elderly people to easily receive nutritionally balanced meals.

[1678] (Example 1)

[1679] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1680] To address the current challenges that make it difficult for the elderly to consistently receive nutritionally balanced meals, there is a need for a system that can accurately understand users' nutrient intake, generate appropriate menus based on that information, and efficiently manage the cooking and delivery processes.

[1681] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1682] In this invention, the server includes means for collecting the user's basic information and meal records; means for storing the collected data and linking it to a user ID; means for analyzing the nutrient intake status based on the stored data and calculating the necessary nutrients and calories; means for generating a well-balanced menu using generative artificial intelligence based on the analysis results and proposing it to the user; means for sharing the generated menu and recipe information with cooking facilities near the user; means for cooking meals based on the shared recipes and delivering them to the user's home; and means for collecting feedback from the user and reflecting it in creating future menus and improving the service. This makes it possible for elderly people to continuously receive nutritionally balanced meals and support their health.

[1683] "User basic information" refers to data about an individual user's basic health and lifestyle habits, such as age, gender, height, weight, allergy information, and food preferences.

[1684] A "meal diary" is data that users record about their daily meals, including specific food names, quantities consumed, and meal times.

[1685] "Means of collection" refers to technical methods or devices for obtaining data entered by users, such as through mobile applications or web forms.

[1686] "Means of preservation" refers to technologies for storing and managing collected data in data storage such as databases.

[1687] A "User ID" is a unique identifier assigned to each user and is used to distinguish a user's data from that of other users.

[1688] "Methods for analyzing nutrient intake" refer to technologies that, based on collected data, identify the amount and types of nutrients a user is consuming and analyze any deficiencies or excesses in necessary nutrients or calories.

[1689] "Generative artificial intelligence" is an artificial intelligence technology that uses advanced algorithms such as machine learning and deep learning to analyze data, make predictions, and provide optimal suggestions.

[1690] "Methods for generating menus" refers to technologies that create well-balanced meal plans based on analysis results and propose them to users.

[1691] A "cooking facility" refers to a facility located near the user that performs cooking, and includes restaurants and catering services.

[1692] "Recipe information" refers to information such as specific cooking methods, necessary ingredients, and cooking procedures based on a menu.

[1693] "Means of cooking" refer to the techniques and equipment used to actually prepare a meal based on shared recipe information.

[1694] "Delivery methods" refer to the methods and technologies used to deliver cooked food to the user's home.

[1695] "Means of collecting feedback" refers to the technologies and methods used to obtain ratings and comments from users.

[1696] "Means of reflecting feedback in service improvement" refers to methods used to create future menus and improve service quality based on the collected feedback.

[1697] This invention relates to a system for elderly people to continuously receive nutritionally balanced meals. This system collects basic information and meal records from the user, analyzes nutrient intake based on this information, and generates an appropriate menu. Furthermore, the generated menu and recipe information are shared with cooking facilities, the cooked meals are delivered to the user's home, and feedback is collected to help create future menus and improve the service.

[1698] This system uses the following hardware and software.

[1699] User:

[1700] Users enter personal information (age, gender, height, weight, allergy information, food preferences, etc.) and daily meal details using a mobile application or web form. Specifically, they use devices such as smartphones or PCs. An example of information a user might enter is "Height 165cm, weight 60kg, likes chicken."

[1701] server:

[1702] The server receives data sent by the user and stores it in a database. This database can be a common relational database system (e.g., PostgreSQL). Next, generative artificial intelligence (e.g., GPT-3) is used to analyze the collected data and identify the user's nutrient intake. For example, analysis of data from a female user in her 70s might reveal that her daily calorie requirement is 1600 kcal and that she is deficient in B vitamins.

[1703] Based on the analysis results, the server uses generative artificial intelligence to generate a well-balanced menu and propose it to the user. For example, it might suggest: "Breakfast: Fruit yogurt, Lunch: Stir-fried chicken liver and blanched spinach, Dinner: Grilled fish and steamed vegetables." An example of a prompt message would be: "Based on data collected from a female user in her 70s, please propose a well-balanced menu that matches the dietary analysis results showing a deficiency in B vitamins."

[1704] The generated menu and its detailed recipe information are shared with the cooking facility closest to the user's area. The server uses the SMTP protocol to send emails to the cooking facility, providing the recipe information. Alternatively, data can be shared via a dedicated API.

[1705] Terminal (Cooking Staff):

[1706] Cooking staff check recipe information sent from the server on their terminals (PCs or tablets) and prepare the necessary ingredients. Specifically, they check the inventory of necessary ingredients in real time and place uncooked ingredients on the cooking counter. Cooking staff cook the ingredients using cooking utensils such as frying pans and ovens while checking the recipe instructions. For example, they might sauté chicken liver for the specified time and boil and cool spinach.

[1707] Terminal (Delivery Staff):

[1708] Delivery staff carefully package the cooked meals and deliver them to the user's home. Using GPS devices, delivery staff ensure the meals arrive precisely at the designated time. Specifically, they carefully pack the food into packaging materials and head towards the user's address while monitoring traffic conditions in real time.

[1709] User:

[1710] After receiving their delivered food and finishing their meal, users submit feedback through the application. They rate the taste, quantity, and quality, and then submit the feedback. For example, one piece of feedback might be, "The stir-fried chicken liver was a little too salty."

[1711] server:

[1712] The server collects user feedback and uses it to create future menus and improve the service. For example, based on feedback such as "the stir-fried chicken liver was a little too salty," the seasoning will be adjusted next time.

[1713] This will provide a system that supports users' health and enables elderly people to continuously receive nutritionally balanced meals.

[1714] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1715] Step 1: Data Collection

[1716] User:

[1717] Users open a mobile application or web form and enter their personal information and daily meal details. Specifically, they use a smartphone or PC to fill in the required fields in the form.

[1718] Input: Personal information entered by the user (age, gender, height, weight, allergy information, food preferences, etc.) and meal details.

[1719] Output: Dataset of user's basic information and meal records

[1720] Step 2: Save Data

[1721] server:

[1722] The server receives data sent by the user and stores it in the database. The database uses a relational database system (e.g., PostgreSQL). The data is associated with the user ID upon storage.

[1723] Input: User's basic information and meal record dataset

[1724] Output: User's basic information and meal records stored in the database

[1725] Step 3: Data Analysis

[1726] server:

[1727] The generative artificial intelligence model (e.g., GPT-3) on the server analyzes the stored data to identify the user's nutrient intake. For example, it calculates the daily calorie requirement and any deficient nutrients.

[1728] Input: User's basic information and meal records stored in the database.

[1729] Output: User's nutrient intake status and specific analysis results

[1730] Step 4: Menu Generation

[1731] server:

[1732] Based on the analysis results, the server uses generative artificial intelligence to generate a well-balanced menu. For example, it might suggest "Breakfast: Fruit yogurt, Lunch: Stir-fried chicken liver and blanched spinach, Dinner: Grilled fish and steamed vegetables."

[1733] Input: User's nutrient intake status and analysis results

[1734] Output: A well-balanced menu and detailed recipe information

[1735] Step 5: Local Sharing

[1736] server:

[1737] The generated menu and recipe information is shared with the cooking facility closest to the user's area. Data is transmitted via SMTP protocol or a dedicated API.

[1738] Input: A well-balanced menu and detailed recipe information

[1739] Output: Recipe information sent to the cooking facility

[1740] Step 6: Preparation for cooking

[1741] Terminal (Cooking Staff):

[1742] Cooking staff check recipe information sent from the server on their devices (PCs or tablets) and prepare the necessary ingredients. They also check the inventory of necessary ingredients in real time and place them on the cooking counter.

[1743] Input: Recipe information sent to the cooking facility

[1744] Output: Prepared ingredients

[1745] Step 7: Cooking

[1746] Terminal (Cooking Staff):

[1747] The cooking staff begins cooking using the prepared ingredients. They use cooking utensils such as frying pans and ovens and cook according to the recipe.

[1748] Input: Prepared ingredients

[1749] Output: Finished dish

[1750] Step 8: Delivery

[1751] Terminal (Delivery Staff):

[1752] Delivery staff properly package the cooked meals and deliver them to the user's home. They use GPS to monitor traffic conditions and deliver at the specified time.

[1753] Input: Finished dish

[1754] Output: Food delivered to the user's home

[1755] Step 9: Gathering Feedback

[1756] User:

[1757] After receiving and eating their delivered food, users submit feedback through the application. They rate the taste, quantity, and quality of the food and then submit it.

[1758] Input: Post-use evaluation of delivered food

[1759] Output: Feedback data

[1760] Step 10: Feedback Analysis

[1761] server:

[1762] The server collects user feedback and uses it to create future menus and improve the service. Based on the feedback, it inputs the next prompt into the generative artificial intelligence and makes adjustments.

[1763] Input: Feedback data

[1764] Output: Improved menu planning and service quality

[1765] In this way, by performing data processing and calculations using specific inputs and outputs, as well as generating AI models and prompts at each step, a system is realized that supports the health of the elderly while maintaining nutritional balance.

[1766] (Application Example 1)

[1767] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the robot 414 will be referred to as the "terminal." For elderly people and users who require nutritional management, ensuring a balanced diet on a daily basis can be difficult. Furthermore, continuously providing menus that consider nutritional balance is time-consuming, making continuous health management difficult. Therefore, based on the user's personal information and meal records, support will be provided to help them maintain a safe and healthy diet.

[1768] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means: The server includes means for collecting the user's basic information and meal records; means for analyzing the intake status of nutrients based on the collected data and calculating the necessary nutrients and calories; means for generating a well-balanced menu based on the analysis results and proposing it to the user; means for sharing the generated menu and recipe information with cooking facilities near the user; means for cooking based on the shared recipes and delivering them to the user's home; means for collecting feedback from the user and reflecting it in improving the service; and means for managing the user's data through a smartphone application, generating menus using generative artificial intelligence, and sharing the results with cooking facilities.

[1769] A "user" refers to anyone who uses the system to input personal information or meal records.

[1770] "Basic information" refers to personal data such as the user's age, gender, height, weight, allergy information, and food preferences.

[1771] A "food diary" refers to the contents of the food a user consumes on a daily basis.

[1772] "Data" refers to basic information and meal records collected from users.

[1773] "Nutrients" refer to components necessary for nutritional balance, such as proteins, vitamins, and minerals.

[1774] "Analysis" refers to the process of evaluating nutrient intake based on collected data.

[1775] "Calories" refer to the amount of energy contained in food.

[1776] A "menu" refers to a meal plan that is designed with a balance of nutrients in mind.

[1777] "Generative artificial intelligence" refers to algorithms and models that generate nutritionally balanced meal plans based on user data.

[1778] A "cooking facility" refers to a place where food is prepared based on the generated menu and recipe information.

[1779] "Feedback" refers to the information that users send back to the system, evaluating the dishes they are served.

[1780] "Service improvement" refers to improving menu creation, cooking, and delivery services for future orders based on the collected feedback information.

[1781] A "smartphone application" refers to a mobile app that allows users to input basic information and meal records, and receive analysis results and meal plan suggestions.

[1782] Modes for carrying out the invention

[1783] System Overview

[1784] This invention is a food delivery system that provides users with nutritionally balanced meals. Users input basic information and meal records through a smartphone application, and based on that data, a meal plan is generated, prepared at the nearest cooking facility to the user, and delivered by delivery staff. User feedback is used to improve the service.

[1785] Specific Embodiments

[1786] 1. Collection of user data

[1787] Users input basic information such as age, gender, height, weight, allergy information, and dietary preferences through a smartphone application. Their daily meals are also recorded. This data is sent to a server and stored in a database.

[1788] 2. Analysis of Nutrients

[1789] The server analyzes the collected basic information and meal records and uses a generative AI model to understand the user's nutrient intake. This analysis can identify which nutrients the user needs to consume and in what quantities.

[1790] 3. Menu generation

[1791] The generation AI model generates optimal menus based on the analysis results. The generated menus include food items with a well-balanced combination of nutrients. For example, for a user who is deficient in a specific vitamin, dishes using ingredients rich in that vitamin will be suggested.

[1792] 4. Data Sharing

[1793] The generated menu and its detailed recipe information are shared with the nearest cooking facility in the user's area. The cooking facility then prepares the dishes based on the received recipe information.

[1794] 5. Food preparation and delivery

[1795] The kitchen staff prepares the meals according to the generated recipes and packages them appropriately. Then, delivery staff deliver the meals to the user's address. This ensures that users receive fresh, nutritionally balanced meals tailored to their time of day.

[1796] 6. Gathering feedback and improving services

[1797] After receiving and eating their delivered food, users provide feedback through a smartphone application. They input ratings regarding taste, quantity, and quality. This feedback is sent to the server and used to create future menus and improve the service.

[1798] Hardware and software to be used

[1799] Hardware: Users use smartphones, while kitchen staff and delivery personnel use tablets or PCs. Servers are used for data storage and analysis.

[1800] Software: A smartphone application is used for inputting basic user information and meal records. SQLite is used for the database, and Flask is used for the backend of the web application. The OpenAI API is used for generative AI models.

[1801] Examples of prompt statements

[1802] The following is an example of a prompt to input into a generative AI model:

[1803] User information:

[1804] Age: 70, Gender: Female, Height: 160cm, Weight: 55kg.

[1805] Allergens: Shrimp, nuts.

[1806] Meal plan: Breakfast: Toast and coffee. Lunch: Chicken salad. Dinner: Braised fish and stir-fried vegetables.

[1807] Please provide a nutritionally balanced menu.

[1808] Example output from a generative AI model for this:

[1809] Breakfast: Fruit yogurt

[1810] Lunch: Stir-fried chicken liver, blanched spinach

[1811] Dinner: Grilled fish and steamed vegetables

[1812] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1813] Step 1: Collect basic user information and meal records.

[1814] Users enter basic information (age, gender, height, weight, allergy information, and dietary preferences) using a smartphone application. They also enter their daily meal details. The entered information is sent to a server and stored in an SQLite database. Specifically, the text-based input data is converted into a database format.

[1815] Input: User's basic information and meal records

[1816] Output: User information and meal records stored in the database

[1817] Step 2: Nutrient Analysis

[1818] The server analyzes nutrient intake based on user information and meal records stored in the database. This analysis uses OpenAI's generative AI model. Specifically, user information and meal records are input to the generative AI model as prompts, and data processing is performed to identify deficient nutrients based on the analysis results obtained.

[1819] Input: User information and meal records in the database

[1820] Output: Analysis results (such as deficient nutrients and required calories)

[1821] Step 3: Menu Generation

[1822] The server uses the OpenAI API to generate a nutritionally balanced menu based on the nutrient analysis results. It inputs prompts into the AI ​​model to obtain the optimal menu for the user. The generated menu includes specific food items and their recipes.

[1823] Input: Nutrient analysis results and prompt text

[1824] Output: A well-balanced menu and its recipe information

[1825] Step 4: Sharing menu information

[1826] The server sends the generated menu and recipe information to the nearest cooking facility in the user's area. The cooking facility then begins preparing the food based on the received recipe information. This step involves transmitting data using communication means.

[1827] Input: Menu and recipe information

[1828] Output: Sending recipe information to cooking facilities

[1829] Step 5: Cooking and delivering the food

[1830] The kitchen staff prepares the dishes based on the received recipe information. The finished dishes are properly packaged and delivered to the user's address by delivery staff. This step includes the actions required for cooking and delivery.

[1831] Input: Recipe information received by the cooking facility

[1832] Output: Cooked food delivered to the user's address.

[1833] Step 6: Gathering feedback and improving the service

[1834] After users have eaten the food they received, they provide feedback through a smartphone application. This feedback is sent to the server and used to generate future menus and improve the service. The server analyzes this feedback and incorporates it into the next menu generation.

[1835] Input: User feedback

[1836] Output: Improved service and menu generation process

[1837] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1838] 1. Data Acquisition Module

[1839] User:

[1840] Users log in to a mobile application or web form and enter personal information (age, gender, height, weight, allergy information, dietary preferences, etc.) and details of their daily meals. For example, user D might record scrambled eggs and toast for breakfast as part of their meal details.

[1841] 2. Dietary Habits Analysis Module

[1842] server:

[1843] The server stores collected personal information and meal records in a database. Based on the stored data, generative artificial intelligence is used to analyze the user's nutrient intake and calculate the necessary nutrients and calories. For example, analyzing user D's data reveals that their daily calorie requirement is 1800 kcal and that they are deficient in vitamin D.

[1844] 3. Emotion Recognition Module

[1845] User:

[1846] The user uses the application to perform voice input and facial recognition. For example, user D launches the application and inputs "Today's meal was very delicious" by voice.

[1847] server:

[1848] The server analyzes voice input and facial recognition data to extract user emotion data. For example, it extracts the emotion "satisfied" from user D's voice input.

[1849] 4. Menu Creation Module

[1850] server:

[1851] The server generates a well-balanced menu based on analysis results and emotional data. Generative artificial intelligence suggests the optimal combination of ingredients and dishes. For example, it suggests a menu to user D that includes dishes rich in vitamin D, such as "grilled salmon, spinach salad, and soy milk smoothie."

[1852] 5. Locally shared modules

[1853] server:

[1854] The generated menu and detailed recipe information are shared with the cooking facility closest to the user's area. For example, recipe information for grilled salmon, spinach salad, and soy milk smoothie is sent to "Nearby Cooking Facility Z" in the area where user D lives.

[1855] 6. Cooking and Delivery Module

[1856] Terminal (Cooking Staff):

[1857] Based on shared recipe information, the cooking staff prepares the dishes for the menu. For example, they check the recipe, gather the necessary ingredients, and begin cooking. After cooking is complete, they properly package the dishes.

[1858] Terminal (Delivery Staff):

[1859] A delivery staff member picks up the prepared food and delivers it to the user's home. For example, grilled salmon, spinach salad, and a soy milk smoothie are delivered to user D's home.

[1860] 7. Feedback Module

[1861] User:

[1862] User D receives the delivered food, finishes eating, and then enters a rating and feedback through the application. For example, they might enter a rating such as, "The grilled salmon was very tasty, but there was too much salad dressing."

[1863] server:

[1864] The server collects user feedback and uses it, along with sentiment data, to create future menus and improve the service. For example, based on user D's feedback, the amount of salad dressing will be adjusted next time, and the sentiment data will be used to create more satisfying menus.

[1865] Through the steps described above, this system allows elderly individuals to easily receive nutritionally balanced meals and provides a more satisfying service by utilizing emotional data. The system is continuously improved through a process that collects basic user information and meal records, generates optimal menus based on analysis results and emotional data, shares them with cooking facilities, handles cooking and delivery, and collects feedback.

[1866] The following describes the processing flow.

[1867] Step 1:

[1868] User:

[1869] Users log in to a mobile application or web form and enter personal information (age, gender, height, weight, allergy information, food preferences, etc.). They also record their daily meals. For example, user D records the scrambled eggs and toast they ate for breakfast.

[1870] Step 2:

[1871] server:

[1872] The server stores personal information and meal records collected from users in a database and formats them appropriately for analysis. User D's personal information and meal record (scrambled eggs and toast) are stored in the database.

[1873] Step 3:

[1874] server:

[1875] Using generative artificial intelligence on the server, the system retrieves users' meal records and basic information from a database and analyzes their nutrient intake. This allows it to identify their daily calorie needs and any nutrient deficiencies. For example, it might determine that user D needs 1800 kcal and is deficient in vitamin D.

[1876] Step 4:

[1877] User:

[1878] The user performs voice input and facial recognition through a mobile application. User D voice-inputs, "Today's meal was very delicious," and the app recognizes the user's facial expression using the camera.

[1879] Step 5:

[1880] server:

[1881] The server analyzes voice input and facial recognition data to extract user emotion data. For example, it recognizes the emotion of "satisfaction" from user D's voice and "smile" from their facial expression.

[1882] Step 6:

[1883] server:

[1884] Based on the analysis results and emotional data, the server generates a well-balanced menu. Generative artificial intelligence suggests the optimal combination of ingredients and dishes. For example, it might suggest a menu to user D consisting of "grilled salmon, spinach salad, and soy milk smoothie" as a dish rich in vitamin D.

[1885] Step 7:

[1886] server:

[1887] The generated menu and detailed recipe information are shared with the cooking facility closest to the user's area. For example, recipe information for grilled salmon, spinach salad, and soy milk smoothie is sent to "Nearby Cooking Facility Z" in the area where user D lives.

[1888] Step 8:

[1889] Terminal (Cooking Staff):

[1890] Based on the shared recipe information, the cooking staff prepares the dishes for the menu. They check the recipe, gather the necessary ingredients, and begin cooking. After cooking is complete, they properly package the food.

[1891] Step 9:

[1892] Terminal (Delivery Staff):

[1893] A delivery staff member picks up the prepared food and delivers it to the user's home. For example, grilled salmon, spinach salad, and a soy milk smoothie are delivered to user D's home.

[1894] Step 10:

[1895] User:

[1896] User D receives the delivered food and enjoys the meal. For example, for dinner, they might have grilled salmon and a spinach salad.

[1897] Step 11:

[1898] User:

[1899] After the meal, users enter ratings and feedback through the application. For example, they might enter a rating such as, "The grilled salmon was very tasty, but there was too much salad dressing."

[1900] Step 12:

[1901] server:

[1902] The server collects user feedback to help create future menus and improve the service. Based on user D's feedback, the amount of salad dressing will be adjusted next time, and emotional data will be used to generate a more satisfying menu.

[1903] Through the steps described above, this system allows elderly individuals to easily receive nutritionally balanced meals and provides a more satisfying service by utilizing emotional data. The system is continuously improved through a process that collects basic user information and meal records, generates optimal menus based on analysis results and emotional data, shares them with cooking facilities, handles cooking and delivery, and collects feedback.

[1904] (Example 2)

[1905] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1906] In modern times, providing easily accessible, nutritionally balanced meals for the elderly and those with difficulty managing their diets is a crucial challenge. However, systems that analyze individual nutrients and calories, and provide menus that take into account individual meal content and emotional states, are extremely limited. Therefore, to address these challenges, there is a need to develop a system that collects individual dietary and emotional data, proposes optimal menus based on this data, and provides consistent support from cooking and delivery to feedback.

[1907] The identification processing performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for collecting the user's basic information and meal records; means for analyzing the intake status of nutrients based on the collected data and calculating the necessary nutrients and calories; means for collecting and analyzing the user's emotional data; means for generating a well-balanced menu based on the analysis results and emotional data and proposing it to the user; means for sharing the generated menu and recipe information with cooking facilities near the user; means for cooking the food based on the shared recipe and delivering it to the user's home; and means for collecting feedback from the user and reflecting it in improving the service. This makes it possible to provide menus based on individual nutritional management and emotional state, enabling the realization of a more satisfying service.

[1908] "User basic information" refers to personal data including the user's age, gender, height, weight, allergy information, and food preferences.

[1909] "Meal records" refer to information that users input and record about their daily meals through applications or web forms.

[1910] "Means of collection" refers to systems and applications that receive data input from users and store it in a database.

[1911] "Methods for analyzing nutrient intake and calculating necessary nutrients and calories" refers to algorithms or programs that analyze a user's nutritional intake based on collected meal records and calculate the necessary nutrients and calorie amounts.

[1912] "Emotional data" refers to information indicating the user's emotional state (e.g., satisfaction, dissatisfaction, joy) collected through voice input and facial recognition.

[1913] "Means for collecting and analyzing emotional data" refers to software and algorithms that collect emotional data through user voice input or facial recognition and then analyze that data.

[1914] "A means of generating and proposing well-balanced menus to users" refers to a generative artificial intelligence system that generates and proposes combinations of ingredients and dishes suitable for the user based on the results of nutrient analysis and emotional data.

[1915] "Means of sharing recipe information with cooking facilities near the user" refers to systems or networks that transmit generated menus and detailed recipe information to cooking facilities near the user's residential area.

[1916] "Means of preparing food and delivering it to the user's home" refers to the means by which a cooking facility prepares food based on shared recipe information and delivers it to the user's home through a delivery service.

[1917] "Means for collecting feedback and using it to improve services" refers to a system that collects ratings and opinions provided by users through the application and uses them to improve the quality of future services and menus.

[1918] This invention is a system that proposes nutritionally balanced meals based on the user's basic information, daily meal records, and emotional data, and then prepares and delivers those meals. A specific embodiment of this system is shown below.

[1919] 1. Data Collection

[1920] User:

[1921] Users log in to a mobile application or web form and enter personal information (age, gender, height, weight, allergy information, dietary preferences, etc.). They also enter details of their daily meals into the application. For example, user D might record the scrambled eggs and toast they ate for breakfast. The application used should ideally have a user-friendly interface.

[1922] 2. Data storage

[1923] server:

[1924] The server stores personal information and meal records submitted by users in a database. Specifically, it uses database management systems such as MySQL or PostgreSQL. The stored data is later used for analysis.

[1925] 3. Nutritional Analysis

[1926] server:

[1927] Based on the stored data, the server uses generative artificial intelligence (e.g., OpenAI GPT-4) to analyze the user's nutrient intake and calculate the necessary nutrients and calories. For example, it might identify that user D's daily calorie requirement is 1800 kcal and that they are deficient in vitamin D.

[1928] 4. Collection and Analysis of Sentimental Data

[1929] User:

[1930] The user uses the application to perform voice input and facial recognition. For example, they might voice input such as, "Today's meal was very delicious."

[1931] server:

[1932] The server uses speech recognition software (e.g., Google Speech-to-Text API) and facial expression analysis algorithms (e.g., Microsoft Face API) to analyze emotional data and extract emotions such as satisfaction levels. The analysis results are stored in a database and used in the next step.

[1933] 5. Menu generation

[1934] server:

[1935] Based on nutritional analysis results and emotional data, the server uses generative artificial intelligence to generate a well-balanced menu. For example, it might suggest a menu rich in vitamin D, such as "grilled salmon, spinach salad, and soy milk smoothie." This menu information is then sent to the cooking facility in the next step.

[1936] 6. Recipe Sharing

[1937] server:

[1938] The server shares the generated menu and detailed recipe information with cooking facilities near the user's location. For example, it sends menu information for grilled salmon, spinach salad, and soy milk smoothie to "nearby cooking facilities" in the user D's area.

[1939] 7. Cooking and Delivery

[1940] Terminal (Cooking Staff):

[1941] The cooking staff prepares the dishes for the menu based on shared recipe information. For example, they gather the necessary ingredients and begin cooking, and once cooking is complete, they properly package the dishes.

[1942] Terminal (Delivery Staff):

[1943] Delivery staff pick up the prepared food and deliver it to the user's home. For example, they might deliver "grilled salmon, spinach salad, and a soy milk smoothie" to user D's home.

[1944] 8. Gathering Feedback

[1945] User:

[1946] User D receives the delivered food, finishes eating, and then enters a rating and feedback through the application. For example, they might enter a rating such as, "The grilled salmon was very tasty, but there was too much salad dressing."

[1947] server:

[1948] The server collects user feedback and uses it, along with sentiment data, to create future menus and improve the service.

[1949] Specific example:

[1950] User D records the scrambled eggs and toast they ate for breakfast using the app.

[1951] The server analyzes daily nutrient intake based on recorded meal data and identifies vitamin D deficiency.

[1952] User D inputs "Today's meal was very delicious" via voice input, and the server extracts the emotion of "satisfaction."

[1953] The server generates a menu of "grilled salmon, spinach salad, and soy milk smoothie" and shares it with the cooking facility.

[1954] Cooking staff prepare the meals based on the menu, and delivery staff deliver the meals to user D.

[1955] User D enters feedback via the app after the meal, and the server adjusts the next menu based on that data.

[1956] Examples of prompts for a generative AI model:

[1957] "Based on the scrambled eggs and toast that User D ate for breakfast, please suggest a well-balanced menu that is rich in vitamin D."

[1958] Therefore, this system allows elderly people and those who have difficulty managing their diet to easily receive nutritionally balanced meals, and also enables the provision of more satisfying services by utilizing emotional data.

[1959] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1960] Step 1: Data Entry

[1961] User:

[1962] Input: Users enter personal information (age, gender, height, weight, allergy information, dietary preferences, etc.) through a mobile application or web form.

[1963] Operation: The user accesses the application and enters the necessary personal information into the input form. They also enter details of their daily meals. For example, user D records the scrambled eggs and toast they ate for breakfast.

[1964] Output: The entered personal information and meal record data are formatted within the application and ready for transmission.

[1965] Step 2: Save Data

[1966] server:

[1967] Input: Personal information and meal record data submitted by the user.

[1968] Operation: The server receives data and saves it to a database such as MySQL or PostgreSQL. It performs data integrity checks and notifies the user if errors occur.

[1969] Output: User personal information and meal record data stored in the database.

[1970] Step 3: Nutritional Analysis

[1971] server:

[1972] Input: User meal record data stored in the database.

[1973] Operation: Using generative artificial intelligence (e.g., OpenAI GPT-4), the system analyzes stored meal record data to determine the user's nutrient intake. Specifically, it calculates the daily calorie requirement and any deficient nutrients based on user D's diet.

[1974] Output: The analysis results provide data on calorie intake and deficient nutrients (e.g., vitamin D). This analysis result is also stored in a database and used for subsequent processing.

[1975] Step 4: Collecting emotional data

[1976] User:

[1977] Input: Users input emotional data into the application using voice input or facial recognition.

[1978] Operation: For example, user D inputs "Today's meal was very delicious" via voice. The application formats the voice and facial expression data and prepares it for transmission to the server.

[1979] Output: The formatted audio data and facial expression data are sent to the server.

[1980] Step 5: Emotion Analysis

[1981] server:

[1982] Input: Voice data and facial expression data sent by the user.

[1983] Operation: Uses speech recognition software (e.g., Google Speech-to-Text API) and facial expression analysis algorithms (e.g., Microsoft Face API) to analyze emotional data and extract emotions such as satisfaction levels.

[1984] Output: Extracted sentiment data (e.g., "satisfied") is obtained. This sentiment data is also stored in the database and used in the next step.

[1985] Step 6: Menu Generation

[1986] server:

[1987] Input: Nutritional analysis results and emotional data.

[1988] Operation: Based on nutritional analysis results and emotional data, it uses generative artificial intelligence to generate a well-balanced menu. For example, it might suggest "grilled salmon, spinach salad, and soy milk smoothie," which are rich in vitamin D.

[1989] Output: The proposed menu information is generated and ready to be sent to the kitchen facility. This information is also stored in the database.

[1990] Step 7: Recipe Sharing

[1991] server:

[1992] Input: Generated menu and detailed recipe information.

[1993] Operation: The server transmits data to cooking facilities near the user. Communication is conducted via the internet or a dedicated network.

[1994] Output: Menu and recipe information sent to the cooking facility.

[1995] Step 8: Cooking

[1996] Terminal (Cooking Staff):

[1997] Input: Menu and recipe information received by the cooking facility.

[1998] Action: Based on this information, the kitchen staff gathers the necessary ingredients and begins cooking. For example, they might prepare grilled salmon, spinach salad, and soy milk smoothies.

[1999] Output: Cooking is complete and the food is properly packaged.

[2000] Step 9: Delivery

[2001] Terminal (Delivery Staff):

[2002] Input: Package of the finished dish.

[2003] Operation: Delivery staff receive the food and prepare it for delivery. They deliver the food to the user's home at the specified time. For example, deliver grilled salmon, spinach salad, and a soy milk smoothie to user D's home.

[2004] Output: Food delivered to the user's home.

[2005] Step 10: Gathering Feedback

[2006] User:

[2007] Input: Ratings and opinions about the delivered food.

[2008] Operation: After user D finishes their meal, they enter their rating and feedback through the application. For example, they might enter the opinion, "The grilled salmon was very tasty, but there was too much salad dressing."

[2009] Output: The input feedback is sent to the server.

[2010] server:

[2011] Input: Feedback submitted by the user.

[2012] Operation: Collects feedback and stores it in a database. Uses the feedback data to create future menus and improve services.

[2013] Output: Feedback information useful for improvement is accumulated in the database, contributing to service enhancement.

[2014] (Application Example 2)

[2015] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[2016] In modern society, many users lead busy lives and lack the time to plan and prepare balanced meals. Furthermore, accurately understanding each individual's nutrient intake and generating optimal menus to supplement any deficiencies is challenging. Moreover, menu suggestions and service improvements that consider user satisfaction and emotions regarding meals are insufficient. Therefore, there is a need for a system that allows users to easily obtain balanced meals while also increasing their satisfaction with those meals.

[2017] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[2018] In this invention, the server includes means for collecting the user's basic information and meal records; means for analyzing the user's nutrient intake based on the collected data and calculating the necessary nutrients and calories; means for generating a well-balanced menu based on the analysis results and emotional data and proposing it to the user; means for sharing the generated menu and recipe information with cooking facilities near the user; means for cooking the meal based on the shared recipe and delivering it to the user's home; means for extracting the user's emotional data through voice input and facial recognition; and means for collecting user feedback and emotional data and reflecting it in improving the service. As a result, users can easily obtain well-balanced meals tailored to their individual nutritional needs and increase their satisfaction with those meals.

[2019] "User basic information" refers to personal data such as age, gender, weight, height, allergy information, and food preferences.

[2020] A "meal diary" is data in which a user specifically records the contents of their daily meals.

[2021] "Nutrient intake status" refers to the amount and balance of each nutrient that the user is consuming through their diet.

[2022] "Required nutrients and calories" refers to the specific nutrients and calories that a user needs to maintain their health and achieve their goals.

[2023] "Analysis results" refer to the results regarding nutrient intake and nutrient deficiencies derived by a generative artificial intelligence system based on the user's basic information and dietary records that have been collected.

[2024] "Emotional data" refers to data such as satisfaction levels and moods extracted through user voice input and facial recognition.

[2025] A "menu" refers to a specific set of dishes or meal combinations suggested to the user.

[2026] "Recipe information" refers to detailed instructions that include how to prepare a dish and a list of necessary ingredients.

[2027] A "cooking facility" is a facility that prepares meals based on menus suggested to users.

[2028] "Feedback" refers to data that users input and submit regarding the food or service they receive, including their evaluations and impressions.

[2029] "Service improvement" means improving future menu suggestions and service content based on collected feedback and sentiment data.

[2030] This invention is a system that collects basic user information and meal records, and generates and proposes well-balanced menus based on the analysis results and emotional data. The aim of this system is to enable users to effectively manage their daily meals and increase their satisfaction with them.

[2031] 1. Data Acquisition Module

[2032] User:

[2033] Users use a mobile application to input personal information (age, gender, height, weight, allergy information, food preferences, etc.) and details of their daily meals. For example, a user might record the scrambled eggs and toast they ate for breakfast.

[2034] 2. Dietary Habits Analysis Module

[2035] server:

[2036] The server stores collected personal information and meal records in a database. Based on the stored data, generative artificial intelligence is used to analyze the user's nutrient intake and calculate the necessary nutrients and calories. For example, by analyzing the user's data, it might identify that the user needs 1800 kcal per day and is deficient in vitamin D.

[2037] 3. Emotion Recognition Module

[2038] User:

[2039] The user uses the application for voice input and facial recognition. For example, the user launches the application and voice-inputs, "Today's meal was very delicious."

[2040] server:

[2041] The server analyzes voice input and facial recognition data to extract user emotion data. For example, it might extract the emotion of "satisfaction" from the user's voice input.

[2042] 4. Menu Creation Module

[2043] server:

[2044] The server generates a well-balanced menu based on analysis results and emotional data. Generative artificial intelligence suggests the optimal combination of ingredients and dishes. For example, it might suggest a menu to the user that includes dishes rich in vitamin D, such as "grilled salmon, spinach salad, and soy milk smoothie."

[2045] 5. Locally shared modules

[2046] server:

[2047] The generated menu and detailed recipe information are shared with the cooking facility closest to the user's area. For example, recipes for grilled salmon, spinach salad, and soy milk smoothie are sent to cooking facilities in the user's area.

[2048] 6. Cooking and Delivery Module

[2049] Terminal (Cooking Staff):

[2050] Based on shared recipe information, the cooking staff prepares the dishes for the menu. For example, they check the recipe, gather the necessary ingredients, and begin cooking. After cooking is complete, they properly package the dishes.

[2051] Terminal (Delivery Staff):

[2052] Cooked food is picked up by delivery staff and delivered to the user's home. For example, grilled salmon, spinach salad, and a soy milk smoothie can be delivered to the user's home.

[2053] 7. Feedback Module

[2054] User:

[2055] After receiving their delivered food and finishing their meal, users enter ratings and feedback through the application. For example, they might write, "The grilled salmon was very tasty, but there was too much salad dressing."

[2056] server:

[2057] The server collects user feedback and uses it, along with sentiment data, to create future menus and improve the service. For example, based on user feedback, the amount of salad dressing might be adjusted next time, and sentiment data might be used to create more satisfying menus.

[2058] The hardware used includes common smartphones (Android / iOS), servers, and terminals for cooking and delivery staff. Software includes Android Studio and iOS development tools for mobile applications, Flask for the server side, SQLite for the database, and TensorFlow for AI models.

[2059] As a concrete example, the following prompt sentences are used in generative AI models:

[2060] User data:

[2061] Age: 30

[2062] Gender: Male

[2063] Weight: 70kg

[2064] Height: 175cm

[2065] Dietary restrictions: None

[2066] Recent meals: Breakfast - Oatmeal with fruits, Lunch - Chicken salad, Dinner - Grilled salmon with vegetables

[2067] User feeling about today's meals: Satisfied

[2068] Generate a balanced menu for the next day considering the user's health needs and preferences.

[2069] In this way, the system allows individual users to easily enjoy nutritionally balanced meals and achieve highly satisfying and smart meal management.

[2070] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[2071] Step 1:

[2072] Users enter personal information and meal records.

[2073] Input: Users use a smartphone application to enter personal information such as age, gender, height, weight, allergy information, and dietary preferences, as well as details of their daily meals.

[2074] Data processing and calculation: The application checks the input data and organizes it according to the format.

[2075] Output: Organized personal information and meal records are sent to the server and stored in the database.

[2076] Specific operation: The user enters the scrambled eggs and toast they ate for breakfast, and the application organizes that information and sends it to the server.

[2077] Step 2:

[2078] The server performs dietary analysis.

[2079] Input: User's personal information and meal records stored in the database.

[2080] Data processing and calculation: The server uses generative artificial intelligence to analyze the user's nutrient intake and calculate the necessary nutrients and calories.

[2081] Output: The calculated analysis results are generated.

[2082] Specific operation: The server analyzes the user's data and identifies that their daily calorie requirement is 1800 kcal and that they are deficient in vitamin D.

[2083] Step 3:

[2084] Users input emotional data.

[2085] Input: The user launches the application and provides emotional data through voice input or facial recognition.

[2086] Data Processing and Calculation: The application performs preprocessing to analyze voice input and facial recognition data.

[2087] Output: Pre-processed emotion data is sent to the server.

[2088] Specific operation: The user voice-inputs "Today's meal was very delicious," and this information is pre-processed before being sent to the server.

[2089] Step 4:

[2090] The server analyzes the emotional data.

[2091] Input: Sentiment data submitted by the user.

[2092] Data processing and calculation: The server uses generative artificial intelligence to analyze emotional data and identify user satisfaction and emotions.

[2093] Output: Emotional data extracted as an analysis result.

[2094] Specific operation: The server extracts the emotion of "satisfaction" from the voice input.

[2095] Step 5:

[2096] The server generates the menu.

[2097] Input: Analysis results and sentiment data.

[2098] Data processing and calculation: The server uses generative artificial intelligence to calculate the optimal combination of ingredients and dishes.

[2099] Output: A well-balanced menu is generated.

[2100] Specific action: A menu is proposed that includes dishes rich in vitamin D, such as "grilled salmon, spinach salad, and soy milk smoothie."

[2101] Step 6:

[2102] The server shares recipe information with the cooking facilities.

[2103] Input: Generated menu and detailed recipe information.

[2104] Data processing and calculation: The server converts menu and recipe information into a specific format and sends it to the nearest cooking facility.

[2105] Output: Shared data for the cooking facility to receive.

[2106] Specific action: Send recipe information for "grilled salmon, spinach salad, and soy milk smoothie" to a cooking facility in the user's area.

[2107] Step 7:

[2108] The cooking facility prepares the food.

[2109] Input: Recipe information shared from the server.

[2110] Data processing and calculation: Cooking staff gather the necessary ingredients based on the recipe and begin cooking.

[2111] Output: Cooked food.

[2112] Specific tasks: Cooking staff will review the recipes and prepare and properly package the grilled salmon, spinach salad, and soy milk smoothie.

[2113] Step 8:

[2114] Delivery staff deliver the food.

[2115] Input: Dishes prepared in a cooking facility.

[2116] Data processing and calculation: Delivery staff pick up the food and deliver it to the user's home.

[2117] Output: Meals delivered to the user's home.

[2118] Specific actions: A delivery staff member delivers grilled salmon, spinach salad, and a soy milk smoothie to the user's home.

[2119] Step 9:

[2120] Users enter feedback

[2121] Input: Your evaluation or comments on the food you received.

[2122] Data processing and calculation: The application collects the input feedback and sends it to the server.

[2123] Output: Collected feedback data.

[2124] Specific operation: The user enters a review stating, "The grilled salmon was very tasty, but there was too much salad dressing," and this information is sent to the server.

[2125] Step 10:

[2126] The server analyzes the feedback and uses it to improve the service.

[2127] Input: User feedback and sentiment data.

[2128] Data processing and calculation: Generative artificial intelligence is used to analyze feedback and utilize it for creating future menus and improving services.

[2129] Output: Improved menu suggestions and service details.

[2130] Specific operation: Based on user feedback, the server adjusts the amount of salad dressing in the next menu.

[2131] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[2132] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[2133] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[2134] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[2135] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[2136] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[2137] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[2138] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[2139] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[2140] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[2141] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[2142] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[2143] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[2144] 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.

[2145] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[2146] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[2147] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[2148] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resour...

Claims

1. Means for collecting basic user information and meal records, Based on the collected data, a means of analyzing nutrient intake and calculating the necessary nutrients and calories, A means of generating a well-balanced menu based on the analysis results and proposing it to the user, A means of sharing the generated menu and recipe information with nearby cooking facilities, A means of cooking meals based on shared recipes and delivering them to the user's home, A means of collecting user feedback and incorporating it into service improvements, A system that includes this.

2. The system according to claim 1, wherein the means for inputting the user's meal record is a mobile application.

3. The system according to claim 1, comprising means for analyzing nutrient intake status using generative artificial intelligence.

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