system

Abstract

We provide the system. [Solution] A means for users to input family structure, food preferences, and allergy information, Means for receiving and storing the input information, A means for automatically generating a weekly menu based on the aforementioned stored information, A means for generating a list of necessary ingredients based on the automatically generated menu, A means for automatically ordering ingredients from an online store based on the generated ingredient list, A system including means for notifying the user of the automatically placed order.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance 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 families, considering daily menus and purchasing all the necessary ingredients has become a major burden. In particular, it is not easy to provide a balanced diet while taking into account family composition, individual food preferences, and allergy information. In such a situation, parents leading busy daily lives have a strong need to save the time and effort of thinking about menus and going shopping.

Means for Solving the Problems

[0005] This invention provides a means for a user to input family structure, food preferences, and allergy information, and a means for receiving and storing the input information. It also includes a means for automatically generating a weekly menu based on the stored information, and a means for generating a list of necessary ingredients based on the automatically generated menu. The invention solves this problem by providing a system that includes a means for automatically ordering ingredients from an online store based on the generated ingredient list, and a means for notifying the user of the automatically ordered items. Furthermore, by providing a means for the user to confirm and modify the notified order details, and a means for confirming the final ingredient order with the online store based on the modified order details, the invention realizes a convenient and flexible system for the user.

[0006] A "user" is a person who uses the system to provide information about their family structure, food preferences, and allergies, and receives services such as menu planning and grocery ordering.

[0007] "Family structure" refers to information about the ages, genders, and number of people belonging to a household.

[0008] "Food preferences" refers to information about the foods, dishes, and seasonings that the user and their family enjoy.

[0009] "Allergy information" refers to information about foods that users or their families cannot eat or ingredients that should be avoided.

[0010] A "menu" refers to a plan that outlines the combination of dishes to be served at each meal throughout the week.

[0011] A "food ingredient list" is a list of various ingredients needed based on a menu.

[0012] An "online store" is a retail store that has a system for providing products for purchase via the internet.

[0013] "Automated ordering" is a process in which the system orders ingredients from an online store without requiring user input or confirmation.

[0014] A "notification" is a message sent by the system to inform the user of information such as the menu or order details.

[0015] "Modification" refers to the act of a user making changes to the menu or order details they have been notified about.

[0016] "Confirmation" refers to the process of finalizing the order details and officially submitting them to the online store. [Brief explanation of the drawing]

[0017] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This 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] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This 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] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This 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] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This 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.

Mode for Carrying Out the Invention

[0018] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0019] First, the terms used in the following description will be explained.

[0020] In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be one 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), APU (Accelerated Processing Unit), etc.

[0021] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0022] 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.

[0023] 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).

[0024] 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."

[0025] [First Embodiment]

[0026] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0027] 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.

[0028] 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).

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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".

[0038] This invention provides a system that automatically generates menus based on input such as family composition, food preferences, and allergy information, and automatically orders the necessary ingredients from an online store.

[0039] System Configuration

[0040] This system consists of the following components:

[0041] 1. User Registration Method

[0042] The device provides a user interface that allows users to input information such as family structure, food preferences, and allergies.

[0043] For example, the user enters information such as "husband 35 years old, wife 33 years old, child 5 years old," "wife has a peanut allergy," and "child likes vegetables."

[0044] 2. Data storage means

[0045] The server saves the entered information to the database.

[0046] For example, information entered by users can be saved to a family structure database or an allergy information database.

[0047] 3. Menu generation method

[0048] The server references the stored information and automatically generates a weekly menu tailored to nutritional balance and family preferences.

[0049] As an example, I suggest chicken steak for dinner on Monday and Japanese-style hamburger steak for dinner on Tuesday.

[0050] 4. Ingredient list generation method

[0051] The server creates a list of necessary ingredients based on the generated menu.

[0052] For example, list ingredients such as 200g of chicken, 1 onion, and 2 carrots.

[0053] 5. Automated ordering method

[0054] The server automatically orders the necessary ingredients via the online store's API.

[0055] For example, the generated ingredient list is sent to an online store in an order format.

[0056] 6. Means of notification

[0057] The server notifies the user's terminal of the order details.

[0058] For example, the user's device will display "Order details: 200g chicken, 1 onion, 2 carrots".

[0059] 7. Verification and Correction Methods

[0060] The user reviews the notified order details and makes corrections as needed.

[0061] For example, a user can change chicken to fish to increase the number of fish dishes available.

[0062] 8. Final confirmation method

[0063] The server then sends the corrected order details to the online store and confirms the order.

[0064] Specific example

[0065] 1. Registration process:

[0066] The user launches the app and enters their family structure as "husband 35 years old, wife 33 years old, child 5 years old," along with "wife has a peanut allergy" and "child likes vegetables."

[0067] 2. Menu generation process:

[0068] Every Monday, the server accesses the database and generates a weekly menu that includes chicken steak on Monday and Japanese-style hamburger steak on Tuesday.

[0069] 3. Generate ingredient list:

[0070] The server lists 200g of chicken, 1 onion, and 2 carrots.

[0071] 4. Automatic ordering:

[0072] The server generates a list of ingredients and places an order through the online store's API.

[0073] 5. Notifications and confirmations:

[0074] The server notifies the user's terminal of the order details, and the user confirms and modifies the order.

[0075] 6. Delivery process:

[0076] The online supermarket delivers groceries to the user's home based on the finalized order details.

[0077] This system allows users to significantly reduce the effort involved in planning daily menus and grocery shopping, and enables them to provide well-balanced meals tailored to their family's needs.

[0078] The following describes the processing flow.

[0079] Program processing steps

[0080] Registration process

[0081] Step 1:

[0082] The user opens the app and enters family information.

[0083] For example, enter "Husband 35 years old, wife 33 years old, child 5 years old".

[0084] The terminal sends the entered data to the server.

[0085] Step 2:

[0086] Users enter their food preferences and allergy information.

[0087] For example, enter "My wife has a peanut allergy" and "My child likes vegetables."

[0088] The terminal sends the entered data to the server.

[0089] Step 3:

[0090] The server saves the information it receives to the database.

[0091] Save the information in the family information table and the food preferences / allergy information table.

[0092] Menu generation process

[0093] Step 4:

[0094] Every Monday, the server starts processing menu generation requests according to a scheduled task.

[0095] Step 5:

[0096] The server retrieves family information, food preferences, and allergy information from the database.

[0097] Step 6:

[0098] Based on the data acquired by the server, it generates a weekly meal plan by referring to a predefined nutritional balance and recipe database.

[0099] For example, generate "Monday's dinner is chicken steak" and "Tuesday's dinner is Japanese-style hamburger steak".

[0100] Step 7:

[0101] The server generates menus, links them to user accounts, and saves them to the database.

[0102] Ingredient list generation process

[0103] Step 8:

[0104] The server references the saved menu and lists the ingredients needed for each dish.

[0105] Step 9:

[0106] The server generates a list of necessary ingredients based on the menu.

[0107] As an example, list "200g of chicken," "1 onion," and "2 carrots."

[0108] Step 10:

[0109] The server optimizes itself to avoid duplication and waste by considering past purchase history and inventory information.

[0110] Collaboration with online supermarkets

[0111] Step 11:

[0112] The server uses the online supermarket's API to convert the generated grocery list into an order format.

[0113] Step 12:

[0114] The server sends order information, including the user's address and desired delivery time, to the online supermarket.

[0115] Notification and confirmation

[0116] Step 13:

[0117] The server sends a confirmation notification to the user's terminal containing the generated menu and ingredient order.

[0118] Step 14:

[0119] The user's device receives a notification, and the user confirms the menu and order details.

[0120] For example, "If you want to increase the number of fish dishes, change the chicken to fish."

[0121] Step 15:

[0122] The user makes corrections, and the device sends the corrected data to the server.

[0123] Delivery process

[0124] Step 16:

[0125] The server performs a final check and then sends the order details to the online supermarket.

[0126] Step 17:

[0127] Online supermarkets prepare groceries based on the order and deliver them to the user's home at the specified time.

[0128] In this way, users can significantly reduce the effort involved in daily meal planning and grocery shopping. Convenience is enhanced because the system automates everything, allowing users to handle tedious processes with minimal awareness.

[0129] (Example 1)

[0130] 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."

[0131] In today's busy lifestyle, planning family meals and efficiently gathering necessary ingredients is difficult for many people. In particular, creating menus that maintain nutritional balance while considering each family member's food preferences and allergy information requires considerable time and effort. There is a need for a system that solves this problem and simplifies family meal preparation.

[0132] 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.

[0133] In this invention, the server includes means for the user to input family composition, food preferences, and allergy information; means for receiving and storing the input information; means for automatically generating a weekly menu based on the stored information; and means for generating the menu according to nutritional balance and family preferences. This makes it possible to automatically generate nutritionally balanced menus tailored to the needs of the household, significantly reducing the effort required for meal preparation.

[0134] A "user" is someone who uses the system to input information such as family structure, food preferences, and allergies.

[0135] "Family structure" refers to information such as the relationships, ages, and genders of individual members within a household, and is data that is considered when the system generates menus.

[0136] "Food preferences" refer to a user's tastes and preferences for specific ingredients or dishes, and are one of the important factors when a system generates menus.

[0137] "Allergy information" refers to information about whether the user or their family members have allergies to specific foods. This information is used by the system to generate menus that do not contain allergens.

[0138] "Input method" refers to the interface that allows users to input data such as family structure, food preferences, and allergy information into the system.

[0139] "Storage method" refers to the process or function by which a server saves information entered by a user to a database.

[0140] "Menu generation method" refers to the process by which a system automatically generates a week's worth of menus based on stored information.

[0141] "Nutritional balance" refers to the appropriate balance of nutrients necessary for maintaining health, and it is a criterion that the system considers when generating menus.

[0142] A "food ingredient list" refers to a list of necessary ingredients based on the generated menu.

[0143] "Automated ordering method" refers to a function or process for automatically ordering a generated list of ingredients from an online store.

[0144] "Notification method" refers to the system's function of notifying the user of the details of an automatically placed order.

[0145] "Confirmation and correction means" refers to a system function that allows users to confirm the order details they have been notified about and make corrections as needed.

[0146] "Final confirmation method" refers to the function used to confirm the final food order with the online store based on the revised order details.

[0147] This invention relates to a system that automatically generates menus based on family composition, food preferences, and allergy information, and automatically orders the necessary ingredients from an online store. A specific embodiment of this system is described below.

[0148] System Configuration

[0149] This system consists of the following components:

[0150] 1. User input means

[0151] The device provides a user interface that allows the user to input information such as family structure, food preferences, and allergies. Examples of such devices include personal computers and smartphones.

[0152] Specifically, users input information through the application such as their family structure ("husband 35 years old, wife 33 years old, child 5 years old"), their wife's peanut allergy, and their child's preference for vegetables.

[0153] 2. Data storage means

[0154] The server stores information entered by the user in a database. Servers are often built as cloud-based services.

[0155] For example, the entered information is stored in a "family structure database" and an "allergy information database."

[0156] 3. Menu generation method

[0157] The server references stored information and uses a generation AI model to automatically generate a weekly menu tailored to nutritional balance and family preferences.

[0158] Specifically, the server has the AI ​​model generate menus such as "chicken steak for dinner on Monday, and Japanese-style hamburger steak for dinner on Tuesday."

[0159] 4. Ingredient list generation method

[0160] The server generates a list of necessary ingredients based on the generated menu.

[0161] Specifically, the server creates a list of ingredients such as "200g chicken, 1 onion, 2 carrots."

[0162] 5. Automated ordering method

[0163] The server automatically orders the necessary ingredients via the online store's API.

[0164] For example, the server sends the generated list of ingredients to the online store as an order breakdown.

[0165] 6. Means of notification

[0166] The server notifies the user's terminal of the order details.

[0167] For example, the server sends a notification to the terminal saying, "Order details: 200g chicken, 1 onion, 2 carrots."

[0168] 7. Verification and Correction Methods

[0169] The user reviews the notified order details and makes corrections as needed.

[0170] For example, users can make modifications through their devices, such as changing "chicken to fish."

[0171] 8. Final confirmation method

[0172] The server then sends the corrected order details to the online store and confirms the order.

[0173] Specifically, the server completes the order based on the finalized details.

[0174] Example of a prompt

[0175] Examples of prompts for a generative AI model are as follows:

[0176] "Please create a week's worth of dinner menus for a family with a 35-year-old husband, a 33-year-old wife, and a 5-year-old child. The wife has a peanut allergy, and the child likes vegetables."

[0177] This system allows users to significantly reduce the effort involved in planning daily menus and grocery shopping, and enables them to provide well-balanced meals tailored to their family's needs.

[0178] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0179] Step 1:

[0180] The user enters information about their family structure, food preferences, and allergies into the device.

[0181] Input: The user uses the application to input information such as family structure ("husband 35 years old, wife 33 years old, child 5 years old"), "wife has a peanut allergy," and "child likes vegetables."

[0182] Output: The input information is stored in the terminal's temporary memory.

[0183] Specific operation: When the user enters data into each input field and presses the "Save" button, the device prepares to send the input data to the server.

[0184] Step 2:

[0185] The terminal sends the information entered by the user to the server.

[0186] Input: Family composition, food preferences, and allergy information stored on the device.

[0187] Output: A request that sends input information to the server.

[0188] Specific operation: The device sends user input data to the server's API endpoint via a POST request.

[0189] Step 3:

[0190] The server saves the received information to the database.

[0191] Input: Family composition, food preferences, and allergy information received by the server from the terminal.

[0192] Output: User information stored in the database.

[0193] Specific operation: The server connects to the database and saves the received data to the appropriate table using an INSERT statement.

[0194] Step 4:

[0195] The server generates a weekly meal plan based on the stored information.

[0196] Input: Family composition, food preferences, and allergy information stored in the database.

[0197] Output: The generated weekly meal plan.

[0198] Specific operation: The server inputs prompt text into the generated AI model and makes a request to generate a menu such as "Chicken steak for dinner on Monday, and Japanese-style hamburger steak for dinner on Tuesday."

[0199] Step 5:

[0200] The server generates a list of necessary ingredients based on the generated menu.

[0201] Input: A generated weekly meal plan.

[0202] Output: List of required ingredients.

[0203] Specific operation: The server extracts the ingredients for each menu item from a database or fixed list, calculates the required quantities, and lists them.

[0204] Step 6:

[0205] The server automatically orders ingredients via the online store's API.

[0206] Input: The generated list of ingredients.

[0207] Output: Order request sent to the online store.

[0208] Specific operation: The server sends order data, including the ingredient list, via a POST request to the online store's API endpoint.

[0209] Step 7:

[0210] The server notifies the user's terminal of the order details.

[0211] Input: Order request submitted to the online store.

[0212] Output: A notification of the order details displayed on the user's device.

[0213] Specific operation: The server generates a notification message containing the order details and sends it to the terminal to inform the user that the order has been submitted.

[0214] Step 8:

[0215] The user reviews the notified order details and makes corrections as needed.

[0216] Input: Order details displayed on the terminal.

[0217] Output: Order details modified by the user.

[0218] Specific actions: The user reviews the order details displayed on the device interface, makes any necessary changes or corrections, and then presses the "Confirm" button.

[0219] Step 9:

[0220] The server will finalize the order on the online store based on the corrected information.

[0221] Input: Order details modified by the user.

[0222] Output: The final food order has been confirmed.

[0223] Specific operation: The server finalizes the modified order details and sends them again to the online store's API endpoint to complete the order.

[0224] (Application Example 1)

[0225] 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."

[0226] Traditional menu planning and grocery ordering systems can take into account user preferences and allergy information, but they have struggled to comprehensively cover the delivery of ordered ingredients. Furthermore, users are required to shop for ingredients daily and review and modify their orders, which adds to their time and effort. A system that solves these problems and enhances user convenience is needed.

[0227] 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.

[0228] In this invention, the server includes means for the user to input family composition, food preferences, and allergy information; means for receiving and storing the input information; means for automatically generating a weekly menu based on the stored information; means for generating a list of necessary ingredients based on the automatically generated menu; means for automatically ordering ingredients from an online store based on the generated ingredient list; means for notifying the user of the automatically ordered items; and means for delivering the ingredients via a delivery service based on the ordered items. This makes it possible for a user to input family composition, food preferences, and allergy information and have the entire process, from automatically generating a menu to ordering ingredients and even delivery, handled in a consistent manner.

[0229] "A means for users to input family structure, food preferences, and allergy information" refers to an interface for users to input their family structure, the food preferences of individual members, and allergy information.

[0230] "Means for receiving and storing the input information" refers to a system for receiving information entered by a user and storing it in a database or similar.

[0231] "Means for automatically generating a weekly menu based on the stored information" refers to algorithms or software for automatically creating a nutritionally balanced weekly menu based on stored family composition, food preferences, and allergy information.

[0232] "Means for generating a list of necessary ingredients based on the automatically generated menu" refers to a program that lists the ingredients necessary for the automatically generated menu and generates that list.

[0233] "Means for automatically ordering ingredients from an online store based on the generated ingredient list" refers to a system that automatically orders ingredients using an online store's API or the like based on the generated ingredient list.

[0234] "Means for notifying the user of the automatically ordered items" refers to an application or interface for notifying the user of the contents of the automatically ordered food items.

[0235] "Means of delivering ingredients via delivery service based on the order details" refers to a system that uses online stores and delivery services selected based on the order details to deliver the necessary ingredients to the user's home.

[0236] System Configuration

[0237] This invention is a system that allows users to input their family structure, food preferences, and allergy information, automatically generates menus, automatically orders necessary ingredients from an online store, and even handles delivery in a seamless manner.

[0238] User registration method

[0239] The system provides an interface to the user's device (e.g., a smartphone or tablet) and includes a means for inputting family structure, food preferences, and allergy information. This information is transmitted to a server and stored in a database.

[0240] Information reception and storage means

[0241] The server receives information entered by the user and stores it in a database. For example, it stores it in a family structure database and an allergy information database.

[0242] Menu generation method

[0243] The server automatically generates a week's worth of menus using a generative AI model based on the stored information. During this process, prompts are used to instruct the AI ​​model, taking into account nutritional balance, family preferences, and allergy information.

[0244] Prompt example:

[0245] System: Please generate a weekly meal plan based on family composition, dietary preferences, and allergy information.

[0246] User input: Family composition is "husband 35 years old, wife 33 years old, child 5 years old". The wife has a peanut allergy, and the child likes vegetables.

[0247] System output: Monday dinner is chicken steak, Tuesday dinner is Japanese-style hamburger, Wednesday dinner is...

[0248] Ingredient list generation method

[0249] The server generates a list of necessary ingredients based on the automatically generated menu. For example, it might list ingredients such as 200g of chicken, 1 onion, and 2 carrots.

[0250] Automatic ordering method

[0251] The server automatically places an order via the online store's API based on the generated list of ingredients. At this time, it sends the order data according to the online store's API format.

[0252] Notification means

[0253] The server notifies the user's terminal of the automatically placed order. For example, it displays to the user, "Order details: 200g chicken, 1 onion, 2 carrots."

[0254] Verification and correction methods

[0255] The user reviews the notified order details and makes any necessary modifications. For example, the user can change chicken to fish to increase the number of fish dishes.

[0256] final means of confirmation

[0257] The server then confirms the user's modified order details again via the online store's API, and completes the order.

[0258] Delivery method

[0259] Based on the finalized order details, the server selects the appropriate delivery service and initiates the process of delivering the ingredients to the user's home.

[0260] Specific example

[0261] Registration process:

[0262] The user launches the smartphone app and enters their family structure as "husband 35 years old, wife 33 years old, child 5 years old," along with "wife has a peanut allergy" and "child likes vegetables."

[0263] Menu generation process:

[0264] The server looks at the database and generates a weekly menu that includes chicken steak for dinner on Monday and Japanese-style hamburger steak for dinner on Tuesday.

[0265] Ingredient list generation:

[0266] The server lists 200g of chicken, 1 onion, and 2 carrots.

[0267] Automatic ordering:

[0268] The server places orders for the generated ingredient list through the online store's API.

[0269] Notifications and confirmations:

[0270] The server notifies the user's terminal of the order details, and the user confirms and modifies the details.

[0271] Delivery process:

[0272] Online stores and delivery services deliver groceries to users' homes based on the finalized order details.

[0273] This significantly reduces the effort users spend on daily meal planning and grocery shopping, making it easy to provide well-balanced meals tailored to their family's needs.

[0274] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0275] Step 1:

[0276] The user enters their family structure, food preferences, and allergy information.

[0277] Input: Family composition (e.g., "Husband 35 years old, wife 33 years old, child 5 years old"), food preferences (e.g., "Child likes vegetables"), and allergy information (e.g., "Wife is allergic to peanuts") are entered via a smartphone app.

[0278] Output: Input information is sent to the server.

[0279] Specific operation: The user enters information into the app's input screen and presses the "Submit" button, which sends the data to the server.

[0280] Step 2:

[0281] The server receives and stores the entered information.

[0282] Input: Family composition, food preferences, and allergy information submitted by the user.

[0283] Output: Family composition data, food preference data, and allergy information saved in the database.

[0284] Specific operation: The server saves the received information in the corresponding tables of the database. For example, add data to the family composition table, preference information table, and allergy information table.

[0285] Step 3:

[0286] The server automatically generates a one-week meal plan based on the saved information.

[0287] Input: Family composition, food preferences, and allergy information saved in the database.

[0288] Output: A one-week meal plan list.

[0289] Specific operation: The server uses the prompt text to give instructions to the generation AI model and receives a one-week meal plan considering family composition, food preferences, and allergy information.

[0290] Step 4:

[0291] The server generates a list of required ingredients based on the automatically generated meal plan.

[0292] Input: A one-week meal plan list.

[0293] Output: A list of required ingredients.

[0294] Specific operation: The server analyzes the meal plan data and lists up the ingredients and their quantities required for each meal. For example, 200g of chicken, 1 onion, 2 carrots, etc.

[0295] Step 5:

[0296] The server automatically places an order on the online store based on the generated ingredient list.

[0297] Input: List of necessary ingredients.

[0298] Output: Order data sent to the online store.

[0299] Specific operation: The server uses the API of the online store to convert the ingredient list into an order format and send the order data.

[0300] Step 6:

[0301] The server notifies the user of the automatically ordered content.

[0302] Input: Sent order data.

[0303] Output: Order confirmation notification displayed on the user's smartphone.

[0304] Specific operation: The server sends a push notification or an email to notify the user of the order details on the user's terminal.

[0305] Step 7:

[0306] The user checks and modifies the notified order details.

[0307] Input: Order confirmation notification. [[ID=四十二]]

[0308] Output: Checked and modified order details.

[0309] Specific operation: The user checks the notified order details in the app, adds or changes ingredients if necessary, and presses the "Confirm" button.

[0310] Step 8:

[0311] Based on the modified order details, the server finally confirms the ingredient order with the online store.

[0312] Input: Checked and modified order details.

[0313] Output: Final order data sent to the online store.

[0314] Specific operation: The server resends the final order data, reflecting the user's modifications, to the online store's API to confirm the order.

[0315] Step 9:

[0316] The server then processes the delivery of ingredients based on the finalized order details.

[0317] Input: Last order data.

[0318] Output: Food items delivered to the user's home.

[0319] Specific operation: The server uses APIs from online stores and delivery services to process food delivery. It may also send delivery completion notifications to the user's smartphone.

[0320] 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.

[0321] This invention is a system that automatically generates menus and orders necessary ingredients from an online store by inputting family structure, food preferences, and allergy information, and combining this with an emotion engine. A key feature is that the emotion engine recognizes the user's emotions and adjusts the menu and notification content accordingly.

[0322] System Configuration

[0323] This system consists of the following components:

[0324] 1. User Registration Method

[0325] The device provides a user interface that allows users to input information such as family structure, food preferences, and allergies.

[0326] For example, the user enters information such as "husband 35 years old, wife 33 years old, child 5 years old," "wife has a peanut allergy," and "child likes vegetables."

[0327] 2. Data storage means

[0328] The server saves the entered information to the database.

[0329] For example, information entered by users can be saved to a family structure database or an allergy information database.

[0330] 3. Means of recognizing emotions (emotion engine)

[0331] The server recognizes emotions from the user's input data and behavioral history.

[0332] For example, if a user is feeling stressed, the system will detect it.

[0333] 4. Menu generation means

[0334] The server references stored information and recognized emotional data to automatically generate a weekly menu tailored to nutritional balance and family preferences.

[0335] For example, I suggest chicken steak for Monday's dinner, a comforting meal, and Japanese-style hamburger steak for Tuesday's dinner, which has a relaxing effect.

[0336] 5. Menu adjustment methods

[0337] The server adjusts the menu based on emotional information obtained by the emotion engine.

[0338] For example, if a user is feeling stressed, the system might suggest a dish with relaxing properties.

[0339] 6. Ingredient List Generation Method

[0340] The server creates a list of necessary ingredients based on the generated menu.

[0341] For example, list ingredients such as 200g of chicken, 1 onion, and 2 carrots.

[0342] 7. Automated ordering method

[0343] The server automatically orders the necessary ingredients via the online store's API.

[0344] For example, the generated ingredient list is sent to an online store in an order format.

[0345] 8. Means of notification

[0346] The server notifies the user's device of a menu based on their order and emotions.

[0347] For example, a notification message might appear stating, "Today, we suggest a relaxing Japanese-style hamburger steak."

[0348] 9. Verification and Correction Methods

[0349] The user reviews the notified order details and makes corrections as needed.

[0350] For example, a user can change chicken to fish to increase the number of fish dishes available.

[0351] 10. Final confirmation method

[0352] The server then sends the corrected order details to the online store and confirms the order.

[0353] Online supermarkets prepare groceries based on the order and deliver them to the user's home at the specified time.

[0354] Specific example

[0355] 1. Registration process:

[0356] The user launches the app and enters their family structure as "husband 35 years old, wife 33 years old, child 5 years old," along with "wife has a peanut allergy" and "child likes vegetables."

[0357] 2. Emotion recognition:

[0358] The server recognizes when a user is experiencing stress based on their behavioral history and input data.

[0359] 3. Menu generation:

[0360] Every Monday, the server accesses the database and generates a weekly menu that includes a relaxing chicken steak on Monday and a Japanese-style hamburger on Tuesday.

[0361] 4. Generate ingredient list:

[0362] The server lists 200g of chicken, 1 onion, and 2 carrots.

[0363] 5. Automatic ordering:

[0364] The server generates a list of ingredients and places an order through the online store's API.

[0365] 6. Notifications and confirmations:

[0366] The server notifies the user's terminal of the order details, and the user confirms and modifies the order.

[0367] For example, we could suggest a menu to reduce stress, and then the user could review it and make revisions to include more fish dishes.

[0368] 7. Delivery process:

[0369] The online supermarket delivers groceries to the user's home based on the finalized order details.

[0370] In this way, users can not only significantly reduce the effort involved in daily meal planning and shopping, but also achieve a more comfortable eating lifestyle by being provided with optimal meals tailored to their emotional state. By automating everything and making adjustments based on emotions, the system can improve the user's quality of life.

[0371] The following describes the processing flow.

[0372] Program processing steps

[0373] Registration process

[0374] Step 1:

[0375] The user opens the app and enters family information.

[0376] For example, enter "Husband 35 years old, wife 33 years old, child 5 years old".

[0377] The terminal sends the entered data to the server.

[0378] Step 2:

[0379] Users enter their food preferences and allergy information.

[0380] For example, enter "My wife has a peanut allergy" and "My child likes vegetables."

[0381] The terminal sends the entered data to the server.

[0382] Step 3:

[0383] The server saves the information it receives to the database.

[0384] Save the information in the family information table and the food preferences / allergy information table.

[0385] Emotion recognition process

[0386] Step 4:

[0387] While the user is using the app, the device collects the user's input and behavioral patterns.

[0388] For example, when a user performs many actions in a short period of time, or gives negative feedback about a particular dish.

[0389] Step 5:

[0390] The server analyzes the collected data, and the emotion engine evaluates the user's emotional state.

[0391] For example, the emotion engine might determine that the user is feeling stressed.

[0392] Menu generation process

[0393] Step 6:

[0394] Every Monday, the server starts processing menu generation requests according to a scheduled task.

[0395] Step 7:

[0396] The server retrieves family information, food preferences and allergy information, and emotional information from the database.

[0397] Step 8:

[0398] Based on the data acquired by the server, the system automatically generates a weekly meal plan by referring to a predefined nutritional balance and recipe database.

[0399] For example, I suggest chicken steak for Monday's dinner, a comforting meal, and Japanese-style hamburger steak for Tuesday's dinner, which has a relaxing effect.

[0400] Step 9:

[0401] The server adjusts the menu content it generates based on the emotional information obtained by the emotion engine.

[0402] For example, if a user is feeling stressed, they might choose a menu item that promotes relaxation.

[0403] Step 10:

[0404] The server saves the adjusted menu to the user's account in the database.

[0405] Ingredient list generation process

[0406] Step 11:

[0407] The server references the saved menu and lists the ingredients needed for each dish.

[0408] Step 12:

[0409] The server generates a list of necessary ingredients based on the menu.

[0410] As an example, list "200g of chicken," "1 onion," and "2 carrots."

[0411] Step 13:

[0412] The server optimizes itself to avoid duplication and waste by considering past purchase history and inventory information.

[0413] Collaboration with online supermarkets

[0414] Step 14:

[0415] The server uses the online supermarket's API to convert the generated grocery list into an order format.

[0416] Step 15:

[0417] The server sends order information, including the user's address and desired delivery time, to the online supermarket.

[0418] Notification and confirmation

[0419] Step 16:

[0420] The server sends a confirmation notification to the user's terminal containing the generated menu and ingredient order.

[0421] Step 17:

[0422] The user's device receives a notification, and the user confirms the menu and order details.

[0423] For example, "If you want to increase the number of fish dishes, change the chicken to fish."

[0424] Step 18:

[0425] The user makes corrections, and the device sends the corrected data to the server.

[0426] Delivery process

[0427] Step 19:

[0428] The server performs a final check and then sends the order details to the online supermarket.

[0429] Step 20:

[0430] Online supermarkets prepare groceries based on the order and deliver them to the user's home at the specified time.

[0431] In this way, users can significantly reduce the effort involved in daily meal planning and grocery shopping. Convenience is enhanced by automating everything and allowing users to handle tedious processes with minimal awareness. Furthermore, by utilizing an emotion engine, optimal meal plans are suggested based on the user's emotional state, leading to a more comfortable and enjoyable eating experience.

[0432] (Example 2)

[0433] 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".

[0434] Conventional menu generation systems generate menus based solely on the family composition, food preferences, and allergy information entered by the user, and therefore cannot provide optimal menus that take into account the user's emotional state. Furthermore, while it is important to provide meals that address a user's emotional state when they are stressed or experiencing a particular emotional condition, conventional systems failed to achieve this. In addition, the automatic ordering, confirmation, and correction functions for ingredients were insufficient, and the system could not fully reduce the burden on the user.

[0435] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0436] In this invention, the server includes means for the user to input family composition, food preferences, and allergy information; server means for receiving and storing the input information; server means for automatically generating a weekly menu based on the stored information and the user's emotional state; server means for generating a list of necessary ingredients based on the automatically generated menu; server means for automatically ordering ingredients from an online store based on the generated ingredient list; and server means for notifying the user terminal of the automatically ordered items. This enables the provision of an optimal menu that takes the user's emotional state into consideration, as well as automatic ordering of ingredients and functions for confirmation and modification.

[0437] "User" refers to an individual or family member who uses the system.

[0438] "Family composition" refers to information about the people who belong to the user's household.

[0439] "Food preferences" refers to the types of ingredients and dishes that the user and their family enjoy consuming.

[0440] "Allergy information" refers to information about allergies held by the user and their family.

[0441] A "server" refers to a computer device that processes, stores, and transmits data within a system.

[0442] "Server configuration" refers to the software and hardware configuration that perform specific processing functions on a server.

[0443] "Automatic generation" refers to the process by which a system autonomously creates data according to a program.

[0444] "Emotional state" refers to the user's current psychological or emotional condition.

[0445] A "food ingredient list" refers to a list of ingredients needed based on the generated menu.

[0446] An "online store" refers to a website or service that allows users to purchase groceries and other items via the internet.

[0447] "Automated ordering" refers to the act of a system ordering specified ingredients from an online store without manual intervention from the user.

[0448] "Notification" refers to the process of transmitting information from a system to a user.

[0449] A "terminal" refers to a device (such as a smartphone or computer) that a user uses to access and operate a system.

[0450] "Confirmation and correction" refers to the act of a user reviewing the information they have been notified about and making changes as necessary.

[0451] "Final ingredient order" refers to the ingredient order that has been confirmed and finalized after user review and modification.

[0452] This invention relates to an automated menu generation system that combines a user's input of family structure, food preferences, and allergy information with an emotion engine. This system uses a server and terminals to provide an optimal menu tailored to the user's emotional state and enables automatic ordering of ingredients.

[0453] 1. User Registration Method

[0454] Users enter family composition, food preferences, and allergy information using devices such as smartphones or computers. These devices provide dedicated applications or web interfaces to facilitate information entry. For example, a user might launch the app and enter information such as "husband 35 years old, wife 33 years old, child 5 years old," "wife has a peanut allergy," and "child likes vegetables."

[0455] 2. Data storage means

[0456] The server saves user input information to a database. Specifically, the entered family structure and allergy information are stored in the corresponding database. The family structure database stores "Husband 35 years old, wife 33 years old, child 5 years old," and the allergy information database stores "Wife: Peanut allergy."

[0457] 3. Emotion recognition means

[0458] The server uses an emotion engine to recognize emotions based on the user's behavior history and input data. This emotion engine analyzes the user's daily behavior data and input information to detect emotions such as stress and joy. For example, if the server detects that the user frequently stays up late, it recognizes that the user is feeling stressed.

[0459] 4. Menu generation means

[0460] The server automatically generates a weekly meal plan based on stored family composition data and emotional information. The server takes emotional information into consideration to automatically generate a nutritionally balanced menu. For example, it might suggest a comforting chicken steak for Monday's dinner and a relaxing Japanese-style hamburger for Tuesday's dinner.

[0461] 5. Menu adjustment methods

[0462] Based on the results of the emotion engine, the server can adjust the menu to match the user's emotional state. For example, if the user is feeling stressed, it will suggest dishes with relaxing effects. This may include suggesting specific herbal teas or menu items containing ingredients known to relieve stress.

[0463] 6. Ingredient List Generation Method

[0464] The server creates a list of necessary ingredients based on the generated menu. This ingredient list specifically details the ingredients needed for each menu item. For example, it might list 200g of chicken, 1 onion, and 2 carrots.

[0465] 7. Automated ordering method

[0466] The server automatically places an order for the generated grocery list via the online store's API. The server sends the order details to the online store's API and confirms the order. For example, a grocery list is sent to an online supermarket, and the ordering process is automated.

[0467] 8. Means of notification

[0468] The server notifies the user's device of the order details and menu. For example, a notification such as "Today we suggest a relaxing Japanese-style hamburger steak" is sent to the user's device.

[0469] 9. Verification and Correction Methods

[0470] Users can review the notified order details and make modifications as needed. On their device screen, users can check the order details and, if necessary, change chicken to fish to increase the amount of fish dishes.

[0471] 10. Final confirmation method

[0472] The server then sends the revised order details to the online store to finalize the order. The online store prepares the ingredients based on the finalized order and delivers them to the user's home.

[0473] In this way, users can not only save time on daily meal planning and shopping, but also enjoy a more comfortable eating experience by being provided with optimal meals tailored to their emotional state. Because the system is fully automated, it is expected to improve the user's quality of life.

[0474] Specific example

[0475] Registration process: The user launches the app and enters their family structure as "husband 35 years old, wife 33 years old, child 5 years old," and states that "wife has a peanut allergy" and "child likes vegetables."

[0476] Emotion Recognition: The server recognizes when a user is experiencing stress based on their behavioral history and input data.

[0477] Menu generation: Every Monday, the server accesses the database and generates a weekly menu that includes relaxing chicken steak on Monday and Japanese-style hamburger steak on Tuesday.

[0478] Ingredient list generation: The server lists 200g of chicken, 1 onion, and 2 carrots.

[0479] Automated ordering: The server places an order for the generated ingredient list via the online store's API.

[0480] Notification and Confirmation: The server notifies the user of the order details on their device, and the user confirms and modifies the order. For example, the server might suggest a menu to reduce stress, and the user can confirm and modify it to include more fish dishes.

[0481] Delivery process: The online supermarket delivers the groceries to the user's home based on the finalized order.

[0482] This system enables the provision of optimal menus tailored to the user's emotional state, as well as automatic ordering, confirmation, and modification functions for ingredients.

[0483] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0484] Step 1: The user enters family composition, food preferences, and allergy information via the device.

[0485] Input: The user enters family information such as "Husband 35 years old, wife 33 years old, child 5 years old" into the input form, allergy information such as "Wife has a peanut allergy," and food preference information such as "Child likes vegetables."

[0486] Data processing: The terminal formats the input data and prepares it for transmission to the server.

[0487] Output: The formatted dataset is sent to the server.

[0488] Step 2: The server receives the entered information and saves it to the database.

[0489] Input: Family composition, allergy information, and food preferences information sent from the device.

[0490] Data processing: The server performs appropriate registration operations in the database and stores the input data in the appropriate tables (family structure table, allergy table, food preference table).

[0491] Output: A save completion notification and information for verifying the saved data are generated.

[0492] Step 3: The server recognizes the user's emotions based on their behavioral history and input data.

[0493] Input: User activity log data and family structure, food preferences, and allergy information entered in Step 1.

[0494] Data Calculation: The emotion engine analyzes user behavior data to calculate emotional states such as stress and joy. Natural language processing and machine learning algorithms are used for these calculations.

[0495] Output: Data representing the user's current emotional state (e.g., high stress level).

[0496] Step 4: The server automatically generates a weekly meal plan based on the stored data and recognized emotion information.

[0497] Input: Family structure data, allergy information, food preferences, perceived emotional state.

[0498] Data processing: The menu generation algorithm generates nutritionally balanced menus based on this data. If the emotional state is stressed, dishes with relaxing effects are prioritized.

[0499] Output: Weekly meal plan data (e.g., Monday's dinner is chicken steak, Tuesday's dinner is Japanese-style hamburger steak).

[0500] Step 5: The server generates a list of necessary ingredients based on the automatically generated menu.

[0501] Input: Generated menu data.

[0502] Data processing: Extract information on ingredients needed for the menu and generate an ingredient list. Calculate the required amount of each ingredient and add it to the list.

[0503] Output: Ingredient list data (Example: 200g chicken, 1 onion, 2 carrots).

[0504] Step 6: The server places an automated order based on the ingredient list via the online store's API.

[0505] Input: Generated ingredient list data.

[0506] Data processing: Send order information to the online store API. Convert the ingredient list into an order format and send it.

[0507] Output: Order confirmation information and order ID.

[0508] Step 7: The server notifies the user terminal of the order details and the generated menu.

[0509] Input: Order confirmation information, order ID, generated menu data.

[0510] Data processing: Generate a notification message and send it to the user's terminal.

[0511] Output: Notification message saying, "Today, we suggest a relaxing Japanese-style hamburger steak."

[0512] Step 8: The user reviews the notified order details and makes any necessary corrections.

[0513] Input: Order details and menu data displayed on the user's terminal.

[0514] Specific actions: The user confirms the order details on the device screen and, if necessary, changes chicken to fish to increase the number of fish dishes.

[0515] Output: Modified order details data.

[0516] Step 9: The server finally sends the corrected order details to the online store and confirms the order.

[0517] Input: Modified order details data.

[0518] Data calculation: Generate final order data reflecting the changes and send it to the online store API.

[0519] Output: Final order confirmation information and shipping information.

[0520] Step 10: The online store prepares the ingredients based on the finalized order and delivers them to the user's home.

[0521] Input: Final order confirmation information and shipping information.

[0522] Specific operation: The online store prepares the ingredients based on the order and delivers them to the user's home at the specified date and time.

[0523] Output: Notification to the user that delivery is complete.

[0524] (Application Example 2)

[0525] 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".

[0526] Conventional menu generation systems automatically generate menus and order ingredients based on the user's food preferences and allergy information. However, they cannot adjust menus based on the user's emotional state or mental health, making it difficult to improve dietary habits in response to the user's stress and mood. Therefore, in order to further improve the user's quality of life, menu generation and automatic ordering that take emotional information into account are necessary.

[0527] 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.

[0528] In this invention, the server includes means for recognizing the user's emotional state using an emotion engine, means for automatically generating a weekly menu based on the stored information and emotional information, and means for automatically ordering ingredients from an online store based on the generated ingredient list. This makes it possible to adjust the menu and order the optimal ingredients based on the user's emotional state.

[0529] "Family structure" refers to information that describes the members of each family, including their ages and relationships.

[0530] "Food preferences" refers to information indicating the types of ingredients and dishes that the user or their family enjoys.

[0531] "Allergy information" refers to information about users or their family members who have allergic reactions to specific foods.

[0532] An "emotion engine" is a means of recognizing a user's emotional state from their input data and behavioral history.

[0533] A "menu" refers to a list of meals for one week.

[0534] A "food ingredient list" is a list of ingredients required based on a menu.

[0535] An "online store" is a store that provides services for selling food and goods via the internet.

[0536] "Notification" is the act of a system informing a user's device of information that requires their attention.

[0537] "Confirmation and Correction" refers to the process by which users reconfirm the information and order details provided by the system and make corrections as necessary.

[0538] "Confirming" means finalizing the revised order details and submitting them to the online store.

[0539] This invention is a system that automatically generates menus and orders necessary ingredients from an online store by having the user input family structure, food preferences, and allergy information, and combining this with an emotion engine. A key feature is that the emotion engine recognizes the user's emotions and adjusts the menus and notification content accordingly.

[0540] System Configuration

[0541] This system consists of the following components:

[0542] 1. User Registration Method

[0543] The server provides a user interface (UI) that allows users to input family structure, food preferences, and allergy information through their terminal.

[0544] For example, the user enters information such as "husband 35 years old, wife 33 years old, child 5 years old," "wife has a peanut allergy," and "child likes vegetables."

[0545] 2. Data storage means

[0546] The server saves the entered information to the database.

[0547] For example, information entered by users can be saved to a family structure database or an allergy information database.

[0548] 3. Means of recognizing emotions (emotion engine)

[0549] The server recognizes emotions from the user's input data and behavioral history.

[0550] For example, if a user is feeling stressed, the system will detect it.

[0551] 4. Menu generation means

[0552] The server automatically generates weekly meal plans tailored to nutritional balance and family preferences, based on stored information and emotional data.

[0553] For example, I suggest chicken steak for Monday's dinner, a comforting meal, and Japanese-style hamburger steak for Tuesday's dinner, which has a relaxing effect.

[0554] 5. Menu adjustment methods

[0555] The server adjusts the menu based on emotional information obtained by the emotion engine.

[0556] For example, if a user is feeling stressed, the system might suggest a dish with relaxing properties.

[0557] 6. Ingredient List Generation Method

[0558] The server creates a list of necessary ingredients based on the generated menu.

[0559] For example, list ingredients such as 200g of chicken, 1 onion, and 2 carrots.

[0560] 7. Automated ordering method

[0561] The server automatically orders the necessary ingredients via the online store's API.

[0562] For example, the generated ingredient list is sent to an online store in an order format.

[0563] 8. Means of notification

[0564] The server notifies the user's device of a menu based on their order and emotions.

[0565] For example, a notification message might appear stating, "Today, we suggest a relaxing Japanese-style hamburger steak."

[0566] 9. Verification and Correction Methods

[0567] The user reviews the notified order details and makes corrections as needed.

[0568] For example, a user can change chicken to fish to increase the number of fish dishes available.

[0569] 10. Final confirmation method

[0570] The server then sends the revised order details to the online store and confirms the order.

[0571] The online store prepares the ingredients based on the order and delivers them to the user's home at the specified time.

[0572] Specific examples of operation

[0573] 1. Registration process:

[0574] The user launches the app and enters their family structure as "husband 35 years old, wife 33 years old, child 5 years old," along with "wife has a peanut allergy" and "child likes vegetables."

[0575] 2. Emotion recognition:

[0576] The server recognizes when a user is experiencing stress based on their behavioral history and input data.

[0577] 3. Menu generation:

[0578] Every Monday, the server accesses the database and generates a weekly menu that includes a relaxing chicken steak on Monday and a Japanese-style hamburger on Tuesday.

[0579] 4. Generate ingredient list:

[0580] The server lists 200g of chicken, 1 onion, and 2 carrots.

[0581] 5. Automatic ordering:

[0582] The server generates a list of ingredients and places an order through the online store's API.

[0583] 6. Notifications and confirmations:

[0584] The server notifies the user's terminal of the order details, and the user confirms and modifies the order.

[0585] For example, we could suggest a menu to reduce stress, and then the user could review it and make revisions to include more fish dishes.

[0586] 7. Delivery process:

[0587] The online store delivers the groceries to the user's home based on the finalized order details.

[0588] Example of a prompt

[0589] Family composition: Husband 35 years old, wife 33 years old, child 5 years old

[0590] Food preferences: Children like vegetables.

[0591] Allergy information: My wife has a peanut allergy.

[0592] Emotions: Stress

[0593] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0594] Step 1:

[0595] The user launches the smartphone app and enters information about their family structure, food preferences, and allergies.

[0596] Input details: Family composition (e.g., husband 35 years old, wife 33 years old, child 5 years old), food preferences (e.g., child likes vegetables), allergy information (e.g., wife has a peanut allergy)

[0597] Output: The terminal sends the input content to the server.

[0598] Step 2:

[0599] The server saves the received information to the database.

[0600] Input data: Family composition data, food preferences data, allergy information data

[0601] Output: User information stored in the database

[0602] Step 3:

[0603] The emotion engine analyzes the user's input data and behavioral history to recognize their emotional state.

[0604] Input content: User activity history, input data

[0605] Output: Emotional information (e.g., stress level)

[0606] Step 4:

[0607] The server automatically generates a weekly meal plan based on stored user information and sentiment data.

[0608] Input content: Database user information, sentiment information

[0609] Output: A week's menu (Example: Chicken steak on Monday, Japanese-style hamburger on Tuesday)

[0610] Step 5:

[0611] The server creates a list of necessary ingredients based on the generated menu.

[0612] Input content: Weekly meal plan

[0613] Output: Ingredient list (Example: 200g chicken, 1 onion, 2 carrots)

[0614] Step 6:

[0615] The server automatically places orders using the online store's API based on the ingredient list.

[0616] Input content: Ingredient list

[0617] Output: Order request to online store

[0618] Step 7:

[0619] The server notifies the user's terminal of the automatically placed order.

[0620] Input details: Order details

[0621] Output: Notification to your smartphone (e.g., today's menu and order details)

[0622] Step 8:

[0623] The user checks the notification content on their device and makes corrections as needed.

[0624] Input details: Notified order details

[0625] Output: Modified order details

[0626] Step 9:

[0627] The server will then finalize the grocery order with the online store based on the revised order details.

[0628] Input details: Modified order details

[0629] Output: Final order data for online store

[0630] Step 10:

[0631] The online store delivers the groceries to the user's home based on the finalized order details.

[0632] Input details: Final order data

[0633] Output: Food delivered to the user's home.

[0634] 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.

[0635] 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.

[0636] 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.

[0637] [Second Embodiment]

[0638] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0639] 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.

[0640] 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).

[0641] 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.

[0642] 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.

[0643] 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).

[0644] 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.

[0645] 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.

[0646] 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.

[0647] 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.

[0648] 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.

[0649] 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".

[0650] This invention provides a system that automatically generates menus based on input such as family composition, food preferences, and allergy information, and automatically orders the necessary ingredients from an online store.

[0651] System Configuration

[0652] This system consists of the following components:

[0653] 1. User Registration Method

[0654] The device provides a user interface that allows users to input information such as family structure, food preferences, and allergies.

[0655] For example, the user enters information such as "husband 35 years old, wife 33 years old, child 5 years old," "wife has a peanut allergy," and "child likes vegetables."

[0656] 2. Data storage means

[0657] The server saves the entered information to the database.

[0658] For example, information entered by users can be saved to a family structure database or an allergy information database.

[0659] 3. Menu generation method

[0660] The server references the stored information and automatically generates a weekly menu tailored to nutritional balance and family preferences.

[0661] As an example, I suggest chicken steak for dinner on Monday and Japanese-style hamburger steak for dinner on Tuesday.

[0662] 4. Ingredient list generation method

[0663] The server creates a list of necessary ingredients based on the generated menu.

[0664] For example, list ingredients such as 200g of chicken, 1 onion, and 2 carrots.

[0665] 5. Automated ordering method

[0666] The server automatically orders the necessary ingredients via the online store's API.

[0667] For example, the generated ingredient list is sent to an online store in an order format.

[0668] 6. Means of notification

[0669] The server notifies the user's terminal of the order details.

[0670] For example, the user's device will display "Order details: 200g chicken, 1 onion, 2 carrots".

[0671] 7. Verification and Correction Methods

[0672] The user reviews the notified order details and makes corrections as needed.

[0673] For example, a user can change chicken to fish to increase the number of fish dishes available.

[0674] 8. Final confirmation method

[0675] The server then sends the corrected order details to the online store and confirms the order.

[0676] Specific example

[0677] 1. Registration process:

[0678] The user launches the app and enters their family structure as "husband 35 years old, wife 33 years old, child 5 years old," along with "wife has a peanut allergy" and "child likes vegetables."

[0679] 2. Menu generation process:

[0680] Every Monday, the server accesses the database and generates a weekly menu that includes chicken steak on Monday and Japanese-style hamburger steak on Tuesday.

[0681] 3. Generate ingredient list:

[0682] The server lists 200g of chicken, 1 onion, and 2 carrots.

[0683] 4. Automatic ordering:

[0684] The server generates a list of ingredients and places an order through the online store's API.

[0685] 5. Notifications and confirmations:

[0686] The server notifies the user's terminal of the order details, and the user confirms and modifies the order.

[0687] 6. Delivery process:

[0688] The online supermarket delivers groceries to the user's home based on the finalized order details.

[0689] This system allows users to significantly reduce the effort involved in planning daily menus and grocery shopping, and enables them to provide well-balanced meals tailored to their family's needs.

[0690] The following describes the processing flow.

[0691] Program processing steps

[0692] Registration process

[0693] Step 1:

[0694] The user opens the app and enters family information.

[0695] For example, enter "Husband 35 years old, wife 33 years old, child 5 years old".

[0696] The terminal sends the entered data to the server.

[0697] Step 2:

[0698] Users enter their food preferences and allergy information.

[0699] For example, enter "My wife has a peanut allergy" and "My child likes vegetables."

[0700] The terminal sends the entered data to the server.

[0701] Step 3:

[0702] The server saves the information it receives to the database.

[0703] Save the information in the family information table and the food preferences / allergy information table.

[0704] Menu generation process

[0705] Step 4:

[0706] Every Monday, the server starts processing menu generation requests according to a scheduled task.

[0707] Step 5:

[0708] The server retrieves family information, food preferences, and allergy information from the database.

[0709] Step 6:

[0710] Based on the data acquired by the server, it generates a weekly meal plan by referring to a predefined nutritional balance and recipe database.

[0711] For example, generate "Monday's dinner is chicken steak" and "Tuesday's dinner is Japanese-style hamburger steak".

[0712] Step 7:

[0713] The server generates menus, links them to user accounts, and saves them to the database.

[0714] Ingredient list generation process

[0715] Step 8:

[0716] The server references the saved menu and lists the ingredients needed for each dish.

[0717] Step 9:

[0718] The server generates a list of necessary ingredients based on the menu.

[0719] As an example, list "200g of chicken," "1 onion," and "2 carrots."

[0720] Step 10:

[0721] The server optimizes itself to avoid duplication and waste by considering past purchase history and inventory information.

[0722] Collaboration with online supermarkets

[0723] Step 11:

[0724] The server uses the online supermarket's API to convert the generated grocery list into an order format.

[0725] Step 12:

[0726] The server sends order information, including the user's address and desired delivery time, to the online supermarket.

[0727] Notification and confirmation

[0728] Step 13:

[0729] The server sends a confirmation notification to the user's terminal containing the generated menu and ingredient order.

[0730] Step 14:

[0731] The user's device receives a notification, and the user confirms the menu and order details.

[0732] For example, "If you want to increase the number of fish dishes, change the chicken to fish."

[0733] Step 15:

[0734] The user makes corrections, and the device sends the corrected data to the server.

[0735] Delivery process

[0736] Step 16:

[0737] The server performs a final check and then sends the order details to the online supermarket.

[0738] Step 17:

[0739] Online supermarkets prepare groceries based on the order and deliver them to the user's home at the specified time.

[0740] In this way, users can significantly reduce the effort involved in daily meal planning and grocery shopping. Convenience is enhanced because the system automates everything, allowing users to handle tedious processes with minimal awareness.

[0741] (Example 1)

[0742] 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."

[0743] In today's busy lifestyle, planning family meals and efficiently gathering necessary ingredients is difficult for many people. In particular, creating menus that maintain nutritional balance while considering each family member's food preferences and allergy information requires considerable time and effort. There is a need for a system that solves this problem and simplifies family meal preparation.

[0744] 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.

[0745] In this invention, the server includes means for the user to input family composition, food preferences, and allergy information; means for receiving and storing the input information; means for automatically generating a weekly menu based on the stored information; and means for generating the menu according to nutritional balance and family preferences. This makes it possible to automatically generate nutritionally balanced menus tailored to the needs of the household, significantly reducing the effort required for meal preparation.

[0746] A "user" is someone who uses the system to input information such as family structure, food preferences, and allergies.

[0747] "Family structure" refers to information such as the relationships, ages, and genders of individual members within a household, and is data that is considered when the system generates menus.

[0748] "Food preferences" refer to a user's tastes and preferences for specific ingredients or dishes, and are one of the important factors when a system generates menus.

[0749] "Allergy information" refers to information about whether the user or their family members have allergies to specific foods. This information is used by the system to generate menus that do not contain allergens.

[0750] "Input method" refers to the interface that allows users to input data such as family structure, food preferences, and allergy information into the system.

[0751] "Storage method" refers to the process or function by which a server saves information entered by a user to a database.

[0752] "Menu generation method" refers to the process by which a system automatically generates a week's worth of menus based on stored information.

[0753] "Nutritional balance" refers to the appropriate balance of nutrients necessary to maintain health, and it is a criterion that the system considers when generating menus.

[0754] A "food ingredient list" refers to a list of necessary ingredients based on the generated menu.

[0755] "Automated ordering method" refers to a function or process for automatically ordering a generated list of ingredients from an online store.

[0756] "Notification method" refers to the system's function of notifying the user of the details of an automatically placed order.

[0757] "Confirmation and correction means" refers to a system function that allows users to confirm the order details they have been notified about and make corrections as needed.

[0758] "Final confirmation method" refers to the function used to confirm the final food order with the online store based on the revised order details.

[0759] This invention relates to a system that automatically generates menus based on family composition, food preferences, and allergy information, and automatically orders the necessary ingredients from an online store. A specific embodiment of this system is described below.

[0760] System Configuration

[0761] This system consists of the following components:

[0762] 1. User input means

[0763] The device provides a user interface that allows the user to input information such as family structure, food preferences, and allergies. Examples of such devices include personal computers and smartphones.

[0764] Specifically, users input information through the application such as their family structure ("husband 35 years old, wife 33 years old, child 5 years old"), their wife's peanut allergy, and their child's preference for vegetables.

[0765] 2. Data storage means

[0766] The server stores information entered by the user in a database. Servers are often built as cloud-based services.

[0767] For example, the entered information is stored in a "family structure database" and an "allergy information database."

[0768] 3. Menu generation method

[0769] The server references stored information and uses a generation AI model to automatically generate a weekly menu tailored to nutritional balance and family preferences.

[0770] Specifically, the server has the AI ​​model generate menus such as "chicken steak for dinner on Monday, and Japanese-style hamburger steak for dinner on Tuesday."

[0771] 4. Ingredient list generation method

[0772] The server generates a list of necessary ingredients based on the generated menu.

[0773] Specifically, the server creates a list of ingredients such as "200g chicken, 1 onion, 2 carrots."

[0774] 5. Automated ordering method

[0775] The server automatically orders the necessary ingredients via the online store's API.

[0776] For example, the server sends the generated list of ingredients to the online store as an order breakdown.

[0777] 6. Means of notification

[0778] The server notifies the user's terminal of the order details.

[0779] For example, the server sends a notification to the terminal saying, "Order details: 200g chicken, 1 onion, 2 carrots."

[0780] 7. Verification and Correction Methods

[0781] The user reviews the notified order details and makes corrections as needed.

[0782] For example, users can make modifications through their devices, such as changing "chicken to fish."

[0783] 8. Final confirmation method

[0784] The server then sends the corrected order details to the online store and confirms the order.

[0785] Specifically, the server completes the order based on the finalized details.

[0786] Example of a prompt

[0787] Examples of prompts for a generative AI model are as follows:

[0788] "Please create a week's worth of dinner menus for a family with a 35-year-old husband, a 33-year-old wife, and a 5-year-old child. The wife has a peanut allergy, and the child likes vegetables."

[0789] This system allows users to significantly reduce the effort involved in planning daily menus and grocery shopping, and enables them to provide well-balanced meals tailored to their family's needs.

[0790] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0791] Step 1:

[0792] The user enters information about their family structure, food preferences, and allergies into the device.

[0793] Input: The user uses the application to input information such as family structure ("husband 35 years old, wife 33 years old, child 5 years old"), "wife has a peanut allergy," and "child likes vegetables."

[0794] Output: The input information is stored in the terminal's temporary memory.

[0795] Specific operation: When the user enters data into each input field and presses the "Save" button, the device prepares to send the input data to the server.

[0796] Step 2:

[0797] The terminal sends the information entered by the user to the server.

[0798] Input: Family composition, food preferences, and allergy information stored on the device.

[0799] Output: A request that sends input information to the server.

[0800] Specific operation: The device sends user input data to the server's API endpoint via a POST request.

[0801] Step 3:

[0802] The server saves the received information to the database.

[0803] Input: Family composition, food preferences, and allergy information received by the server from the terminal.

[0804] Output: User information stored in the database.

[0805] Specific operation: The server connects to the database and saves the received data to the appropriate table using an INSERT statement.

[0806] Step 4:

[0807] The server generates a weekly meal plan based on the stored information.

[0808] Input: Family composition, food preferences, and allergy information stored in the database.

[0809] Output: The generated weekly meal plan.

[0810] Specific operation: The server inputs prompt text into the generated AI model and makes a request to generate a menu such as "Chicken steak for dinner on Monday, and Japanese-style hamburger steak for dinner on Tuesday."

[0811] Step 5:

[0812] The server generates a list of necessary ingredients based on the generated menu.

[0813] Input: A generated weekly meal plan.

[0814] Output: List of required ingredients.

[0815] Specific operation: The server extracts the ingredients for each menu item from a database or fixed list, calculates the required quantities, and lists them.

[0816] Step 6:

[0817] The server automatically orders ingredients via the online store's API.

[0818] Input: The generated list of ingredients.

[0819] Output: Order request sent to the online store.

[0820] Specific operation: The server sends order data, including the ingredient list, via a POST request to the online store's API endpoint.

[0821] Step 7:

[0822] The server notifies the user's terminal of the order details.

[0823] Input: Order request submitted to the online store.

[0824] Output: A notification of the order details displayed on the user's device.

[0825] Specific operation: The server generates a notification message containing the order details and sends it to the terminal to inform the user that the order has been submitted.

[0826] Step 8:

[0827] The user reviews the notified order details and makes corrections as needed.

[0828] Input: Order details displayed on the terminal.

[0829] Output: Order details modified by the user.

[0830] Specific actions: The user reviews the order details displayed on the device interface, makes any necessary changes or corrections, and then presses the "Confirm" button.

[0831] Step 9:

[0832] The server will finalize the order on the online store based on the corrected information.

[0833] Input: Order details modified by the user.

[0834] Output: The final food order has been confirmed.

[0835] Specific operation: The server finalizes the modified order details and sends them again to the online store's API endpoint to complete the order.

[0836] (Application Example 1)

[0837] 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."

[0838] Traditional menu planning and grocery ordering systems can take into account user preferences and allergy information, but they have struggled to comprehensively cover the delivery of ordered ingredients. Furthermore, users are required to shop for ingredients daily and review and modify their orders, which adds to their time and effort. A system that solves these problems and enhances user convenience is needed.

[0839] 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.

[0840] In this invention, the server includes means for the user to input family composition, food preferences, and allergy information; means for receiving and storing the input information; means for automatically generating a weekly menu based on the stored information; means for generating a list of necessary ingredients based on the automatically generated menu; means for automatically ordering ingredients from an online store based on the generated ingredient list; means for notifying the user of the automatically ordered items; and means for delivering the ingredients via a delivery service based on the ordered items. This makes it possible for a user to input family composition, food preferences, and allergy information and have the entire process, from automatically generating a menu to ordering ingredients and even delivery, handled in a consistent manner.

[0841] "A means for users to input family structure, food preferences, and allergy information" refers to an interface for users to input their family structure, the food preferences of individual members, and allergy information.

[0842] "Means for receiving and storing the input information" refers to a system for receiving information entered by a user and storing it in a database or similar.

[0843] "Means for automatically generating a weekly menu based on the stored information" refers to algorithms or software for automatically creating a nutritionally balanced weekly menu based on stored family composition, food preferences, and allergy information.

[0844] "Means for generating a list of necessary ingredients based on the automatically generated menu" refers to a program that lists the ingredients necessary for the automatically generated menu and generates that list.

[0845] "Means for automatically ordering ingredients from an online store based on the generated ingredient list" refers to a system that automatically orders ingredients using an online store's API or the like based on the generated ingredient list.

[0846] "Means for notifying the user of the automatically placed order" refers to an application or interface for notifying the user of the contents of the automatically placed order.

[0847] "Means of delivering ingredients via delivery service based on the aforementioned order details" refers to a system that utilizes online stores and delivery services selected based on the order details to deliver the necessary ingredients to the user's home.

[0848] System Configuration

[0849] This invention is a system that allows users to input their family structure, food preferences, and allergy information, automatically generates menus, automatically orders necessary ingredients from an online store, and even handles delivery in a seamless manner.

[0850] User registration method

[0851] The system provides an interface to the user's device (e.g., a smartphone or tablet) and includes a means for inputting family structure, food preferences, and allergy information. This information is transmitted to a server and stored in a database.

[0852] Information reception and storage means

[0853] The server receives information entered by the user and stores it in a database. For example, it stores it in a family structure database and an allergy information database.

[0854] Menu generation method

[0855] The server automatically generates a week's worth of menus using a generative AI model based on the stored information. During this process, prompts are used to instruct the AI ​​model, taking into account nutritional balance, family preferences, and allergy information.

[0856] Prompt example:

[0857] System: Please generate a weekly meal plan based on family composition, dietary preferences, and allergy information.

[0858] User input: Family composition is "husband 35 years old, wife 33 years old, child 5 years old". The wife has a peanut allergy, and the child likes vegetables.

[0859] System output: Monday dinner is chicken steak, Tuesday dinner is Japanese-style hamburger, Wednesday dinner is...

[0860] Ingredient list generation method

[0861] The server generates a list of necessary ingredients based on the automatically generated menu. For example, it might list ingredients such as 200g of chicken, 1 onion, and 2 carrots.

[0862] Automatic ordering method

[0863] The server automatically places an order via the online store's API based on the generated list of ingredients. At this time, it sends the order data according to the online store's API format.

[0864] Notification means

[0865] The server notifies the user's terminal of the automatically placed order. For example, it displays to the user, "Order details: 200g chicken, 1 onion, 2 carrots."

[0866] Verification and correction methods

[0867] The user reviews the notified order details and makes any necessary modifications. For example, the user can change chicken to fish to increase the number of fish dishes.

[0868] final means of confirmation

[0869] The server then confirms the user's modified order details again via the online store's API, and completes the order.

[0870] Delivery method

[0871] Based on the finalized order details, the server selects the appropriate delivery service and initiates the process of delivering the ingredients to the user's home.

[0872] Specific example

[0873] Registration process:

[0874] The user launches the smartphone app and enters their family structure as "husband 35 years old, wife 33 years old, child 5 years old," along with "wife has a peanut allergy" and "child likes vegetables."

[0875] Menu generation process:

[0876] The server looks at the database and generates a weekly menu that includes chicken steak for dinner on Monday and Japanese-style hamburger steak for dinner on Tuesday.

[0877] Ingredient list generation:

[0878] The server lists 200g of chicken, 1 onion, and 2 carrots.

[0879] Automatic ordering:

[0880] The server places orders for the generated ingredient list through the online store's API.

[0881] Notifications and confirmations:

[0882] The server notifies the user's terminal of the order details, and the user confirms and modifies the details.

[0883] Delivery process:

[0884] Online stores and delivery services deliver groceries to users' homes based on the finalized order details.

[0885] This significantly reduces the effort users spend on daily meal planning and grocery shopping, making it easy to provide well-balanced meals tailored to their family's needs.

[0886] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0887] Step 1:

[0888] The user enters their family structure, food preferences, and allergy information.

[0889] Input: Family composition (e.g., "Husband 35 years old, wife 33 years old, child 5 years old"), food preferences (e.g., "Child likes vegetables"), and allergy information (e.g., "Wife is allergic to peanuts") are entered via a smartphone app.

[0890] Output: Input information is sent to the server.

[0891] Specific operation: The user enters information into the app's input screen and presses the "Submit" button, which sends the data to the server.

[0892] Step 2:

[0893] The server receives and stores the entered information.

[0894] Input: Family composition, food preferences, and allergy information submitted by the user.

[0895] Output: Family structure data, food preference data, and allergy information stored in the database.

[0896] Specific operation: The server saves the received information to the corresponding table in the database. For example, it adds data to the family structure table, preference information table, and allergy information table.

[0897] Step 3:

[0898] The server automatically generates a weekly meal plan based on the stored information.

[0899] Input: Family composition, food preferences, and allergy information stored in the database.

[0900] Output: A weekly meal plan list.

[0901] Specific operation: The server issues instructions to the generated AI model using prompt messages and receives a weekly menu that takes into account family composition, food preferences, and allergy information.

[0902] Step 4:

[0903] The server generates a list of necessary ingredients based on the automatically generated menu.

[0904] Input: A weekly meal plan.

[0905] Output: List of required ingredients.

[0906] Specific operation: The server analyzes the menu data and lists the ingredients and quantities needed for each meal. For example, 200g of chicken, 1 onion, 2 carrots, etc.

[0907] Step 5:

[0908] The server automatically places an order with the online store based on the generated list of ingredients.

[0909] Input: List of required ingredients.

[0910] Output: Order data sent to the online store.

[0911] Specific operation: The server uses the online store's API to convert the ingredient list into an order format and send the order data.

[0912] Step 6:

[0913] The server notifies the user of the automatically placed order.

[0914] Input: Submitted order data.

[0915] Output: An order confirmation notification displayed on the user's smartphone.

[0916] Specific operation: The server sends a push notification or email to the user's device to inform them of the order details.

[0917] Step 7:

[0918] The user reviews and modifies the order details they have been notified about.

[0919] Input: Order confirmation notification.

[0920] Output: Confirmed and corrected order details.

[0921] Specific actions: The user checks the notified order details in the app, adds or changes ingredients as needed, and presses the "Confirm" button.

[0922] Step 8:

[0923] The server then finalizes the grocery order to the online store based on the revised order details.

[0924] Input: Confirmed and corrected order details.

[0925] Output: Final order data sent to the online store.

[0926] Specific operation: The server resends the final order data, reflecting the user's modifications, to the online store's API to confirm the order.

[0927] Step 9:

[0928] The server then processes the delivery of ingredients based on the finalized order details.

[0929] Input: Last order data.

[0930] Output: Food items delivered to the user's home.

[0931] Specific operation: The server uses APIs from online stores and delivery services to process food delivery. It may also send delivery completion notifications to the user's smartphone.

[0932] 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.

[0933] This invention is a system that automatically generates menus and orders necessary ingredients from an online store by inputting family structure, food preferences, and allergy information, and combining this with an emotion engine. A key feature is that the emotion engine recognizes the user's emotions and adjusts the menu and notification content accordingly.

[0934] System Configuration

[0935] This system consists of the following components:

[0936] 1. User Registration Method

[0937] The device provides a user interface that allows users to input information such as family structure, food preferences, and allergies.

[0938] For example, the user enters information such as "husband 35 years old, wife 33 years old, child 5 years old," "wife has a peanut allergy," and "child likes vegetables."

[0939] 2. Data storage means

[0940] The server saves the entered information to the database.

[0941] For example, information entered by users can be saved to a family structure database or an allergy information database.

[0942] 3. Means of recognizing emotions (emotion engine)

[0943] The server recognizes emotions from the user's input data and behavioral history.

[0944] For example, if a user is feeling stressed, the system will detect it.

[0945] 4. Menu generation means

[0946] The server references stored information and recognized emotional data to automatically generate a weekly menu tailored to nutritional balance and family preferences.

[0947] For example, I suggest chicken steak for Monday's dinner, a comforting meal, and Japanese-style hamburger steak for Tuesday's dinner, which has a relaxing effect.

[0948] 5. Menu adjustment methods

[0949] The server adjusts the menu based on emotional information obtained by the emotion engine.

[0950] For example, if a user is feeling stressed, the system might suggest a dish with relaxing properties.

[0951] 6. Ingredient List Generation Method

[0952] The server creates a list of necessary ingredients based on the generated menu.

[0953] For example, list ingredients such as 200g of chicken, 1 onion, and 2 carrots.

[0954] 7. Automated ordering method

[0955] The server automatically orders the necessary ingredients via the online store's API.

[0956] For example, the generated ingredient list is sent to an online store in an order format.

[0957] 8. Means of notification

[0958] The server notifies the user's device of a menu based on their order and emotions.

[0959] For example, a notification message might appear stating, "Today, we suggest a relaxing Japanese-style hamburger steak."

[0960] 9. Verification and Correction Methods

[0961] The user reviews the notified order details and makes corrections as needed.

[0962] For example, a user can change chicken to fish to increase the number of fish dishes available.

[0963] 10. Final confirmation method

[0964] The server then sends the corrected order details to the online store and confirms the order.

[0965] Online supermarkets prepare groceries based on the order and deliver them to the user's home at the specified time.

[0966] Specific example

[0967] 1. Registration process:

[0968] The user launches the app and enters their family structure as "husband 35 years old, wife 33 years old, child 5 years old," along with "wife has a peanut allergy" and "child likes vegetables."

[0969] 2. Emotion recognition:

[0970] The server recognizes when a user is experiencing stress based on their behavioral history and input data.

[0971] 3. Menu generation:

[0972] Every Monday, the server accesses the database and generates a weekly menu that includes a relaxing chicken steak on Monday and a Japanese-style hamburger on Tuesday.

[0973] 4. Generate ingredient list:

[0974] The server lists 200g of chicken, 1 onion, and 2 carrots.

[0975] 5. Automatic ordering:

[0976] The server generates a list of ingredients and places an order through the online store's API.

[0977] 6. Notifications and confirmations:

[0978] The server notifies the user's terminal of the order details, and the user confirms and modifies the order.

[0979] For example, we could suggest a menu to reduce stress, and then the user could review it and make revisions to include more fish dishes.

[0980] 7. Delivery process:

[0981] The online supermarket delivers groceries to the user's home based on the finalized order details.

[0982] In this way, users can not only significantly reduce the effort involved in daily meal planning and shopping, but also achieve a more comfortable eating lifestyle by being provided with optimal meals tailored to their emotional state. By automating everything and making adjustments based on emotions, the system can improve the user's quality of life.

[0983] The following describes the processing flow.

[0984] Program processing steps

[0985] Registration process

[0986] Step 1:

[0987] The user opens the app and enters family information.

[0988] For example, enter "Husband 35 years old, wife 33 years old, child 5 years old".

[0989] The terminal sends the entered data to the server.

[0990] Step 2:

[0991] Users enter their food preferences and allergy information.

[0992] For example, enter "My wife has a peanut allergy" and "My child likes vegetables."

[0993] The terminal sends the entered data to the server.

[0994] Step 3:

[0995] The server saves the information it receives to the database.

[0996] Save the information in the family information table and the food preferences / allergy information table.

[0997] Emotion recognition process

[0998] Step 4:

[0999] While the user is using the app, the device collects the user's input and behavioral patterns.

[1000] For example, when a user performs many actions in a short period of time, or gives negative feedback about a particular dish.

[1001] Step 5:

[1002] The server analyzes the collected data, and the emotion engine evaluates the user's emotional state.

[1003] For example, the emotion engine might determine that the user is feeling stressed.

[1004] Menu generation process

[1005] Step 6:

[1006] Every Monday, the server starts processing menu generation requests according to a scheduled task.

[1007] Step 7:

[1008] The server retrieves family information, food preferences and allergy information, and emotional information from the database.

[1009] Step 8:

[1010] Based on the data acquired by the server, the system automatically generates a weekly meal plan by referring to a predefined nutritional balance and recipe database.

[1011] For example, I suggest chicken steak for Monday's dinner, a comforting meal, and Japanese-style hamburger steak for Tuesday's dinner, which has a relaxing effect.

[1012] Step 9:

[1013] The server adjusts the menu content it generates based on the emotional information obtained by the emotion engine.

[1014] For example, if a user is feeling stressed, they might choose a menu item that promotes relaxation.

[1015] Step 10:

[1016] The server saves the adjusted menu to the user's account in the database.

[1017] Ingredient list generation process

[1018] Step 11:

[1019] The server references the saved menu and lists the ingredients needed for each dish.

[1020] Step 12:

[1021] The server generates a list of necessary ingredients based on the menu.

[1022] As an example, list "200g of chicken," "1 onion," and "2 carrots."

[1023] Step 13:

[1024] The server optimizes itself to avoid duplication and waste by considering past purchase history and inventory information.

[1025] Collaboration with online supermarkets

[1026] Step 14:

[1027] The server uses the online supermarket's API to convert the generated grocery list into an order format.

[1028] Step 15:

[1029] The server sends order information, including the user's address and desired delivery time, to the online supermarket.

[1030] Notification and confirmation

[1031] Step 16:

[1032] The server sends a confirmation notification to the user's terminal containing the generated menu and ingredient order.

[1033] Step 17:

[1034] The user's device receives a notification, and the user confirms the menu and order details.

[1035] For example, "If you want to increase the number of fish dishes, change the chicken to fish."

[1036] Step 18:

[1037] The user makes corrections, and the device sends the corrected data to the server.

[1038] Delivery process

[1039] Step 19:

[1040] The server performs a final check and then sends the order details to the online supermarket.

[1041] Step 20:

[1042] Online supermarkets prepare groceries based on the order and deliver them to the user's home at the specified time.

[1043] In this way, users can significantly reduce the effort involved in daily meal planning and grocery shopping. Convenience is enhanced by automating everything and allowing users to handle tedious processes with minimal awareness. Furthermore, by utilizing an emotion engine, optimal meal plans are suggested based on the user's emotional state, leading to a more comfortable and enjoyable eating experience.

[1044] (Example 2)

[1045] 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".

[1046] Conventional menu generation systems generate menus based solely on the family composition, food preferences, and allergy information entered by the user, and therefore cannot provide optimal menus that take into account the user's emotional state. Furthermore, while it is important to provide meals that address a user's emotional state when they are stressed or experiencing a particular emotional condition, conventional systems failed to achieve this. In addition, the automatic ordering, confirmation, and correction functions for ingredients were insufficient, and the system could not fully reduce the burden on the user.

[1047] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1048] In this invention, the server includes means for the user to input family composition, food preferences, and allergy information; server means for receiving and storing the input information; server means for automatically generating a weekly menu based on the stored information and the user's emotional state; server means for generating a list of necessary ingredients based on the automatically generated menu; server means for automatically ordering ingredients from an online store based on the generated ingredient list; and server means for notifying the user terminal of the automatically ordered items. This enables the provision of an optimal menu that takes the user's emotional state into consideration, as well as automatic ordering of ingredients and functions for confirmation and modification.

[1049] "User" refers to an individual or family member who uses the system.

[1050] "Family composition" refers to information about the people who belong to the user's household.

[1051] "Food preferences" refers to the types of ingredients and dishes that the user and their family enjoy consuming.

[1052] "Allergy information" refers to information about allergies held by the user and their family.

[1053] A "server" refers to a computer device that processes, stores, and transmits data within a system.

[1054] "Server configuration" refers to the software and hardware configuration that perform specific processing functions on a server.

[1055] "Automatic generation" refers to the process by which a system autonomously creates data according to a program.

[1056] "Emotional state" refers to the user's current psychological or emotional condition.

[1057] A "food ingredient list" refers to a list of ingredients needed based on the generated menu.

[1058] An "online store" refers to a website or service that allows users to purchase groceries and other items via the internet.

[1059] "Automated ordering" refers to the act of a system ordering specified ingredients from an online store without manual intervention from the user.

[1060] "Notification" refers to the process of transmitting information from a system to a user.

[1061] A "terminal" refers to a device (such as a smartphone or computer) that a user uses to access and operate a system.

[1062] "Confirmation and correction" refers to the act of a user reviewing the information they have been notified about and making changes as necessary.

[1063] "Final ingredient order" refers to the ingredient order that has been confirmed and finalized after user review and modification.

[1064] This invention relates to an automated menu generation system that combines a user's input of family structure, food preferences, and allergy information with an emotion engine. This system uses a server and terminals to provide an optimal menu tailored to the user's emotional state and enables automatic ordering of ingredients.

[1065] 1. User Registration Method

[1066] Users enter family composition, food preferences, and allergy information using devices such as smartphones or computers. These devices provide dedicated applications or web interfaces to facilitate information entry. For example, a user might launch the app and enter information such as "husband 35 years old, wife 33 years old, child 5 years old," "wife has a peanut allergy," and "child likes vegetables."

[1067] 2. Data storage means

[1068] The server saves user input information to a database. Specifically, the entered family structure and allergy information are stored in the corresponding database. The family structure database stores "Husband 35 years old, wife 33 years old, child 5 years old," and the allergy information database stores "Wife: Peanut allergy."

[1069] 3. Emotion recognition means

[1070] The server uses an emotion engine to recognize emotions based on the user's behavior history and input data. This emotion engine analyzes the user's daily behavior data and input information to detect emotions such as stress and joy. For example, if the server detects that the user frequently stays up late, it recognizes that the user is feeling stressed.

[1071] 4. Menu generation means

[1072] The server automatically generates a weekly meal plan based on stored family composition data and emotional information. The server takes emotional information into consideration to automatically generate a nutritionally balanced menu. For example, it might suggest a comforting chicken steak for Monday's dinner and a relaxing Japanese-style hamburger for Tuesday's dinner.

[1073] 5. Menu adjustment methods

[1074] Based on the results of the emotion engine, the server can adjust the menu to match the user's emotional state. For example, if the user is feeling stressed, it will suggest dishes with relaxing effects. This may include suggesting specific herbal teas or menu items containing ingredients known to relieve stress.

[1075] 6. Ingredient List Generation Method

[1076] The server creates a list of necessary ingredients based on the generated menu. This ingredient list specifically details the ingredients needed for each menu item. For example, it might list 200g of chicken, 1 onion, and 2 carrots.

[1077] 7. Automated ordering method

[1078] The server automatically places an order for the generated grocery list via the online store's API. The server sends the order details to the online store's API and confirms the order. For example, a grocery list is sent to an online supermarket, and the ordering process is automated.

[1079] 8. Means of notification

[1080] The server notifies the user's device of the order details and menu. For example, a notification such as "Today we suggest a relaxing Japanese-style hamburger steak" is sent to the user's device.

[1081] 9. Verification and Correction Methods

[1082] Users can review the notified order details and make modifications as needed. On their device screen, users can check the order details and, if necessary, change chicken to fish to increase the amount of fish dishes.

[1083] 10. Final confirmation method

[1084] The server then sends the revised order details to the online store to finalize the order. The online store prepares the ingredients based on the finalized order and delivers them to the user's home.

[1085] In this way, users can not only save time on daily meal planning and shopping, but also enjoy a more comfortable eating experience by receiving optimal meal plans tailored to their emotional state. Because the system is fully automated, it is expected to improve the user's quality of life.

[1086] Specific example

[1087] Registration process: The user launches the app and enters their family structure as "husband 35 years old, wife 33 years old, child 5 years old," and states that "wife has a peanut allergy" and "child likes vegetables."

[1088] Emotion Recognition: The server recognizes when a user is experiencing stress based on their behavioral history and input data.

[1089] Menu generation: Every Monday, the server accesses the database and generates a weekly menu that includes relaxing chicken steak on Monday and Japanese-style hamburger steak on Tuesday.

[1090] Ingredient list generation: The server lists 200g of chicken, 1 onion, and 2 carrots.

[1091] Automated ordering: The server places an order for the generated ingredient list via the online store's API.

[1092] Notification and Confirmation: The server notifies the user of the order details on their device, and the user confirms and modifies the order. For example, the server might suggest a menu to reduce stress, and the user can confirm and modify it to include more fish dishes.

[1093] Delivery process: The online supermarket delivers the groceries to the user's home based on the finalized order.

[1094] This system enables the provision of optimal menus tailored to the user's emotional state, as well as automatic ordering, confirmation, and modification functions for ingredients.

[1095] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1096] Step 1: The user enters family composition, food preferences, and allergy information via the device.

[1097] Input: The user enters family information such as "Husband 35 years old, wife 33 years old, child 5 years old" into the input form, allergy information such as "Wife has a peanut allergy," and food preference information such as "Child likes vegetables."

[1098] Data processing: The terminal formats the input data and prepares it for transmission to the server.

[1099] Output: The formatted dataset is sent to the server.

[1100] Step 2: The server receives the entered information and saves it to the database.

[1101] Input: Family composition, allergy information, and food preferences information sent from the device.

[1102] Data processing: The server performs appropriate registration operations in the database and stores the input data in the appropriate tables (family structure table, allergy table, food preference table).

[1103] Output: A save completion notification and information for verifying the saved data are generated.

[1104] Step 3: The server recognizes the user's emotions based on their behavioral history and input data.

[1105] Input: User activity log data and family structure, food preferences, and allergy information entered in Step 1.

[1106] Data Calculation: The emotion engine analyzes user behavior data to calculate emotional states such as stress and joy. Natural language processing and machine learning algorithms are used for these calculations.

[1107] Output: Data representing the user's current emotional state (e.g., high stress level).

[1108] Step 4: The server automatically generates a weekly meal plan based on the stored data and recognized emotion information.

[1109] Input: Family structure data, allergy information, food preferences, perceived emotional state.

[1110] Data processing: The menu generation algorithm generates nutritionally balanced menus based on this data. If the emotional state is stressed, dishes with relaxing effects are prioritized.

[1111] Output: Weekly meal plan data (e.g., Monday's dinner is chicken steak, Tuesday's dinner is Japanese-style hamburger steak).

[1112] Step 5: The server generates a list of necessary ingredients based on the automatically generated menu.

[1113] Input: Generated menu data.

[1114] Data processing: Extract information on ingredients needed for the menu and generate an ingredient list. Calculate the required amount of each ingredient and add it to the list.

[1115] Output: Ingredient list data (Example: 200g chicken, 1 onion, 2 carrots).

[1116] Step 6: The server places an automated order based on the ingredient list via the online store's API.

[1117] Input: Generated ingredient list data.

[1118] Data processing: Send order information to the online store API. Convert the ingredient list into an order format and send it.

[1119] Output: Order confirmation information and order ID.

[1120] Step 7: The server notifies the user terminal of the order details and the generated menu.

[1121] Input: Order confirmation information, order ID, generated menu data.

[1122] Data processing: Generate a notification message and send it to the user's terminal.

[1123] Output: Notification message saying, "Today, we suggest a relaxing Japanese-style hamburger steak."

[1124] Step 8: The user reviews the notified order details and makes any necessary corrections.

[1125] Input: Order details and menu data displayed on the user's terminal.

[1126] Specific actions: The user confirms the order details on the device screen and, if necessary, changes chicken to fish to increase the number of fish dishes.

[1127] Output: Modified order details data.

[1128] Step 9: The server finally sends the corrected order details to the online store and confirms the order.

[1129] Input: Modified order details data.

[1130] Data calculation: Generate final order data reflecting the changes and send it to the online store API.

[1131] Output: Final order confirmation information and shipping information.

[1132] Step 10: The online store prepares the ingredients based on the finalized order and delivers them to the user's home.

[1133] Input: Final order confirmation information and shipping information.

[1134] Specific operation: The online store prepares the ingredients based on the order and delivers them to the user's home at the specified date and time.

[1135] Output: Notification to the user that delivery is complete.

[1136] (Application Example 2)

[1137] 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."

[1138] Conventional menu generation systems automatically generate menus and order ingredients based on the user's food preferences and allergy information. However, they cannot adjust menus based on the user's emotional state or mental health, making it difficult to improve dietary habits in response to the user's stress and mood. Therefore, in order to further improve the user's quality of life, menu generation and automatic ordering that take emotional information into account are necessary.

[1139] 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.

[1140] In this invention, the server includes means for recognizing the user's emotional state using an emotion engine, means for automatically generating a weekly menu based on the stored information and emotional information, and means for automatically ordering ingredients from an online store based on the generated ingredient list. This makes it possible to adjust the menu and order the optimal ingredients based on the user's emotional state.

[1141] "Family structure" refers to information that describes the members of each family, including their ages and relationships.

[1142] "Food preferences" refers to information indicating the types of ingredients and dishes that the user or their family enjoys.

[1143] "Allergy information" refers to information about users or their family members who have allergic reactions to specific foods.

[1144] An "emotion engine" is a means of recognizing a user's emotional state from their input data and behavioral history.

[1145] A "menu" refers to a list of meals for one week.

[1146] A "food ingredient list" is a list of ingredients required based on a menu.

[1147] An "online store" is a store that provides services for selling food and goods via the internet.

[1148] "Notification" is the act of a system informing a user's device of information that requires their attention.

[1149] "Confirmation and Correction" refers to the process by which users reconfirm the information and order details provided by the system and make corrections as necessary.

[1150] "Confirming" means finalizing the revised order details and submitting them to the online store.

[1151] This invention is a system that automatically generates menus and orders necessary ingredients from an online store by having the user input family structure, food preferences, and allergy information, and combining this with an emotion engine. A key feature is that the emotion engine recognizes the user's emotions and adjusts the menus and notification content accordingly.

[1152] System Configuration

[1153] This system consists of the following components:

[1154] 1. User Registration Method

[1155] The server provides a user interface (UI) that allows users to input family structure, food preferences, and allergy information through their terminal.

[1156] For example, the user enters information such as "husband 35 years old, wife 33 years old, child 5 years old," "wife has a peanut allergy," and "child likes vegetables."

[1157] 2. Data storage means

[1158] The server saves the entered information to the database.

[1159] For example, information entered by users can be saved to a family structure database or an allergy information database.

[1160] 3. Emotion recognition means (emotion engine)

[1161] The server recognizes emotions from the user's input data and behavioral history.

[1162] For example, if a user is feeling stressed, the system will detect it.

[1163] 4. Menu generation means

[1164] The server automatically generates weekly meal plans tailored to nutritional balance and family preferences, based on stored information and emotional data.

[1165] For example, I suggest chicken steak for Monday's dinner, a comforting meal, and Japanese-style hamburger steak for Tuesday's dinner, which has a relaxing effect.

[1166] 5. Menu adjustment methods

[1167] The server adjusts the menu based on emotional information obtained by the emotion engine.

[1168] For example, if a user is feeling stressed, we might suggest a dish that has a relaxing effect.

[1169] 6. Ingredient List Generation Method

[1170] The server creates a list of necessary ingredients based on the generated menu.

[1171] For example, list ingredients such as 200g of chicken, 1 onion, and 2 carrots.

[1172] 7. Automated ordering method

[1173] The server automatically orders the necessary ingredients via the online store's API.

[1174] For example, the generated ingredient list is sent to an online store in an order format.

[1175] 8. Means of notification

[1176] The server notifies the user's device of a menu based on their order and emotions.

[1177] For example, a notification message might appear stating, "Today, we suggest a relaxing Japanese-style hamburger steak."

[1178] 9. Verification and Correction Methods

[1179] The user reviews the notified order details and makes corrections as needed.

[1180] For example, a user can change chicken to fish to increase the number of fish dishes available.

[1181] 10. Final confirmation method

[1182] The server then sends the revised order details to the online store and confirms the order.

[1183] The online store prepares the ingredients based on the order and delivers them to the user's home at the specified time.

[1184] Specific examples of operation

[1185] 1. Registration process:

[1186] The user launches the app and enters their family structure as "husband 35 years old, wife 33 years old, child 5 years old," along with "wife has a peanut allergy" and "child likes vegetables."

[1187] 2. Emotion recognition:

[1188] The server recognizes when a user is experiencing stress based on their behavioral history and input data.

[1189] 3. Menu generation:

[1190] Every Monday, the server accesses the database and generates a weekly menu that includes a relaxing chicken steak on Monday and a Japanese-style hamburger on Tuesday.

[1191] 4. Generate ingredient list:

[1192] The server lists 200g of chicken, 1 onion, and 2 carrots.

[1193] 5. Automatic ordering:

[1194] The server generates a list of ingredients and places an order through the online store's API.

[1195] 6. Notifications and confirmations:

[1196] The server notifies the user's terminal of the order details, and the user confirms and modifies the order.

[1197] For example, we could suggest a menu to reduce stress, and then the user could review it and make revisions to include more fish dishes.

[1198] 7. Delivery process:

[1199] The online store delivers the groceries to the user's home based on the finalized order details.

[1200] Example of a prompt

[1201] Family composition: Husband 35 years old, wife 33 years old, child 5 years old

[1202] Food preferences: Children like vegetables.

[1203] Allergy information: My wife has a peanut allergy.

[1204] Emotions: Stress

[1205] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1206] Step 1:

[1207] The user launches the smartphone app and enters information about their family structure, food preferences, and allergies.

[1208] Input details: Family composition (e.g., husband 35 years old, wife 33 years old, child 5 years old), food preferences (e.g., child likes vegetables), allergy information (e.g., wife has a peanut allergy)

[1209] Output: The terminal sends the input content to the server.

[1210] Step 2:

[1211] The server saves the received information to the database.

[1212] Input data: Family composition data, food preferences data, allergy information data

[1213] Output: User information stored in the database

[1214] Step 3:

[1215] The emotion engine analyzes the user's input data and behavioral history to recognize their emotional state.

[1216] Input content: User activity history, input data

[1217] Output: Emotional information (e.g., stress level)

[1218] Step 4:

[1219] The server automatically generates a weekly meal plan based on stored user information and sentiment data.

[1220] Input content: Database user information, sentiment information

[1221] Output: A week's menu (Example: Chicken steak on Monday, Japanese-style hamburger on Tuesday)

[1222] Step 5:

[1223] The server creates a list of necessary ingredients based on the generated menu.

[1224] Input content: Weekly meal plan

[1225] Output: Ingredient list (Example: 200g chicken, 1 onion, 2 carrots)

[1226] Step 6:

[1227] The server automatically places orders using the online store's API based on the ingredient list.

[1228] Input content: Ingredient list

[1229] Output: Order request to online store

[1230] Step 7:

[1231] The server notifies the user's terminal of the automatically placed order.

[1232] Input details: Order details

[1233] Output: Notification to your smartphone (e.g., today's menu and order details)

[1234] Step 8:

[1235] The user checks the notification content on their device and makes corrections as needed.

[1236] Input details: Notified order details

[1237] Output: Modified order details

[1238] Step 9:

[1239] The server will then finalize the grocery order with the online store based on the revised order details.

[1240] Input details: Modified order details

[1241] Output: Final order data for online store

[1242] Step 10:

[1243] The online store delivers the groceries to the user's home based on the finalized order details.

[1244] Input details: Final order data

[1245] Output: Food delivered to the user's home.

[1246] 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.

[1247] 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.

[1248] 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.

[1249] [Third Embodiment]

[1250] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[1251] 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.

[1252] 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).

[1253] 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.

[1254] 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.

[1255] 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).

[1256] 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.

[1257] 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.

[1258] 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.

[1259] 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.

[1260] 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.

[1261] 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".

[1262] This invention provides a system that automatically generates menus based on input such as family composition, food preferences, and allergy information, and automatically orders the necessary ingredients from an online store.

[1263] System Configuration

[1264] This system consists of the following components:

[1265] 1. User Registration Method

[1266] The device provides a user interface that allows users to input information such as family structure, food preferences, and allergies.

[1267] For example, the user enters information such as "husband 35 years old, wife 33 years old, child 5 years old," "wife has a peanut allergy," and "child likes vegetables."

[1268] 2. Data storage means

[1269] The server saves the entered information to the database.

[1270] For example, information entered by users can be saved to a family structure database or an allergy information database.

[1271] 3. Menu generation method

[1272] The server references the stored information and automatically generates a weekly menu tailored to nutritional balance and family preferences.

[1273] As an example, I suggest chicken steak for dinner on Monday and Japanese-style hamburger steak for dinner on Tuesday.

[1274] 4. Ingredient list generation method

[1275] The server creates a list of necessary ingredients based on the generated menu.

[1276] For example, list ingredients such as 200g of chicken, 1 onion, and 2 carrots.

[1277] 5. Automated ordering method

[1278] The server automatically orders the necessary ingredients via the online store's API.

[1279] For example, the generated ingredient list is sent to an online store in an order format.

[1280] 6. Means of notification

[1281] The server notifies the user's terminal of the order details.

[1282] For example, the user's device will display "Order details: 200g chicken, 1 onion, 2 carrots".

[1283] 7. Verification and Correction Methods

[1284] The user reviews the notified order details and makes corrections as needed.

[1285] For example, a user can change chicken to fish to increase the number of fish dishes available.

[1286] 8. Final confirmation method

[1287] The server then sends the corrected order details to the online store and confirms the order.

[1288] Specific example

[1289] 1. Registration process:

[1290] The user launches the app and enters their family structure as "husband 35 years old, wife 33 years old, child 5 years old," along with "wife has a peanut allergy" and "child likes vegetables."

[1291] 2. Menu generation process:

[1292] Every Monday, the server accesses the database and generates a weekly menu that includes chicken steak on Monday and Japanese-style hamburger steak on Tuesday.

[1293] 3. Generate ingredient list:

[1294] The server lists 200g of chicken, 1 onion, and 2 carrots.

[1295] 4. Automatic ordering:

[1296] The server generates a list of ingredients and places an order through the online store's API.

[1297] 5. Notifications and confirmations:

[1298] The server notifies the user's terminal of the order details, and the user confirms and modifies the order.

[1299] 6. Delivery process:

[1300] The online supermarket delivers groceries to the user's home based on the finalized order details.

[1301] This system allows users to significantly reduce the effort involved in planning daily menus and grocery shopping, and enables them to provide well-balanced meals tailored to their family's needs.

[1302] The following describes the processing flow.

[1303] Program processing steps

[1304] Registration process

[1305] Step 1:

[1306] The user opens the app and enters family information.

[1307] For example, enter "Husband 35 years old, wife 33 years old, child 5 years old".

[1308] The terminal sends the entered data to the server.

[1309] Step 2:

[1310] Users enter their food preferences and allergy information.

[1311] For example, enter "My wife has a peanut allergy" and "My child likes vegetables."

[1312] The terminal sends the entered data to the server.

[1313] Step 3:

[1314] The server saves the information it receives to the database.

[1315] Save the information in the family information table and the food preferences / allergy information table.

[1316] Menu generation process

[1317] Step 4:

[1318] Every Monday, the server starts processing menu generation requests according to a scheduled task.

[1319] Step 5:

[1320] The server retrieves family information, food preferences, and allergy information from the database.

[1321] Step 6:

[1322] Based on the data acquired by the server, it generates a weekly meal plan by referring to a predefined nutritional balance and recipe database.

[1323] For example, generate "Monday's dinner is chicken steak" and "Tuesday's dinner is Japanese-style hamburger steak".

[1324] Step 7:

[1325] The server generates menus, links them to user accounts, and saves them to the database.

[1326] Ingredient list generation process

[1327] Step 8:

[1328] The server references the saved menu and lists the ingredients needed for each dish.

[1329] Step 9:

[1330] The server generates a list of necessary ingredients based on the menu.

[1331] As an example, list "200g of chicken," "1 onion," and "2 carrots."

[1332] Step 10:

[1333] The server optimizes itself to avoid duplication and waste by considering past purchase history and inventory information.

[1334] Collaboration with online supermarkets

[1335] Step 11:

[1336] The server uses the online supermarket's API to convert the generated grocery list into an order format.

[1337] Step 12:

[1338] The server sends order information, including the user's address and desired delivery time, to the online supermarket.

[1339] Notification and confirmation

[1340] Step 13:

[1341] The server sends a confirmation notification to the user's terminal containing the generated menu and ingredient order.

[1342] Step 14:

[1343] The user's device receives a notification, and the user confirms the menu and order details.

[1344] For example, "If you want to increase the number of fish dishes, change the chicken to fish."

[1345] Step 15:

[1346] The user makes corrections, and the device sends the corrected data to the server.

[1347] Delivery process

[1348] Step 16:

[1349] The server performs a final check and then sends the order details to the online supermarket.

[1350] Step 17:

[1351] Online supermarkets prepare groceries based on the order and deliver them to the user's home at the specified time.

[1352] In this way, users can significantly reduce the effort involved in daily meal planning and grocery shopping. Convenience is enhanced because the system automates everything, allowing users to handle tedious processes with minimal awareness.

[1353] (Example 1)

[1354] 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."

[1355] In today's busy lifestyle, planning family meals and efficiently gathering necessary ingredients is difficult for many people. In particular, creating menus that maintain nutritional balance while considering each family member's food preferences and allergy information requires considerable time and effort. There is a need for a system that solves this problem and simplifies family meal preparation.

[1356] 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.

[1357] In this invention, the server includes means for the user to input family composition, food preferences, and allergy information; means for receiving and storing the input information; means for automatically generating a weekly menu based on the stored information; and means for generating the menu according to nutritional balance and family preferences. This makes it possible to automatically generate nutritionally balanced menus tailored to the needs of the household, significantly reducing the effort required for meal preparation.

[1358] A "user" is someone who uses the system to input information such as family structure, food preferences, and allergies.

[1359] "Family structure" refers to information such as the relationships, ages, and genders of individual members within a household, and is data that is considered when the system generates menus.

[1360] "Food preferences" refer to a user's tastes and preferences for specific ingredients or dishes, and are one of the important factors when a system generates menus.

[1361] "Allergy information" refers to information about whether the user or their family members have allergies to specific foods. This information is used by the system to generate menus that do not contain allergens.

[1362] "Input method" refers to the interface that allows users to input data such as family structure, food preferences, and allergy information into the system.

[1363] "Storage method" refers to the process or function by which a server saves information entered by a user to a database.

[1364] "Menu generation method" refers to the process by which a system automatically generates a week's worth of menus based on stored information.

[1365] "Nutritional balance" refers to the appropriate balance of nutrients necessary to maintain health, and it is a criterion that the system considers when generating menus.

[1366] A "food ingredient list" refers to a list of necessary ingredients based on the generated menu.

[1367] "Automated ordering method" refers to a function or process for automatically ordering a generated list of ingredients from an online store.

[1368] "Notification method" refers to the system's function of notifying the user of the details of an automatically placed order.

[1369] "Confirmation and correction means" refers to a system function that allows users to confirm the order details they have been notified about and make corrections as needed.

[1370] "Final confirmation method" refers to the function used to confirm the final food order with the online store based on the revised order details.

[1371] This invention relates to a system that automatically generates menus based on family composition, food preferences, and allergy information, and automatically orders the necessary ingredients from an online store. A specific embodiment of this system is described below.

[1372] System Configuration

[1373] This system consists of the following components:

[1374] 1. User input means

[1375] The device provides a user interface that allows the user to input information such as family structure, food preferences, and allergies. Examples of such devices include personal computers and smartphones.

[1376] Specifically, users input information through the application such as their family structure ("husband 35 years old, wife 33 years old, child 5 years old"), their wife's peanut allergy, and their child's preference for vegetables.

[1377] 2. Data storage means

[1378] The server stores information entered by the user in a database. Servers are often built as cloud-based services.

[1379] For example, the entered information is stored in a "family structure database" and an "allergy information database."

[1380] 3. Menu generation method

[1381] The server references stored information and uses a generation AI model to automatically generate a weekly menu tailored to nutritional balance and family preferences.

[1382] Specifically, the server has the AI ​​model generate menus such as "chicken steak for dinner on Monday, and Japanese-style hamburger steak for dinner on Tuesday."

[1383] 4. Ingredient list generation method

[1384] The server generates a list of necessary ingredients based on the generated menu.

[1385] Specifically, the server creates a list of ingredients such as "200g chicken, 1 onion, 2 carrots."

[1386] 5. Automated ordering method

[1387] The server automatically orders the necessary ingredients via the online store's API.

[1388] For example, the server sends the generated list of ingredients to the online store as an order breakdown.

[1389] 6. Means of notification

[1390] The server notifies the user's terminal of the order details.

[1391] For example, the server sends a notification to the terminal saying, "Order details: 200g chicken, 1 onion, 2 carrots."

[1392] 7. Verification and Correction Methods

[1393] The user reviews the notified order details and makes corrections as needed.

[1394] For example, users can make modifications through their devices, such as changing "chicken to fish."

[1395] 8. Final confirmation method

[1396] The server then sends the corrected order details to the online store and confirms the order.

[1397] Specifically, the server completes the order based on the finalized details.

[1398] Example of a prompt

[1399] Examples of prompts for a generative AI model are as follows:

[1400] "Please create a week's worth of dinner menus for a family with a 35-year-old husband, a 33-year-old wife, and a 5-year-old child. The wife has a peanut allergy, and the child likes vegetables."

[1401] This system allows users to significantly reduce the effort involved in planning daily menus and grocery shopping, and enables them to provide well-balanced meals tailored to their family's needs.

[1402] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1403] Step 1:

[1404] The user enters information about their family structure, food preferences, and allergies into the device.

[1405] Input: The user uses the application to input information such as family structure ("husband 35 years old, wife 33 years old, child 5 years old"), "wife has a peanut allergy," and "child likes vegetables."

[1406] Output: The input information is stored in the terminal's temporary memory.

[1407] Specific operation: When the user enters data into each input field and presses the "Save" button, the device prepares to send the input data to the server.

[1408] Step 2:

[1409] The terminal sends the information entered by the user to the server.

[1410] Input: Family composition, food preferences, and allergy information stored on the device.

[1411] Output: A request that sends input information to the server.

[1412] Specific operation: The device sends user input data to the server's API endpoint via a POST request.

[1413] Step 3:

[1414] The server saves the received information to the database.

[1415] Input: Family composition, food preferences, and allergy information received by the server from the terminal.

[1416] Output: User information stored in the database.

[1417] Specific operation: The server connects to the database and saves the received data to the appropriate table using an INSERT statement.

[1418] Step 4:

[1419] The server generates a weekly meal plan based on the stored information.

[1420] Input: Family composition, food preferences, and allergy information stored in the database.

[1421] Output: The generated weekly meal plan.

[1422] Specific operation: The server inputs prompt text into the generated AI model and makes a request to generate a menu such as "Chicken steak for dinner on Monday, and Japanese-style hamburger steak for dinner on Tuesday."

[1423] Step 5:

[1424] The server generates a list of necessary ingredients based on the generated menu.

[1425] Input: A generated weekly meal plan.

[1426] Output: List of required ingredients.

[1427] Specific operation: The server extracts the ingredients for each menu item from a database or fixed list, calculates the required quantities, and lists them.

[1428] Step 6:

[1429] The server automatically orders ingredients via the online store's API.

[1430] Input: The generated list of ingredients.

[1431] Output: Order request sent to the online store.

[1432] Specific operation: The server sends order data, including the ingredient list, via a POST request to the online store's API endpoint.

[1433] Step 7:

[1434] The server notifies the user's terminal of the order details.

[1435] Input: Order request submitted to the online store.

[1436] Output: A notification of the order details displayed on the user's device.

[1437] Specific operation: The server generates a notification message containing the order details and sends it to the terminal to inform the user that the order has been submitted.

[1438] Step 8:

[1439] The user reviews the notified order details and makes corrections as needed.

[1440] Input: Order details displayed on the terminal.

[1441] Output: Order details modified by the user.

[1442] Specific actions: The user reviews the order details displayed on the device interface, makes any necessary changes or corrections, and then presses the "Confirm" button.

[1443] Step 9:

[1444] The server will finalize the order on the online store based on the corrected information.

[1445] Input: Order details modified by the user.

[1446] Output: The final food order has been confirmed.

[1447] Specific operation: The server finalizes the modified order details and sends them again to the online store's API endpoint to complete the order.

[1448] (Application Example 1)

[1449] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1450] Traditional menu planning and grocery ordering systems can take into account user preferences and allergy information, but they have struggled to comprehensively cover the delivery of ordered ingredients. Furthermore, users are required to shop for ingredients daily and review and modify their orders, which adds to their time and effort. A system that solves these problems and enhances user convenience is needed.

[1451] 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.

[1452] In this invention, the server includes means for the user to input family composition, food preferences, and allergy information; means for receiving and storing the input information; means for automatically generating a weekly menu based on the stored information; means for generating a list of necessary ingredients based on the automatically generated menu; means for automatically ordering ingredients from an online store based on the generated ingredient list; means for notifying the user of the automatically ordered items; and means for delivering the ingredients via a delivery service based on the ordered items. This makes it possible for a user to input family composition, food preferences, and allergy information and have the entire process, from automatically generating a menu to ordering ingredients and even delivery, handled in a consistent manner.

[1453] "A means for users to input family structure, food preferences, and allergy information" refers to an interface for users to input their family structure, the food preferences of individual members, and allergy information.

[1454] "Means for receiving and storing the input information" refers to a system for receiving information entered by a user and storing it in a database or similar.

[1455] "Means for automatically generating a weekly menu based on the stored information" refers to algorithms or software for automatically creating a nutritionally balanced weekly menu based on stored family composition, food preferences, and allergy information.

[1456] "Means for generating a list of necessary ingredients based on the automatically generated menu" refers to a program that lists the ingredients necessary for the automatically generated menu and generates that list.

[1457] "Means for automatically ordering ingredients from an online store based on the generated ingredient list" refers to a system that automatically orders ingredients using an online store's API or the like based on the generated ingredient list.

[1458] "Means for notifying the user of the automatically placed order" refers to an application or interface for notifying the user of the contents of the automatically placed order.

[1459] "Means of delivering ingredients via delivery service based on the aforementioned order details" refers to a system that utilizes online stores and delivery services selected based on the order details to deliver the necessary ingredients to the user's home.

[1460] System Configuration

[1461] This invention is a system that allows users to input their family structure, food preferences, and allergy information, automatically generates menus, automatically orders necessary ingredients from an online store, and even handles delivery in a seamless manner.

[1462] User registration method

[1463] The system provides an interface to the user's device (e.g., a smartphone or tablet) and includes a means for inputting family structure, food preferences, and allergy information. This information is transmitted to a server and stored in a database.

[1464] Information reception and storage means

[1465] The server receives information entered by the user and stores it in a database. For example, it stores it in a family structure database and an allergy information database.

[1466] Menu generation method

[1467] The server automatically generates a week's worth of menus using a generative AI model based on the stored information. During this process, prompts are used to instruct the AI ​​model, taking into account nutritional balance, family preferences, and allergy information.

[1468] Prompt example:

[1469] System: Please generate a weekly meal plan based on family composition, dietary preferences, and allergy information.

[1470] User input: Family composition is "husband 35 years old, wife 33 years old, child 5 years old". The wife has a peanut allergy, and the child likes vegetables.

[1471] System output: Monday dinner is chicken steak, Tuesday dinner is Japanese-style hamburger, Wednesday dinner is...

[1472] Ingredient list generation method

[1473] The server generates a list of necessary ingredients based on the automatically generated menu. For example, it might list ingredients such as 200g of chicken, 1 onion, and 2 carrots.

[1474] Automatic ordering method

[1475] The server automatically places an order via the online store's API based on the generated list of ingredients. At this time, it sends the order data according to the online store's API format.

[1476] Notification means

[1477] The server notifies the user's terminal of the automatically placed order. For example, it displays to the user, "Order details: 200g chicken, 1 onion, 2 carrots."

[1478] Verification and correction methods

[1479] The user reviews the notified order details and makes any necessary modifications. For example, the user can change chicken to fish to increase the number of fish dishes.

[1480] final means of confirmation

[1481] The server then confirms the user's modified order details again via the online store's API, and completes the order.

[1482] Delivery method

[1483] Based on the finalized order details, the server selects the appropriate delivery service and initiates the process of delivering the ingredients to the user's home.

[1484] Specific example

[1485] Registration process:

[1486] The user launches the smartphone app and enters their family structure as "husband 35 years old, wife 33 years old, child 5 years old," along with "wife has a peanut allergy" and "child likes vegetables."

[1487] Menu generation process:

[1488] The server looks at the database and generates a weekly menu that includes chicken steak for dinner on Monday and Japanese-style hamburger steak for dinner on Tuesday.

[1489] Ingredient list generation:

[1490] The server lists 200g of chicken, 1 onion, and 2 carrots.

[1491] Automatic ordering:

[1492] The server places orders for the generated ingredient list through the online store's API.

[1493] Notifications and confirmations:

[1494] The server notifies the user's terminal of the order details, and the user confirms and modifies the details.

[1495] Delivery process:

[1496] Online stores and delivery services deliver groceries to users' homes based on the finalized order details.

[1497] This significantly reduces the effort users spend on daily meal planning and grocery shopping, making it easy to provide well-balanced meals tailored to their family's needs.

[1498] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1499] Step 1:

[1500] The user enters their family structure, food preferences, and allergy information.

[1501] Input: Family composition (e.g., "Husband 35 years old, wife 33 years old, child 5 years old"), food preferences (e.g., "Child likes vegetables"), and allergy information (e.g., "Wife is allergic to peanuts") are entered via a smartphone app.

[1502] Output: Input information is sent to the server.

[1503] Specific operation: The user enters information into the app's input screen and presses the "Submit" button, which sends the data to the server.

[1504] Step 2:

[1505] The server receives and stores the entered information.

[1506] Input: Family composition, food preferences, and allergy information submitted by the user.

[1507] Output: Family structure data, food preference data, and allergy information stored in the database.

[1508] Specific operation: The server saves the received information to the corresponding table in the database. For example, it adds data to the family structure table, preference information table, and allergy information table.

[1509] Step 3:

[1510] The server automatically generates a weekly meal plan based on the stored information.

[1511] Input: Family composition, food preferences, and allergy information stored in the database.

[1512] Output: A weekly meal plan list.

[1513] Specific operation: The server issues instructions to the generated AI model using prompt messages and receives a weekly menu that takes into account family composition, food preferences, and allergy information.

[1514] Step 4:

[1515] The server generates a list of necessary ingredients based on the automatically generated menu.

[1516] Input: A weekly meal plan.

[1517] Output: List of required ingredients.

[1518] Specific operation: The server analyzes the menu data and lists the ingredients and quantities needed for each meal. For example, 200g of chicken, 1 onion, 2 carrots, etc.

[1519] Step 5:

[1520] The server automatically places an order with the online store based on the generated list of ingredients.

[1521] Input: List of required ingredients.

[1522] Output: Order data sent to the online store.

[1523] Specific operation: The server uses the online store's API to convert the ingredient list into an order format and send the order data.

[1524] Step 6:

[1525] The server notifies the user of the automatically placed order.

[1526] Input: Submitted order data.

[1527] Output: An order confirmation notification displayed on the user's smartphone.

[1528] Specific operation: The server sends a push notification or email to the user's device to inform them of the order details.

[1529] Step 7:

[1530] The user reviews and modifies the order details they have been notified about.

[1531] Input: Order confirmation notification.

[1532] Output: Confirmed and corrected order details.

[1533] Specific actions: The user checks the notified order details in the app, adds or changes ingredients as needed, and presses the "Confirm" button.

[1534] Step 8:

[1535] The server then finalizes the grocery order to the online store based on the revised order details.

[1536] Input: Confirmed and corrected order details.

[1537] Output: Final order data sent to the online store.

[1538] Specific operation: The server resends the final order data, reflecting the user's modifications, to the online store's API to confirm the order.

[1539] Step 9:

[1540] The server then processes the delivery of ingredients based on the finalized order details.

[1541] Input: Last order data.

[1542] Output: Food items delivered to the user's home.

[1543] Specific operation: The server uses APIs from online stores and delivery services to process food delivery. It may also send delivery completion notifications to the user's smartphone.

[1544] 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.

[1545] This invention is a system that automatically generates menus and orders necessary ingredients from an online store by inputting family structure, food preferences, and allergy information, and combining this with an emotion engine. A key feature is that the emotion engine recognizes the user's emotions and adjusts the menu and notification content accordingly.

[1546] System Configuration

[1547] This system consists of the following components:

[1548] 1. User Registration Method

[1549] The device provides a user interface that allows users to input information such as family structure, food preferences, and allergies.

[1550] For example, the user enters information such as "husband 35 years old, wife 33 years old, child 5 years old," "wife has a peanut allergy," and "child likes vegetables."

[1551] 2. Data storage means

[1552] The server saves the entered information to the database.

[1553] For example, information entered by users can be saved to a family structure database or an allergy information database.

[1554] 3. Means of recognizing emotions (emotion engine)

[1555] The server recognizes emotions from the user's input data and behavioral history.

[1556] For example, if a user is feeling stressed, the system will detect it.

[1557] 4. Menu generation means

[1558] The server references stored information and recognized emotional data to automatically generate a weekly menu tailored to nutritional balance and family preferences.

[1559] For example, I suggest chicken steak for Monday's dinner, a comforting meal, and Japanese-style hamburger steak for Tuesday's dinner, which has a relaxing effect.

[1560] 5. Menu adjustment methods

[1561] The server adjusts the menu based on emotional information obtained by the emotion engine.

[1562] For example, if a user is feeling stressed, the system might suggest a dish with relaxing properties.

[1563] 6. Ingredient List Generation Method

[1564] The server creates a list of necessary ingredients based on the generated menu.

[1565] For example, list ingredients such as 200g of chicken, 1 onion, and 2 carrots.

[1566] 7. Automated ordering method

[1567] The server automatically orders the necessary ingredients via the online store's API.

[1568] For example, the generated ingredient list is sent to an online store in an order format.

[1569] 8. Means of notification

[1570] The server notifies the user's device of a menu based on their order and emotions.

[1571] For example, a notification message might appear stating, "Today, we suggest a relaxing Japanese-style hamburger steak."

[1572] 9. Verification and Correction Methods

[1573] The user reviews the notified order details and makes corrections as needed.

[1574] For example, a user can change chicken to fish to increase the number of fish dishes available.

[1575] 10. Final confirmation method

[1576] The server then sends the corrected order details to the online store and confirms the order.

[1577] Online supermarkets prepare groceries based on the order and deliver them to the user's home at the specified time.

[1578] Specific example

[1579] 1. Registration process:

[1580] The user launches the app and enters their family structure as "husband 35 years old, wife 33 years old, child 5 years old," along with "wife has a peanut allergy" and "child likes vegetables."

[1581] 2. Emotion recognition:

[1582] The server recognizes when a user is experiencing stress based on their behavioral history and input data.

[1583] 3. Menu generation:

[1584] Every Monday, the server accesses the database and generates a weekly menu that includes a relaxing chicken steak on Monday and a Japanese-style hamburger on Tuesday.

[1585] 4. Generate ingredient list:

[1586] The server lists 200g of chicken, 1 onion, and 2 carrots.

[1587] 5. Automatic ordering:

[1588] The server generates a list of ingredients and places an order through the online store's API.

[1589] 6. Notifications and confirmations:

[1590] The server notifies the user's terminal of the order details, and the user confirms and modifies the order.

[1591] For example, we could suggest a menu to reduce stress, and then the user could review it and make revisions to include more fish dishes.

[1592] 7. Delivery process:

[1593] The online supermarket delivers groceries to the user's home based on the finalized order details.

[1594] In this way, users can not only significantly reduce the effort involved in daily meal planning and shopping, but also achieve a more comfortable eating lifestyle by being provided with optimal meals tailored to their emotional state. By automating everything and making adjustments based on emotions, the system can improve the user's quality of life.

[1595] The following describes the processing flow.

[1596] Program processing steps

[1597] Registration process

[1598] Step 1:

[1599] The user opens the app and enters family information.

[1600] For example, enter "Husband 35 years old, wife 33 years old, child 5 years old".

[1601] The terminal sends the entered data to the server.

[1602] Step 2:

[1603] Users enter their food preferences and allergy information.

[1604] For example, enter "My wife has a peanut allergy" and "My child likes vegetables."

[1605] The terminal sends the entered data to the server.

[1606] Step 3:

[1607] The server saves the information it receives to the database.

[1608] Save the information in the family information table and the food preferences / allergy information table.

[1609] Emotion recognition process

[1610] Step 4:

[1611] While the user is using the app, the device collects the user's input and behavioral patterns.

[1612] For example, when a user performs many actions in a short period of time, or gives negative feedback about a particular dish.

[1613] Step 5:

[1614] The server analyzes the collected data, and the emotion engine evaluates the user's emotional state.

[1615] For example, the emotion engine might determine that the user is feeling stressed.

[1616] Menu generation process

[1617] Step 6:

[1618] Every Monday, the server starts processing menu generation requests according to a scheduled task.

[1619] Step 7:

[1620] The server retrieves family information, food preferences and allergy information, and emotional information from the database.

[1621] Step 8:

[1622] Based on the data acquired by the server, the system automatically generates a weekly meal plan by referring to a predefined nutritional balance and recipe database.

[1623] For example, I suggest chicken steak for Monday's dinner, a comforting meal, and Japanese-style hamburger steak for Tuesday's dinner, which has a relaxing effect.

[1624] Step 9:

[1625] The server adjusts the menu content it generates based on the emotional information obtained by the emotion engine.

[1626] For example, if a user is feeling stressed, they might choose a menu item that promotes relaxation.

[1627] Step 10:

[1628] The server saves the adjusted menu to the user's account in the database.

[1629] Ingredient list generation process

[1630] Step 11:

[1631] The server references the saved menu and lists the ingredients needed for each dish.

[1632] Step 12:

[1633] The server generates a list of necessary ingredients based on the menu.

[1634] As an example, list "200g of chicken," "1 onion," and "2 carrots."

[1635] Step 13:

[1636] The server optimizes itself to avoid duplication and waste by considering past purchase history and inventory information.

[1637] Collaboration with online supermarkets

[1638] Step 14:

[1639] The server uses the online supermarket's API to convert the generated grocery list into an order format.

[1640] Step 15:

[1641] The server sends order information, including the user's address and desired delivery time, to the online supermarket.

[1642] Notification and confirmation

[1643] Step 16:

[1644] The server sends a confirmation notification to the user's terminal containing the generated menu and ingredient order.

[1645] Step 17:

[1646] The user's device receives a notification, and the user confirms the menu and order details.

[1647] For example, "If you want to increase the number of fish dishes, change the chicken to fish."

[1648] Step 18:

[1649] The user makes corrections, and the device sends the corrected data to the server.

[1650] Delivery process

[1651] Step 19:

[1652] The server performs a final check and then sends the order details to the online supermarket.

[1653] Step 20:

[1654] Online supermarkets prepare groceries based on the order and deliver them to the user's home at the specified time.

[1655] In this way, users can significantly reduce the effort involved in daily meal planning and grocery shopping. Convenience is enhanced by automating everything and allowing users to handle tedious processes with minimal awareness. Furthermore, by utilizing an emotion engine, optimal meal plans are suggested based on the user's emotional state, leading to a more comfortable and enjoyable eating experience.

[1656] (Example 2)

[1657] 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."

[1658] Conventional menu generation systems generate menus based solely on the family composition, food preferences, and allergy information entered by the user, and therefore cannot provide optimal menus that take into account the user's emotional state. Furthermore, while it is important to provide meals that address a user's emotional state when they are stressed or experiencing a particular emotional condition, conventional systems failed to achieve this. In addition, the automatic ordering, confirmation, and correction functions for ingredients were insufficient, and the system could not fully reduce the burden on the user.

[1659] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1660] In this invention, the server includes means for the user to input family composition, food preferences, and allergy information; server means for receiving and storing the input information; server means for automatically generating a weekly menu based on the stored information and the user's emotional state; server means for generating a list of necessary ingredients based on the automatically generated menu; server means for automatically ordering ingredients from an online store based on the generated ingredient list; and server means for notifying the user terminal of the automatically ordered items. This enables the provision of an optimal menu that takes the user's emotional state into consideration, as well as automatic ordering of ingredients and functions for confirmation and modification.

[1661] "User" refers to an individual or family member who uses the system.

[1662] "Family composition" refers to information about the people who belong to the user's household.

[1663] "Food preferences" refers to the types of ingredients and dishes that the user and their family enjoy consuming.

[1664] "Allergy information" refers to information about allergies held by the user and their family.

[1665] A "server" refers to a computer device that processes, stores, and transmits data within a system.

[1666] "Server configuration" refers to the software and hardware configuration that perform specific processing functions on a server.

[1667] "Automatic generation" refers to the process by which a system autonomously creates data according to a program.

[1668] "Emotional state" refers to the user's current psychological or emotional condition.

[1669] A "food ingredient list" refers to a list of ingredients needed based on the generated menu.

[1670] An "online store" refers to a website or service that allows users to purchase groceries and other items via the internet.

[1671] "Automated ordering" refers to the act of a system ordering specified ingredients from an online store without manual intervention from the user.

[1672] "Notification" refers to the process of transmitting information from a system to a user.

[1673] A "terminal" refers to a device (such as a smartphone or computer) that a user uses to access and operate a system.

[1674] "Confirmation and correction" refers to the act of a user reviewing the information they have been notified about and making changes as necessary.

[1675] "Final ingredient order" refers to the ingredient order that has been confirmed and finalized after user review and modification.

[1676] This invention relates to an automated menu generation system that combines a user's input of family structure, food preferences, and allergy information with an emotion engine. This system uses a server and terminals to provide an optimal menu tailored to the user's emotional state and enables automatic ordering of ingredients.

[1677] 1. User Registration Method

[1678] Users enter family composition, food preferences, and allergy information using devices such as smartphones or computers. These devices provide dedicated applications or web interfaces to facilitate information entry. For example, a user might launch the app and enter information such as "husband 35 years old, wife 33 years old, child 5 years old," "wife has a peanut allergy," and "child likes vegetables."

[1679] 2. Data storage means

[1680] The server saves user input information to a database. Specifically, the entered family structure and allergy information are stored in the corresponding database. The family structure database stores "Husband 35 years old, wife 33 years old, child 5 years old," and the allergy information database stores "Wife: Peanut allergy."

[1681] 3. Emotion recognition means

[1682] The server uses an emotion engine to recognize emotions based on the user's behavior history and input data. This emotion engine analyzes the user's daily behavior data and input information to detect emotions such as stress and joy. For example, if the server detects that the user frequently stays up late, it recognizes that the user is feeling stressed.

[1683] 4. Menu generation means

[1684] The server automatically generates a weekly meal plan based on stored family composition data and emotional information. The server takes emotional information into consideration to automatically generate a nutritionally balanced menu. For example, it might suggest a comforting chicken steak for Monday's dinner and a relaxing Japanese-style hamburger for Tuesday's dinner.

[1685] 5. Menu adjustment methods

[1686] Based on the results of the emotion engine, the server can adjust the menu to match the user's emotional state. For example, if the user is feeling stressed, it will suggest dishes with relaxing effects. This may include suggesting specific herbal teas or menu items containing ingredients known to relieve stress.

[1687] 6. Ingredient List Generation Method

[1688] The server creates a list of necessary ingredients based on the generated menu. This ingredient list specifically details the ingredients needed for each menu item. For example, it might list 200g of chicken, 1 onion, and 2 carrots.

[1689] 7. Automated ordering method

[1690] The server automatically places an order for the generated grocery list via the online store's API. The server sends the order details to the online store's API and confirms the order. For example, a grocery list is sent to an online supermarket, and the ordering process is automated.

[1691] 8. Means of notification

[1692] The server notifies the user's device of the order details and menu. For example, a notification such as "Today we suggest a relaxing Japanese-style hamburger steak" is sent to the user's device.

[1693] 9. Verification and Correction Methods

[1694] Users can review the notified order details and make modifications as needed. On their device screen, users can check the order details and, if necessary, change chicken to fish to increase the amount of fish dishes.

[1695] 10. Final confirmation method

[1696] The server then sends the revised order details to the online store to finalize the order. The online store prepares the ingredients based on the finalized order and delivers them to the user's home.

[1697] In this way, users can not only save time on daily meal planning and shopping, but also enjoy a more comfortable eating experience by being provided with optimal meals tailored to their emotional state. Because the system is fully automated, it is expected to improve the user's quality of life.

[1698] Specific example

[1699] Registration process: The user launches the app and enters their family structure as "husband 35 years old, wife 33 years old, child 5 years old," and states that "wife has a peanut allergy" and "child likes vegetables."

[1700] Emotion Recognition: The server recognizes when a user is experiencing stress based on their behavioral history and input data.

[1701] Menu generation: Every Monday, the server accesses the database and generates a weekly menu that includes relaxing chicken steak on Monday and Japanese-style hamburger steak on Tuesday.

[1702] Ingredient list generation: The server lists 200g of chicken, 1 onion, and 2 carrots.

[1703] Automated ordering: The server places an order for the generated ingredient list via the online store's API.

[1704] Notification and Confirmation: The server notifies the user of the order details on their device, and the user confirms and modifies the order. For example, the server might suggest a menu to reduce stress, and the user can confirm and modify it to include more fish dishes.

[1705] Delivery process: The online supermarket delivers the groceries to the user's home based on the finalized order.

[1706] This system enables the provision of optimal menus tailored to the user's emotional state, as well as automatic ordering, confirmation, and modification functions for ingredients.

[1707] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1708] Step 1: The user enters family composition, food preferences, and allergy information via the device.

[1709] Input: The user enters family information such as "Husband 35 years old, wife 33 years old, child 5 years old" into the input form, allergy information such as "Wife has a peanut allergy," and food preference information such as "Child likes vegetables."

[1710] Data processing: The terminal formats the input data and prepares it for transmission to the server.

[1711] Output: The formatted dataset is sent to the server.

[1712] Step 2: The server receives the entered information and saves it to the database.

[1713] Input: Family composition, allergy information, and food preferences information sent from the device.

[1714] Data processing: The server performs appropriate registration operations in the database and stores the input data in the appropriate tables (family structure table, allergy table, food preference table).

[1715] Output: A save completion notification and information for verifying the saved data are generated.

[1716] Step 3: The server recognizes the user's emotions based on their behavioral history and input data.

[1717] Input: User activity log data and family structure, food preferences, and allergy information entered in Step 1.

[1718] Data Calculation: The emotion engine analyzes user behavior data to calculate emotional states such as stress and joy. Natural language processing and machine learning algorithms are used for these calculations.

[1719] Output: Data representing the user's current emotional state (e.g., high stress level).

[1720] Step 4: The server automatically generates a weekly meal plan based on the stored data and recognized emotion information.

[1721] Input: Family structure data, allergy information, food preferences, perceived emotional state.

[1722] Data processing: The menu generation algorithm generates nutritionally balanced menus based on this data. If the emotional state is stressed, dishes with relaxing effects are prioritized.

[1723] Output: Weekly meal plan data (e.g., Monday's dinner is chicken steak, Tuesday's dinner is Japanese-style hamburger steak).

[1724] Step 5: The server generates a list of necessary ingredients based on the automatically generated menu.

[1725] Input: Generated menu data.

[1726] Data processing: Extract information on ingredients needed for the menu and generate an ingredient list. Calculate the required amount of each ingredient and add it to the list.

[1727] Output: Ingredient list data (Example: 200g chicken, 1 onion, 2 carrots).

[1728] Step 6: The server places an automated order based on the ingredient list via the online store's API.

[1729] Input: Generated ingredient list data.

[1730] Data processing: Send order information to the online store API. Convert the ingredient list into an order format and send it.

[1731] Output: Order confirmation information and order ID.

[1732] Step 7: The server notifies the user terminal of the order details and the generated menu.

[1733] Input: Order confirmation information, order ID, generated menu data.

[1734] Data processing: Generate a notification message and send it to the user's terminal.

[1735] Output: Notification message saying, "Today, we suggest a relaxing Japanese-style hamburger steak."

[1736] Step 8: The user reviews the notified order details and makes any necessary corrections.

[1737] Input: Order details and menu data displayed on the user's terminal.

[1738] Specific actions: The user confirms the order details on the device screen and, if necessary, changes chicken to fish to increase the number of fish dishes.

[1739] Output: Modified order details data.

[1740] Step 9: The server finally sends the corrected order details to the online store and confirms the order.

[1741] Input: Modified order details data.

[1742] Data calculation: Generate final order data reflecting the changes and send it to the online store API.

[1743] Output: Final order confirmation information and shipping information.

[1744] Step 10: The online store prepares the ingredients based on the finalized order and delivers them to the user's home.

[1745] Input: Final order confirmation information and shipping information.

[1746] Specific operation: The online store prepares the ingredients based on the order and delivers them to the user's home at the specified date and time.

[1747] Output: Notification to the user that delivery is complete.

[1748] (Application Example 2)

[1749] 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."

[1750] Conventional menu generation systems automatically generate menus and order ingredients based on the user's food preferences and allergy information. However, they cannot adjust menus based on the user's emotional state or mental health, making it difficult to improve dietary habits in response to the user's stress and mood. Therefore, in order to further improve the user's quality of life, menu generation and automatic ordering that take emotional information into account are necessary.

[1751] 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.

[1752] In this invention, the server includes means for recognizing the user's emotional state using an emotion engine, means for automatically generating a weekly menu based on the stored information and emotional information, and means for automatically ordering ingredients from an online store based on the generated ingredient list. This makes it possible to adjust the menu and order the optimal ingredients based on the user's emotional state.

[1753] "Family structure" refers to information that describes the members of each family, including their ages and relationships.

[1754] "Food preferences" refers to information indicating the types of ingredients and dishes that the user or their family enjoys.

[1755] "Allergy information" refers to information about users or their family members who have allergic reactions to specific foods.

[1756] An "emotion engine" is a means of recognizing a user's emotional state from their input data and behavioral history.

[1757] A "menu" refers to a list of meals for one week.

[1758] A "food ingredient list" is a list of ingredients required based on a menu.

[1759] An "online store" is a store that provides services for selling food and goods via the internet.

[1760] "Notification" is the act of a system informing a user's device of information that requires their attention.

[1761] "Confirmation and Correction" refers to the process by which users reconfirm the information and order details provided by the system and make corrections as necessary.

[1762] "Confirming" means finalizing the revised order details and submitting them to the online store.

[1763] This invention is a system that automatically generates menus and orders necessary ingredients from an online store by having the user input family structure, food preferences, and allergy information, and combining this with an emotion engine. A key feature is that the emotion engine recognizes the user's emotions and adjusts the menus and notification content accordingly.

[1764] System Configuration

[1765] This system consists of the following components:

[1766] 1. User Registration Method

[1767] The server provides a user interface (UI) that allows users to input family structure, food preferences, and allergy information through their terminal.

[1768] For example, the user enters information such as "husband 35 years old, wife 33 years old, child 5 years old," "wife has a peanut allergy," and "child likes vegetables."

[1769] 2. Data storage means

[1770] The server saves the entered information to the database.

[1771] For example, information entered by users can be saved to a family structure database or an allergy information database.

[1772] 3. Means of recognizing emotions (emotion engine)

[1773] The server recognizes emotions from the user's input data and behavioral history.

[1774] For example, if a user is feeling stressed, the system will detect it.

[1775] 4. Menu generation means

[1776] The server automatically generates weekly meal plans tailored to nutritional balance and family preferences, based on stored information and emotional data.

[1777] For example, I suggest chicken steak for Monday's dinner, a comforting meal, and Japanese-style hamburger steak for Tuesday's dinner, which has a relaxing effect.

[1778] 5. Menu adjustment methods

[1779] The server adjusts the menu based on emotional information obtained by the emotion engine.

[1780] For example, if a user is feeling stressed, the system might suggest a dish with relaxing properties.

[1781] 6. Ingredient List Generation Method

[1782] The server creates a list of necessary ingredients based on the generated menu.

[1783] For example, list ingredients such as 200g of chicken, 1 onion, and 2 carrots.

[1784] 7. Automated ordering method

[1785] The server automatically orders the necessary ingredients via the online store's API.

[1786] For example, the generated ingredient list is sent to an online store in an order format.

[1787] 8. Means of notification

[1788] The server notifies the user's device of a menu based on their order and emotions.

[1789] For example, a notification message might appear stating, "Today, we suggest a relaxing Japanese-style hamburger steak."

[1790] 9. Verification and Correction Methods

[1791] The user reviews the notified order details and makes corrections as needed.

[1792] For example, a user can change chicken to fish to increase the number of fish dishes available.

[1793] 10. Final confirmation method

[1794] The server then sends the revised order details to the online store and confirms the order.

[1795] The online store prepares the ingredients based on the order and delivers them to the user's home at the specified time.

[1796] Specific examples of operation

[1797] 1. Registration process:

[1798] The user launches the app and enters their family structure as "husband 35 years old, wife 33 years old, child 5 years old," along with "wife has a peanut allergy" and "child likes vegetables."

[1799] 2. Emotion recognition:

[1800] The server recognizes when a user is experiencing stress based on their behavioral history and input data.

[1801] 3. Menu generation:

[1802] Every Monday, the server accesses the database and generates a weekly menu that includes a relaxing chicken steak on Monday and a Japanese-style hamburger on Tuesday.

[1803] 4. Generate ingredient list:

[1804] The server lists 200g of chicken, 1 onion, and 2 carrots.

[1805] 5. Automatic ordering:

[1806] The server generates a list of ingredients and places an order through the online store's API.

[1807] 6. Notifications and confirmations:

[1808] The server notifies the user's terminal of the order details, and the user confirms and modifies the order.

[1809] For example, we could suggest a menu to reduce stress, and then the user could review it and make revisions to include more fish dishes.

[1810] 7. Delivery process:

[1811] The online store delivers the groceries to the user's home based on the finalized order details.

[1812] Example of a prompt

[1813] Family composition: Husband 35 years old, wife 33 years old, child 5 years old

[1814] Food preferences: Children like vegetables.

[1815] Allergy information: My wife has a peanut allergy.

[1816] Emotions: Stress

[1817] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1818] Step 1:

[1819] The user launches the smartphone app and enters information about their family structure, food preferences, and allergies.

[1820] Input details: Family composition (e.g., husband 35 years old, wife 33 years old, child 5 years old), food preferences (e.g., child likes vegetables), allergy information (e.g., wife has a peanut allergy)

[1821] Output: The terminal sends the input content to the server.

[1822] Step 2:

[1823] The server saves the received information to the database.

[1824] Input data: Family composition data, food preferences data, allergy information data

[1825] Output: User information stored in the database

[1826] Step 3:

[1827] The emotion engine analyzes the user's input data and behavioral history to recognize their emotional state.

[1828] Input content: User activity history, input data

[1829] Output: Emotional information (e.g., stress level)

[1830] Step 4:

[1831] The server automatically generates a weekly meal plan based on stored user information and sentiment data.

[1832] Input content: Database user information, sentiment information

[1833] Output: A week's menu (Example: Chicken steak on Monday, Japanese-style hamburger on Tuesday)

[1834] Step 5:

[1835] The server creates a list of necessary ingredients based on the generated menu.

[1836] Input content: Weekly meal plan

[1837] Output: Ingredient list (Example: 200g chicken, 1 onion, 2 carrots)

[1838] Step 6:

[1839] The server automatically places orders using the online store's API based on the ingredient list.

[1840] Input content: Ingredient list

[1841] Output: Order request to online store

[1842] Step 7:

[1843] The server notifies the user's terminal of the automatically placed order.

[1844] Input details: Order details

[1845] Output: Notification to your smartphone (e.g., today's menu and order details)

[1846] Step 8:

[1847] The user checks the notification content on their device and makes corrections as needed.

[1848] Input details: Notified order details

[1849] Output: Modified order details

[1850] Step 9:

[1851] The server will then finalize the grocery order with the online store based on the revised order details.

[1852] Input details: Modified order details

[1853] Output: Final order data for online store

[1854] Step 10:

[1855] The online store delivers the groceries to the user's home based on the finalized order details.

[1856] Input details: Final order data

[1857] Output: Food delivered to the user's home.

[1858] 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.

[1859] 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.

[1860] 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.

[1861] [Fourth Embodiment]

[1862] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1863] 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.

[1864] 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).

[1865] 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.

[1866] 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.

[1867] 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).

[1868] 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.

[1869] 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.

[1870] 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.

[1871] 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.

[1872] 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.

[1873] 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.

[1874] 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".

[1875] This invention provides a system that automatically generates menus based on input such as family composition, food preferences, and allergy information, and automatically orders the necessary ingredients from an online store.

[1876] System Configuration

[1877] This system consists of the following components:

[1878] 1. User Registration Method

[1879] The device provides a user interface that allows users to input information such as family structure, food preferences, and allergies.

[1880] For example, the user enters information such as "husband 35 years old, wife 33 years old, child 5 years old," "wife has a peanut allergy," and "child likes vegetables."

[1881] 2. Data storage means

[1882] The server saves the entered information to the database.

[1883] For example, information entered by users can be saved to a family structure database or an allergy information database.

[1884] 3. Menu generation method

[1885] The server references the stored information and automatically generates a weekly menu tailored to nutritional balance and family preferences.

[1886] As an example, I suggest chicken steak for dinner on Monday and Japanese-style hamburger steak for dinner on Tuesday.

[1887] 4. Ingredient list generation method

[1888] The server creates a list of necessary ingredients based on the generated menu.

[1889] For example, list ingredients such as 200g of chicken, 1 onion, and 2 carrots.

[1890] 5. Automated ordering method

[1891] The server automatically orders the necessary ingredients via the online store's API.

[1892] For example, the generated ingredient list is sent to an online store in an order format.

[1893] 6. Means of notification

[1894] The server notifies the user's terminal of the order details.

[1895] For example, the user's device will display "Order details: 200g chicken, 1 onion, 2 carrots".

[1896] 7. Verification and Correction Methods

[1897] The user reviews the notified order details and makes corrections as needed.

[1898] For example, a user can change chicken to fish to increase the number of fish dishes available.

[1899] 8. Final confirmation method

[1900] The server then sends the corrected order details to the online store and confirms the order.

[1901] Specific example

[1902] 1. Registration process:

[1903] The user launches the app and enters their family structure as "husband 35 years old, wife 33 years old, child 5 years old," along with "wife has a peanut allergy" and "child likes vegetables."

[1904] 2. Menu generation process:

[1905] Every Monday, the server accesses the database and generates a weekly menu that includes chicken steak on Monday and Japanese-style hamburger steak on Tuesday.

[1906] 3. Generate ingredient list:

[1907] The server lists 200g of chicken, 1 onion, and 2 carrots.

[1908] 4. Automatic ordering:

[1909] The server generates a list of ingredients and places an order through the online store's API.

[1910] 5. Notifications and confirmations:

[1911] The server notifies the user's terminal of the order details, and the user confirms and modifies the order.

[1912] 6. Delivery process:

[1913] The online supermarket delivers groceries to the user's home based on the finalized order details.

[1914] This system allows users to significantly reduce the effort involved in planning daily menus and grocery shopping, and enables them to provide well-balanced meals tailored to their family's needs.

[1915] The following describes the processing flow.

[1916] Program processing steps

[1917] Registration process

[1918] Step 1:

[1919] The user opens the app and enters family information.

[1920] For example, enter "Husband 35 years old, wife 33 years old, child 5 years old".

[1921] The terminal sends the entered data to the server.

[1922] Step 2:

[1923] Users enter their food preferences and allergy information.

[1924] For example, enter "My wife has a peanut allergy" and "My child likes vegetables."

[1925] The terminal sends the entered data to the server.

[1926] Step 3:

[1927] The server saves the information it receives to the database.

[1928] Save the information in the family information table and the food preferences / allergy information table.

[1929] Menu generation process

[1930] Step 4:

[1931] Every Monday, the server starts processing menu generation requests according to a scheduled task.

[1932] Step 5:

[1933] The server retrieves family information, food preferences, and allergy information from the database.

[1934] Step 6:

[1935] Based on the data acquired by the server, it generates a weekly meal plan by referring to a predefined nutritional balance and recipe database.

[1936] For example, generate "Monday's dinner is chicken steak" and "Tuesday's dinner is Japanese-style hamburger steak".

[1937] Step 7:

[1938] The server generates menus, links them to user accounts, and saves them to the database.

[1939] Ingredient list generation process

[1940] Step 8:

[1941] The server references the saved menu and lists the ingredients needed for each dish.

[1942] Step 9:

[1943] The server generates a list of necessary ingredients based on the menu.

[1944] As an example, list "200g of chicken," "1 onion," and "2 carrots."

[1945] Step 10:

[1946] The server optimizes itself to avoid duplication and waste by considering past purchase history and inventory information.

[1947] Collaboration with online supermarkets

[1948] Step 11:

[1949] The server uses the online supermarket's API to convert the generated grocery list into an order format.

[1950] Step 12:

[1951] The server sends order information, including the user's address and desired delivery time, to the online supermarket.

[1952] Notification and confirmation

[1953] Step 13:

[1954] The server sends a confirmation notification to the user's terminal containing the generated menu and ingredient order.

[1955] Step 14:

[1956] The user's device receives a notification, and the user confirms the menu and order details.

[1957] For example, "If you want to increase the number of fish dishes, change the chicken to fish."

[1958] Step 15:

[1959] The user makes corrections, and the device sends the corrected data to the server.

[1960] Delivery process

[1961] Step 16:

[1962] The server performs a final check and then sends the order details to the online supermarket.

[1963] Step 17:

[1964] Online supermarkets prepare groceries based on the order and deliver them to the user's home at the specified time.

[1965] In this way, users can significantly reduce the effort involved in daily meal planning and grocery shopping. Convenience is enhanced because the system automates everything, allowing users to handle tedious processes with minimal awareness.

[1966] (Example 1)

[1967] 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".

[1968] In today's busy lifestyle, planning family meals and efficiently gathering necessary ingredients is difficult for many people. In particular, creating menus that maintain nutritional balance while considering each family member's food preferences and allergy information requires considerable time and effort. There is a need for a system that solves this problem and simplifies family meal preparation.

[1969] 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.

[1970] In this invention, the server includes means for the user to input family composition, food preferences, and allergy information; means for receiving and storing the input information; means for automatically generating a weekly menu based on the stored information; and means for generating the menu according to nutritional balance and family preferences. This makes it possible to automatically generate nutritionally balanced menus tailored to the needs of the household, significantly reducing the effort required for meal preparation.

[1971] A "user" is someone who uses the system to input information such as family structure, food preferences, and allergies.

[1972] "Family structure" refers to information such as the relationships, ages, and genders of individual members within a household, and is data that is considered when the system generates menus.

[1973] "Food preferences" refer to a user's tastes and preferences for specific ingredients or dishes, and are one of the important factors when a system generates menus.

[1974] "Allergy information" refers to information about whether the user or their family members have allergies to specific foods. This information is used by the system to generate menus that do not contain allergens.

[1975] "Input method" refers to the interface that allows users to input data such as family structure, food preferences, and allergy information into the system.

[1976] "Storage method" refers to the process or function by which a server saves information entered by a user to a database.

[1977] "Menu generation method" refers to the process by which a system automatically generates a week's worth of menus based on stored information.

[1978] "Nutritional balance" refers to the appropriate balance of nutrients necessary to maintain health, and it is a criterion that the system considers when generating menus.

[1979] A "food ingredient list" refers to a list of necessary ingredients based on the generated menu.

[1980] "Automated ordering method" refers to a function or process for automatically ordering a generated list of ingredients from an online store.

[1981] "Notification method" refers to the system's function of notifying the user of the details of an automatically placed order.

[1982] "Confirmation and correction means" refers to a system function that allows users to confirm the order details they have been notified about and make corrections as needed.

[1983] "Final confirmation method" refers to the function used to confirm the final food order with the online store based on the revised order details.

[1984] This invention relates to a system that automatically generates menus based on family composition, food preferences, and allergy information, and automatically orders the necessary ingredients from an online store. A specific embodiment of this system is described below.

[1985] System Configuration

[1986] This system consists of the following components:

[1987] 1. User input means

[1988] The device provides a user interface that allows the user to input information such as family structure, food preferences, and allergies. Examples of such devices include personal computers and smartphones.

[1989] Specifically, users input information through the application such as their family structure ("husband 35 years old, wife 33 years old, child 5 years old"), their wife's peanut allergy, and their child's preference for vegetables.

[1990] 2. Data storage means

[1991] The server stores information entered by the user in a database. Servers are often built as cloud-based services.

[1992] For example, the entered information is stored in a "family structure database" and an "allergy information database."

[1993] 3. Menu generation method

[1994] The server references stored information and uses a generation AI model to automatically generate a weekly menu tailored to nutritional balance and family preferences.

[1995] Specifically, the server has the AI ​​model generate menus such as "chicken steak for dinner on Monday, and Japanese-style hamburger steak for dinner on Tuesday."

[1996] 4. Ingredient list generation method

[1997] The server generates a list of necessary ingredients based on the generated menu.

[1998] Specifically, the server creates a list of ingredients such as "200g chicken, 1 onion, 2 carrots."

[1999] 5. Automated ordering method

[2000] The server automatically orders the necessary ingredients via the online store's API.

[2001] For example, the server sends the generated list of ingredients to the online store as an order breakdown.

[2002] 6. Means of notification

[2003] The server notifies the user's terminal of the order details.

[2004] For example, the server sends a notification to the terminal saying, "Order details: 200g chicken, 1 onion, 2 carrots."

[2005] 7. Verification and Correction Methods

[2006] The user reviews the notified order details and makes corrections as needed.

[2007] For example, users can make modifications through their devices, such as changing "chicken to fish."

[2008] 8. Final confirmation method

[2009] The server then sends the corrected order details to the online store and confirms the order.

[2010] Specifically, the server completes the order based on the finalized details.

[2011] Example of a prompt

[2012] Examples of prompts for a generative AI model are as follows:

[2013] "Please create a week's worth of dinner menus for a family with a 35-year-old husband, a 33-year-old wife, and a 5-year-old child. The wife has a peanut allergy, and the child likes vegetables."

[2014] This system allows users to significantly reduce the effort involved in planning daily menus and grocery shopping, and enables them to provide well-balanced meals tailored to their family's needs.

[2015] The flow of the specific processing in Example 1 will be explained using Figure 11.

[2016] Step 1:

[2017] The user enters information about their family structure, food preferences, and allergies into the device.

[2018] Input: The user uses the application to input information such as family structure ("husband 35 years old, wife 33 years old, child 5 years old"), "wife has a peanut allergy," and "child likes vegetables."

[2019] Output: The input information is stored in the terminal's temporary memory.

[2020] Specific operation: When the user enters data into each input field and presses the "Save" button, the device prepares to send the input data to the server.

[2021] Step 2:

[2022] The terminal sends the information entered by the user to the server.

[2023] Input: Family composition, food preferences, and allergy information stored on the device.

[2024] Output: A request that sends input information to the server.

[2025] Specific operation: The device sends user input data to the server's API endpoint via a POST request.

[2026] Step 3:

[2027] The server saves the received information to the database.

[2028] Input: Family composition, food preferences, and allergy information received by the server from the terminal.

[2029] Output: User information stored in the database.

[2030] Specific operation: The server connects to the database and saves the received data to the appropriate table using an INSERT statement.

[2031] Step 4:

[2032] The server generates a weekly meal plan based on the stored information.

[2033] Input: Family composition, food preferences, and allergy information stored in the database.

[2034] Output: The generated weekly meal plan.

[2035] Specific operation: The server inputs prompt text into the generated AI model and makes a request to generate a menu such as "Chicken steak for dinner on Monday, and Japanese-style hamburger steak for dinner on Tuesday."

[2036] Step 5:

[2037] The server generates a list of necessary ingredients based on the generated menu.

[2038] Input: A generated weekly meal plan.

[2039] Output: List of required ingredients.

[2040] Specific operation: The server extracts the ingredients for each menu item from a database or fixed list, calculates the required quantities, and lists them.

[2041] Step 6:

[2042] The server automatically orders ingredients via the online store's API.

[2043] Input: The generated list of ingredients.

[2044] Output: Order request sent to the online store.

[2045] Specific operation: The server sends order data, including the ingredient list, via a POST request to the online store's API endpoint.

[2046] Step 7:

[2047] The server notifies the user's terminal of the order details.

[2048] Input: Order request submitted to the online store.

[2049] Output: A notification of the order details displayed on the user's device.

[2050] Specific operation: The server generates a notification message containing the order details and sends it to the terminal to inform the user that the order has been submitted.

[2051] Step 8:

[2052] The user reviews the notified order details and makes corrections as needed.

[2053] Input: Order details displayed on the terminal.

[2054] Output: Order details modified by the user.

[2055] Specific actions: The user reviews the order details displayed on the device interface, makes any necessary changes or corrections, and then presses the "Confirm" button.

[2056] Step 9:

[2057] The server will finalize the order on the online store based on the corrected information.

[2058] Input: Order details modified by the user.

[2059] Output: The final food order has been confirmed.

[2060] Specific operation: The server finalizes the modified order details and sends them again to the online store's API endpoint to complete the order.

[2061] (Application Example 1)

[2062] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[2063] Traditional menu planning and grocery ordering systems can take into account user preferences and allergy information, but they have struggled to comprehensively cover the delivery of ordered ingredients. Furthermore, users are required to shop for ingredients daily and review and modify their orders, which adds to their time and effort. A system that solves these problems and enhances user convenience is needed.

[2064] 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.

[2065] In this invention, the server includes means for the user to input family composition, food preferences, and allergy information; means for receiving and storing the input information; means for automatically generating a weekly menu based on the stored information; means for generating a list of necessary ingredients based on the automatically generated menu; means for automatically ordering ingredients from an online store based on the generated ingredient list; means for notifying the user of the automatically ordered items; and means for delivering the ingredients via a delivery service based on the ordered items. This makes it possible for a user to input family composition, food preferences, and allergy information and have the entire process, from automatically generating a menu to ordering ingredients and even delivery, handled in a consistent manner.

[2066] "A means for users to input family structure, food preferences, and allergy information" refers to an interface for users to input their family structure, the food preferences of individual members, and allergy information.

[2067] "Means for receiving and storing the input information" refers to a system for receiving information entered by a user and storing it in a database or similar.

[2068] "Means for automatically generating a weekly menu based on the stored information" refers to algorithms or software for automatically creating a nutritionally balanced weekly menu based on stored family composition, food preferences, and allergy information.

[2069] "Means for generating a list of necessary ingredients based on the automatically generated menu" refers to a program that lists the ingredients necessary for the automatically generated menu and generates that list.

[2070] "Means for automatically ordering ingredients from an online store based on the generated ingredient list" refers to a system that automatically orders ingredients using an online store's API or the like based on the generated ingredient list.

[2071] "Means for notifying the user of the automatically placed order" refers to an application or interface for notifying the user of the contents of the automatically placed order.

[2072] "Means of delivering ingredients via delivery service based on the aforementioned order details" refers to a system that utilizes online stores and delivery services selected based on the order details to deliver the necessary ingredients to the user's home.

[2073] System Configuration

[2074] This invention is a system that allows users to input their family structure, food preferences, and allergy information, automatically generates menus, automatically orders necessary ingredients from an online store, and even handles delivery in a seamless manner.

[2075] User registration method

[2076] The system provides an interface to the user's device (e.g., a smartphone or tablet) and includes a means for inputting family structure, food preferences, and allergy information. This information is transmitted to a server and stored in a database.

[2077] Information reception and storage means

[2078] The server receives information entered by the user and stores it in a database. For example, it stores it in a family structure database and an allergy information database.

[2079] Menu generation method

[2080] The server automatically generates a week's worth of menus using a generative AI model based on the stored information. During this process, prompts are used to instruct the AI ​​model, taking into account nutritional balance, family preferences, and allergy information.

[2081] Prompt example:

[2082] System: Please generate a weekly meal plan based on family composition, dietary preferences, and allergy information.

[2083] User input: Family composition is "husband 35 years old, wife 33 years old, child 5 years old". The wife has a peanut allergy, and the child likes vegetables.

[2084] System output: Monday dinner is chicken steak, Tuesday dinner is Japanese-style hamburger, Wednesday dinner is...

[2085] Ingredient list generation method

[2086] The server generates a list of necessary ingredients based on the automatically generated menu. For example, it might list ingredients such as 200g of chicken, 1 onion, and 2 carrots.

[2087] Automatic ordering method

[2088] The server automatically places an order via the online store's API based on the generated list of ingredients. At this time, it sends the order data according to the online store's API format.

[2089] Notification means

[2090] The server notifies the user's terminal of the automatically placed order. For example, it displays to the user, "Order details: 200g chicken, 1 onion, 2 carrots."

[2091] Verification and correction methods

[2092] The user reviews the notified order details and makes any necessary modifications. For example, the user can change chicken to fish to increase the number of fish dishes.

[2093] final means of confirmation

[2094] The server then confirms the user's modified order details again via the online store's API, and completes the order.

[2095] Delivery method

[2096] Based on the finalized order details, the server selects the appropriate delivery service and initiates the process of delivering the ingredients to the user's home.

[2097] Specific example

[2098] Registration process:

[2099] The user launches the smartphone app and enters their family structure as "husband 35 years old, wife 33 years old, child 5 years old," along with "wife has a peanut allergy" and "child likes vegetables."

[2100] Menu generation process:

[2101] The server looks at the database and generates a weekly menu that includes chicken steak for dinner on Monday and Japanese-style hamburger steak for dinner on Tuesday.

[2102] Ingredient list generation:

[2103] The server lists 200g of chicken, 1 onion, and 2 carrots.

[2104] Automatic ordering:

[2105] The server places orders for the generated ingredient list through the online store's API.

[2106] Notifications and confirmations:

[2107] The server notifies the user's terminal of the order details, and the user confirms and modifies the details.

[2108] Delivery process:

[2109] Online stores and delivery services deliver groceries to users' homes based on the finalized order details.

[2110] This significantly reduces the effort users spend on daily meal planning and grocery shopping, making it easy to provide well-balanced meals tailored to their family's needs.

[2111] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[2112] Step 1:

[2113] The user enters their family structure, food preferences, and allergy information.

[2114] Input: Family composition (e.g., "Husband 35 years old, wife 33 years old, child 5 years old"), food preferences (e.g., "Child likes vegetables"), and allergy information (e.g., "Wife is allergic to peanuts") are entered via a smartphone app.

[2115] Output: Input information is sent to the server.

[2116] Specific operation: The user enters information into the app's input screen and presses the "Submit" button, which sends the data to the server.

[2117] Step 2:

[2118] The server receives and stores the entered information.

[2119] Input: Family composition, food preferences, and allergy information submitted by the user.

[2120] Output: Family structure data, food preference data, and allergy information stored in the database.

[2121] Specific operation: The server saves the received information to the corresponding table in the database. For example, it adds data to the family structure table, preference information table, and allergy information table.

[2122] Step 3:

[2123] The server automatically generates a weekly meal plan based on the stored information.

[2124] Input: Family composition, food preferences, and allergy information stored in the database.

[2125] Output: A weekly meal plan list.

[2126] Specific operation: The server issues instructions to the generated AI model using prompt messages and receives a weekly menu that takes into account family composition, food preferences, and allergy information.

[2127] Step 4:

[2128] The server generates a list of necessary ingredients based on the automatically generated menu.

[2129] Input: A weekly meal plan.

[2130] Output: List of required ingredients.

[2131] Specific operation: The server analyzes the menu data and lists the ingredients and quantities needed for each meal. For example, 200g of chicken, 1 onion, 2 carrots, etc.

[2132] Step 5:

[2133] The server automatically places an order with the online store based on the generated list of ingredients.

[2134] Input: List of required ingredients.

[2135] Output: Order data sent to the online store.

[2136] Specific operation: The server uses the online store's API to convert the ingredient list into an order format and send the order data.

[2137] Step 6:

[2138] The server notifies the user of the automatically placed order.

[2139] Input: Submitted order data.

[2140] Output: An order confirmation notification displayed on the user's smartphone.

[2141] Specific operation: The server sends a push notification or email to the user's device to inform them of the order details.

[2142] Step 7:

[2143] The user reviews and modifies the order details they have been notified about.

[2144] Input: Order confirmation notification.

[2145] Output: Confirmed and corrected order details.

[2146] Specific actions: The user checks the notified order details in the app, adds or changes ingredients as needed, and presses the "Confirm" button.

[2147] Step 8:

[2148] The server then finalizes the grocery order to the online store based on the revised order details.

[2149] Input: Confirmed and corrected order details.

[2150] Output: Final order data sent to the online store.

[2151] Specific operation: The server resends the final order data, reflecting the user's modifications, to the online store's API to confirm the order.

[2152] Step 9:

[2153] The server then processes the delivery of ingredients based on the finalized order details.

[2154] Input: Last order data.

[2155] Output: Food items delivered to the user's home.

[2156] Specific operation: The server uses APIs from online stores and delivery services to process food delivery. It may also send delivery completion notifications to the user's smartphone.

[2157] 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.

[2158] This invention is a system that automatically generates menus and orders necessary ingredients from an online store by inputting family structure, food preferences, and allergy information, and combining this with an emotion engine. A key feature is that the emotion engine recognizes the user's emotions and adjusts the menu and notification content accordingly.

[2159] System Configuration

[2160] This system consists of the following components:

[2161] 1. User Registration Method

[2162] The device provides a user interface that allows users to input information such as family structure, food preferences, and allergies.

[2163] For example, the user enters information such as "husband 35 years old, wife 33 years old, child 5 years old," "wife has a peanut allergy," and "child likes vegetables."

[2164] 2. Data storage means

[2165] The server saves the entered information to the database.

[2166] For example, information entered by users can be saved to a family structure database or an allergy information database.

[2167] 3. Means of recognizing emotions (emotion engine)

[2168] The server recognizes emotions from the user's input data and behavioral history.

[2169] For example, if a user is feeling stressed, the system will detect it.

[2170] 4. Menu generation means

[2171] The server references stored information and recognized emotional data to automatically generate a weekly menu tailored to nutritional balance and family preferences.

[2172] For example, I suggest chicken steak for Monday's dinner, a comforting meal, and Japanese-style hamburger steak for Tuesday's dinner, which has a relaxing effect.

[2173] 5. Menu adjustment methods

[2174] The server adjusts the menu based on emotional information obtained by the emotion engine.

[2175] For example, if a user is feeling stressed, the system might suggest a dish with relaxing properties.

[2176] 6. Ingredient List Generation Method

[2177] The server creates a list of necessary ingredients based on the generated menu.

[2178] For example, list ingredients such as 200g of chicken, 1 onion, and 2 carrots.

[2179] 7. Automated ordering method

[2180] The server automatically orders the necessary ingredients via the online store's API.

[2181] For example, the generated ingredient list is sent to an online store in an order format.

[2182] 8. Means of notification

[2183] The server notifies the user's device of a menu based on their order and emotions.

[2184] For example, a notification message might appear stating, "Today, we suggest a relaxing Japanese-style hamburger steak."

[2185] 9. Verification and Correction Methods

[2186] The user reviews the notified order details and makes corrections as needed.

[2187] For example, a user can change chicken to fish to increase the number of fish dishes available.

[2188] 10. Final confirmation method

[2189] The server then sends the corrected order details to the online store and confirms the order.

[2190] Online supermarkets prepare groceries based on the order and deliver them to the user's home at the specified time.

[2191] Specific example

[2192] 1. Registration process:

[2193] The user launches the app and enters their family structure as "husband 35 years old, wife 33 years old, child 5 years old," along with "wife has a peanut allergy" and "child likes vegetables."

[2194] 2. Emotion recognition:

[2195] The server recognizes when a user is experiencing stress based on their behavioral history and input data.

[2196] 3. Menu generation:

[2197] Every Monday, the server accesses the database and generates a weekly menu that includes a relaxing chicken steak on Monday and a Japanese-style hamburger on Tuesday.

[2198] 4. Generate ingredient list:

[2199] The server lists 200g of chicken, 1 onion, and 2 carrots.

[2200] 5. Automatic ordering:

[2201] The server generates a list of ingredients and places an order through the online store's API.

[2202] 6. Notifications and confirmations:

[2203] The server notifies the user's terminal of the order details, and the user confirms and modifies the order.

[2204] For example, we could suggest a menu to reduce stress, and then the user could review it and make revisions to include more fish dishes.

[2205] 7. Delivery process:

[2206] The online supermarket delivers groceries to the user's home based on the finalized order details.

[2207] In this way, users can not only significantly reduce the effort involved in daily meal planning and shopping, but also achieve a more comfortable eating lifestyle by being provided with optimal meals tailored to their emotional state. By automating everything and making adjustments based on emotions, the system can improve the user's quality of life.

[2208] The following describes the processing flow.

[2209] Program processing steps

[2210] Registration process

[2211] Step 1:

[2212] The user opens the app and enters family information.

[2213] For example, enter "Husband 35 years old, wife 33 years old, child 5 years old".

[2214] The terminal sends the entered data to the server.

[2215] Step 2:

[2216] Users enter their food preferences and allergy information.

[2217] For example, enter "My wife has a peanut allergy" and "My child likes vegetables."

[2218] The terminal sends the entered data to the server.

[2219] Step 3:

[2220] The server saves the information it receives to the database.

[2221] Save the information in the family information table and the food preferences / allergy information table.

[2222] Emotion recognition process

[2223] Step 4:

[2224] While the user is using the app, the device collects the user's input and behavioral patterns.

[2225] For example, when a user performs many actions in a short period of time, or gives negative feedback about a particular dish.

[2226] Step 5:

[2227] The server analyzes the collected data, and the emotion engine evaluates the user's emotional state.

[2228] For example, the emotion engine might determine that the user is feeling stressed.

[2229] Menu generation process

[2230] Step 6:

[2231] Every Monday, the server starts processing menu generation requests according to a scheduled task.

[2232] Step 7:

[2233] The server retrieves family information, food preferences and allergy information, and emotional information from the database.

[2234] Step 8:

[2235] Based on the data acquired by the server, the system automatically generates a weekly meal plan by referring to a predefined nutritional balance and recipe database.

[2236] For example, I suggest chicken steak for Monday's dinner, a comforting meal, and Japanese-style hamburger steak for Tuesday's dinner, which has a relaxing effect.

[2237] Step 9:

[2238] The server adjusts the menu content it generates based on the emotional information obtained by the emotion engine.

[2239] For example, if a user is feeling stressed, they might choose a menu item that promotes relaxation.

[2240] Step 10:

[2241] The server saves the adjusted menu to the user's account in the database.

[2242] Ingredient list generation process

[2243] Step 11:

[2244] The server references the saved menu and lists the ingredients needed for each dish.

[2245] Step 12:

[2246] The server generates a list of necessary ingredients based on the menu.

[2247] As an example, list "200g of chicken," "1 onion," and "2 carrots."

[2248] Step 13:

[2249] The server optimizes itself to avoid duplication and waste by considering past purchase history and inventory information.

[2250] Collaboration with online supermarkets

[2251] Step 14:

[2252] The server uses the online supermarket's API to convert the generated grocery list into an order format.

[2253] Step 15:

[2254] The server sends order information, including the user's address and desired delivery time, to the online supermarket.

[2255] Notification and confirmation

[2256] Step 16:

[2257] The server sends a confirmation notification to the user's terminal containing the generated menu and ingredient order.

[2258] Step 17:

[2259] The user's device receives a notification, and the user confirms the menu and order details.

[2260] For example, "If you want to increase the number of fish dishes, change the chicken to fish."

[2261] Step 18:

[2262] The user makes corrections, and the device sends the corrected data to the server.

[2263] Delivery process

[2264] Step 19:

[2265] The server performs a final check and then sends the order details to the online supermarket.

[2266] Step 20:

[2267] Online supermarkets prepare groceries based on the order and deliver them to the user's home at the specified time.

[2268] In this way, users can significantly reduce the effort involved in daily meal planning and grocery shopping. Convenience is enhanced by automating everything and allowing users to handle tedious processes with minimal awareness. Furthermore, by utilizing an emotion engine, optimal meal plans are suggested based on the user's emotional state, leading to a more comfortable and enjoyable eating experience.

[2269] (Example 2)

[2270] 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".

[2271] Conventional menu generation systems generate menus based solely on the family composition, food preferences, and allergy information entered by the user, and therefore cannot provide optimal menus that take into account the user's emotional state. Furthermore, while it is important to provide meals that address a user's emotional state when they are stressed or experiencing a particular emotional condition, conventional systems failed to achieve this. In addition, the automatic ordering, confirmation, and correction functions for ingredients were insufficient, and the system could not fully reduce the burden on the user.

[2272] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[2273] In this invention, the server includes means for the user to input family composition, food preferences, and allergy information; server means for receiving and storing the input information; server means for automatically generating a weekly menu based on the stored information and the user's emotional state; server means for generating a list of necessary ingredients based on the automatically generated menu; server means for automatically ordering ingredients from an online store based on the generated ingredient list; and server means for notifying the user terminal of the automatically ordered items. This enables the provision of an optimal menu that takes the user's emotional state into consideration, as well as automatic ordering of ingredients and functions for confirmation and modification.

[2274] "User" refers to an individual or family member who uses the system.

[2275] "Family composition" refers to information about the people who belong to the user's household.

[2276] "Food preferences" refers to the types of ingredients and dishes that the user and their family enjoy consuming.

[2277] "Allergy information" refers to information about allergies held by the user and their family.

[2278] A "server" refers to a computer device that processes, stores, and transmits data within a system.

[2279] "Server configuration" refers to the software and hardware configuration that perform specific processing functions on a server.

[2280] "Automatic generation" refers to the process by which a system autonomously creates data according to a program.

[2281] "Emotional state" refers to the user's current psychological or emotional condition.

[2282] A "food ingredient list" refers to a list of ingredients needed based on the generated menu.

[2283] An "online store" refers to a website or service that allows users to purchase groceries and other items via the internet.

[2284] "Automated ordering" refers to the act of a system ordering specified ingredients from an online store without manual intervention from the user.

[2285] "Notification" refers to the process of transmitting information from a system to a user.

[2286] A "terminal" refers to a device (such as a smartphone or computer) that a user uses to access and operate a system.

[2287] "Confirmation and correction" refers to the act of a user reviewing the information they have been notified about and making changes as necessary.

[2288] "Final ingredient order" refers to the ingredient order that has been confirmed and finalized after user review and modification.

[2289] This invention relates to an automated menu generation system that combines a user's input of family structure, food preferences, and allergy information with an emotion engine. This system uses a server and terminals to provide an optimal menu tailored to the user's emotional state and enables automatic ordering of ingredients.

[2290] 1. User Registration Method

[2291] Users enter family composition, food preferences, and allergy information using devices such as smartphones or computers. These devices provide dedicated applications or web interfaces to facilitate information entry. For example, a user might launch the app and enter information such as "husband 35 years old, wife 33 years old, child 5 years old," "wife has a peanut allergy," and "child likes vegetables."

[2292] 2. Data storage means

[2293] The server saves user input information to a database. Specifically, the entered family structure and allergy information are stored in the corresponding database. The family structure database stores "Husband 35 years old, wife 33 years old, child 5 years old," and the allergy information database stores "Wife: Peanut allergy."

[2294] 3. Emotion recognition means

[2295] The server uses an emotion engine to recognize emotions based on the user's behavior history and input data. This emotion engine analyzes the user's daily behavior data and input information to detect emotions such as stress and joy. For example, if the server detects that the user frequently stays up late, it recognizes that the user is feeling stressed.

[2296] 4. Menu generation means

[2297] The server automatically generates a weekly meal plan based on stored family composition data and emotional information. The server takes emotional information into consideration to automatically generate a nutritionally balanced menu. For example, it might suggest a comforting chicken steak for Monday's dinner and a relaxing Japanese-style hamburger for Tuesday's dinner.

[2298] 5. Menu adjustment methods

[2299] Based on the results of the emotion engine, the server can adjust the menu to match the user's emotional state. For example, if the user is feeling stressed, it will suggest dishes with relaxing effects. This may include suggesting specific herbal teas or menu items containing ingredients known to relieve stress.

[2300] 6. Ingredient List Generation Method

[2301] The server creates a list of necessary ingredients based on the generated menu. This ingredient list specifically details the ingredients needed for each menu item. For example, it might list 200g of chicken, 1 onion, and 2 carrots.

[2302] 7. Automated ordering method

[2303] The server automatically places an order for the generated grocery list via the online store's API. The server sends the order details to the online store's API and confirms the order. For example, a grocery list is sent to an online supermarket, and the ordering process is automated.

[2304] 8. Means of notification

[2305] The server notifies the user's device of the order details and menu. For example, a notification such as "Today we suggest a relaxing Japanese-style hamburger steak" is sent to the user's device.

[2306] 9. Verification and Correction Methods

[2307] Users can review the notified order details and make modifications as needed. On their device screen, users can check the order details and, if necessary, change chicken to fish to increase the amount of fish dishes.

[2308] 10. Final confirmation method

[2309] The server then sends the revised order details to the online store to finalize the order. The online store prepares the ingredients based on the finalized order and delivers them to the user's home.

[2310] In this way, users can not only save time on daily meal planning and shopping, but also enjoy a more comfortable eating experience by being provided with optimal meals tailored to their emotional state. Because the system is fully automated, it is expected to improve the user's quality of life.

[2311] Specific example

[2312] Registration process: The user launches the app and enters their family structure as "husband 35 years old, wife 33 years old, child 5 years old," and states that "wife has a peanut allergy" and "child likes vegetables."

[2313] Emotion Recognition: The server recognizes when a user is experiencing stress based on their behavioral history and input data.

[2314] Menu generation: Every Monday, the server accesses the database and generates a weekly menu that includes relaxing chicken steak on Monday and Japanese-style hamburger steak on Tuesday.

[2315] Ingredient list generation: The server lists 200g of chicken, 1 onion, and 2 carrots.

[2316] Automated ordering: The server places an order for the generated ingredient list via the online store's API.

[2317] Notification and Confirmation: The server notifies the user of the order details on their device, and the user confirms and modifies the order. For example, the server might suggest a menu to reduce stress, and the user can confirm and modify it to include more fish dishes.

[2318] Delivery process: The online supermarket delivers the groceries to the user's home based on the finalized order.

[2319] This system enables the provision of optimal menus tailored to the user's emotional state, as well as automatic ordering, confirmation, and modification functions for ingredients.

[2320] The flow of the specific processing in Example 2 will be explained using Figure 13.

[2321] Step 1: The user enters family composition, food preferences, and allergy information via the device.

[2322] Input: The user enters family information such as "Husband 35 years old, wife 33 years old, child 5 years old" into the input form, allergy information such as "Wife has a peanut allergy," and food preference information such as "Child likes vegetables."

[2323] Data processing: The terminal formats the input data and prepares it for transmission to the server.

[2324] Output: The formatted dataset is sent to the server.

[2325] Step 2: The server receives the entered information and saves it to the database.

[2326] Input: Family composition, allergy information, and food preferences information sent from the device.

[2327] Data processing: The server performs appropriate registration operations in the database and stores the input data in the appropriate tables (family structure table, allergy table, food preference table).

[2328] Output: A save completion notification and information for verifying the saved data are generated.

[2329] Step 3: The server recognizes the user's emotions based on their behavioral history and input data.

[2330] Input: User activity log data and family structure, food preferences, and allergy information entered in Step 1.

[2331] Data Calculation: The emotion engine analyzes user behavior data to calculate emotional states such as stress and joy. Natural language processing and machine learning algorithms are used for these calculations.

[2332] Output: Data representing the user's current emotional state (e.g., high stress level).

[2333] Step 4: The server automatically generates a weekly meal plan based on the stored data and recognized emotion information.

[2334] Input: Family structure data, allergy information, food preferences, perceived emotional state.

[2335] Data processing: The menu generation algorithm generates nutritionally balanced menus based on this data. If the emotional state is stressed, dishes with relaxing effects are prioritized.

[2336] Output: Weekly meal plan data (e.g., Monday's dinner is chicken steak, Tuesday's dinner is Japanese-style hamburger steak).

[2337] Step 5: The server generates a list of necessary ingredients based on the automatically generated menu.

[2338] Input: Generated menu data.

[2339] Data processing: Extract information on ingredients needed for the menu and generate an ingredient list. Calculate the required amount of each ingredient and add it to the list.

[2340] Output: Ingredient list data (Example: 200g chicken, 1 onion, 2 carrots).

[2341] Step 6: The server places an automated order based on the ingredient list via the online store's API.

[2342] Input: Generated ingredient list data.

[2343] Data processing: Send order information to the online store API. Convert the ingredient list into an order format and send it.

[2344] Output: Order confirmation information and order ID.

[2345] Step 7: The server notifies the user terminal of the order details and the generated menu.

[2346] Input: Order confirmation information, order ID, generated menu data.

[2347] Data processing: Generate a notification message and send it to the user's terminal.

[2348] Output: Notification message saying, "Today, we suggest a relaxing Japanese-style hamburger steak."

[2349] Step 8: The user reviews the notified order details and makes any necessary corrections.

[2350] Input: Order details and menu data displayed on the user's terminal.

[2351] Specific actions: The user confirms the order details on the device screen and, if necessary, changes chicken to fish to increase the number of fish dishes.

[2352] Output: Modified order details data.

[2353] Step 9: The server finally sends the corrected order details to the online store and confirms the order.

[2354] Input: Modified order details data.

[2355] Data calculation: Generate final order data reflecting the changes and send it to the online store API.

[2356] Output: Final order confirmation information and shipping information.

[2357] Step 10: The online store prepares the ingredients based on the finalized order and delivers them to the user's home.

[2358] Input: Final order confirmation information and shipping information.

[2359] Specific operation: The online store prepares the ingredients based on the order and delivers them to the user's home at the specified date and time.

[2360] Output: Notification to the user that delivery is complete.

[2361] (Application Example 2)

[2362] 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".

[2363] Conventional menu generation systems automatically generate menus and order ingredients based on the user's food preferences and allergy information. However, they cannot adjust menus based on the user's emotional state or mental health, making it difficult to improve dietary habits in response to the user's stress and mood. Therefore, in order to further improve the user's quality of life, menu generation and automatic ordering that take emotional information into account are necessary.

[2364] 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.

[2365] In this invention, the server includes means for recognizing the user's emotional state using an emotion engine, means for automatically generating a weekly menu based on the stored information and emotional information, and means for automatically ordering ingredients from an online store based on the generated ingredient list. This makes it possible to adjust the menu and order the optimal ingredients based on the user's emotional state.

[2366] "Family structure" refers to information that describes the members of each family, including their ages and relationships.

[2367] "Food preferences" refers to information indicating the types of ingredients and dishes that the user or their family enjoys.

[2368] "Allergy information" refers to information about users or their family members who have allergic reactions to specific foods.

[2369] An "emotion engine" is a means of recognizing a user's emotional state from their input data and behavioral history.

[2370] A "menu" refers to a list of meals for one week.

[2371] A "food ingredient list" is a list of ingredients required based on a menu.

[2372] An "online store" is a store that provides services for selling food and goods via the internet.

[2373] "Notification" is the act of a system informing a user's device of information that requires their attention.

[2374] "Confirmation and Correction" refers to the process by which users reconfirm the information and order details provided by the system and make corrections as necessary.

[2375] "Confirming" means finalizing the revised order details and submitting them to the online store.

[2376] This invention is a system that automatically generates menus and orders necessary ingredients from an online store by having the user input family structure, food preferences, and allergy information, and combining this with an emotion engine. A key feature is that the emotion engine recognizes the user's emotions and adjusts the menus and notification content accordingly.

[2377] System Configuration

[2378] This system consists of the following components:

[2379] 1. User Registration Method

[2380] The server provides a user interface (UI) that allows users to input family structure, food preferences, and allergy information through their terminal.

[2381] For example, the user enters information such as "husband 35 years old, wife 33 years old, child 5 years old," "wife has a peanut allergy," and "child likes vegetables."

[2382] 2. Data storage means

[2383] The server saves the entered information to the database.

[2384] For example, information entered by users can be saved to a family structure database or an allergy information database.

[2385] 3. Means of recognizing emotions (emotion engine)

[2386] The server recognizes emotions from the user's input data and behavioral history.

[2387] For example, if a user is feeling stressed, the system will detect it.

[2388] 4. Menu generation means

[2389] The server automatically generates weekly meal plans tailored to nutritional balance and family preferences, based on stored information and emotional data.

[2390] For example, I suggest chicken steak for Monday's dinner, a comforting meal, and Japanese-style hamburger steak for Tuesday's dinner, which has a relaxing effect.

[2391] 5. Menu adjustment methods

[2392] The server adjusts the menu based on emotional information obtained by the emotion engine.

[2393] For example, if a user is feeling stressed, the system might suggest a dish with relaxing properties.

[2394] 6. Ingredient List Generation Method

[2395] The server creates a list of necessary ingredients based on the generated menu.

[2396] For example, list ingredients such as 200g of chicken, 1 onion, and 2 carrots.

[2397] 7. Automated ordering method

[2398] The server automatically orders the necessary ingredients via the online store's API.

[2399] For example, the generated ingredient list is sent to an online store in an order format.

[2400] 8. Means of notification

[2401] The server notifies the user's device of a menu based on their order and emotions.

[2402] For example, a notification message might appear stating, "Today, we suggest a relaxing Japanese-style hamburger steak."

[2403] 9. Verification and Correction Methods

[2404] The user reviews the notified order details and makes corrections as needed.

[2405] For example, a user can change chicken to fish to increase the number of fish dishes available.

[2406] 10. Final confirmation method

[2407] The server then sends the revised order details to the online store and confirms the order.

[2408] The online store prepares the ingredients based on the order and delivers them to the user's home at the specified time.

[2409] Specific examples of operation

[2410] 1. Registration process:

[2411] The user launches the app and enters their family structure as "husband 35 years old, wife 33 years old, child 5 years old," along with "wife has a peanut allergy" and "child likes vegetables."

[2412] 2. Emotion recognition:

[2413] The server recognizes when a user is experiencing stress based on their behavioral history and input data.

[2414] 3. Menu generation:

[2415] Every Monday, the server accesses the database and generates a weekly menu that includes a relaxing chicken steak on Monday and a Japanese-style hamburger on Tuesday.

[2416] 4. Generate ingredient list:

[2417] The server lists 200g of chicken, 1 onion, and 2 carrots.

[2418] 5. Automatic ordering:

[2419] The server generates a list of ingredients and places an order through the online store's API.

[2420] 6. Notifications and confirmations:

[2421] The server notifies the user's terminal of the order details, and the user confirms and modifies the order.

[2422] For example, we could suggest a menu to reduce stress, and then the user could review it and make revisions to include more fish dishes.

[2423] 7. Delivery process:

[2424] The online store delivers the groceries to the user's home based on the finalized order details.

[2425] Example of a prompt

[2426] Family composition: Husband 35 years old, wife 33 years old, child 5 years old

[2427] Food preferences: Children like vegetables.

[2428] Allergy information: My wife has a peanut allergy.

[2429] Emotions: Stress

[2430] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[2431] Step 1:

[2432] The user launches the smartphone app and enters information about their family structure, food preferences, and allergies.

[2433] Input details: Family composition (e.g., husband 35 years old, wife 33 years old, child 5 years old), food preferences (e.g., child likes vegetables), allergy information (e.g., wife has a peanut allergy)

[2434] Output: The terminal sends the input content to the server.

[2435] Step 2:

[2436] The server saves the received information to the database.

[2437] Input data: Family composition data, food preferences data, allergy information data

[2438] Output: User information stored in the database

[2439] Step 3:

[2440] The emotion engine analyzes the user's input data and behavioral history to recognize their emotional state.

[2441] Input content: User activity history, input data

[2442] Output: Emotional information (e.g., stress level)

[2443] Step 4:

[2444] The server automatically generates a weekly meal plan based on stored user information and sentiment data.

[2445] Input content: Database user information, sentiment information

[2446] Output: A week's menu (Example: Chicken steak on Monday, Japanese-style hamburger on Tuesday)

[2447] Step 5:

[2448] The server creates a list of necessary ingredients based on the generated menu.

[2449] Input content: Weekly meal plan

[2450] Output: Ingredient list (Example: 200g chicken, 1 onion, 2 carrots)

[2451] Step 6:

[2452] The server automatically places orders using the online store's API based on the ingredient list.

[2453] Input content: Ingredient list

[2454] Output: Order request to online store

[2455] Step 7:

[2456] The server notifies the user's terminal of the automatically placed order.

[2457] Input details: Order details

[2458] Output: Notification to your smartphone (e.g., today's menu and order details)

[2459] Step 8:

[2460] The user checks the notification content on their device and makes corrections as needed.

[2461] Input details: Notified order details

[2462] Output: Modified order details

[2463] Step 9:

[2464] The server will then finalize the grocery order with the online store based on the revised order details.

[2465] Input details: Modified order details

[2466] Output: Final order data for online store

[2467] Step 10:

[2468] The online store delivers the groceries to the user's home based on the finalized order details.

[2469] Input details: Final order data

[2470] Output: Food delivered to the user's home.

[2471] 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.

[2472] 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.

[2473] 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.

[2474] 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.

[2475] 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.

[2476] 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.

[2477] 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.

[2478] 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.

[2479] 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."

[2480] 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.

[2481] 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.

[2482] 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.

[2483] 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.

[2484] 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.

[2485] 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.

[2486] 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.

[2487] 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.

[2488] 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...

Claims

1. A means for users to input family structure, food preferences, and allergy information, Means for receiving and storing the input information, A means for automatically generating a weekly menu based on the aforementioned stored information, A means for generating a list of necessary ingredients based on the automatically generated menu, A means for automatically ordering ingredients from an online store based on the generated ingredient list, A system including means for notifying the user of the automatically placed order.

2. The system according to claim 1, further comprising means for the user to confirm and modify the notified order details.

3. The system according to claim 1, further comprising means for confirming the final food order with the online store based on the aforementioned modified order details.

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