System

Abstract

A system is provided.SOLUTION: A system comprising: means for inputting a family structure, a physical condition, a climate, past meals, and preferences of a user; means for receiving the input information; generative AI model means for generating a nutritionally balanced menu using the received information; and means for presenting the generated menu to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Planning a menu for home use often requires a great deal of time and effort. It is particularly difficult to provide nutritionally balanced meals that take into account the family structure, individual physical condition, and climate. Furthermore, in households with infants and young children, preparing appropriate baby food according to their age is often a burden on childcare. There is a need for a system that addresses these issues and allows users to efficiently prepare healthy meals. [Means for solving the problem]

[0005] The present invention provides a system including a generative AI model that receives and analyzes user input information such as family composition, physical condition, climate, past dietary habits, and preferences to generate nutritionally balanced meals. Specifically, the system includes a user input means, an information receiving means, a generative AI model means, and a means for presenting the generated menu to the user. The generative AI model also has functions for generating baby food recipes based on the child's age and selecting different ingredients and cooking methods based on past dietary habits. In this way, users can efficiently prepare nutritionally balanced meals at home.

[0006] "User" refers to a person who uses the system and inputs information such as family composition and health condition.

[0007] "Family composition" refers to information indicating the number of members in the household and their roles (adults, children, babies, etc.).

[0008] "Physical condition" refers to information indicating the health condition and symptoms of the user or family members (e.g., feeling a bit sick, feeling healthy, specific allergies, etc.).

[0009] "Climate" refers to information indicating the state of the external environment (e.g., season, temperature, humidity, etc.) when considering a menu.

[0010] "Past meals" refers to information detailing meals previously consumed by the user and family.

[0011] "Preferences" refers to information about ingredients and dishes that the user or their family likes, and ingredients and dishes that they dislike.

[0012] "Input means" refers to an interface that allows a user to input information such as family composition, physical condition, and weather into the system.

[0013] The "receiving means" refers to the function of the server receiving information input via the input means.

[0014] "Generative AI model" refers to the artificial intelligence algorithms and processes that generate optimal menus based on the information they receive.

[0015] "Presentation means" refers to an interface that presents the generated menu to the user visually or audibly.

[0016] "Baby food" refers to a specific diet provided to infants and young children according to their developmental stage, and consists primarily of soft, easily digestible ingredients.

[0017] "Menu" refers to a list of meals planned for consumption by a user and their family over a period of time.

[0018] "Nutritional balance" means that a particular meal contains the nutrients (e.g., protein, vitamins, minerals, etc.) needed daily in the right proportions. [Brief explanation of the drawings]

[0019] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8]FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0020] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0021] First, the terms used in the following description will be explained.

[0022] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0023] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0024] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0025] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0026] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0027] [First embodiment]

[0028] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0029] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0030] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0031] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0032] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0033] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0034] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0035] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0036] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0037] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0038] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0039] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0040] This invention provides a system that enables users to prepare nutritionally balanced meals at home, and is mainly composed of a user, a terminal, and a server.

[0041] Overall system overview

[0042] The user uses the device to input information such as family composition, physical condition, weather, past meals, and favorite and disliked ingredients. The device then sends this information to the server, which then uses a generative AI model to generate an optimal menu. The generated menu is then sent to the device and presented to the user.

[0043] Detailed System Description

[0044] User side

[0045] 1. The user launches an application on the device.

[0046] 2. The user enters the number of family members and their roles (e.g., two adults, one child, one baby), their recent health condition (e.g., feeling a bit under the weather), the current weather (e.g., winter), past meals (e.g., curry rice), favorite dishes and ingredients (e.g., Japanese food), and ingredients they dislike (e.g., spicy food).

[0047] 3. The device sends this information to the server.

[0048] Server side

[0049] 4. The server receives the input data.

[0050] 5. The server analyzes the received data and passes it to the generative AI model.

[0051] 6. The server generates the optimal menu using a generative AI model. The generative AI model takes into account:

[0052] Appropriate ingredients and portions based on family size and number of people.

[0053] A nutritionally balanced diet suited to your physical condition and the weather.

[0054] Consider your past dietary habits and change the ingredients and cooking methods.

[0055] A menu that reflects your likes and dislikes.

[0056] Suggestions for safe baby food according to the age of infants and toddlers.

[0057] Specific examples

[0058] The menu generated by the server looks like this:

[0059] For adults and a 5-year-old child: Chicken and vegetable hotpot, miso soup with lots of ingredients, and boiled spinach.

[0060] For babies (8 months): Softly simmered carrots and chicken, pumpkin puree.

[0061] Terminal side

[0062] 7. The server sends the generated menu to the terminal.

[0063] 8. The device visually presents the menu to the user, who can review the suggested menu and make adjustments or changes as needed.

[0064] Collect feedback (optional)

[0065] 9. After the meal, the user enters their satisfaction level and comments.

[0066] 10. The device sends the feedback information to the server.

[0067] 11. The server analyzes the received feedback and uses it as training data for the generative AI model. This feedback can improve the accuracy of the next menu suggestion.

[0068] Example

[0069] For example, if a user has a cold on a cold winter day, the system might suggest the following menu: a nutritious chicken and vegetable hotpot, a hearty miso soup, and a vitamin-rich spinach salad. For a baby, it might suggest softly simmered carrots and chicken, or pumpkin puree, which are easy to digest. This system allows users to efficiently provide healthy meals.

[0070] The processing flow will be explained below.

[0071] Step 1:

[0072] The user launches an application on the device.

[0073] Step 2:

[0074] The device displays an information input form to the user, which includes information such as family composition, physical condition, weather, past dietary habits, and food preferences and dislikes.

[0075] Step 3:

[0076] The user enters the required data into an information entry form, for example:

[0077] Family composition: 2 adults, 1 5-year-old child, 1 8-month-old baby

[0078] Health: I've been feeling a bit sick lately

[0079] Climate: Winter

[0080] Past meals: Curry rice

[0081] Favorite food: Japanese food

[0082] Foods I dislike: Spicy foods

[0083] Step 4:

[0084] The terminal collects information input by the user and transmits it to the server.

[0085] Step 5:

[0086] The server analyzes the information it receives and understands the individual's family composition, physical condition, weather, past dietary habits, and food preferences and dislikes.

[0087] Step 6:

[0088] The server calls the generative AI model based on the received data. The generative AI model generates a menu using the following steps:

[0089] Calculate the nutrients you need based on your family composition, physical condition, and climate.

[0090] Consider what you have eaten recently and select a menu that does not overlap with your previous meals.

[0091] Customize menus to reflect the user's preferences and dislikes.

[0092] We recommend appropriate baby foods based on the age of the infant.

[0093] Step 7:

[0094] The server uses a generative AI model to generate the optimal menu. For example, it generates the following menu:

[0095] For adults and children aged 5 and over: Chicken and vegetable hotpot, miso soup with lots of ingredients, and boiled spinach

[0096] For 8-month-old babies: soft-boiled carrots and chicken, pumpkin puree

[0097] Step 8:

[0098] The server sends the generated menu to the terminal.

[0099] Step 9:

[0100] The device visually displays the menu to the user, who can review the suggested menu and make adjustments or changes as needed.

[0101] Step 10:

[0102] (Optional) After the meal, the user enters feedback information, such as satisfaction level and comments.

[0103] Step 11:

[0104] (Optional) The device sends feedback information to the server.

[0105] Step 12:

[0106] (Optional) The server analyzes the received feedback and uses it as training data for the generative AI model, thereby improving the accuracy of the next menu suggestion.

[0107] The above is the flow of specific processing steps of this system.

[0108] Example 1

[0109] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0110] Traditionally, when preparing nutritionally balanced meals at home, users have had to spend time and effort creating menus that take into account information such as family composition, physical condition, weather, past dietary habits, and food preferences and dislikes. However, it is difficult to comprehensively assess these diverse factors and propose an appropriate menu each time. There is also a need for systems that can suggest baby foods that reflect changes in past dietary habits and the child's growth. This requires a system that allows users to create menus efficiently and accurately.

[0111] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0112] In this invention, the server includes means for inputting the user's family composition, physical condition, weather, past meal contents, and preferences, means for receiving the input information, means for analyzing the received information, means for passing the analyzed information to a generative AI model, means for generating an optimal menu using the generative AI model, and means for presenting the generated menu to the user, thereby enabling the user to efficiently and accurately prepare a nutritionally balanced menu that meets the needs of their family.

[0113] The "user" is the person who uses this system to plan a menu and inputs data such as information about their family and ingredients they like and dislike.

[0114] A "terminal" is a device that a user uses to input information and communicate with a server, such as a smartphone, tablet, or PC.

[0115] The "server" is a central processing unit that receives and analyzes information sent by users and generates menus using generative AI models.

[0116] "Family composition" refers to information such as the number, ages, and roles of members in the user's household, and is an important factor in generating menus.

[0117] "Physical condition" refers to the current health condition of the user or family, and includes a cold, a specific illness, allergies, etc.

[0118] "Climate" refers to the current weather conditions, such as information about how the suitability of a dish changes depending on the season or weather.

[0119] "Past meal content" refers to the history of meals the user or their family has eaten in the past, and is used to ensure variety in menus.

[0120] "Preferences" refers to the dishes and ingredients that the user or their family prefers, as well as things they want to avoid.

[0121] A "generative AI model" is an artificial intelligence model that generates optimal menus based on received information.

[0122] A "prompt" is a set of instructions input to a generative AI model, a document that details the necessary requirements and conditions.

[0123] "Feedback information" refers to information such as satisfaction and comments entered by the user after a meal, and is used to improve the accuracy of menu suggestions for the next time.

[0124] This invention provides a system that allows users to prepare nutritionally balanced meals at home, and is primarily composed of a user, a terminal, and a server. Specifically, the user uses the terminal to input information about their family composition, physical condition, weather, past meals, preferences, and disliked ingredients, and the terminal then sends this information to the server. The server then passes the received information to a generative AI model to generate an optimal menu. The generated menu is then sent to the terminal and presented to the user.

[0125] User operations

[0126] 1. A user launches an application on a device, such as a smartphone, tablet, or PC.

[0127] 2. The user enters information such as family composition (e.g., two adults, one child, one baby), physical condition (e.g., feeling a bit under the weather), weather (e.g., winter), past meals (e.g., curry rice), favorite dishes and ingredients (e.g., Japanese food), and disliked ingredients (e.g., spicy food). The device is equipped with an interface for entering information, and the user enters information using text boxes and drop-down menus.

[0128] Terminal handling

[0129] The device sends the information entered by the user to the server using an API, with the data formatted in JSON.

[0130] Server Processing

[0131] The server analyzes the information received from the device. A Python library (e.g., pandas) or a dedicated analysis module is used for the analysis. The analyzed information is then converted into a prompt statement format and passed to a generative AI model (e.g., OpenAI GPT-4). An example of a prompt statement is shown below.

[0132] Prompt Sentence Examples

[0133] "Our family consists of two adults, one five-year-old child, and one eight-month-old baby. I've been feeling a bit under the weather lately, so I'd like to know what kind of nutritionally balanced meal we can make for cold winter days. In the past, our meal was curry and rice. I like Japanese food, but I don't like spicy food."

[0134] Processing generative AI models

[0135] When the generative AI model receives the prompt, it generates an appropriate menu. Factors taken into consideration include family composition and number of people, physical condition, weather, past dietary habits, food preferences and dislikes, and baby food suggestions based on the infant's age. For example, the following menu may be generated:

[0136] Specific examples

[0137] For adults and a 5-year-old child: Chicken and vegetable hotpot, miso soup with lots of ingredients, and boiled spinach.

[0138] For babies (8 months): Softly simmered carrots and chicken, pumpkin puree.

[0139] Menu presentation

[0140] The server sends the generated menu to the device. The menu is formatted in JSON format or similar and sent to the device.

[0141] The device visually presents the menu to the user, who can review the suggested menu and make adjustments or changes as needed.

[0142] Collect feedback (optional)

[0143] After eating, users can enter feedback such as their satisfaction level and comments. The device sends this feedback information to the server, which analyzes the received feedback and reflects it as learning data for the generative AI model. This improves the accuracy of the next menu suggestion.

[0144] The above is a specific embodiment for carrying out the present invention.

[0145] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0146] Step 1:

[0147] The user launches an application on the device.

[0148] What happens: A user taps an application icon on a device such as a smartphone, tablet, or computer.

[0149] Input: Applications installed on the device.

[0150] Output: Application launch.

[0151] Step 2:

[0152] The user inputs information about family composition, physical condition, weather, past meals, preferences, and disliked ingredients.

[0153] What it does: Enter information into an application's input screen using text boxes and drop-down menus.

[0154] Input: User's family composition (e.g., 2 adults, 1 child, 1 baby), physical condition (e.g., feeling a bit under the weather), weather (e.g., winter), past meals (e.g., curry rice), preferences and disliked foods (e.g., likes Japanese food, dislikes spicy food), etc.

[0155] Output: The input data.

[0156] Step 3:

[0157] The terminal transmits the input information to the server.

[0158] How it works: After the user enters the information, they press the submit button, and the device sends the data to the server via the API. The data is formatted in JSON format.

[0159] Input: Information entered by the user.

[0160] Output: Information sent to the server.

[0161] Step 4:

[0162] The server analyzes the received information.

[0163] How it works: The server uses a parsing function to analyze the received data and break down each item (family composition, physical condition, climate, etc.). This analysis uses Python libraries (e.g., pandas).

[0164] Input: Information sent from the device.

[0165] Output: Parsed data.

[0166] Step 5:

[0167] The server passes the analyzed information to the generative AI model.

[0168] How it works: A prompt is generated based on the analysis data and passed to a generative AI model (e.g., OpenAI GPT-4). The prompt includes the analysis results.

[0169] Input: Analysis data.

[0170] Output: The prompt sentence that is passed to the generative AI model.

[0171] Step 6:

[0172] The server uses a generative AI model to generate the optimal menu.

[0173] How it works: The generative AI model receives prompts and generates appropriate menu suggestions based on family composition, physical condition, weather, past dietary habits, food preferences and dislikes, and the age of the infant.

[0174] Input: The prompt sentence passed to the generative AI model.

[0175] Output: The generated menu data.

[0176] Step 7:

[0177] The server sends the generated menu to the terminal.

[0178] Operation: The generated menu data is formatted in JSON format or similar and sent to the device.

[0179] Input: Generated menu data.

[0180] Output: Menu data sent to the device.

[0181] Step 8:

[0182] The terminal visually presents the menu to the user.

[0183] Operation: The device displays the menu data received within the application. The user can visually check the detailed information of each menu item.

[0184] Input: Menu data sent from the server.

[0185] Output: Menu information displayed on the screen.

[0186] Step 9 (Optional):

[0187] After the meal, the user enters their satisfaction level and comments.

[0188] How it works: The user enters their satisfaction and comments on the application's feedback screen and presses the submit button.

[0189] Input: User feedback information.

[0190] Output: The input feedback data.

[0191] Step 10 (Optional):

[0192] The terminal sends feedback information to the server.

[0193] Operation: The entered feedback information is sent to the server via the API.

[0194] Input: Feedback data entered by the user.

[0195] Output: Feedback information sent to the server.

[0196] Step 11 (Optional):

[0197] The server analyzes the feedback and reflects it in the generative AI model.

[0198] How it works: The server analyzes the feedback data and uses it to retrain the generative AI model, improving the accuracy of future menu suggestions.

[0199] Input: Feedback information sent from the device.

[0200] Output: An updated generative AI model.

[0201] (Application example 1)

[0202] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0203] In modern families, preparing nutritionally balanced meals with busy schedules and limited resources is challenging. It's particularly challenging to efficiently plan appropriate menus, as it requires consideration of family composition, health status, weather conditions, past dietary history, and preferences. Furthermore, the process of ordering meals based on the menu can be cumbersome. There is a need for a system that solves these problems, simplifies home meal preparation, and provides healthy meals.

[0204] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0205] In this invention, the server includes means for inputting a user's household composition, health condition, weather conditions, previous meal history, and preferences, means for receiving the input information, a generation AI model means for generating a nutritionally balanced menu using the received information, means for filtering and displaying information related to meal options suggested based on the generated menu, and means for ordering the selected meal options. This allows the user to receive a suggested menu optimal for their household composition and health condition, and to efficiently order meals based on the suggested menu.

[0206] "User" refers to an individual or household that uses the system.

[0207] "Family composition" refers to information indicating the number and roles of members in a household.

[0208] "Health status" refers to information about the health of the user and their family members, such as their recent physical condition and medical history.

[0209] "Weather conditions" refers to information about the current climate and season.

[0210] "Past dietary history" refers to the contents of meals that the user and their family have eaten in the past.

[0211] "Preferences" refers to information about dishes and ingredients that the user and their family like, as well as dishes and ingredients that they dislike or want to avoid.

[0212] "Input means" refers to devices or software that allow a user to provide information about their household configuration, health status, weather conditions, past dietary history, and preferences to the system.

[0213] "Means for receiving" refers to an interface for receiving input information on the server side.

[0214] "Generative AI model means" refers to artificial intelligence technology and its model for generating optimal menus based on received information.

[0215] "Filtering and display means" refers to functionality for filtering and displaying suggested meal options to the user based on the generated menu.

[0216] "Means for ordering" refers to a function that provides a process for the user to actually order the meal options selected.

[0217] Overall system overview

[0218] This system consists of a user, a device, and a server, and helps users prepare nutritionally balanced meals at home. Users use their device to input information about their household composition, health status, weather conditions, past meal history, and preferences, and the server uses a generative AI model to generate an optimal menu. Based on the menu, meal options are filtered and presented to the user. The user can then select the desired meal option and place an order.

[0219] User side

[0220] 1. The user launches an application on the device.

[0221] 2. The user enters information about their household, health condition, weather conditions, past meal history, preferences, budget, and desired delivery time.

[0222] 3. The device sends this information to the server.

[0223] Server side

[0224] 1. The server receives the input data.

[0225] 2. The server analyzes the received data and passes it to the generative AI model.

[0226] 3. The server generates the optimal menu using a generative AI model that takes into account:

[0227] Appropriate ingredients and portions based on household size and number of people.

[0228] A nutritionally balanced diet tailored to your health and weather conditions.

[0229] Consider your past eating history and change ingredients and cooking methods.

[0230] A menu that reflects your preferences and dislikes.

[0231] 4. The server filters the available meal options based on the generated menu and presents them to the user.

[0232] 5. The server provides the information to process the order for the meal options selected by the user.

[0233] Specific examples

[0234] For example, if a user has a cold on a cold winter day, the system may suggest the following menu items: nutritious chicken and vegetable hotpot, hearty miso soup, and vitamin-rich spinach ohitashi. For babies, it suggests easy-to-digest dishes such as softly simmered carrots and chicken, and pumpkin puree. Based on the suggested menu items, appropriate meal options are filtered and presented to the user. The user can then select the desired meal and complete the order.

[0235] Hardware and software used

[0236] Hardware: User device (smartphone or tablet), server.

[0237] Software: terminal application, server receiving interface, generative AI model (e.g., OpenAI's GPT), filtering and display functions, order processing system.

[0238] Prompt Sentence Examples

[0239] The user provided the following information:

[0240] Family composition: 2 adults, 1 child (age 5)

[0241] Health condition: I've been feeling a bit under the weather lately

[0242] Weather conditions: Winter

[0243] Past meal history: Curry rice

[0244] Favorite food: Japanese food

[0245] Foods I dislike: Spicy foods

[0246] Budget: 5,000 yen

[0247] Delivery time: 2023-11-20 18:30

[0248] Use this information to generate optimal menus and suggest food options for the user to order.

[0249] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0250] Step 1:

[0251] The user launches an application on the device.

[0252] Users input information about their household composition, health condition, weather conditions, past meal history, preferences, budget, and desired delivery time.

[0253] Input: household composition, health condition, weather conditions, past meal history, preferences, budget, desired delivery time.

[0254] Output: The input data on the terminal.

[0255] Step 2:

[0256] The device sends the input data to the server

[0257] The terminal transmits the information input by the user to the server as a data packet.

[0258] Input: Input data on the terminal.

[0259] Output: Data packets sent to the server.

[0260] Step 3:

[0261] The server receives the input data

[0262] The server receives the data packets sent from the terminal and performs preprocessing for analysis.

[0263] Input: The data packet sent to the server.

[0264] Output: Structured data to be parsed.

[0265] Step 4:

[0266] The server passes the data to the generative AI model

[0267] The server inputs the analyzed data into the generative AI model and requests it to generate the optimal menu. Specifically, the server passes the data to the generative AI model in the form of a "prompt sentence."

[0268] Input: Structured data.

[0269] Output: A prompt to the generative AI model.

[0270] Step 5:

[0271] Generative AI model generates optimal menu

[0272] The generative AI model generates the optimal menu based on prompts from the server, taking into account household composition, health status, weather conditions, past meal history, and preferences.

[0273] Input: Prompt statement.

[0274] Output: Optimal menu.

[0275] Step 6:

[0276] Filtering meal options based on server-generated menus

[0277] Based on the generated menu, the server selects the optimal meal options from affiliated restaurants and meal kit services and presents the filtered results to the user.

[0278] Input: Optimal menu.

[0279] Output: A filtered list of meal options.

[0280] Step 7:

[0281] The device presents meal options to the user.

[0282] The terminal displays the list of meal options received from the server to the user and prompts them to make a selection.

[0283] Input: A filtered list of meal options.

[0284] Output: The meal options presented to the user.

[0285] Step 8:

[0286] The user selects a meal option and places an order

[0287] The user selects the desired meal options from the presented options and confirms the order.

[0288] Input: A list of meal options.

[0289] Output: Confirmed order data.

[0290] Step 9:

[0291] The server processes the order data and sends the order information to the restaurant or meal kit service

[0292] The server processes the user's order data and transmits the order information to the appropriate restaurant or meal kit service.

[0293] Input: Confirmed order data.

[0294] Output: Order information to a restaurant or meal kit service.

[0295] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0296] This invention provides a system for users to prepare nutritionally balanced meals at home, and is mainly composed of a user, a terminal, a server, and an emotion engine.

[0297] Overall system overview

[0298] The user uses the device to input information such as family composition, physical condition, weather, past meals, and favorite and disliked ingredients, and the emotion engine then recognizes the user's emotions. The device then sends this information to the server, which then uses a generative AI model to generate an optimal menu. The generated menu is then sent to the device and presented to the user.

[0299] Detailed System Description

[0300] User side

[0301] 1. The user launches an application on the device.

[0302] 2. The user enters the number of family members and their roles (e.g., two adults, one child, one baby), their recent health condition (e.g., feeling a bit under the weather), the current weather (e.g., winter), past meals (e.g., curry rice), favorite dishes and ingredients (e.g., Japanese food), and ingredients they dislike (e.g., spicy food).

[0303] 3. The user's emotions are recognized from their voice and facial expressions through the camera and microphone installed on the device.

[0304] 4. The device sends this information (family composition, physical condition, weather, past meals, favorite and disliked ingredients, and recognized emotions) to the server.

[0305] Server side

[0306] 5. The server analyzes the received information to understand the user's family structure, physical condition, weather, past dietary habits, favorite and disliked ingredients, and emotional information.

[0307] 6. The server calls the generative AI model based on the received data. The generative AI model generates a menu using the following steps:

[0308] Calculate the nutrients you need based on your family composition, physical condition, and climate.

[0309] Consider what you have eaten recently and select a menu that does not overlap with your previous meals.

[0310] Customize menus to reflect the user's preferences and dislikes.

[0311] We recommend appropriate baby foods based on the age of the infant.

[0312] The recognized emotional information is used to adjust the menu to suit the user's mood.

[0313] Specific examples

[0314] The menu generated by the server looks like this:

[0315] For adults and children aged 5 and over: Chicken and vegetable hotpot, miso soup with lots of ingredients, and boiled spinach

[0316] For 8-month-old babies: soft-boiled carrots and chicken, pumpkin puree

[0317] For example, if the user has a tired expression, a menu using ingredients that have a relaxing effect may be suggested.

[0318] Terminal side

[0319] 7. The server sends the generated menu to the terminal.

[0320] 8. The device visually presents the menu to the user, who can review the suggested menu and make adjustments or changes as needed.

[0321] Collect feedback (optional)

[0322] 9. After the meal, the user enters their satisfaction level and comments.

[0323] 10. The device sends the feedback information to the server.

[0324] 11. The server analyzes the received feedback and uses it as training data for the generative AI model, thereby improving the accuracy of the next menu suggestion.

[0325] Example

[0326] For example, if a user has a cold on a cold winter day and looks tired, the system might suggest the following menu: a nutritious chicken and vegetable hotpot, a hearty miso soup, and a vitamin-rich spinach salad. For a baby, it might suggest softly simmered carrots and chicken, or pumpkin puree, which are easy to digest. This system allows users to efficiently provide healthy meals. Furthermore, the menu is adjusted based on the user's emotions, so the user is provided with a meal that suits their mood, improving meal satisfaction.

[0327] The processing flow will be explained below.

[0328] Step 1:

[0329] The user launches an application on the device.

[0330] Step 2:

[0331] The device displays an information input form to the user, which includes information such as family composition, physical condition, weather, past dietary habits, and food preferences and dislikes.

[0332] Step 3:

[0333] The user enters the required data into the information entry form, specifically the following information:

[0334] Family composition: 2 adults, 1 5-year-old child, 1 8-month-old baby

[0335] Health: I've been feeling a bit sick lately

[0336] Climate: Winter

[0337] Past meals: Curry rice

[0338] Favorite food: Japanese food

[0339] Foods I dislike: Spicy foods

[0340] Step 4:

[0341] The device stores the user's input data and activates the emotion engine, which activates the camera and microphone to recognize the user's facial expressions and voice.

[0342] Step 5:

[0343] The user speaks to the device or faces the device's camera, and their voice and facial expressions are collected. For example, in response to a question such as "How are you feeling today?", they can respond with "I'm a little tired."

[0344] Step 6:

[0345] The emotion engine analyzes the user's voice and facial expression data to recognize the user's emotional state (e.g., tired, stressed).

[0346] Step 7:

[0347] The terminal transmits the input information and the emotion information recognized by the emotion engine to the server.

[0348] Step 8:

[0349] The server analyzes the received information to understand the user's family composition, physical condition, weather, past dietary habits, favorite and disliked ingredients, and emotional information.

[0350] Step 9:

[0351] The server calls the generative AI model based on the received data. The generative AI model generates a menu using the following steps:

[0352] Calculate the nutrients you need based on your family composition, physical condition, and climate.

[0353] Consider what you have eaten recently and select a menu that does not overlap with your previous meals.

[0354] Customize menus to reflect the user's preferences and dislikes.

[0355] We recommend appropriate baby foods based on the age of the infant.

[0356] Recognized emotional information (tired, stressed, etc.) is used to tailor a menu to suit the user's mood.

[0357] Step 10:

[0358] The server generates the final menu and sends it to the device. For example, it generates the following menu:

[0359] For adults and children aged 5 and over: Chicken and vegetable hot pot (using relaxing dashi stock), miso soup with lots of ingredients, and boiled spinach

[0360] For 8-month-old babies: soft-boiled carrots and chicken, pumpkin puree

[0361] Step 11:

[0362] The device visually displays the generated menu to the user, who can review the suggested menu and make adjustments or changes as needed.

[0363] Step 12:

[0364] (Optional) After the meal, the user enters their satisfaction and feedback on the menu. Enter the feedback information according to the displayed form.

[0365] Step 13:

[0366] (Optional) The device sends feedback information to the server.

[0367] Step 14:

[0368] (Optional) The server analyzes the received feedback and uses it as training data for the generative AI model, thereby improving the accuracy of the next menu suggestion.

[0369] The above is the flow of specific processing steps of this system.

[0370] Example 2

[0371] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0372] In modern families, preparing nutritionally balanced meals is important, but it is not easy to consider a wide range of factors, such as family composition, health status, weather, past dietary habits, and preferences.In addition, there is a lack of systems that can provide appropriate meals for children's developmental stages or menus that suit the user's emotional state, making it difficult to cook accurately and efficiently.

[0373] The specification process by the specification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for inputting the user's family structure, physical condition, climate, past meal contents, emotions, and preferences, a means for receiving the input information, and a generation AI model means for generating a nutritionally balanced menu using the received information. This makes it possible to generate and present an optimal menu based on the user's family structure, health condition, climate, meal history, and emotional information.

[0374] "Family structure" refers to the number and roles of all individuals living together in the household.

[0375] "Physical condition" refers to the health condition of the user and their family, and includes illness, fatigue, stress, and other conditions.

[0376] "Climate" refers to current weather conditions and environmental factors such as season, temperature, and humidity.

[0377] "Past meals" refers to a record of meals eaten by the user and their family over a certain period of time in the past.

[0378] "Emotion" refers to the user's psychological state, including states such as fatigue, stress, and joy.

[0379] "Preferences" refers to the ingredients and dishes that the user and their family like, as well as ingredients that they dislike.

[0380] "Input means" refers to a device or interface that allows a user to input information, including a keyboard, a touch screen, a voice input device, etc.

[0381] "Means for receiving" refers to a mechanism by which the terminal transmits input information to the server and the server receives the information.

[0382] "Generative AI model means" refers to an artificial intelligence algorithm that analyzes received data and generates an optimal menu.

[0383] "Presentation means" refers to a device or interface for providing the generated menu to the user visually or audibly.

[0384] "Baby food recipes" refer to specific cooking methods for providing nutritionally balanced meals according to the developmental stage of infants and young children.

[0385] The "function to select ingredients and cooking methods" refers to a function that recommends new ingredients and cooking methods based on past meal content and the user's preferences.

[0386] This invention provides a system for users to prepare nutritionally balanced meals at home, and is mainly composed of a user, a terminal, a server, and an emotion engine.

[0387] User side

[0388] First, the user launches the application on their device, then enters information such as the number of family members and their roles (e.g., two adults, one child, and one baby), their recent health condition (e.g., feeling a bit under the weather), the current weather (e.g., winter), past meals (e.g., curry rice), favorite dishes and ingredients (e.g., Japanese food), and disliked ingredients (e.g., spicy food).

[0389] The emotion engine recognizes emotions from the user's voice and facial expressions through the device's built-in camera and microphone. For example, if the user is feeling tired or stressed, that information will be recognized.

[0390] Terminal side

[0391] The device sends this information (family composition, physical condition, weather, past dietary habits, favorite and disliked ingredients, and recognized emotions) to a server. At this time, the data is encrypted using security protocols (e.g., SSL / TLS) to ensure secure communication.

[0392] Server side

[0393] The server analyzes the received information to understand the family composition, health status, weather, past dietary habits, food preferences, dislikes, and emotional information. Based on the analysis results, a generative AI model is used to generate a menu using the following steps:

[0394] Generative AI model processing steps

[0395] 1. Calculate your nutritional needs based on your family composition, physical condition, and climate.

[0396] 2. Consider past meal choices and select a menu that does not overlap.

[0397] 3. Customize menus to reflect the user's preferences and dislikes.

[0398] 4. Suggest appropriate baby foods according to the infant's age.

[0399] 5. Use the recognized emotional information to tailor the menu to suit the user's mood.

[0400] As a concrete example, the menu generated by the server is as follows:

[0401] For adults and children aged 5 and over: Chicken and vegetable hotpot, miso soup with lots of ingredients, and boiled spinach

[0402] For 8-month-old babies: soft-boiled carrots and chicken, pumpkin puree

[0403] Display on the terminal

[0404] The server sends the generated menu to the device, which then visually presents the received menu to the user, who can then review the proposed menu and make adjustments or changes as needed. For example, the user can exclude certain ingredients from the proposed menu.

[0405] Collect feedback (optional)

[0406] After eating, users input their satisfaction level and comments. This information is sent to the server via their device. The server analyzes the received feedback and uses it as training data for the generative AI model to improve the accuracy of the next menu suggestion.

[0407] Prompt Sentence Examples

[0408] If the user looks tired on a winter's day, the server might suggest a warm, nutritious meal, such as chicken and vegetable hotpot, hearty miso soup, or boiled spinach.

[0409] This invention not only enables users to efficiently prepare nutritionally balanced meals, but also improves meal satisfaction because the menu is adjusted based on the user's emotions.

[0410] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0411] Step 1:

[0412] The user launches an application on the device.

[0413] Action: Tap or click an application to launch it.

[0414] Input: None.

[0415] Output: The application's welcome screen is displayed.

[0416] Step 2:

[0417] The user inputs information about their family composition, physical condition, weather, past meals, and favorite and disliked ingredients.

[0418] How it works: The user enters information into an input form displayed on the application screen, such as family composition (e.g., two adults, one child, one baby), recent health condition (e.g., feeling a bit under the weather), current weather (e.g., winter), past meals (e.g., curry rice), favorite dishes and ingredients (e.g., Japanese food), and disliked ingredients (e.g., spicy food).

[0419] Input: Information entered by the user.

[0420] Output: The entered information is saved on the device.

[0421] Step 3:

[0422] The device recognizes the user's emotions.

[0423] How it works: The device's built-in camera and microphone capture the user's voice and facial expressions, which are then analyzed using emotion recognition algorithms.

[0424] Input: User's voice data and facial expression data.

[0425] Output: Emotional information (e.g., fatigue, stress) is obtained as the analysis result.

[0426] Step 4:

[0427] The device sends this information (family composition, physical condition, weather, past meals, favorite and disliked ingredients, and recognized emotions) to the server.

[0428] Operation: The device compiles the input information and emotion information and sends it to the server. The data is encrypted using a communication protocol (e.g., SSL / TLS) before being sent.

[0429] Input: Aggregated user information and sentiment information.

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

[0431] Step 5:

[0432] The server analyzes the received information.

[0433] How it works: The server analyzes information about the user's family structure, physical condition, weather, past meals, favorite and disliked ingredients, and emotional information. It also compares this information with past data stored in a database.

[0434] Input: User information and emotion information sent to the server.

[0435] Output: As a result of the analysis, relevant data is extracted.

[0436] Step 6:

[0437] The server generates a menu using a generative AI model.

[0438] How it works: The server calls the generative AI model and generates a menu using the following steps:

[0439] Calculate the nutrients you need based on your family composition, physical condition, and climate.

[0440] Consider past meal choices and select a menu that does not overlap.

[0441] Customize menus to reflect the user's preferences and dislikes.

[0442] We recommend appropriate baby foods based on the age of the infant.

[0443] The recognized emotional information is used to adjust the menu to suit the user's mood.

[0444] Input: Parsed user information and sentiment information.

[0445] Output: The generated menu.

[0446] Step 7:

[0447] The server sends the generated menu to the terminal.

[0448] Operation: The server sends the generated menu data to the device. The data is sent securely using a communication protocol (e.g., HTTP / HTTPS).

[0449] Input: Generated menu data.

[0450] Output: Menu data is sent to the terminal.

[0451] Step 8:

[0452] The terminal presents the menu to the user.

[0453] Operation: The device visually displays the received menu to the user, for example, displaying the menu details on the application screen.

[0454] Input: Received menu data.

[0455] Output: The menu displayed to the user.

[0456] Step 9:

[0457] After the meal, the user enters their satisfaction and comments (optional).

[0458] What happens: A user fills out a feedback form in an application to rate their satisfaction and provide comments.

[0459] Input: User feedback information (e.g., taste rating, portion size, ease of cooking).

[0460] Output: Feedback information is saved to the device.

[0461] Step 10:

[0462] The terminal sends feedback information to the server.

[0463] Operation: The terminal sends the input feedback information to the server. The feedback information is also encrypted before being sent.

[0464] Input: User feedback information.

[0465] Output: The feedback information is sent to the server.

[0466] Step 11:

[0467] The server analyzes the feedback.

[0468] Operation: The server analyzes the feedback information it receives and uses it as training data for the generative AI model.

[0469] Input: Received feedback information.

[0470] Output: Training data used for next menu suggestion.

[0471] (Application example 2)

[0472] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0473] In modern households, it is difficult to plan an appropriate menu that takes into account each member's health condition, preferences, and weather. Providing a daily diet that satisfies all household members and is nutritionally balanced can be a burden. In particular, it is necessary to consider the effort required to plan a menu each time, including baby food, and to suggest meals that respond to the user's emotional state.

[0474] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0475] In this invention, the server includes means for inputting a user's family composition, physical condition, climate, past meal contents, and preferences, means for receiving the input information, a generation AI model means for generating a nutritionally balanced menu using the received information, means for recognizing the user's emotions, means for adjusting the menu based on the user's emotional information, and means for presenting the generated menu to the user. This not only enables the user to easily prepare a nutritionally balanced meal that suits the physical condition, preferences, and climate of each member, but also allows for a more satisfying meal by suggesting a menu that reflects the user's emotional state.

[0476] A "user" is a person who uses the system to manage meals for the household.

[0477] "Family structure" refers to the type and number of members in a household, including, for example, adults, children, babies, etc.

[0478] "Physical condition" refers to the current health condition of the user and their family, and includes, for example, having a cold or feeling tired.

[0479] "Climate" refers to the current weather conditions of the external environment, including, for example, winter, summer, rain, etc.

[0480] "Past meal contents" refers to the history of meals served at home, including specific dishes such as curry rice and salad.

[0481] "Preferences" refers to the likes and dislikes of the user and their family, and includes information such as liking Japanese food and dislike of spicy food.

[0482] "Input means" refers to a device or application that allows a user to provide information about family composition, physical condition, weather, past meals, and preferences to the system.

[0483] "Means for receiving" refers to a device or application that has the function of sending input information to a server and receiving it.

[0484] "Generative AI model means" refers to an artificial intelligence model for generating an optimal menu based on received information.

[0485] "Means for recognizing emotions" refers to devices such as cameras and microphones that can grasp emotions from the user's facial expressions and voice.

[0486] "Means for adjusting menu based on emotional information" refers to a function for adjusting menu based on the recognized emotional state of the user.

[0487] "Presentation means" refers to a device such as a display or speaker that provides the generated menu to the user visually or audibly.

[0488] The "system" refers to the overall mechanism that combines the above means to prepare nutritionally balanced meals in the user's home.

[0489] This invention is a system for users to prepare nutritionally balanced meals at home, and is mainly composed of a user, a terminal, a server, and an emotion engine. A specific embodiment of this system is described below.

[0490] Overall system overview

[0491] The user uses a device (such as a smartphone) to input information such as family composition, physical condition, weather, past meals, and favorite and disliked ingredients, and the emotion engine then recognizes the user's emotions. The device then sends this information to the server, which then uses a generative AI model to generate an optimal menu. The generated menu is then sent to the device and presented to the user visually or audibly.

[0492] Detailed System Description

[0493] User side

[0494] 1. The user launches the application on their device. The user inputs their own and their family's health status and preferences, and provides current weather information.

[0495] 2. The emotion engine recognizes emotions from the user's voice and facial expressions through the camera and microphone installed on the device.

[0496] 3. The device sends this information (family composition, physical condition, weather, past meals, favorite and disliked ingredients, and recognized emotions) to the server.

[0497] Server side

[0498] 1. The server analyzes the information it receives, including information about the user's family structure, physical condition, weather, past meals, favorite and disliked ingredients, and emotional information.

[0499] 2. The server generates an optimal menu by calling a generative AI model based on the received data. The generative AI model calculates necessary nutrients, takes into account recent meals, and customizes the menu to reflect the user's preferences and dislikes. It also uses recognized emotional information to adjust the menu to suit the user's mood.

[0500] Specific examples

[0501] For example, if the user has a cold on a cold winter day, the server might generate the following menu:

[0502] For adults and children: Chicken and vegetable hotpot, miso soup with lots of ingredients, and boiled spinach

[0503] For babies: soft boiled carrots and chicken, pumpkin puree

[0504] If the user is feeling tired, a menu containing ingredients that have a relaxing effect may be suggested.

[0505] Collect feedback (optional)

[0506] 1. After eating, the user enters their satisfaction level and comments.

[0507] 2. The device sends the feedback information to the server.

[0508] 3. The server analyzes the received feedback and uses it as training data for the generative AI model, thereby improving the accuracy of the next menu suggestion.

[0509] Prompt Sentence Examples

[0510] If the user is suffering from a cold on a cold winter day and looks tired, the system will suggest the following meal plans:

[0511] Family consists of 2 adults, 1 child, and 1 baby

[0512] Her favorite food is Japanese food, and her least favorite food is spicy food.

[0513] Users are feeling fatigued

[0514] My last meal was curry rice

[0515] Output:

[0516] For adults and children aged 5 and over: Chicken and vegetable hotpot, miso soup with lots of ingredients, and boiled spinach

[0517] For 8-month-old babies: soft-boiled carrots and chicken, pumpkin puree

[0518] In this way, the user can efficiently prepare healthy meals, and the satisfaction of the meal is improved because the menu is adjusted based on the user's emotional state.

[0519] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0520] Step 1:

[0521] The user launches the application on the device and inputs information such as family composition, physical condition, weather, past meals, and favorite and disliked ingredients. The necessary information is then saved as input data on the device.

[0522] Step 2:

[0523] The user uses the device's built-in camera and microphone to recognize their voice and facial expressions. The device then uses an emotion engine to recognize the user's emotions and generate emotion data. The emotion data is output in the form of, for example, "tired," "energetic," or "sad."

[0524] Step 3:

[0525] The device then sends the information entered in step 1, including family composition, physical condition, weather, past meals, and preferred and disliked ingredients, along with the emotional data generated in step 2, to the server.

[0526] Step 4:

[0527] The server analyzes the received information, obtaining information on family composition, physical condition, weather, past dietary habits, favorite and disliked ingredients, and the user's emotional state. Based on this information, the server preprocesses the data based on the relevant information in the database. Specifically, it checks the consistency of the data and begins calculating the necessary nutrients.

[0528] Step 5:

[0529] The server calls the generative AI model and passes the received data as input. The generative AI model performs the following calculations to generate a menu:

[0530] Calculate the nutrients you need based on your family composition, physical condition, and climate.

[0531] Consider what you have eaten recently and select a menu that does not overlap with your previous meals.

[0532] Customize menus to reflect the user's preferences and dislikes.

[0533] We suggest baby foods appropriate for the infant's age.

[0534] To adjust a menu to match a user's mood using emotional information.

[0535] Step 6:

[0536] The server transmits the generated menu to the terminal, which generates menu data that is visually displayed on the terminal.

[0537] Step 7:

[0538] The device presents the menu to the user, who can then view it on the device screen and make adjustments or changes as needed.

[0539] Step 8: (Optional)

[0540] After eating, users input their satisfaction level and comments, which generates feedback data.

[0541] Step 9: (Optional)

[0542] The device sends feedback information to the server, which analyzes the received feedback and uses it as training data for the generative AI model, thereby improving the accuracy of the next menu suggestion.

[0543] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0544] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0545] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0546] [Second embodiment]

[0547] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0548] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0549] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0550] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0551] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0552] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0553] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0554] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0555] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0556] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0557] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0558] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0559] This invention provides a system that enables users to prepare nutritionally balanced meals at home, and is mainly composed of a user, a terminal, and a server.

[0560] Overall system overview

[0561] The user uses the device to input information such as family composition, physical condition, weather, past meals, and favorite and disliked ingredients. The device then sends this information to the server, which then uses a generative AI model to generate an optimal menu. The generated menu is then sent to the device and presented to the user.

[0562] Detailed System Description

[0563] User side

[0564] 1. The user launches an application on the device.

[0565] 2. The user enters the number of family members and their roles (e.g., two adults, one child, one baby), their recent health condition (e.g., feeling a bit under the weather), the current weather (e.g., winter), past meals (e.g., curry rice), favorite dishes and ingredients (e.g., Japanese food), and ingredients they dislike (e.g., spicy food).

[0566] 3. The device sends this information to the server.

[0567] Server side

[0568] 4. The server receives the input data.

[0569] 5. The server analyzes the received data and passes it to the generative AI model.

[0570] 6. The server generates the optimal menu using a generative AI model. The generative AI model takes into account:

[0571] Appropriate ingredients and portions based on family size and number of people.

[0572] A nutritionally balanced diet suited to your physical condition and the weather.

[0573] Consider your past dietary habits and change the ingredients and cooking methods.

[0574] A menu that reflects your likes and dislikes.

[0575] Suggestions for safe baby food according to the age of infants and toddlers.

[0576] Specific examples

[0577] The menu generated by the server looks like this:

[0578] For adults and a 5-year-old child: Chicken and vegetable hotpot, miso soup with lots of ingredients, and boiled spinach.

[0579] For babies (8 months): Softly simmered carrots and chicken, pumpkin puree.

[0580] Terminal side

[0581] 7. The server sends the generated menu to the terminal.

[0582] 8. The device visually presents the menu to the user, who can review the suggested menu and make adjustments or changes as needed.

[0583] Collect feedback (optional)

[0584] 9. After the meal, the user enters their satisfaction level and comments.

[0585] 10. The device sends the feedback information to the server.

[0586] 11. The server analyzes the received feedback and uses it as training data for the generative AI model. This feedback can improve the accuracy of the next menu suggestion.

[0587] Example

[0588] For example, if a user has a cold on a cold winter day, the system might suggest the following menu: a nutritious chicken and vegetable hotpot, a hearty miso soup, and a vitamin-rich spinach salad. For a baby, it might suggest softly simmered carrots and chicken, or pumpkin puree, which are easy to digest. This system allows users to efficiently provide healthy meals.

[0589] The processing flow will be explained below.

[0590] Step 1:

[0591] The user launches an application on the device.

[0592] Step 2:

[0593] The device displays an information input form to the user, which includes information such as family composition, physical condition, weather, past dietary habits, and food preferences and dislikes.

[0594] Step 3:

[0595] The user enters the required data into an information entry form, for example:

[0596] Family composition: 2 adults, 1 5-year-old child, 1 8-month-old baby

[0597] Health: I've been feeling a bit sick lately

[0598] Climate: Winter

[0599] Past meals: Curry rice

[0600] Favorite food: Japanese food

[0601] Foods I dislike: Spicy foods

[0602] Step 4:

[0603] The terminal collects information input by the user and transmits it to the server.

[0604] Step 5:

[0605] The server analyzes the information it receives and understands the individual's family composition, physical condition, weather, past dietary habits, and food preferences and dislikes.

[0606] Step 6:

[0607] The server calls the generative AI model based on the received data. The generative AI model generates a menu using the following steps:

[0608] Calculate the nutrients you need based on your family composition, physical condition, and climate.

[0609] Consider what you have eaten recently and select a menu that does not overlap with your previous meals.

[0610] Customize menus to reflect the user's preferences and dislikes.

[0611] We recommend appropriate baby foods based on the age of the infant.

[0612] Step 7:

[0613] The server uses a generative AI model to generate the optimal menu. For example, it generates the following menu:

[0614] For adults and children aged 5 and over: Chicken and vegetable hotpot, miso soup with lots of ingredients, and boiled spinach

[0615] For 8-month-old babies: soft-boiled carrots and chicken, pumpkin puree

[0616] Step 8:

[0617] The server sends the generated menu to the terminal.

[0618] Step 9:

[0619] The device visually displays the menu to the user, who can review the suggested menu and make adjustments or changes as needed.

[0620] Step 10:

[0621] (Optional) After the meal, the user enters feedback information, such as satisfaction level and comments.

[0622] Step 11:

[0623] (Optional) The device sends feedback information to the server.

[0624] Step 12:

[0625] (Optional) The server analyzes the received feedback and uses it as training data for the generative AI model, thereby improving the accuracy of the next menu suggestion.

[0626] The above is the flow of specific processing steps of this system.

[0627] Example 1

[0628] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0629] Traditionally, when preparing nutritionally balanced meals at home, users have had to spend time and effort creating menus that take into account information such as family composition, physical condition, weather, past dietary habits, and food preferences and dislikes. However, it is difficult to comprehensively assess these diverse factors and propose an appropriate menu each time. There is also a need for systems that can suggest baby foods that reflect changes in past dietary habits and the child's growth. This requires a system that allows users to create menus efficiently and accurately.

[0630] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0631] In this invention, the server includes means for inputting the user's family composition, physical condition, weather, past meal contents, and preferences, means for receiving the input information, means for analyzing the received information, means for passing the analyzed information to a generative AI model, means for generating an optimal menu using the generative AI model, and means for presenting the generated menu to the user, thereby enabling the user to efficiently and accurately prepare a nutritionally balanced menu that meets the needs of their family.

[0632] The "user" is the person who uses this system to plan a menu and inputs data such as information about their family and ingredients they like and dislike.

[0633] A "terminal" is a device that a user uses to input information and communicate with a server, such as a smartphone, tablet, or PC.

[0634] The "server" is a central processing unit that receives and analyzes information sent by users and generates menus using generative AI models.

[0635] "Family composition" refers to information such as the number, ages, and roles of members in the user's household, and is an important factor in generating menus.

[0636] "Physical condition" refers to the current health condition of the user or family, and includes a cold, a specific illness, allergies, etc.

[0637] "Climate" refers to the current weather conditions, such as information about how the suitability of a dish changes depending on the season or weather.

[0638] "Past meal content" refers to the history of meals the user or their family has eaten in the past, and is used to ensure variety in menus.

[0639] "Preferences" refers to the dishes and ingredients that the user or their family prefers, as well as things they want to avoid.

[0640] A "generative AI model" is an artificial intelligence model that generates optimal menus based on received information.

[0641] A "prompt" is a set of instructions input to a generative AI model, a document that details the necessary requirements and conditions.

[0642] "Feedback information" refers to information such as satisfaction and comments entered by the user after a meal, and is used to improve the accuracy of menu suggestions for the next time.

[0643] This invention provides a system that allows users to prepare nutritionally balanced meals at home, and is primarily composed of a user, a terminal, and a server. Specifically, the user uses the terminal to input information about their family composition, physical condition, weather, past meals, preferences, and disliked ingredients, and the terminal then sends this information to the server. The server then passes the received information to a generative AI model to generate an optimal menu. The generated menu is then sent to the terminal and presented to the user.

[0644] User operations

[0645] 1. A user launches an application on a device, such as a smartphone, tablet, or PC.

[0646] 2. The user enters information such as family composition (e.g., two adults, one child, one baby), physical condition (e.g., feeling a bit under the weather), weather (e.g., winter), past meals (e.g., curry rice), favorite dishes and ingredients (e.g., Japanese food), and disliked ingredients (e.g., spicy food). The device is equipped with an interface for entering information, and the user enters information using text boxes and drop-down menus.

[0647] Terminal handling

[0648] The device sends the information entered by the user to the server using an API, with the data formatted in JSON.

[0649] Server Processing

[0650] The server analyzes the information received from the device. A Python library (e.g., pandas) or a dedicated analysis module is used for the analysis. The analyzed information is then converted into a prompt statement format and passed to a generative AI model (e.g., OpenAI GPT-4). An example of a prompt statement is shown below.

[0651] Prompt Sentence Examples

[0652] "Our family consists of two adults, one five-year-old child, and one eight-month-old baby. I've been feeling a bit under the weather lately, so I'd like to know what kind of nutritionally balanced meal we can make for cold winter days. In the past, our meal was curry and rice. I like Japanese food, but I don't like spicy food."

[0653] Processing generative AI models

[0654] When the generative AI model receives the prompt, it generates an appropriate menu. Factors taken into consideration include family composition and number of people, physical condition, weather, past dietary habits, food preferences and dislikes, and baby food suggestions based on the infant's age. For example, the following menu may be generated:

[0655] Specific examples

[0656] For adults and a 5-year-old child: Chicken and vegetable hotpot, miso soup with lots of ingredients, and boiled spinach.

[0657] For babies (8 months): Softly simmered carrots and chicken, pumpkin puree.

[0658] Menu presentation

[0659] The server sends the generated menu to the device. The menu is formatted in JSON format or similar and sent to the device.

[0660] The device visually presents the menu to the user, who can review the suggested menu and make adjustments or changes as needed.

[0661] Collect feedback (optional)

[0662] After eating, users can enter feedback such as their satisfaction level and comments. The device sends this feedback information to the server, which analyzes the received feedback and reflects it as learning data for the generative AI model. This improves the accuracy of the next menu suggestion.

[0663] The above is a specific embodiment for carrying out the present invention.

[0664] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0665] Step 1:

[0666] The user launches an application on the device.

[0667] What happens: A user taps an application icon on a device such as a smartphone, tablet, or computer.

[0668] Input: Applications installed on the device.

[0669] Output: Application launch.

[0670] Step 2:

[0671] The user inputs information about family composition, physical condition, weather, past meals, preferences, and disliked ingredients.

[0672] What it does: Enter information into an application's input screen using text boxes and drop-down menus.

[0673] Input: User's family composition (e.g., 2 adults, 1 child, 1 baby), physical condition (e.g., feeling a bit under the weather), weather (e.g., winter), past meals (e.g., curry rice), preferences and disliked foods (e.g., likes Japanese food, dislikes spicy food), etc.

[0674] Output: The input data.

[0675] Step 3:

[0676] The terminal transmits the input information to the server.

[0677] How it works: After the user enters the information, they press the submit button, and the device sends the data to the server via the API. The data is formatted in JSON format.

[0678] Input: Information entered by the user.

[0679] Output: Information sent to the server.

[0680] Step 4:

[0681] The server analyzes the received information.

[0682] How it works: The server uses a parsing function to analyze the received data and break down each item (family composition, physical condition, climate, etc.). This analysis uses Python libraries (e.g., pandas).

[0683] Input: Information sent from the device.

[0684] Output: Parsed data.

[0685] Step 5:

[0686] The server passes the analyzed information to the generative AI model.

[0687] How it works: A prompt is generated based on the analysis data and passed to a generative AI model (e.g., OpenAI GPT-4). The prompt includes the analysis results.

[0688] Input: Analysis data.

[0689] Output: The prompt sentence that is passed to the generative AI model.

[0690] Step 6:

[0691] The server uses a generative AI model to generate the optimal menu.

[0692] How it works: The generative AI model receives prompts and generates appropriate menu suggestions based on family composition, physical condition, weather, past dietary habits, food preferences and dislikes, and the age of the infant.

[0693] Input: The prompt sentence passed to the generative AI model.

[0694] Output: The generated menu data.

[0695] Step 7:

[0696] The server sends the generated menu to the terminal.

[0697] Operation: The generated menu data is formatted in JSON format or similar and sent to the device.

[0698] Input: Generated menu data.

[0699] Output: Menu data sent to the device.

[0700] Step 8:

[0701] The terminal visually presents the menu to the user.

[0702] Operation: The device displays the menu data received within the application. The user can visually check the detailed information of each menu item.

[0703] Input: Menu data sent from the server.

[0704] Output: Menu information displayed on the screen.

[0705] Step 9 (Optional):

[0706] After the meal, the user enters their satisfaction level and comments.

[0707] How it works: The user enters their satisfaction and comments on the application's feedback screen and presses the submit button.

[0708] Input: User feedback information.

[0709] Output: The input feedback data.

[0710] Step 10 (Optional):

[0711] The terminal sends feedback information to the server.

[0712] Operation: The entered feedback information is sent to the server via the API.

[0713] Input: Feedback data entered by the user.

[0714] Output: Feedback information sent to the server.

[0715] Step 11 (Optional):

[0716] The server analyzes the feedback and reflects it in the generative AI model.

[0717] How it works: The server analyzes the feedback data and uses it to retrain the generative AI model, improving the accuracy of future menu suggestions.

[0718] Input: Feedback information sent from the device.

[0719] Output: An updated generative AI model.

[0720] (Application example 1)

[0721] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0722] In modern families, preparing nutritionally balanced meals with busy schedules and limited resources is challenging. It's particularly challenging to efficiently plan appropriate menus, as it requires consideration of family composition, health status, weather conditions, past dietary history, and preferences. Furthermore, the process of ordering meals based on the menu can be cumbersome. There is a need for a system that solves these problems, simplifies home meal preparation, and provides healthy meals.

[0723] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0724] In this invention, the server includes means for inputting a user's household composition, health condition, weather conditions, previous meal history, and preferences, means for receiving the input information, a generation AI model means for generating a nutritionally balanced menu using the received information, means for filtering and displaying information related to meal options suggested based on the generated menu, and means for ordering the selected meal options. This allows the user to receive a suggested menu optimal for their household composition and health condition, and to efficiently order meals based on the suggested menu.

[0725] "User" refers to an individual or household that uses the system.

[0726] "Family composition" refers to information indicating the number and roles of members in a household.

[0727] "Health status" refers to information about the health of the user and their family members, such as their recent physical condition and medical history.

[0728] "Weather conditions" refers to information about the current climate and season.

[0729] "Past dietary history" refers to the contents of meals that the user and their family have eaten in the past.

[0730] "Preferences" refers to information about dishes and ingredients that the user and their family like, as well as dishes and ingredients that they dislike or want to avoid.

[0731] "Input means" refers to devices or software that allow a user to provide information about their household configuration, health status, weather conditions, past dietary history, and preferences to the system.

[0732] "Means for receiving" refers to an interface for receiving input information on the server side.

[0733] "Generative AI model means" refers to artificial intelligence technology and its model for generating optimal menus based on received information.

[0734] "Filtering and display means" refers to functionality for filtering and displaying suggested meal options to the user based on the generated menu.

[0735] "Means for ordering" refers to a function that provides a process for the user to actually order the meal options selected.

[0736] Overall system overview

[0737] This system consists of a user, a device, and a server, and helps users prepare nutritionally balanced meals at home. Users use their device to input information about their household composition, health status, weather conditions, past meal history, and preferences, and the server uses a generative AI model to generate an optimal menu. Based on the menu, meal options are filtered and presented to the user. The user can then select the desired meal option and place an order.

[0738] User side

[0739] 1. The user launches an application on the device.

[0740] 2. The user enters information about their household, health condition, weather conditions, past meal history, preferences, budget, and desired delivery time.

[0741] 3. The device sends this information to the server.

[0742] Server side

[0743] 1. The server receives the input data.

[0744] 2. The server analyzes the received data and passes it to the generative AI model.

[0745] 3. The server generates the optimal menu using a generative AI model that takes into account:

[0746] Appropriate ingredients and portions based on household size and number of people.

[0747] A nutritionally balanced diet tailored to your health and weather conditions.

[0748] Consider your past eating history and change ingredients and cooking methods.

[0749] A menu that reflects your preferences and dislikes.

[0750] 4. The server filters the available meal options based on the generated menu and presents them to the user.

[0751] 5. The server provides the information to process the order for the meal options selected by the user.

[0752] Specific examples

[0753] For example, if a user has a cold on a cold winter day, the system may suggest the following menu items: nutritious chicken and vegetable hotpot, hearty miso soup, and vitamin-rich spinach ohitashi. For babies, it suggests easy-to-digest dishes such as softly simmered carrots and chicken, and pumpkin puree. Based on the suggested menu items, appropriate meal options are filtered and presented to the user. The user can then select the desired meal and complete the order.

[0754] Hardware and software used

[0755] Hardware: User device (smartphone or tablet), server.

[0756] Software: terminal application, server receiving interface, generative AI model (e.g., OpenAI's GPT), filtering and display functions, order processing system.

[0757] Prompt Sentence Examples

[0758] The user provided the following information:

[0759] Family composition: 2 adults, 1 child (age 5)

[0760] Health condition: I've been feeling a bit under the weather lately

[0761] Weather conditions: Winter

[0762] Past meal history: Curry rice

[0763] Favorite food: Japanese food

[0764] Foods I dislike: Spicy foods

[0765] Budget: 5,000 yen

[0766] Delivery time: 2023-11-20 18:30

[0767] Use this information to generate optimal menus and suggest food options for the user to order.

[0768] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0769] Step 1:

[0770] The user launches an application on the device.

[0771] Users input information about their household composition, health condition, weather conditions, past meal history, preferences, budget, and desired delivery time.

[0772] Input: household composition, health condition, weather conditions, past meal history, preferences, budget, desired delivery time.

[0773] Output: The input data on the terminal.

[0774] Step 2:

[0775] The device sends the input data to the server

[0776] The terminal transmits the information input by the user to the server as a data packet.

[0777] Input: Input data on the terminal.

[0778] Output: Data packets sent to the server.

[0779] Step 3:

[0780] The server receives the input data

[0781] The server receives the data packets sent from the terminal and performs preprocessing for analysis.

[0782] Input: The data packet sent to the server.

[0783] Output: Structured data to be parsed.

[0784] Step 4:

[0785] The server passes the data to the generative AI model

[0786] The server inputs the analyzed data into the generative AI model and requests it to generate the optimal menu. Specifically, the server passes the data to the generative AI model in the form of a "prompt sentence."

[0787] Input: Structured data.

[0788] Output: A prompt to the generative AI model.

[0789] Step 5:

[0790] Generative AI model generates optimal menu

[0791] The generative AI model generates the optimal menu based on prompts from the server, taking into account household composition, health status, weather conditions, past meal history, and preferences.

[0792] Input: Prompt statement.

[0793] Output: Optimal menu.

[0794] Step 6:

[0795] Filtering meal options based on server-generated menus

[0796] Based on the generated menu, the server selects the optimal meal options from affiliated restaurants and meal kit services and presents the filtered results to the user.

[0797] Input: Optimal menu.

[0798] Output: A filtered list of meal options.

[0799] Step 7:

[0800] The device presents meal options to the user.

[0801] The terminal displays the list of meal options received from the server to the user and prompts them to make a selection.

[0802] Input: A filtered list of meal options.

[0803] Output: The meal options presented to the user.

[0804] Step 8:

[0805] The user selects a meal option and places an order

[0806] The user selects the desired meal options from the presented options and confirms the order.

[0807] Input: A list of meal options.

[0808] Output: Confirmed order data.

[0809] Step 9:

[0810] The server processes the order data and sends the order information to the restaurant or meal kit service

[0811] The server processes the user's order data and transmits the order information to the appropriate restaurant or meal kit service.

[0812] Input: Confirmed order data.

[0813] Output: Order information to a restaurant or meal kit service.

[0814] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0815] This invention provides a system for users to prepare nutritionally balanced meals at home, and is mainly composed of a user, a terminal, a server, and an emotion engine.

[0816] Overall system overview

[0817] The user uses the device to input information such as family composition, physical condition, weather, past meals, and favorite and disliked ingredients, and the emotion engine then recognizes the user's emotions. The device then sends this information to the server, which then uses a generative AI model to generate an optimal menu. The generated menu is then sent to the device and presented to the user.

[0818] Detailed System Description

[0819] User side

[0820] 1. The user launches an application on the device.

[0821] 2. The user enters the number of family members and their roles (e.g., two adults, one child, one baby), their recent health condition (e.g., feeling a bit under the weather), the current weather (e.g., winter), past meals (e.g., curry rice), favorite dishes and ingredients (e.g., Japanese food), and ingredients they dislike (e.g., spicy food).

[0822] 3. The user's emotions are recognized from their voice and facial expressions through the camera and microphone installed on the device.

[0823] 4. The device sends this information (family composition, physical condition, weather, past meals, favorite and disliked ingredients, and recognized emotions) to the server.

[0824] Server side

[0825] 5. The server analyzes the received information to understand the user's family structure, physical condition, weather, past dietary habits, favorite and disliked ingredients, and emotional information.

[0826] 6. The server calls the generative AI model based on the received data. The generative AI model generates a menu using the following steps:

[0827] Calculate the nutrients you need based on your family composition, physical condition, and climate.

[0828] Consider what you have eaten recently and select a menu that does not overlap with your previous meals.

[0829] Customize menus to reflect the user's preferences and dislikes.

[0830] We recommend appropriate baby foods based on the age of the infant.

[0831] The recognized emotional information is used to adjust the menu to suit the user's mood.

[0832] Specific examples

[0833] The menu generated by the server looks like this:

[0834] For adults and children aged 5 and over: Chicken and vegetable hotpot, miso soup with lots of ingredients, and boiled spinach

[0835] For 8-month-old babies: soft-boiled carrots and chicken, pumpkin puree

[0836] For example, if the user has a tired expression, a menu using ingredients that have a relaxing effect may be suggested.

[0837] Terminal side

[0838] 7. The server sends the generated menu to the terminal.

[0839] 8. The device visually presents the menu to the user, who can review the suggested menu and make adjustments or changes as needed.

[0840] Collect feedback (optional)

[0841] 9. After the meal, the user enters their satisfaction level and comments.

[0842] 10. The device sends the feedback information to the server.

[0843] 11. The server analyzes the received feedback and uses it as training data for the generative AI model, thereby improving the accuracy of the next menu suggestion.

[0844] Example

[0845] For example, if a user has a cold on a cold winter day and looks tired, the system might suggest the following menu: a nutritious chicken and vegetable hotpot, a hearty miso soup, and a vitamin-rich spinach salad. For a baby, it might suggest softly simmered carrots and chicken, or pumpkin puree, which are easy to digest. This system allows users to efficiently provide healthy meals. Furthermore, the menu is adjusted based on the user's emotions, so the user is provided with a meal that suits their mood, improving meal satisfaction.

[0846] The processing flow will be explained below.

[0847] Step 1:

[0848] The user launches an application on the device.

[0849] Step 2:

[0850] The device displays an information input form to the user, which includes information such as family composition, physical condition, weather, past dietary habits, and food preferences and dislikes.

[0851] Step 3:

[0852] The user enters the required data into the information entry form, specifically the following information:

[0853] Family composition: 2 adults, 1 5-year-old child, 1 8-month-old baby

[0854] Health: I've been feeling a bit sick lately

[0855] Climate: Winter

[0856] Past meals: Curry rice

[0857] Favorite food: Japanese food

[0858] Foods I dislike: Spicy foods

[0859] Step 4:

[0860] The device stores the user's input data and activates the emotion engine, which activates the camera and microphone to recognize the user's facial expressions and voice.

[0861] Step 5:

[0862] The user speaks to the device or faces the device's camera, and their voice and facial expressions are collected. For example, in response to a question such as "How are you feeling today?", they can respond with "I'm a little tired."

[0863] Step 6:

[0864] The emotion engine analyzes the user's voice and facial expression data to recognize the user's emotional state (e.g., tired, stressed).

[0865] Step 7:

[0866] The terminal transmits the input information and the emotion information recognized by the emotion engine to the server.

[0867] Step 8:

[0868] The server analyzes the received information to understand the user's family composition, physical condition, weather, past dietary habits, favorite and disliked ingredients, and emotional information.

[0869] Step 9:

[0870] The server calls the generative AI model based on the received data. The generative AI model generates a menu using the following steps:

[0871] Calculate the nutrients you need based on your family composition, physical condition, and climate.

[0872] Consider what you have eaten recently and select a menu that does not overlap with your previous meals.

[0873] Customize menus to reflect the user's preferences and dislikes.

[0874] We recommend appropriate baby foods based on the age of the infant.

[0875] Recognized emotional information (tired, stressed, etc.) is used to tailor a menu to suit the user's mood.

[0876] Step 10:

[0877] The server generates the final menu and sends it to the device. For example, it generates the following menu:

[0878] For adults and children aged 5 and over: Chicken and vegetable hot pot (using relaxing dashi stock), miso soup with lots of ingredients, and boiled spinach

[0879] For 8-month-old babies: soft-boiled carrots and chicken, pumpkin puree

[0880] Step 11:

[0881] The device visually displays the generated menu to the user, who can review the suggested menu and make adjustments or changes as needed.

[0882] Step 12:

[0883] (Optional) After the meal, the user enters their satisfaction and feedback on the menu. Enter the feedback information according to the displayed form.

[0884] Step 13:

[0885] (Optional) The device sends feedback information to the server.

[0886] Step 14:

[0887] (Optional) The server analyzes the received feedback and uses it as training data for the generative AI model, thereby improving the accuracy of the next menu suggestion.

[0888] The above is the flow of specific processing steps of this system.

[0889] Example 2

[0890] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0891] In modern families, preparing nutritionally balanced meals is important, but it is not easy to consider a wide range of factors, such as family composition, health status, weather, past dietary habits, and preferences.In addition, there is a lack of systems that can provide appropriate meals for children's developmental stages or menus that suit the user's emotional state, making it difficult to cook accurately and efficiently.

[0892] The specification process by the specification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for inputting the user's family structure, physical condition, climate, past meal contents, emotions, and preferences, a means for receiving the input information, and a generation AI model means for generating a nutritionally balanced menu using the received information. This makes it possible to generate and present an optimal menu based on the user's family structure, health condition, climate, meal history, and emotional information.

[0893] "Family structure" refers to the number and roles of all individuals living together in the household.

[0894] "Physical condition" refers to the health condition of the user and their family, and includes illness, fatigue, stress, and other conditions.

[0895] "Climate" refers to current weather conditions and environmental factors such as season, temperature, and humidity.

[0896] "Past meals" refers to a record of meals eaten by the user and their family over a certain period of time in the past.

[0897] "Emotion" refers to the user's psychological state, including states such as fatigue, stress, and joy.

[0898] "Preferences" refers to the ingredients and dishes that the user and their family like, as well as ingredients that they dislike.

[0899] "Input means" refers to a device or interface that allows a user to input information, including a keyboard, a touch screen, a voice input device, etc.

[0900] "Means for receiving" refers to a mechanism by which the terminal transmits input information to the server and the server receives the information.

[0901] "Generative AI model means" refers to an artificial intelligence algorithm that analyzes received data and generates an optimal menu.

[0902] "Presentation means" refers to a device or interface for providing the generated menu to the user visually or audibly.

[0903] "Baby food recipes" refer to specific cooking methods for providing nutritionally balanced meals according to the developmental stage of infants and young children.

[0904] The "function to select ingredients and cooking methods" refers to a function that recommends new ingredients and cooking methods based on past meal content and the user's preferences.

[0905] This invention provides a system for users to prepare nutritionally balanced meals at home, and is mainly composed of a user, a terminal, a server, and an emotion engine.

[0906] User side

[0907] First, the user launches the application on their device, then enters information such as the number of family members and their roles (e.g., two adults, one child, and one baby), their recent health condition (e.g., feeling a bit under the weather), the current weather (e.g., winter), past meals (e.g., curry rice), favorite dishes and ingredients (e.g., Japanese food), and disliked ingredients (e.g., spicy food).

[0908] The emotion engine recognizes emotions from the user's voice and facial expressions through the device's built-in camera and microphone. For example, if the user is feeling tired or stressed, that information will be recognized.

[0909] Terminal side

[0910] The device sends this information (family composition, physical condition, weather, past dietary habits, favorite and disliked ingredients, and recognized emotions) to a server. At this time, the data is encrypted using security protocols (e.g., SSL / TLS) to ensure secure communication.

[0911] Server side

[0912] The server analyzes the received information to understand the family composition, health status, weather, past dietary habits, food preferences, dislikes, and emotional information. Based on the analysis results, a generative AI model is used to generate a menu using the following steps:

[0913] Generative AI model processing steps

[0914] 1. Calculate your nutritional needs based on your family composition, physical condition, and climate.

[0915] 2. Consider past meal choices and select a menu that does not overlap.

[0916] 3. Customize menus to reflect the user's preferences and dislikes.

[0917] 4. Suggest appropriate baby foods according to the infant's age.

[0918] 5. Use the recognized emotional information to tailor the menu to suit the user's mood.

[0919] As a concrete example, the menu generated by the server is as follows:

[0920] For adults and children aged 5 and over: Chicken and vegetable hotpot, miso soup with lots of ingredients, and boiled spinach

[0921] For 8-month-old babies: soft-boiled carrots and chicken, pumpkin puree

[0922] Display on the terminal

[0923] The server sends the generated menu to the device, which then visually presents the received menu to the user, who can then review the proposed menu and make adjustments or changes as needed. For example, the user can exclude certain ingredients from the proposed menu.

[0924] Collect feedback (optional)

[0925] After eating, users input their satisfaction level and comments. This information is sent to the server via their device. The server analyzes the received feedback and uses it as training data for the generative AI model to improve the accuracy of the next menu suggestion.

[0926] Prompt Sentence Examples

[0927] If the user looks tired on a winter's day, the server might suggest a warm, nutritious meal, such as chicken and vegetable hotpot, hearty miso soup, or boiled spinach.

[0928] This invention not only enables users to efficiently prepare nutritionally balanced meals, but also improves meal satisfaction because the menu is adjusted based on the user's emotions.

[0929] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0930] Step 1:

[0931] The user launches an application on the device.

[0932] Action: Tap or click an application to launch it.

[0933] Input: None.

[0934] Output: The application's welcome screen is displayed.

[0935] Step 2:

[0936] The user inputs information about their family composition, physical condition, weather, past meals, and favorite and disliked ingredients.

[0937] How it works: The user enters information into an input form displayed on the application screen, such as family composition (e.g., two adults, one child, one baby), recent health condition (e.g., feeling a bit under the weather), current weather (e.g., winter), past meals (e.g., curry rice), favorite dishes and ingredients (e.g., Japanese food), and disliked ingredients (e.g., spicy food).

[0938] Input: Information entered by the user.

[0939] Output: The entered information is saved on the device.

[0940] Step 3:

[0941] The device recognizes the user's emotions.

[0942] How it works: The device's built-in camera and microphone capture the user's voice and facial expressions, which are then analyzed using emotion recognition algorithms.

[0943] Input: User's voice data and facial expression data.

[0944] Output: Emotional information (e.g., fatigue, stress) is obtained as the analysis result.

[0945] Step 4:

[0946] The device sends this information (family composition, physical condition, weather, past meals, favorite and disliked ingredients, and recognized emotions) to the server.

[0947] Operation: The device compiles the input information and emotion information and sends it to the server. The data is encrypted using a communication protocol (e.g., SSL / TLS) before being sent.

[0948] Input: Aggregated user information and sentiment information.

[0949] Output: The data is sent to the server.

[0950] Step 5:

[0951] The server analyzes the received information.

[0952] How it works: The server analyzes information about the user's family structure, physical condition, weather, past meals, favorite and disliked ingredients, and emotional information. It also compares this information with past data stored in a database.

[0953] Input: User information and emotion information sent to the server.

[0954] Output: As a result of the analysis, relevant data is extracted.

[0955] Step 6:

[0956] The server generates a menu using a generative AI model.

[0957] How it works: The server calls the generative AI model and generates a menu using the following steps:

[0958] Calculate the nutrients you need based on your family composition, physical condition, and climate.

[0959] Consider past meal choices and select a menu that does not overlap.

[0960] Customize menus to reflect the user's preferences and dislikes.

[0961] We recommend appropriate baby foods based on the age of the infant.

[0962] The recognized emotional information is used to adjust the menu to suit the user's mood.

[0963] Input: Parsed user information and sentiment information.

[0964] Output: The generated menu.

[0965] Step 7:

[0966] The server sends the generated menu to the terminal.

[0967] Operation: The server sends the generated menu data to the device. The data is sent securely using a communication protocol (e.g., HTTP / HTTPS).

[0968] Input: Generated menu data.

[0969] Output: Menu data is sent to the terminal.

[0970] Step 8:

[0971] The terminal presents the menu to the user.

[0972] Operation: The device visually displays the received menu to the user, for example, displaying the menu details on the application screen.

[0973] Input: Received menu data.

[0974] Output: The menu displayed to the user.

[0975] Step 9:

[0976] After the meal, the user enters their satisfaction and comments (optional).

[0977] What happens: A user fills out a feedback form in an application to rate their satisfaction and provide comments.

[0978] Input: User feedback information (e.g., taste rating, portion size, ease of cooking).

[0979] Output: Feedback information is saved to the device.

[0980] Step 10:

[0981] The terminal sends feedback information to the server.

[0982] Operation: The terminal sends the input feedback information to the server. The feedback information is also encrypted before being sent.

[0983] Input: User feedback information.

[0984] Output: The feedback information is sent to the server.

[0985] Step 11:

[0986] The server analyzes the feedback.

[0987] Operation: The server analyzes the feedback information it receives and uses it as training data for the generative AI model.

[0988] Input: Received feedback information.

[0989] Output: Training data used for next menu suggestion.

[0990] (Application example 2)

[0991] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0992] In modern households, it is difficult to plan an appropriate menu that takes into account each member's health condition, preferences, and weather. Providing a daily diet that satisfies all household members and is nutritionally balanced can be a burden. In particular, it is necessary to consider the effort required to plan a menu each time, including baby food, and to suggest meals that respond to the user's emotional state.

[0993] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0994] In this invention, the server includes means for inputting a user's family composition, physical condition, climate, past meal contents, and preferences, means for receiving the input information, a generation AI model means for generating a nutritionally balanced menu using the received information, means for recognizing the user's emotions, means for adjusting the menu based on the user's emotional information, and means for presenting the generated menu to the user. This not only enables the user to easily prepare a nutritionally balanced meal that suits the physical condition, preferences, and climate of each member, but also allows for a more satisfying meal by suggesting a menu that reflects the user's emotional state.

[0995] A "user" is a person who uses the system to manage meals for the household.

[0996] "Family structure" refers to the type and number of members in a household, including, for example, adults, children, babies, etc.

[0997] "Physical condition" refers to the current health condition of the user and their family, and includes, for example, having a cold or feeling tired.

[0998] "Climate" refers to the current weather conditions of the external environment, including, for example, winter, summer, rain, etc.

[0999] "Past meal contents" refers to the history of meals served at home, including specific dishes such as curry rice and salad.

[1000] "Preferences" refers to the likes and dislikes of the user and their family, and includes information such as liking Japanese food and dislike of spicy food.

[1001] "Input means" refers to a device or application that allows a user to provide information about family composition, physical condition, weather, past meals, and preferences to the system.

[1002] "Means for receiving" refers to a device or application that has the function of sending input information to a server and receiving it.

[1003] "Generative AI model means" refers to an artificial intelligence model for generating an optimal menu based on received information.

[1004] "Means for recognizing emotions" refers to devices such as cameras and microphones that can grasp emotions from the user's facial expressions and voice.

[1005] "Means for adjusting menu based on emotional information" refers to a function for adjusting menu based on the recognized emotional state of the user.

[1006] "Presentation means" refers to a device such as a display or speaker that provides the generated menu to the user visually or audibly.

[1007] The "system" refers to the overall mechanism that combines the above means to prepare nutritionally balanced meals in the user's home.

[1008] This invention is a system for users to prepare nutritionally balanced meals at home, and is mainly composed of a user, a terminal, a server, and an emotion engine. A specific embodiment of this system is described below.

[1009] Overall system overview

[1010] The user uses a device (such as a smartphone) to input information such as family composition, physical condition, weather, past meals, and favorite and disliked ingredients, and the emotion engine then recognizes the user's emotions. The device then sends this information to the server, which then uses a generative AI model to generate an optimal menu. The generated menu is then sent to the device and presented to the user visually or audibly.

[1011] Detailed System Description

[1012] User side

[1013] 1. The user launches the application on their device. The user inputs their own and their family's health status and preferences, and provides current weather information.

[1014] 2. The emotion engine recognizes emotions from the user's voice and facial expressions through the camera and microphone installed on the device.

[1015] 3. The device sends this information (family composition, physical condition, weather, past meals, favorite and disliked ingredients, and recognized emotions) to the server.

[1016] Server side

[1017] 1. The server analyzes the information it receives, including information about the user's family structure, physical condition, weather, past meals, favorite and disliked ingredients, and emotional information.

[1018] 2. The server generates an optimal menu by calling a generative AI model based on the received data. The generative AI model calculates necessary nutrients, takes into account recent meals, and customizes the menu to reflect the user's preferences and dislikes. It also uses recognized emotional information to adjust the menu to suit the user's mood.

[1019] Specific examples

[1020] For example, if the user has a cold on a cold winter day, the server might generate the following menu:

[1021] For adults and children: Chicken and vegetable hotpot, miso soup with lots of ingredients, and boiled spinach

[1022] For babies: soft boiled carrots and chicken, pumpkin puree

[1023] If the user is feeling tired, a menu containing ingredients that have a relaxing effect may be suggested.

[1024] Collect feedback (optional)

[1025] 1. After eating, the user enters their satisfaction level and comments.

[1026] 2. The device sends the feedback information to the server.

[1027] 3. The server analyzes the received feedback and uses it as training data for the generative AI model, thereby improving the accuracy of the next menu suggestion.

[1028] Prompt Sentence Examples

[1029] If the user is suffering from a cold on a cold winter day and looks tired, the system will suggest the following meal plans:

[1030] Family consists of 2 adults, 1 child, and 1 baby

[1031] Her favorite food is Japanese food, and her least favorite food is spicy food.

[1032] Users are feeling fatigued

[1033] My last meal was curry rice

[1034] Output:

[1035] For adults and children aged 5 and over: Chicken and vegetable hotpot, miso soup with lots of ingredients, and boiled spinach

[1036] For 8-month-old babies: soft-boiled carrots and chicken, pumpkin puree

[1037] In this way, the user can efficiently prepare healthy meals, and the satisfaction of the meal is improved because the menu is adjusted based on the user's emotional state.

[1038] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1039] Step 1:

[1040] The user launches the application on the device and inputs information such as family composition, physical condition, weather, past meals, and favorite and disliked ingredients. The necessary information is then saved as input data on the device.

[1041] Step 2:

[1042] The user uses the device's built-in camera and microphone to recognize their voice and facial expressions. The device then uses an emotion engine to recognize the user's emotions and generate emotion data. The emotion data is output in the form of, for example, "tired," "energetic," or "sad."

[1043] Step 3:

[1044] The device then sends the information entered in step 1, including family composition, physical condition, weather, past meals, and preferred and disliked ingredients, along with the emotional data generated in step 2, to the server.

[1045] Step 4:

[1046] The server analyzes the received information, obtaining information on family composition, physical condition, weather, past dietary habits, favorite and disliked ingredients, and the user's emotional state. Based on this information, the server preprocesses the data based on the relevant information in the database. Specifically, it checks the consistency of the data and begins calculating the necessary nutrients.

[1047] Step 5:

[1048] The server calls the generative AI model and passes the received data as input. The generative AI model performs the following calculations to generate a menu:

[1049] Calculate the nutrients you need based on your family composition, physical condition, and climate.

[1050] Consider what you have eaten recently and select a menu that does not overlap with your previous meals.

[1051] Customize menus to reflect the user's preferences and dislikes.

[1052] We suggest baby foods appropriate for the infant's age.

[1053] To adjust a menu to match a user's mood using emotional information.

[1054] Step 6:

[1055] The server transmits the generated menu to the terminal, which generates menu data that is visually displayed on the terminal.

[1056] Step 7:

[1057] The device presents the menu to the user, who can then view it on the device screen and make adjustments or changes as needed.

[1058] Step 8: (Optional)

[1059] After eating, users input their satisfaction level and comments, which generates feedback data.

[1060] Step 9: (Optional)

[1061] The device sends feedback information to the server, which analyzes the received feedback and uses it as training data for the generative AI model, thereby improving the accuracy of the next menu suggestion.

[1062] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1063] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1064] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1065] [Third embodiment]

[1066] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1067] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1068] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1069] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1070] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1071] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1072] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1073] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1074] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1075] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1076] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1077] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[1078] This invention provides a system that enables users to prepare nutritionally balanced meals at home, and is mainly composed of a user, a terminal, and a server.

[1079] Overall system overview

[1080] The user uses the device to input information such as family composition, physical condition, weather, past meals, and favorite and disliked ingredients. The device then sends this information to the server, which then uses a generative AI model to generate an optimal menu. The generated menu is then sent to the device and presented to the user.

[1081] Detailed System Description

[1082] User side

[1083] 1. The user launches an application on the device.

[1084] 2. The user enters the number of family members and their roles (e.g., two adults, one child, one baby), their recent health condition (e.g., feeling a bit under the weather), the current weather (e.g., winter), past meals (e.g., curry rice), favorite dishes and ingredients (e.g., Japanese food), and ingredients they dislike (e.g., spicy food).

[1085] 3. The device sends this information to the server.

[1086] Server side

[1087] 4. The server receives the input data.

[1088] 5. The server analyzes the received data and passes it to the generative AI model.

[1089] 6. The server generates the optimal menu using a generative AI model. The generative AI model takes into account:

[1090] Appropriate ingredients and portions based on family size and number of people.

[1091] A nutritionally balanced diet suited to your physical condition and the weather.

[1092] Consider your past dietary habits and change the ingredients and cooking methods.

[1093] A menu that reflects your likes and dislikes.

[1094] Suggestions for safe baby food according to the age of infants and toddlers.

[1095] Specific examples

[1096] The menu generated by the server looks like this:

[1097] For adults and a 5-year-old child: Chicken and vegetable hotpot, miso soup with lots of ingredients, and boiled spinach.

[1098] For babies (8 months): Softly simmered carrots and chicken, pumpkin puree.

[1099] Terminal side

[1100] 7. The server sends the generated menu to the terminal.

[1101] 8. The device visually presents the menu to the user, who can review the suggested menu and make adjustments or changes as needed.

[1102] Collect feedback (optional)

[1103] 9. After the meal, the user enters their satisfaction level and comments.

[1104] 10. The device sends the feedback information to the server.

[1105] 11. The server analyzes the received feedback and uses it as training data for the generative AI model. This feedback can improve the accuracy of the next menu suggestion.

[1106] Example

[1107] For example, if a user has a cold on a cold winter day, the system might suggest the following menu: a nutritious chicken and vegetable hotpot, a hearty miso soup, and a vitamin-rich spinach salad. For a baby, it might suggest softly simmered carrots and chicken, or pumpkin puree, which are easy to digest. This system allows users to efficiently provide healthy meals.

[1108] The processing flow will be explained below.

[1109] Step 1:

[1110] The user launches an application on the device.

[1111] Step 2:

[1112] The device displays an information input form to the user, which includes information such as family composition, physical condition, weather, past dietary habits, and food preferences and dislikes.

[1113] Step 3:

[1114] The user enters the required data into an information entry form, for example:

[1115] Family composition: 2 adults, 1 5-year-old child, 1 8-month-old baby

[1116] Health: I've been feeling a bit sick lately

[1117] Climate: Winter

[1118] Past meals: Curry rice

[1119] Favorite food: Japanese food

[1120] Foods I dislike: Spicy foods

[1121] Step 4:

[1122] The terminal collects information input by the user and transmits it to the server.

[1123] Step 5:

[1124] The server analyzes the information it receives and understands the individual's family composition, physical condition, weather, past dietary habits, and food preferences and dislikes.

[1125] Step 6:

[1126] The server calls the generative AI model based on the received data. The generative AI model generates a menu using the following steps:

[1127] Calculate the nutrients you need based on your family composition, physical condition, and climate.

[1128] Consider what you have eaten recently and select a menu that does not overlap with your previous meals.

[1129] Customize menus to reflect the user's preferences and dislikes.

[1130] We recommend appropriate baby foods based on the age of the infant.

[1131] Step 7:

[1132] The server uses a generative AI model to generate the optimal menu. For example, it generates the following menu:

[1133] For adults and children aged 5 and over: Chicken and vegetable hotpot, miso soup with lots of ingredients, and boiled spinach

[1134] For 8-month-old babies: soft-boiled carrots and chicken, pumpkin puree

[1135] Step 8:

[1136] The server sends the generated menu to the terminal.

[1137] Step 9:

[1138] The device visually displays the menu to the user, who can review the suggested menu and make adjustments or changes as needed.

[1139] Step 10:

[1140] (Optional) After the meal, the user enters feedback information, such as satisfaction level and comments.

[1141] Step 11:

[1142] (Optional) The device sends feedback information to the server.

[1143] Step 12:

[1144] (Optional) The server analyzes the received feedback and uses it as training data for the generative AI model, thereby improving the accuracy of the next menu suggestion.

[1145] The above is the flow of specific processing steps of this system.

[1146] Example 1

[1147] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1148] Traditionally, when preparing nutritionally balanced meals at home, users have had to spend time and effort creating menus that take into account information such as family composition, physical condition, weather, past dietary habits, and food preferences and dislikes. However, it is difficult to comprehensively assess these diverse factors and propose an appropriate menu each time. There is also a need for systems that can suggest baby foods that reflect changes in past dietary habits and the child's growth. This requires a system that allows users to create menus efficiently and accurately.

[1149] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1150] In this invention, the server includes means for inputting the user's family composition, physical condition, weather, past meal contents, and preferences, means for receiving the input information, means for analyzing the received information, means for passing the analyzed information to a generative AI model, means for generating an optimal menu using the generative AI model, and means for presenting the generated menu to the user, thereby enabling the user to efficiently and accurately prepare a nutritionally balanced menu that meets the needs of their family.

[1151] The "user" is the person who uses this system to plan a menu and inputs data such as information about their family and ingredients they like and dislike.

[1152] A "terminal" is a device that a user uses to input information and communicate with a server, such as a smartphone, tablet, or PC.

[1153] The "server" is a central processing unit that receives and analyzes information sent by users and generates menus using generative AI models.

[1154] "Family composition" refers to information such as the number, ages, and roles of members in the user's household, and is an important factor in generating menus.

[1155] "Physical condition" refers to the current health condition of the user or family, and includes a cold, a specific illness, allergies, etc.

[1156] "Climate" refers to the current weather conditions, such as information about how the suitability of a dish changes depending on the season or weather.

[1157] "Past meal content" refers to the history of meals the user or their family has eaten in the past, and is used to ensure variety in menus.

[1158] "Preferences" refers to the dishes and ingredients that the user or their family prefers, as well as things they want to avoid.

[1159] A "generative AI model" is an artificial intelligence model that generates optimal menus based on received information.

[1160] A "prompt" is a set of instructions input to a generative AI model, a document that details the necessary requirements and conditions.

[1161] "Feedback information" refers to information such as satisfaction and comments entered by the user after a meal, and is used to improve the accuracy of menu suggestions for the next time.

[1162] This invention provides a system that allows users to prepare nutritionally balanced meals at home, and is primarily composed of a user, a terminal, and a server. Specifically, the user uses the terminal to input information about their family composition, physical condition, weather, past meals, preferences, and disliked ingredients, and the terminal then sends this information to the server. The server then passes the received information to a generative AI model to generate an optimal menu. The generated menu is then sent to the terminal and presented to the user.

[1163] User operations

[1164] 1. A user launches an application on a device, such as a smartphone, tablet, or PC.

[1165] 2. The user enters information such as family composition (e.g., two adults, one child, one baby), physical condition (e.g., feeling a bit under the weather), weather (e.g., winter), past meals (e.g., curry rice), favorite dishes and ingredients (e.g., Japanese food), and disliked ingredients (e.g., spicy food). The device is equipped with an interface for entering information, and the user enters information using text boxes and drop-down menus.

[1166] Terminal handling

[1167] The device sends the information entered by the user to the server using an API, with the data formatted in JSON.

[1168] Server Processing

[1169] The server analyzes the information received from the device. A Python library (e.g., pandas) or a dedicated analysis module is used for the analysis. The analyzed information is then converted into a prompt statement format and passed to a generative AI model (e.g., OpenAI GPT-4). An example of a prompt statement is shown below.

[1170] Prompt Sentence Examples

[1171] "Our family consists of two adults, one five-year-old child, and one eight-month-old baby. I've been feeling a bit under the weather lately, so I'd like to know what kind of nutritionally balanced meal we can make for cold winter days. In the past, our meal was curry and rice. I like Japanese food, but I don't like spicy food."

[1172] Processing generative AI models

[1173] When the generative AI model receives the prompt, it generates an appropriate menu. Factors taken into consideration include family composition and number of people, physical condition, weather, past dietary habits, food preferences and dislikes, and baby food suggestions based on the infant's age. For example, the following menu may be generated:

[1174] Specific examples

[1175] For adults and a 5-year-old child: Chicken and vegetable hotpot, miso soup with lots of ingredients, and boiled spinach.

[1176] For babies (8 months): Softly simmered carrots and chicken, pumpkin puree.

[1177] Menu presentation

[1178] The server sends the generated menu to the device. The menu is formatted in JSON format or similar and sent to the device.

[1179] The device visually presents the menu to the user, who can review the suggested menu and make adjustments or changes as needed.

[1180] Collect feedback (optional)

[1181] After eating, users can enter feedback such as their satisfaction level and comments. The device sends this feedback information to the server, which analyzes the received feedback and reflects it as learning data for the generative AI model. This improves the accuracy of the next menu suggestion.

[1182] The above is a specific embodiment for carrying out the present invention.

[1183] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1184] Step 1:

[1185] The user launches an application on the device.

[1186] What happens: A user taps an application icon on a device such as a smartphone, tablet, or computer.

[1187] Input: Applications installed on the device.

[1188] Output: Application launch.

[1189] Step 2:

[1190] The user inputs information about family composition, physical condition, weather, past meals, preferences, and disliked ingredients.

[1191] What it does: Enter information into an application's input screen using text boxes and drop-down menus.

[1192] Input: User's family composition (e.g., 2 adults, 1 child, 1 baby), physical condition (e.g., feeling a bit under the weather), weather (e.g., winter), past meals (e.g., curry rice), preferences and disliked foods (e.g., likes Japanese food, dislikes spicy food), etc.

[1193] Output: The input data.

[1194] Step 3:

[1195] The terminal transmits the input information to the server.

[1196] How it works: After the user enters the information, they press the submit button, and the device sends the data to the server via the API. The data is formatted in JSON format.

[1197] Input: Information entered by the user.

[1198] Output: Information sent to the server.

[1199] Step 4:

[1200] The server analyzes the received information.

[1201] How it works: The server uses a parsing function to analyze the received data and break down each item (family composition, physical condition, climate, etc.). This analysis uses Python libraries (e.g., pandas).

[1202] Input: Information sent from the device.

[1203] Output: Parsed data.

[1204] Step 5:

[1205] The server passes the analyzed information to the generative AI model.

[1206] How it works: A prompt is generated based on the analysis data and passed to a generative AI model (e.g., OpenAI GPT-4). The prompt includes the analysis results.

[1207] Input: Analysis data.

[1208] Output: The prompt sentence that is passed to the generative AI model.

[1209] Step 6:

[1210] The server uses a generative AI model to generate the optimal menu.

[1211] How it works: The generative AI model receives prompts and generates appropriate menu suggestions based on family composition, physical condition, weather, past dietary habits, food preferences and dislikes, and the age of the infant.

[1212] Input: The prompt sentence passed to the generative AI model.

[1213] Output: The generated menu data.

[1214] Step 7:

[1215] The server sends the generated menu to the terminal.

[1216] Operation: The generated menu data is formatted in JSON format or similar and sent to the device.

[1217] Input: Generated menu data.

[1218] Output: Menu data sent to the device.

[1219] Step 8:

[1220] The terminal visually presents the menu to the user.

[1221] Operation: The device displays the menu data received within the application. The user can visually check the detailed information of each menu item.

[1222] Input: Menu data sent from the server.

[1223] Output: Menu information displayed on the screen.

[1224] Step 9 (Optional):

[1225] After the meal, the user enters their satisfaction level and comments.

[1226] How it works: The user enters their satisfaction and comments on the application's feedback screen and presses the submit button.

[1227] Input: User feedback information.

[1228] Output: The input feedback data.

[1229] Step 10 (Optional):

[1230] The terminal sends feedback information to the server.

[1231] Operation: The entered feedback information is sent to the server via the API.

[1232] Input: Feedback data entered by the user.

[1233] Output: Feedback information sent to the server.

[1234] Step 11 (Optional):

[1235] The server analyzes the feedback and reflects it in the generative AI model.

[1236] How it works: The server analyzes the feedback data and uses it to retrain the generative AI model, improving the accuracy of future menu suggestions.

[1237] Input: Feedback information sent from the device.

[1238] Output: An updated generative AI model.

[1239] (Application example 1)

[1240] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1241] In modern families, preparing nutritionally balanced meals with busy schedules and limited resources is challenging. It's particularly challenging to efficiently plan appropriate menus, as it requires consideration of family composition, health status, weather conditions, past dietary history, and preferences. Furthermore, the process of ordering meals based on the menu can be cumbersome. There is a need for a system that solves these problems, simplifies home meal preparation, and provides healthy meals.

[1242] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1243] In this invention, the server includes means for inputting a user's household composition, health condition, weather conditions, previous meal history, and preferences, means for receiving the input information, a generation AI model means for generating a nutritionally balanced menu using the received information, means for filtering and displaying information related to meal options suggested based on the generated menu, and means for ordering the selected meal options. This allows the user to receive a suggested menu optimal for their household composition and health condition, and to efficiently order meals based on the suggested menu.

[1244] "User" refers to an individual or household that uses the system.

[1245] "Family composition" refers to information indicating the number and roles of members in a household.

[1246] "Health status" refers to information about the health of the user and their family members, such as their recent physical condition and medical history.

[1247] "Weather conditions" refers to information about the current climate and season.

[1248] "Past dietary history" refers to the contents of meals that the user and their family have eaten in the past.

[1249] "Preferences" refers to information about dishes and ingredients that the user and their family like, as well as dishes and ingredients that they dislike or want to avoid.

[1250] "Input means" refers to devices or software that allow a user to provide information about their household configuration, health status, weather conditions, past dietary history, and preferences to the system.

[1251] "Means for receiving" refers to an interface for receiving input information on the server side.

[1252] "Generative AI model means" refers to artificial intelligence technology and its model for generating optimal menus based on received information.

[1253] "Filtering and display means" refers to functionality for filtering and displaying suggested meal options to the user based on the generated menu.

[1254] "Means for ordering" refers to a function that provides a process for the user to actually order the meal options selected.

[1255] Overall system overview

[1256] This system consists of a user, a device, and a server, and helps users prepare nutritionally balanced meals at home. Users use their device to input information about their household composition, health status, weather conditions, past meal history, and preferences, and the server uses a generative AI model to generate an optimal menu. Based on the menu, meal options are filtered and presented to the user. The user can then select the desired meal option and place an order.

[1257] User side

[1258] 1. The user launches an application on the device.

[1259] 2. The user enters information about their household, health condition, weather conditions, past meal history, preferences, budget, and desired delivery time.

[1260] 3. The device sends this information to the server.

[1261] Server side

[1262] 1. The server receives the input data.

[1263] 2. The server analyzes the received data and passes it to the generative AI model.

[1264] 3. The server generates the optimal menu using a generative AI model that takes into account:

[1265] Appropriate ingredients and portions based on household size and number of people.

[1266] A nutritionally balanced diet tailored to your health and weather conditions.

[1267] Consider your past eating history and change ingredients and cooking methods.

[1268] A menu that reflects your preferences and dislikes.

[1269] 4. The server filters the available meal options based on the generated menu and presents them to the user.

[1270] 5. The server provides the information to process the order for the meal options selected by the user.

[1271] Specific examples

[1272] For example, if a user has a cold on a cold winter day, the system may suggest the following menu items: nutritious chicken and vegetable hotpot, hearty miso soup, and vitamin-rich spinach ohitashi. For babies, it suggests easy-to-digest dishes such as softly simmered carrots and chicken, and pumpkin puree. Based on the suggested menu items, appropriate meal options are filtered and presented to the user. The user can then select the desired meal and complete the order.

[1273] Hardware and software used

[1274] Hardware: User device (smartphone or tablet), server.

[1275] Software: terminal application, server receiving interface, generative AI model (e.g., OpenAI's GPT), filtering and display functions, order processing system.

[1276] Prompt Sentence Examples

[1277] The user provided the following information:

[1278] Family composition: 2 adults, 1 child (age 5)

[1279] Health condition: I've been feeling a bit under the weather lately

[1280] Weather conditions: Winter

[1281] Past meal history: Curry rice

[1282] Favorite food: Japanese food

[1283] Foods I dislike: Spicy foods

[1284] Budget: 5,000 yen

[1285] Delivery time: 2023-11-20 18:30

[1286] Use this information to generate optimal menus and suggest food options for the user to order.

[1287] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1288] Step 1:

[1289] The user launches an application on the device.

[1290] Users input information about their household composition, health condition, weather conditions, past meal history, preferences, budget, and desired delivery time.

[1291] Input: household composition, health condition, weather conditions, past meal history, preferences, budget, desired delivery time.

[1292] Output: The input data on the terminal.

[1293] Step 2:

[1294] The device sends the input data to the server

[1295] The terminal transmits the information input by the user to the server as a data packet.

[1296] Input: Input data on the terminal.

[1297] Output: Data packets sent to the server.

[1298] Step 3:

[1299] The server receives the input data

[1300] The server receives the data packets sent from the terminal and performs preprocessing for analysis.

[1301] Input: The data packet sent to the server.

[1302] Output: Structured data to be parsed.

[1303] Step 4:

[1304] The server passes the data to the generative AI model

[1305] The server inputs the analyzed data into the generative AI model and requests it to generate the optimal menu. Specifically, the server passes the data to the generative AI model in the form of a "prompt sentence."

[1306] Input: Structured data.

[1307] Output: A prompt to the generative AI model.

[1308] Step 5:

[1309] Generative AI model generates optimal menu

[1310] The generative AI model generates the optimal menu based on prompts from the server, taking into account household composition, health status, weather conditions, past meal history, and preferences.

[1311] Input: Prompt statement.

[1312] Output: Optimal menu.

[1313] Step 6:

[1314] Filtering meal options based on server-generated menus

[1315] Based on the generated menu, the server selects the optimal meal options from affiliated restaurants and meal kit services and presents the filtered results to the user.

[1316] Input: Optimal menu.

[1317] Output: A filtered list of meal options.

[1318] Step 7:

[1319] The device presents meal options to the user.

[1320] The terminal displays the list of meal options received from the server to the user and prompts them to make a selection.

[1321] Input: A filtered list of meal options.

[1322] Output: The meal options presented to the user.

[1323] Step 8:

[1324] The user selects a meal option and places an order

[1325] The user selects the desired meal options from the presented options and confirms the order.

[1326] Input: A list of meal options.

[1327] Output: Confirmed order data.

[1328] Step 9:

[1329] The server processes the order data and sends the order information to the restaurant or meal kit service

[1330] The server processes the user's order data and transmits the order information to the appropriate restaurant or meal kit service.

[1331] Input: Confirmed order data.

[1332] Output: Order information to a restaurant or meal kit service.

[1333] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1334] This invention provides a system for users to prepare nutritionally balanced meals at home, and is mainly composed of a user, a terminal, a server, and an emotion engine.

[1335] Overall system overview

[1336] The user uses the device to input information such as family composition, physical condition, weather, past meals, and favorite and disliked ingredients, and the emotion engine then recognizes the user's emotions. The device then sends this information to the server, which then uses a generative AI model to generate an optimal menu. The generated menu is then sent to the device and presented to the user.

[1337] Detailed System Description

[1338] User side

[1339] 1. The user launches an application on the device.

[1340] 2. The user enters the number of family members and their roles (e.g., two adults, one child, one baby), their recent health condition (e.g., feeling a bit under the weather), the current weather (e.g., winter), past meals (e.g., curry rice), favorite dishes and ingredients (e.g., Japanese food), and ingredients they dislike (e.g., spicy food).

[1341] 3. The user's emotions are recognized from their voice and facial expressions through the camera and microphone installed on the device.

[1342] 4. The device sends this information (family composition, physical condition, weather, past meals, favorite and disliked ingredients, and recognized emotions) to the server.

[1343] Server side

[1344] 5. The server analyzes the received information to understand the user's family structure, physical condition, weather, past dietary habits, favorite and disliked ingredients, and emotional information.

[1345] 6. The server calls the generative AI model based on the received data. The generative AI model generates a menu using the following steps:

[1346] Calculate the nutrients you need based on your family composition, physical condition, and climate.

[1347] Consider what you have eaten recently and select a menu that does not overlap with your previous meals.

[1348] Customize menus to reflect the user's preferences and dislikes.

[1349] We recommend appropriate baby foods based on the age of the infant.

[1350] The recognized emotional information is used to adjust the menu to suit the user's mood.

[1351] Specific examples

[1352] The menu generated by the server looks like this:

[1353] For adults and children aged 5 and over: Chicken and vegetable hotpot, miso soup with lots of ingredients, and boiled spinach

[1354] For 8-month-old babies: soft-boiled carrots and chicken, pumpkin puree

[1355] For example, if the user has a tired expression, a menu using ingredients that have a relaxing effect may be suggested.

[1356] Terminal side

[1357] 7. The server sends the generated menu to the terminal.

[1358] 8. The device visually presents the menu to the user, who can review the suggested menu and make adjustments or changes as needed.

[1359] Collect feedback (optional)

[1360] 9. After the meal, the user enters their satisfaction level and comments.

[1361] 10. The device sends the feedback information to the server.

[1362] 11. The server analyzes the received feedback and uses it as training data for the generative AI model, thereby improving the accuracy of the next menu suggestion.

[1363] Example

[1364] For example, if a user has a cold on a cold winter day and looks tired, the system might suggest the following menu: a nutritious chicken and vegetable hotpot, a hearty miso soup, and a vitamin-rich spinach salad. For a baby, it might suggest softly simmered carrots and chicken, or pumpkin puree, which are easy to digest. This system allows users to efficiently provide healthy meals. Furthermore, the menu is adjusted based on the user's emotions, so the user is provided with a meal that suits their mood, improving meal satisfaction.

[1365] The processing flow will be explained below.

[1366] Step 1:

[1367] The user launches an application on the device.

[1368] Step 2:

[1369] The device displays an information input form to the user, which includes information such as family composition, physical condition, weather, past dietary habits, and food preferences and dislikes.

[1370] Step 3:

[1371] The user enters the required data into the information entry form, specifically the following information:

[1372] Family composition: 2 adults, 1 5-year-old child, 1 8-month-old baby

[1373] Health: I've been feeling a bit sick lately

[1374] Climate: Winter

[1375] Past meals: Curry rice

[1376] Favorite food: Japanese food

[1377] Foods I dislike: Spicy foods

[1378] Step 4:

[1379] The device stores the user's input data and activates the emotion engine, which activates the camera and microphone to recognize the user's facial expressions and voice.

[1380] Step 5:

[1381] The user speaks to the device or faces the device's camera, and their voice and facial expressions are collected. For example, in response to a question such as "How are you feeling today?", they can respond with "I'm a little tired."

[1382] Step 6:

[1383] The emotion engine analyzes the user's voice and facial expression data to recognize the user's emotional state (e.g., tired, stressed).

[1384] Step 7:

[1385] The terminal transmits the input information and the emotion information recognized by the emotion engine to the server.

[1386] Step 8:

[1387] The server analyzes the received information to understand the user's family composition, physical condition, weather, past dietary habits, favorite and disliked ingredients, and emotional information.

[1388] Step 9:

[1389] The server calls the generative AI model based on the received data. The generative AI model generates a menu using the following steps:

[1390] Calculate the nutrients you need based on your family composition, physical condition, and climate.

[1391] Consider what you have eaten recently and select a menu that does not overlap with your previous meals.

[1392] Customize menus to reflect the user's preferences and dislikes.

[1393] We recommend appropriate baby foods based on the age of the infant.

[1394] Recognized emotional information (tired, stressed, etc.) is used to tailor a menu to suit the user's mood.

[1395] Step 10:

[1396] The server generates the final menu and sends it to the device. For example, it generates the following menu:

[1397] For adults and children aged 5 and over: Chicken and vegetable hot pot (using relaxing dashi stock), miso soup with lots of ingredients, and boiled spinach

[1398] For 8-month-old babies: soft-boiled carrots and chicken, pumpkin puree

[1399] Step 11:

[1400] The device visually displays the generated menu to the user, who can review the suggested menu and make adjustments or changes as needed.

[1401] Step 12:

[1402] (Optional) After the meal, the user enters their satisfaction and feedback on the menu. Enter the feedback information according to the displayed form.

[1403] Step 13:

[1404] (Optional) The device sends feedback information to the server.

[1405] Step 14:

[1406] (Optional) The server analyzes the received feedback and uses it as training data for the generative AI model, thereby improving the accuracy of the next menu suggestion.

[1407] The above is the flow of specific processing steps of this system.

[1408] Example 2

[1409] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1410] In modern families, preparing nutritionally balanced meals is important, but it is not easy to consider a wide range of factors, such as family composition, health status, weather, past dietary habits, and preferences.In addition, there is a lack of systems that can provide appropriate meals for children's developmental stages or menus that suit the user's emotional state, making it difficult to cook accurately and efficiently.

[1411] The specification process by the specification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for inputting the user's family structure, physical condition, climate, past meal contents, emotions, and preferences, a means for receiving the input information, and a generation AI model means for generating a nutritionally balanced menu using the received information. This makes it possible to generate and present an optimal menu based on the user's family structure, health condition, climate, meal history, and emotional information.

[1412] "Family structure" refers to the number and roles of all individuals living together in the household.

[1413] "Physical condition" refers to the health condition of the user and their family, and includes illness, fatigue, stress, and other conditions.

[1414] "Climate" refers to current weather conditions and environmental factors such as season, temperature, and humidity.

[1415] "Past meals" refers to a record of meals eaten by the user and their family over a certain period of time in the past.

[1416] "Emotion" refers to the user's psychological state, including states such as fatigue, stress, and joy.

[1417] "Preferences" refers to the ingredients and dishes that the user and their family like, as well as ingredients that they dislike.

[1418] "Input means" refers to a device or interface that allows a user to input information, including a keyboard, a touch screen, a voice input device, etc.

[1419] "Means for receiving" refers to a mechanism by which the terminal transmits input information to the server and the server receives the information.

[1420] "Generative AI model means" refers to an artificial intelligence algorithm that analyzes received data and generates an optimal menu.

[1421] "Presentation means" refers to a device or interface for providing the generated menu to the user visually or audibly.

[1422] "Baby food recipes" refer to specific cooking methods for providing nutritionally balanced meals according to the developmental stage of infants and young children.

[1423] The "function to select ingredients and cooking methods" refers to a function that recommends new ingredients and cooking methods based on past meal content and the user's preferences.

[1424] This invention provides a system for users to prepare nutritionally balanced meals at home, and is mainly composed of a user, a terminal, a server, and an emotion engine.

[1425] User side

[1426] First, the user launches the application on their device, then enters information such as the number of family members and their roles (e.g., two adults, one child, and one baby), their recent health condition (e.g., feeling a bit under the weather), the current weather (e.g., winter), past meals (e.g., curry rice), favorite dishes and ingredients (e.g., Japanese food), and disliked ingredients (e.g., spicy food).

[1427] The emotion engine recognizes emotions from the user's voice and facial expressions through the device's built-in camera and microphone. For example, if the user is feeling tired or stressed, that information will be recognized.

[1428] Terminal side

[1429] The device sends this information (family composition, physical condition, weather, past dietary habits, favorite and disliked ingredients, and recognized emotions) to a server. At this time, the data is encrypted using security protocols (e.g., SSL / TLS) to ensure secure communication.

[1430] Server side

[1431] The server analyzes the received information to understand the family composition, health status, weather, past dietary habits, food preferences, dislikes, and emotional information. Based on the analysis results, a generative AI model is used to generate a menu using the following steps:

[1432] Generative AI model processing steps

[1433] 1. Calculate your nutritional needs based on your family composition, physical condition, and climate.

[1434] 2. Consider past meal choices and select a menu that does not overlap.

[1435] 3. Customize menus to reflect the user's preferences and dislikes.

[1436] 4. Suggest appropriate baby foods according to the infant's age.

[1437] 5. Use the recognized emotional information to tailor the menu to suit the user's mood.

[1438] As a concrete example, the menu generated by the server is as follows:

[1439] For adults and children aged 5 and over: Chicken and vegetable hotpot, miso soup with lots of ingredients, and boiled spinach

[1440] For 8-month-old babies: soft-boiled carrots and chicken, pumpkin puree

[1441] Display on the terminal

[1442] The server sends the generated menu to the device, which then visually presents the received menu to the user, who can then review the proposed menu and make adjustments or changes as needed. For example, the user can exclude certain ingredients from the proposed menu.

[1443] Collect feedback (optional)

[1444] After eating, users input their satisfaction level and comments. This information is sent to the server via their device. The server analyzes the received feedback and uses it as training data for the generative AI model to improve the accuracy of the next menu suggestion.

[1445] Prompt Sentence Examples

[1446] If the user looks tired on a winter's day, the server might suggest a warm, nutritious meal, such as chicken and vegetable hotpot, hearty miso soup, or boiled spinach.

[1447] This invention not only enables users to efficiently prepare nutritionally balanced meals, but also improves meal satisfaction because the menu is adjusted based on the user's emotions.

[1448] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1449] Step 1:

[1450] The user launches an application on the device.

[1451] Action: Tap or click an application to launch it.

[1452] Input: None.

[1453] Output: The application's welcome screen is displayed.

[1454] Step 2:

[1455] The user inputs information about their family composition, physical condition, weather, past meals, and favorite and disliked ingredients.

[1456] How it works: The user enters information into an input form displayed on the application screen, such as family composition (e.g., two adults, one child, one baby), recent health condition (e.g., feeling a bit under the weather), current weather (e.g., winter), past meals (e.g., curry rice), favorite dishes and ingredients (e.g., Japanese food), and disliked ingredients (e.g., spicy food).

[1457] Input: Information entered by the user.

[1458] Output: The entered information is saved on the device.

[1459] Step 3:

[1460] The device recognizes the user's emotions.

[1461] How it works: The device's built-in camera and microphone capture the user's voice and facial expressions, which are then analyzed using emotion recognition algorithms.

[1462] Input: User's voice data and facial expression data.

[1463] Output: Emotional information (e.g., fatigue, stress) is obtained as the analysis result.

[1464] Step 4:

[1465] The device sends this information (family composition, physical condition, weather, past meals, favorite and disliked ingredients, and recognized emotions) to the server.

[1466] Operation: The device compiles the input information and emotion information and sends it to the server. The data is encrypted using a communication protocol (e.g., SSL / TLS) before being sent.

[1467] Input: Aggregated user information and sentiment information.

[1468] Output: The data is sent to the server.

[1469] Step 5:

[1470] The server analyzes the received information.

[1471] How it works: The server analyzes information about the user's family structure, physical condition, weather, past meals, favorite and disliked ingredients, and emotional information. It also compares this information with past data stored in a database.

[1472] Input: User information and emotion information sent to the server.

[1473] Output: As a result of the analysis, relevant data is extracted.

[1474] Step 6:

[1475] The server generates a menu using a generative AI model.

[1476] How it works: The server calls the generative AI model and generates a menu using the following steps:

[1477] Calculate the nutrients you need based on your family composition, physical condition, and climate.

[1478] Consider past meal choices and select a menu that does not overlap.

[1479] Customize menus to reflect the user's preferences and dislikes.

[1480] We recommend appropriate baby foods based on the age of the infant.

[1481] The recognized emotional information is used to adjust the menu to suit the user's mood.

[1482] Input: Parsed user information and sentiment information.

[1483] Output: The generated menu.

[1484] Step 7:

[1485] The server sends the generated menu to the terminal.

[1486] Operation: The server sends the generated menu data to the device. The data is sent securely using a communication protocol (e.g., HTTP / HTTPS).

[1487] Input: Generated menu data.

[1488] Output: Menu data is sent to the terminal.

[1489] Step 8:

[1490] The terminal presents the menu to the user.

[1491] Operation: The device visually displays the received menu to the user, for example, displaying the menu details on the application screen.

[1492] Input: Received menu data.

[1493] Output: The menu displayed to the user.

[1494] Step 9:

[1495] After the meal, the user enters their satisfaction and comments (optional).

[1496] What happens: A user fills out a feedback form in an application to rate their satisfaction and provide comments.

[1497] Input: User feedback information (e.g., taste rating, portion size, ease of cooking).

[1498] Output: Feedback information is saved to the device.

[1499] Step 10:

[1500] The terminal sends feedback information to the server.

[1501] Operation: The terminal sends the input feedback information to the server. The feedback information is also encrypted before being sent.

[1502] Input: User feedback information.

[1503] Output: The feedback information is sent to the server.

[1504] Step 11:

[1505] The server analyzes the feedback.

[1506] Operation: The server analyzes the feedback information it receives and uses it as training data for the generative AI model.

[1507] Input: Received feedback information.

[1508] Output: Training data used for next menu suggestion.

[1509] (Application example 2)

[1510] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1511] In modern households, it is difficult to plan an appropriate menu that takes into account each member's health condition, preferences, and weather. Providing a daily diet that satisfies all household members and is nutritionally balanced can be a burden. In particular, it is necessary to consider the effort required to plan a menu each time, including baby food, and to suggest meals that respond to the user's emotional state.

[1512] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1513] In this invention, the server includes means for inputting a user's family composition, physical condition, climate, past meal contents, and preferences, means for receiving the input information, a generation AI model means for generating a nutritionally balanced menu using the received information, means for recognizing the user's emotions, means for adjusting the menu based on the user's emotional information, and means for presenting the generated menu to the user. This not only enables the user to easily prepare a nutritionally balanced meal that suits the physical condition, preferences, and climate of each member, but also allows for a more satisfying meal by suggesting a menu that reflects the user's emotional state.

[1514] A "user" is a person who uses the system to manage meals for the household.

[1515] "Family structure" refers to the type and number of members in a household, including, for example, adults, children, babies, etc.

[1516] "Physical condition" refers to the current health condition of the user and their family, and includes, for example, having a cold or feeling tired.

[1517] "Climate" refers to the current weather conditions of the external environment, including, for example, winter, summer, rain, etc.

[1518] "Past meal contents" refers to the history of meals served at home, including specific dishes such as curry rice and salad.

[1519] "Preferences" refers to the likes and dislikes of the user and their family, and includes information such as liking Japanese food and dislike of spicy food.

[1520] "Input means" refers to a device or application that allows a user to provide information about family composition, physical condition, weather, past meals, and preferences to the system.

[1521] "Means for receiving" refers to a device or application that has the function of sending input information to a server and receiving it.

[1522] "Generative AI model means" refers to an artificial intelligence model for generating an optimal menu based on received information.

[1523] "Means for recognizing emotions" refers to devices such as cameras and microphones that can grasp emotions from the user's facial expressions and voice.

[1524] "Means for adjusting menu based on emotional information" refers to a function for adjusting menu based on the recognized emotional state of the user.

[1525] "Presentation means" refers to a device such as a display or speaker that provides the generated menu to the user visually or audibly.

[1526] The "system" refers to the overall mechanism that combines the above means to prepare nutritionally balanced meals in the user's home.

[1527] This invention is a system for users to prepare nutritionally balanced meals at home, and is mainly composed of a user, a terminal, a server, and an emotion engine. A specific embodiment of this system is described below.

[1528] Overall system overview

[1529] The user uses a device (such as a smartphone) to input information such as family composition, physical condition, weather, past meals, and favorite and disliked ingredients, and the emotion engine then recognizes the user's emotions. The device then sends this information to the server, which then uses a generative AI model to generate an optimal menu. The generated menu is then sent to the device and presented to the user visually or audibly.

[1530] Detailed System Description

[1531] User side

[1532] 1. The user launches the application on their device. The user inputs their own and their family's health status and preferences, and provides current weather information.

[1533] 2. The emotion engine recognizes emotions from the user's voice and facial expressions through the camera and microphone installed on the device.

[1534] 3. The device sends this information (family composition, physical condition, weather, past meals, favorite and disliked ingredients, and recognized emotions) to the server.

[1535] Server side

[1536] 1. The server analyzes the information it receives, including information about the user's family structure, physical condition, weather, past meals, favorite and disliked ingredients, and emotional information.

[1537] 2. The server generates an optimal menu by calling a generative AI model based on the received data. The generative AI model calculates necessary nutrients, takes into account recent meals, and customizes the menu to reflect the user's preferences and dislikes. It also uses recognized emotional information to adjust the menu to suit the user's mood.

[1538] Specific examples

[1539] For example, if the user has a cold on a cold winter day, the server might generate the following menu:

[1540] For adults and children: Chicken and vegetable hotpot, miso soup with lots of ingredients, and boiled spinach

[1541] For babies: soft boiled carrots and chicken, pumpkin puree

[1542] If the user is feeling tired, a menu containing ingredients that have a relaxing effect may be suggested.

[1543] Collect feedback (optional)

[1544] 1. After eating, the user enters their satisfaction level and comments.

[1545] 2. The device sends the feedback information to the server.

[1546] 3. The server analyzes the received feedback and uses it as training data for the generative AI model, thereby improving the accuracy of the next menu suggestion.

[1547] Prompt Sentence Examples

[1548] If the user is suffering from a cold on a cold winter day and looks tired, the system will suggest the following meal plans:

[1549] Family consists of 2 adults, 1 child, and 1 baby

[1550] Her favorite food is Japanese food, and her least favorite food is spicy food.

[1551] Users are feeling fatigued

[1552] My last meal was curry rice

[1553] Output:

[1554] For adults and children aged 5 and over: Chicken and vegetable hotpot, miso soup with lots of ingredients, and boiled spinach

[1555] For 8-month-old babies: soft-boiled carrots and chicken, pumpkin puree

[1556] In this way, the user can efficiently prepare healthy meals, and the satisfaction of the meal is improved because the menu is adjusted based on the user's emotional state.

[1557] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1558] Step 1:

[1559] The user launches the application on the device and inputs information such as family composition, physical condition, weather, past meals, and favorite and disliked ingredients. The necessary information is then saved as input data on the device.

[1560] Step 2:

[1561] The user uses the device's built-in camera and microphone to recognize their voice and facial expressions. The device then uses an emotion engine to recognize the user's emotions and generate emotion data. The emotion data is output in the form of, for example, "tired," "energetic," or "sad."

[1562] Step 3:

[1563] The device then sends the information entered in step 1, including family composition, physical condition, weather, past meals, and preferred and disliked ingredients, along with the emotional data generated in step 2, to the server.

[1564] Step 4:

[1565] The server analyzes the received information, obtaining information on family composition, physical condition, weather, past dietary habits, favorite and disliked ingredients, and the user's emotional state. Based on this information, the server preprocesses the data based on the relevant information in the database. Specifically, it checks the consistency of the data and begins calculating the necessary nutrients.

[1566] Step 5:

[1567] The server calls the generative AI model and passes the received data as input. The generative AI model performs the following calculations to generate a menu:

[1568] Calculate the nutrients you need based on your family composition, physical condition, and climate.

[1569] Consider what you have eaten recently and select a menu that does not overlap with your previous meals.

[1570] Customize menus to reflect the user's preferences and dislikes.

[1571] We suggest baby foods appropriate for the infant's age.

[1572] To adjust a menu to match a user's mood using emotional information.

[1573] Step 6:

[1574] The server transmits the generated menu to the terminal, which generates menu data that is visually displayed on the terminal.

[1575] Step 7:

[1576] The device presents the menu to the user, who can then view it on the device screen and make adjustments or changes as needed.

[1577] Step 8: (Optional)

[1578] After eating, users input their satisfaction level and comments, which generates feedback data.

[1579] Step 9: (Optional)

[1580] The device sends feedback information to the server, which analyzes the received feedback and uses it as training data for the generative AI model, thereby improving the accuracy of the next menu suggestion.

[1581] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1582] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1583] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1584] [Fourth embodiment]

[1585] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1586] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1587] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1588] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1589] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1590] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1591] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1592] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1593] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1594] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1595] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1596] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1597] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1598] This invention provides a system that enables users to prepare nutritionally balanced meals at home, and is mainly composed of a user, a terminal, and a server.

[1599] Overall system overview

[1600] The user uses the device to input information such as family composition, physical condition, weather, past meals, and favorite and disliked ingredients. The device then sends this information to the server, which then uses a generative AI model to generate an optimal menu. The generated menu is then sent to the device and presented to the user.

[1601] Detailed System Description

[1602] User side

[1603] 1. The user launches an application on the device.

[1604] 2. The user enters the number of family members and their roles (e.g., two adults, one child, one baby), their recent health condition (e.g., feeling a bit under the weather), the current weather (e.g., winter), past meals (e.g., curry rice), favorite dishes and ingredients (e.g., Japanese food), and ingredients they dislike (e.g., spicy food).

[1605] 3. The device sends this information to the server.

[1606] Server side

[1607] 4. The server receives the input data.

[1608] 5. The server analyzes the received data and passes it to the generative AI model.

[1609] 6. The server generates the optimal menu using a generative AI model. The generative AI model takes into account:

[1610] Appropriate ingredients and portions based on family size and number of people.

[1611] A nutritionally balanced diet suited to your physical condition and the weather.

[1612] Consider your past dietary habits and change the ingredients and cooking methods.

[1613] A menu that reflects your likes and dislikes.

[1614] Suggestions for safe baby food according to the age of infants and toddlers.

[1615] Specific examples

[1616] The menu generated by the server looks like this:

[1617] For adults and a 5-year-old child: Chicken and vegetable hotpot, miso soup with lots of ingredients, and boiled spinach.

[1618] For babies (8 months): Softly simmered carrots and chicken, pumpkin puree.

[1619] Terminal side

[1620] 7. The server sends the generated menu to the terminal.

[1621] 8. The device visually presents the menu to the user, who can review the suggested menu and make adjustments or changes as needed.

[1622] Collect feedback (optional)

[1623] 9. After the meal, the user enters their satisfaction level and comments.

[1624] 10. The device sends the feedback information to the server.

[1625] 11. The server analyzes the received feedback and uses it as training data for the generative AI model. This feedback can improve the accuracy of the next menu suggestion.

[1626] Example

[1627] For example, if a user has a cold on a cold winter day, the system might suggest the following menu: a nutritious chicken and vegetable hotpot, a hearty miso soup, and a vitamin-rich spinach salad. For a baby, it might suggest softly simmered carrots and chicken, or pumpkin puree, which are easy to digest. This system allows users to efficiently provide healthy meals.

[1628] The processing flow will be explained below.

[1629] Step 1:

[1630] The user launches an application on the device.

[1631] Step 2:

[1632] The device displays an information input form to the user, which includes information such as family composition, physical condition, weather, past dietary habits, and food preferences and dislikes.

[1633] Step 3:

[1634] The user enters the required data into an information entry form, for example:

[1635] Family composition: 2 adults, 1 5-year-old child, 1 8-month-old baby

[1636] Health: I've been feeling a bit sick lately

[1637] Climate: Winter

[1638] Past meals: Curry rice

[1639] Favorite food: Japanese food

[1640] Foods I dislike: Spicy foods

[1641] Step 4:

[1642] The terminal collects information input by the user and transmits it to the server.

[1643] Step 5:

[1644] The server analyzes the information it receives and understands the individual's family composition, physical condition, weather, past dietary habits, and food preferences and dislikes.

[1645] Step 6:

[1646] The server calls the generative AI model based on the received data. The generative AI model generates a menu using the following steps:

[1647] Calculate the nutrients you need based on your family composition, physical condition, and climate.

[1648] Consider what you have eaten recently and select a menu that does not overlap with your previous meals.

[1649] Customize menus to reflect the user's preferences and dislikes.

[1650] We recommend appropriate baby foods based on the age of the infant.

[1651] Step 7:

[1652] The server uses a generative AI model to generate the optimal menu. For example, it generates the following menu:

[1653] For adults and children aged 5 and over: Chicken and vegetable hotpot, miso soup with lots of ingredients, and boiled spinach

[1654] For 8-month-old babies: soft-boiled carrots and chicken, pumpkin puree

[1655] Step 8:

[1656] The server sends the generated menu to the terminal.

[1657] Step 9:

[1658] The device visually displays the menu to the user, who can review the suggested menu and make adjustments or changes as needed.

[1659] Step 10:

[1660] (Optional) After the meal, the user enters feedback information, such as satisfaction level and comments.

[1661] Step 11:

[1662] (Optional) The device sends feedback information to the server.

[1663] Step 12:

[1664] (Optional) The server analyzes the received feedback and uses it as training data for the generative AI model, thereby improving the accuracy of the next menu suggestion.

[1665] The above is the flow of specific processing steps of this system.

[1666] Example 1

[1667] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1668] Traditionally, when preparing nutritionally balanced meals at home, users have had to spend time and effort creating menus that take into account information such as family composition, physical condition, weather, past dietary habits, and food preferences and dislikes. However, it is difficult to comprehensively assess these diverse factors and propose an appropriate menu each time. There is also a need for systems that can suggest baby foods that reflect changes in past dietary habits and the child's growth. This requires a system that allows users to create menus efficiently and accurately.

[1669] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1670] In this invention, the server includes means for inputting the user's family composition, physical condition, weather, past meal contents, and preferences, means for receiving the input information, means for analyzing the received information, means for passing the analyzed information to a generative AI model, means for generating an optimal menu using the generative AI model, and means for presenting the generated menu to the user, thereby enabling the user to efficiently and accurately prepare a nutritionally balanced menu that meets the needs of their family.

[1671] The "user" is the person who uses this system to plan a menu and inputs data such as information about their family and ingredients they like and dislike.

[1672] A "terminal" is a device that a user uses to input information and communicate with a server, such as a smartphone, tablet, or PC.

[1673] The "server" is a central processing unit that receives and analyzes information sent by users and generates menus using generative AI models.

[1674] "Family composition" refers to information such as the number, ages, and roles of members in the user's household, and is an important factor in generating menus.

[1675] "Physical condition" refers to the current health condition of the user or family, and includes a cold, a specific illness, allergies, etc.

[1676] "Climate" refers to the current weather conditions, such as information about how the suitability of a dish changes depending on the season or weather.

[1677] "Past meal content" refers to the history of meals the user or their family has eaten in the past, and is used to ensure variety in menus.

[1678] "Preferences" refers to the dishes and ingredients that the user or their family prefers, as well as things they want to avoid.

[1679] A "generative AI model" is an artificial intelligence model that generates optimal menus based on received information.

[1680] A "prompt" is a set of instructions input to a generative AI model, a document that details the necessary requirements and conditions.

[1681] "Feedback information" refers to information such as satisfaction and comments entered by the user after a meal, and is used to improve the accuracy of menu suggestions for the next time.

[1682] This invention provides a system that allows users to prepare nutritionally balanced meals at home, and is primarily composed of a user, a terminal, and a server. Specifically, the user uses the terminal to input information about their family composition, physical condition, weather, past meals, preferences, and disliked ingredients, and the terminal then sends this information to the server. The server then passes the received information to a generative AI model to generate an optimal menu. The generated menu is then sent to the terminal and presented to the user.

[1683] User operations

[1684] 1. A user launches an application on a device, such as a smartphone, tablet, or PC.

[1685] 2. The user enters information such as family composition (e.g., two adults, one child, one baby), physical condition (e.g., feeling a bit under the weather), weather (e.g., winter), past meals (e.g., curry rice), favorite dishes and ingredients (e.g., Japanese food), and disliked ingredients (e.g., spicy food). The device is equipped with an interface for entering information, and the user enters information using text boxes and drop-down menus.

[1686] Terminal handling

[1687] The device sends the information entered by the user to the server using an API, with the data formatted in JSON.

[1688] Server Processing

[1689] The server analyzes the information received from the device. A Python library (e.g., pandas) or a dedicated analysis module is used for the analysis. The analyzed information is then converted into a prompt statement format and passed to a generative AI model (e.g., OpenAI GPT-4). An example of a prompt statement is shown below.

[1690] Prompt Sentence Examples

[1691] "Our family consists of two adults, one five-year-old child, and one eight-month-old baby. I've been feeling a bit under the weather lately, so I'd like to know what kind of nutritionally balanced meal we can make for cold winter days. In the past, our meal was curry and rice. I like Japanese food, but I don't like spicy food."

[1692] Processing generative AI models

[1693] When the generative AI model receives the prompt, it generates an appropriate menu. Factors taken into consideration include family composition and number of people, physical condition, weather, past dietary habits, food preferences and dislikes, and baby food suggestions based on the infant's age. For example, the following menu may be generated:

[1694] Specific examples

[1695] For adults and a 5-year-old child: Chicken and vegetable hotpot, miso soup with lots of ingredients, and boiled spinach.

[1696] For babies (8 months): Softly simmered carrots and chicken, pumpkin puree.

[1697] Menu presentation

[1698] The server sends the generated menu to the device. The menu is formatted in JSON format or similar and sent to the device.

[1699] The device visually presents the menu to the user, who can review the suggested menu and make adjustments or changes as needed.

[1700] Collect feedback (optional)

[1701] After eating, users can enter feedback such as their satisfaction level and comments. The device sends this feedback information to the server, which analyzes the received feedback and reflects it as learning data for the generative AI model. This improves the accuracy of the next menu suggestion.

[1702] The above is a specific embodiment for carrying out the present invention.

[1703] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1704] Step 1:

[1705] The user launches an application on the device.

[1706] What happens: A user taps an application icon on a device such as a smartphone, tablet, or computer.

[1707] Input: Applications installed on the device.

[1708] Output: Application launch.

[1709] Step 2:

[1710] The user inputs information about family composition, physical condition, weather, past meals, preferences, and disliked ingredients.

[1711] What it does: Enter information into an application's input screen using text boxes and drop-down menus.

[1712] Input: User's family composition (e.g., 2 adults, 1 child, 1 baby), physical condition (e.g., feeling a bit under the weather), weather (e.g., winter), past meals (e.g., curry rice), preferences and disliked foods (e.g., likes Japanese food, dislikes spicy food), etc.

[1713] Output: The input data.

[1714] Step 3:

[1715] The terminal transmits the input information to the server.

[1716] How it works: After the user enters the information, they press the submit button, and the device sends the data to the server via the API. The data is formatted in JSON format.

[1717] Input: Information entered by the user.

[1718] Output: Information sent to the server.

[1719] Step 4:

[1720] The server analyzes the received information.

[1721] How it works: The server uses a parsing function to analyze the received data and break down each item (family composition, physical condition, climate, etc.). This analysis uses Python libraries (e.g., pandas).

[1722] Input: Information sent from the device.

[1723] Output: Parsed data.

[1724] Step 5:

[1725] The server passes the analyzed information to the generative AI model.

[1726] How it works: A prompt is generated based on the analysis data and passed to a generative AI model (e.g., OpenAI GPT-4). The prompt includes the analysis results.

[1727] Input: Analysis data.

[1728] Output: The prompt sentence that is passed to the generative AI model.

[1729] Step 6:

[1730] The server uses a generative AI model to generate the optimal menu.

[1731] How it works: The generative AI model receives prompts and generates appropriate menu suggestions based on family composition, physical condition, weather, past dietary habits, food preferences and dislikes, and the age of the infant.

[1732] Input: The prompt sentence passed to the generative AI model.

[1733] Output: The generated menu data.

[1734] Step 7:

[1735] The server sends the generated menu to the terminal.

[1736] Operation: The generated menu data is formatted in JSON format or similar and sent to the device.

[1737] Input: Generated menu data.

[1738] Output: Menu data sent to the device.

[1739] Step 8:

[1740] The terminal visually presents the menu to the user.

[1741] Operation: The device displays the menu data received within the application. The user can visually check the detailed information of each menu item.

[1742] Input: Menu data sent from the server.

[1743] Output: Menu information displayed on the screen.

[1744] Step 9 (Optional):

[1745] After the meal, the user enters their satisfaction level and comments.

[1746] How it works: The user enters their satisfaction and comments on the application's feedback screen and presses the submit button.

[1747] Input: User feedback information.

[1748] Output: The input feedback data.

[1749] Step 10 (Optional):

[1750] The terminal sends feedback information to the server.

[1751] Operation: The entered feedback information is sent to the server via the API.

[1752] Input: Feedback data entered by the user.

[1753] Output: Feedback information sent to the server.

[1754] Step 11 (Optional):

[1755] The server analyzes the feedback and reflects it in the generative AI model.

[1756] How it works: The server analyzes the feedback data and uses it to retrain the generative AI model, improving the accuracy of future menu suggestions.

[1757] Input: Feedback information sent from the device.

[1758] Output: An updated generative AI model.

[1759] (Application example 1)

[1760] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1761] In modern families, preparing nutritionally balanced meals with busy schedules and limited resources is challenging. It's particularly challenging to efficiently plan appropriate menus, as it requires consideration of family composition, health status, weather conditions, past dietary history, and preferences. Furthermore, the process of ordering meals based on the menu can be cumbersome. There is a need for a system that solves these problems, simplifies home meal preparation, and provides healthy meals.

[1762] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1763] In this invention, the server includes means for inputting a user's household composition, health condition, weather conditions, previous meal history, and preferences, means for receiving the input information, a generation AI model means for generating a nutritionally balanced menu using the received information, means for filtering and displaying information related to meal options suggested based on the generated menu, and means for ordering the selected meal options. This allows the user to receive a suggested menu optimal for their household composition and health condition, and to efficiently order meals based on the suggested menu.

[1764] "User" refers to an individual or household that uses the system.

[1765] "Family composition" refers to information indicating the number and roles of members in a household.

[1766] "Health status" refers to information about the health of the user and their family members, such as their recent physical condition and medical history.

[1767] "Weather conditions" refers to information about the current climate and season.

[1768] "Past dietary history" refers to the contents of meals that the user and their family have eaten in the past.

[1769] "Preferences" refers to information about dishes and ingredients that the user and their family like, as well as dishes and ingredients that they dislike or want to avoid.

[1770] "Input means" refers to devices or software that allow a user to provide information about their household configuration, health status, weather conditions, past dietary history, and preferences to the system.

[1771] "Means for receiving" refers to an interface for receiving input information on the server side.

[1772] "Generative AI model means" refers to artificial intelligence technology and its model for generating optimal menus based on received information.

[1773] "Filtering and display means" refers to functionality for filtering and displaying suggested meal options to the user based on the generated menu.

[1774] "Means for ordering" refers to a function that provides a process for the user to actually order the meal options selected.

[1775] Overall system overview

[1776] This system consists of a user, a device, and a server, and helps users prepare nutritionally balanced meals at home. Users use their device to input information about their household composition, health status, weather conditions, past meal history, and preferences, and the server uses a generative AI model to generate an optimal menu. Based on the menu, meal options are filtered and presented to the user. The user can then select the desired meal option and place an order.

[1777] User side

[1778] 1. The user launches an application on the device.

[1779] 2. The user enters information about their household, health condition, weather conditions, past meal history, preferences, budget, and desired delivery time.

[1780] 3. The device sends this information to the server.

[1781] Server side

[1782] 1. The server receives the input data.

[1783] 2. The server analyzes the received data and passes it to the generative AI model.

[1784] 3. The server generates the optimal menu using a generative AI model that takes into account:

[1785] Appropriate ingredients and portions based on household size and number of people.

[1786] A nutritionally balanced diet tailored to your health and weather conditions.

[1787] Consider your past eating history and change ingredients and cooking methods.

[1788] A menu that reflects your preferences and dislikes.

[1789] 4. The server filters the available meal options based on the generated menu and presents them to the user.

[1790] 5. The server provides the information to process the order for the meal options selected by the user.

[1791] Specific examples

[1792] For example, if a user has a cold on a cold winter day, the system may suggest the following menu items: nutritious chicken and vegetable hotpot, hearty miso soup, and vitamin-rich spinach ohitashi. For babies, it suggests easy-to-digest dishes such as softly simmered carrots and chicken, and pumpkin puree. Based on the suggested menu items, appropriate meal options are filtered and presented to the user. The user can then select the desired meal and complete the order.

[1793] Hardware and software used

[1794] Hardware: User device (smartphone or tablet), server.

[1795] Software: terminal application, server receiving interface, generative AI model (e.g., OpenAI's GPT), filtering and display functions, order processing system.

[1796] Prompt Sentence Examples

[1797] The user provided the following information:

[1798] Family composition: 2 adults, 1 child (age 5)

[1799] Health condition: I've been feeling a bit under the weather lately

[1800] Weather conditions: Winter

[1801] Past meal history: Curry rice

[1802] Favorite food: Japanese food

[1803] Foods I dislike: Spicy foods

[1804] Budget: 5,000 yen

[1805] Delivery time: 2023-11-20 18:30

[1806] Use this information to generate optimal menus and suggest food options for the user to order.

[1807] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1808] Step 1:

[1809] The user launches an application on the device.

[1810] Users input information about their household composition, health condition, weather conditions, past meal history, preferences, budget, and desired delivery time.

[1811] Input: household composition, health condition, weather conditions, past meal history, preferences, budget, desired delivery time.

[1812] Output: The input data on the terminal.

[1813] Step 2:

[1814] The device sends the input data to the server

[1815] The terminal transmits the information input by the user to the server as a data packet.

[1816] Input: Input data on the terminal.

[1817] Output: Data packets sent to the server.

[1818] Step 3:

[1819] The server receives the input data

[1820] The server receives the data packets sent from the terminal and performs preprocessing for analysis.

[1821] Input: The data packet sent to the server.

[1822] Output: Structured data to be parsed.

[1823] Step 4:

[1824] The server passes the data to the generative AI model

[1825] The server inputs the analyzed data into the generative AI model and requests it to generate the optimal menu. Specifically, the server passes the data to the generative AI model in the form of a "prompt sentence."

[1826] Input: Structured data.

[1827] Output: A prompt to the generative AI model.

[1828] Step 5:

[1829] Generative AI model generates optimal menu

[1830] The generative AI model generates the optimal menu based on prompts from the server, taking into account household composition, health status, weather conditions, past meal history, and preferences.

[1831] Input: Prompt statement.

[1832] Output: Optimal menu.

[1833] Step 6:

[1834] Filtering meal options based on server-generated menus

[1835] Based on the generated menu, the server selects the optimal meal options from affiliated restaurants and meal kit services and presents the filtered results to the user.

[1836] Input: Optimal menu.

[1837] Output: A filtered list of meal options.

[1838] Step 7:

[1839] The device presents meal options to the user.

[1840] The terminal displays the list of meal options received from the server to the user and prompts them to make a selection.

[1841] Input: A filtered list of meal options.

[1842] Output: The meal options presented to the user.

[1843] Step 8:

[1844] The user selects a meal option and places an order

[1845] The user selects the desired meal options from the presented options and confirms the order.

[1846] Input: A list of meal options.

[1847] Output: Confirmed order data.

[1848] Step 9:

[1849] The server processes the order data and sends the order information to the restaurant or meal kit service

[1850] The server processes the user's order data and transmits the order information to the appropriate restaurant or meal kit service.

[1851] Input: Confirmed order data.

[1852] Output: Order information to a restaurant or meal kit service.

[1853] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1854] This invention provides a system for users to prepare nutritionally balanced meals at home, and is mainly composed of a user, a terminal, a server, and an emotion engine.

[1855] Overall system overview

[1856] The user uses the device to input information such as family composition, physical condition, weather, past meals, and favorite and disliked ingredients, and the emotion engine then recognizes the user's emotions. The device then sends this information to the server, which then uses a generative AI model to generate an optimal menu. The generated menu is then sent to the device and presented to the user.

[1857] Detailed System Description

[1858] User side

[1859] 1. The user launches an application on the device.

[1860] 2. The user enters the number of family members and their roles (e.g., two adults, one child, one baby), their recent health condition (e.g., feeling a bit under the weather), the current weather (e.g., winter), past meals (e.g., curry rice), favorite dishes and ingredients (e.g., Japanese food), and ingredients they dislike (e.g., spicy food).

[1861] 3. The user's emotions are recognized from their voice and facial expressions through the camera and microphone installed on the device.

[1862] 4. The device sends this information (family composition, physical condition, weather, past meals, favorite and disliked ingredients, and recognized emotions) to the server.

[1863] Server side

[1864] 5. The server analyzes the received information to understand the user's family structure, physical condition, weather, past dietary habits, favorite and disliked ingredients, and emotional information.

[1865] 6. The server calls the generative AI model based on the received data. The generative AI model generates a menu using the following steps:

[1866] Calculate the nutrients you need based on your family composition, physical condition, and climate.

[1867] Consider what you have eaten recently and select a menu that does not overlap with your previous meals.

[1868] Customize menus to reflect the user's preferences and dislikes.

[1869] We recommend appropriate baby foods based on the age of the infant.

[1870] The recognized emotional information is used to adjust the menu to suit the user's mood.

[1871] Specific examples

[1872] The menu generated by the server looks like this:

[1873] For adults and children aged 5 and over: Chicken and vegetable hotpot, miso soup with lots of ingredients, and boiled spinach

[1874] For 8-month-old babies: soft-boiled carrots and chicken, pumpkin puree

[1875] For example, if the user has a tired expression, a menu using ingredients that have a relaxing effect may be suggested.

[1876] Terminal side

[1877] 7. The server sends the generated menu to the terminal.

[1878] 8. The device visually presents the menu to the user, who can review the suggested menu and make adjustments or changes as needed.

[1879] Collect feedback (optional)

[1880] 9. After the meal, the user enters their satisfaction level and comments.

[1881] 10. The device sends the feedback information to the server.

[1882] 11. The server analyzes the received feedback and uses it as training data for the generative AI model, thereby improving the accuracy of the next menu suggestion.

[1883] Example

[1884] For example, if a user has a cold on a cold winter day and looks tired, the system might suggest the following menu: a nutritious chicken and vegetable hotpot, a hearty miso soup, and a vitamin-rich spinach salad. For a baby, it might suggest softly simmered carrots and chicken, or pumpkin puree, which are easy to digest. This system allows users to efficiently provide healthy meals. Furthermore, the menu is adjusted based on the user's emotions, so the user is provided with a meal that suits their mood, improving meal satisfaction.

[1885] The processing flow will be explained below.

[1886] Step 1:

[1887] The user launches an application on the device.

[1888] Step 2:

[1889] The device displays an information input form to the user, which includes information such as family composition, physical condition, weather, past dietary habits, and food preferences and dislikes.

[1890] Step 3:

[1891] The user enters the required data into the information entry form, specifically the following information:

[1892] Family composition: 2 adults, 1 5-year-old child, 1 8-month-old baby

[1893] Health: I've been feeling a bit sick lately

[1894] Climate: Winter

[1895] Past meals: Curry rice

[1896] Favorite food: Japanese food

[1897] Foods I dislike: Spicy foods

[1898] Step 4:

[1899] The device stores the user's input data and activates the emotion engine, which activates the camera and microphone to recognize the user's facial expressions and voice.

[1900] Step 5:

[1901] The user speaks to the device or faces the device's camera, and their voice and facial expressions are collected. For example, in response to a question such as "How are you feeling today?", they can respond with "I'm a little tired."

[1902] Step 6:

[1903] The emotion engine analyzes the user's voice and facial expression data to recognize the user's emotional state (e.g., tired, stressed).

[1904] Step 7:

[1905] The terminal transmits the input information and the emotion information recognized by the emotion engine to the server.

[1906] Step 8:

[1907] The server analyzes the received information to understand the user's family composition, physical condition, weather, past dietary habits, favorite and disliked ingredients, and emotional information.

[1908] Step 9:

[1909] The server calls the generative AI model based on the received data. The generative AI model generates a menu using the following steps:

[1910] Calculate the nutrients you need based on your family composition, physical condition, and climate.

[1911] Consider what you have eaten recently and select a menu that does not overlap with your previous meals.

[1912] Customize menus to reflect the user's preferences and dislikes.

[1913] We recommend appropriate baby foods based on the age of the infant.

[1914] Recognized emotional information (tired, stressed, etc.) is used to tailor a menu to suit the user's mood.

[1915] Step 10:

[1916] The server generates the final menu and sends it to the device. For example, it generates the following menu:

[1917] For adults and children aged 5 and over: Chicken and vegetable hot pot (using relaxing dashi stock), miso soup with lots of ingredients, and boiled spinach

[1918] For 8-month-old babies: soft-boiled carrots and chicken, pumpkin puree

[1919] Step 11:

[1920] The device visually displays the generated menu to the user, who can review the suggested menu and make adjustments or changes as needed.

[1921] Step 12:

[1922] (Optional) After the meal, the user enters their satisfaction and feedback on the menu. Enter the feedback information according to the displayed form.

[1923] Step 13:

[1924] (Optional) The device sends feedback information to the server.

[1925] Step 14:

[1926] (Optional) The server analyzes the received feedback and uses it as training data for the generative AI model, thereby improving the accuracy of the next menu suggestion.

[1927] The above is the flow of specific processing steps of this system.

[1928] Example 2

[1929] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1930] In modern families, preparing nutritionally balanced meals is important, but it is not easy to consider a wide range of factors, such as family composition, health status, weather, past dietary habits, and preferences.In addition, there is a lack of systems that can provide appropriate meals for children's developmental stages or menus that suit the user's emotional state, making it difficult to cook accurately and efficiently.

[1931] The specification process by the specification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for inputting the user's family structure, physical condition, climate, past meal contents, emotions, and preferences, a means for receiving the input information, and a generation AI model means for generating a nutritionally balanced menu using the received information. This makes it possible to generate and present an optimal menu based on the user's family structure, health condition, climate, meal history, and emotional information.

[1932] "Family structure" refers to the number and roles of all individuals living together in the household.

[1933] "Physical condition" refers to the health condition of the user and their family, and includes illness, fatigue, stress, and other conditions.

[1934] "Climate" refers to current weather conditions and environmental factors such as season, temperature, and humidity.

[1935] "Past meals" refers to a record of meals eaten by the user and their family over a certain period of time in the past.

[1936] "Emotion" refers to the user's psychological state, including states such as fatigue, stress, and joy.

[1937] "Preferences" refers to the ingredients and dishes that the user and their family like, as well as ingredients that they dislike.

[1938] "Input means" refers to a device or interface that allows a user to input information, including a keyboard, a touch screen, a voice input device, etc.

[1939] "Means for receiving" refers to a mechanism by which the terminal transmits input information to the server and the server receives the information.

[1940] "Generative AI model means" refers to an artificial intelligence algorithm that analyzes received data and generates an optimal menu.

[1941] "Presentation means" refers to a device or interface for providing the generated menu to the user visually or audibly.

[1942] "Baby food recipes" refer to specific cooking methods for providing nutritionally balanced meals according to the developmental stage of infants and young children.

[1943] The "function to select ingredients and cooking methods" refers to a function that recommends new ingredients and cooking methods based on past meal content and the user's preferences.

[1944] This invention provides a system for users to prepare nutritionally balanced meals at home, and is mainly composed of a user, a terminal, a server, and an emotion engine.

[1945] User side

[1946] First, the user launches the application on their device, then enters information such as the number of family members and their roles (e.g., two adults, one child, and one baby), their recent health condition (e.g., feeling a bit under the weather), the current weather (e.g., winter), past meals (e.g., curry rice), favorite dishes and ingredients (e.g., Japanese food), and disliked ingredients (e.g., spicy food).

[1947] The emotion engine recognizes emotions from the user's voice and facial expressions through the device's built-in camera and microphone. For example, if the user is feeling tired or stressed, that information will be recognized.

[1948] Terminal side

[1949] The device sends this information (family composition, physical condition, weather, past dietary habits, favorite and disliked ingredients, and recognized emotions) to a server. At this time, the data is encrypted using security protocols (e.g., SSL / TLS) to ensure secure communication.

[1950] Server side

[1951] The server analyzes the received information to understand the family composition, health status, weather, past dietary habits, food preferences, dislikes, and emotional information. Based on the analysis results, a generative AI model is used to generate a menu using the following steps:

[1952] Generative AI model processing steps

[1953] 1. Calculate your nutritional needs based on your family composition, physical condition, and climate.

[1954] 2. Consider past meal choices and select a menu that does not overlap.

[1955] 3. Customize menus to reflect the user's preferences and dislikes.

[1956] 4. Suggest appropriate baby foods according to the infant's age.

[1957] 5. Use the recognized emotional information to tailor the menu to suit the user's mood.

[1958] As a concrete example, the menu generated by the server is as follows:

[1959] For adults and children aged 5 and over: Chicken and vegetable hotpot, miso soup with lots of ingredients, and boiled spinach

[1960] For 8-month-old babies: soft-boiled carrots and chicken, pumpkin puree

[1961] Display on the terminal

[1962] The server sends the generated menu to the device, which then visually presents the received menu to the user, who can then review the proposed menu and make adjustments or changes as needed. For example, the user can exclude certain ingredients from the proposed menu.

[1963] Collect feedback (optional)

[1964] After eating, users input their satisfaction level and comments. This information is sent to the server via their device. The server analyzes the received feedback and uses it as training data for the generative AI model to improve the accuracy of the next menu suggestion.

[1965] Prompt Sentence Examples

[1966] If the user looks tired on a winter's day, the server might suggest a warm, nutritious meal, such as chicken and vegetable hotpot, hearty miso soup, or boiled spinach.

[1967] This invention not only enables users to efficiently prepare nutritionally balanced meals, but also improves meal satisfaction because the menu is adjusted based on the user's emotions.

[1968] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1969] Step 1:

[1970] The user launches an application on the device.

[1971] Action: Tap or click an application to launch it.

[1972] Input: None.

[1973] Output: The application's welcome screen is displayed.

[1974] Step 2:

[1975] The user inputs information about their family composition, physical condition, weather, past meals, and favorite and disliked ingredients.

[1976] How it works: The user enters information into an input form displayed on the application screen, such as family composition (e.g., two adults, one child, one baby), recent health condition (e.g., feeling a bit under the weather), current weather (e.g., winter), past meals (e.g., curry rice), favorite dishes and ingredients (e.g., Japanese food), and disliked ingredients (e.g., spicy food).

[1977] Input: Information entered by the user.

[1978] Output: The entered information is saved on the device.

[1979] Step 3:

[1980] The device recognizes the user's emotions.

[1981] How it works: The device's built-in camera and microphone capture the user's voice and facial expressions, which are then analyzed using emotion recognition algorithms.

[1982] Input: User's voice data and facial expression data.

[1983] Output: Emotional information (e.g., fatigue, stress) is obtained as the analysis result.

[1984] Step 4:

[1985] The device sends this information (family composition, physical condition, weather, past meals, favorite and disliked ingredients, and recognized emotions) to the server.

[1986] Operation: The device compiles the input information and emotion information and sends it to the server. The data is encrypted using a communication protocol (e.g., SSL / TLS) before being sent.

[1987] Input: Aggregated user information and sentiment information.

[1988] Output: The data is sent to the server.

[1989] Step 5:

[1990] The server analyzes the received information.

[1991] How it works: The server analyzes information about the user's family structure, physical condition, weather, past meals, favorite and disliked ingredients, and emotional information. It also compares this information with past data stored in a database.

[1992] Input: User information and emotion information sent to the server.

[1993] Output: As a result of the analysis, relevant data is extracted.

[1994] Step 6:

[1995] The server generates a menu using a generative AI model.

[1996] How it works: The server calls the generative AI model and generates a menu using the following steps:

[1997] Calculate the nutrients you need based on your family composition, physical condition, and climate.

[1998] Consider past meal choices and select a menu that does not overlap.

[1999] Customize menus to reflect the user's preferences and dislikes.

[2000] We recommend appropriate baby foods based on the age of the infant.

[2001] The recognized emotional information is used to adjust the menu to suit the user's mood.

[2002] Input: Parsed user information and sentiment information.

[2003] Output: The generated menu.

[2004] Step 7:

[2005] The server sends the generated menu to the terminal.

[2006] Operation: The server sends the generated menu data to the device. The data is sent securely using a communication protocol (e.g., HTTP / HTTPS).

[2007] Input: Generated menu data.

[2008] Output: Menu data is sent to the terminal.

[2009] Step 8:

[2010] The terminal presents the menu to the user.

[2011] Operation: The device visually displays the received menu to the user, for example, displaying the menu details on the application screen.

[2012] Input: Received menu data.

[2013] Output: The menu displayed to the user.

[2014] Step 9:

[2015] After the meal, the user enters their satisfaction and comments (optional).

[2016] What happens: A user fills out a feedback form in an application to rate their satisfaction and provide comments.

[2017] Input: User feedback information (e.g., taste rating, portion size, ease of cooking).

[2018] Output: Feedback information is saved to the device.

[2019] Step 10:

[2020] The terminal sends feedback information to the server.

[2021] Operation: The terminal sends the input feedback information to the server. The feedback information is also encrypted before being sent.

[2022] Input: User feedback information.

[2023] Output: The feedback information is sent to the server.

[2024] Step 11:

[2025] The server analyzes the feedback.

[2026] Operation: The server analyzes the feedback information it receives and uses it as training data for the generative AI model.

[2027] Input: Received feedback information.

[2028] Output: Training data used for next menu suggestion.

[2029] (Application example 2)

[2030] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2031] In modern households, it is difficult to plan an appropriate menu that takes into account each member's health condition, preferences, and weather. Providing a daily diet that satisfies all household members and is nutritionally balanced can be a burden. In particular, it is necessary to consider the effort required to plan a menu each time, including baby food, and to suggest meals that respond to the user's emotional state.

[2032] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[2033] In this invention, the server includes means for inputting a user's family composition, physical condition, climate, past meal contents, and preferences, means for receiving the input information, a generation AI model means for generating a nutritionally balanced menu using the received information, means for recognizing the user's emotions, means for adjusting the menu based on the user's emotional information, and means for presenting the generated menu to the user. This not only enables the user to easily prepare a nutritionally balanced meal that suits the physical condition, preferences, and climate of each member, but also allows for a more satisfying meal by suggesting a menu that reflects the user's emotional state.

[2034] A "user" is a person who uses the system to manage meals for the household.

[2035] "Family structure" refers to the type and number of members in a household, including, for example, adults, children, babies, etc.

[2036] "Physical condition" refers to the current health condition of the user and their family, and includes, for example, having a cold or feeling tired.

[2037] "Climate" refers to the current weather conditions of the external environment, including, for example, winter, summer, rain, etc.

[2038] "Past meal contents" refers to the history of meals served at home, including specific dishes such as curry rice and salad.

[2039] "Preferences" refers to the likes and dislikes of the user and their family, and includes information such as liking Japanese food and dislike of spicy food.

[2040] "Input means" refers to a device or application that allows a user to provide information about family composition, physical condition, weather, past meals, and preferences to the system.

[2041] "Means for receiving" refers to a device or application that has the function of sending input information to a server and receiving it.

[2042] "Generative AI model means" refers to an artificial intelligence model for generating an optimal menu based on received information.

[2043] "Means for recognizing emotions" refers to devices such as cameras and microphones that can grasp emotions from the user's facial expressions and voice.

[2044] "Means for adjusting menu based on emotional information" refers to a function for adjusting menu based on the recognized emotional state of the user.

[2045] "Presentation means" refers to a device such as a display or speaker that provides the generated menu to the user visually or audibly.

[2046] The "system" refers to the overall mechanism that combines the above means to prepare nutritionally balanced meals in the user's home.

[2047] This invention is a system for users to prepare nutritionally balanced meals at home, and is mainly composed of a user, a terminal, a server, and an emotion engine. A specific embodiment of this system is described below.

[2048] Overall system overview

[2049] The user uses a device (such as a smartphone) to input information such as family composition, physical condition, weather, past meals, and favorite and disliked ingredients, and the emotion engine then recognizes the user's emotions. The device then sends this information to the server, which then uses a generative AI model to generate an optimal menu. The generated menu is then sent to the device and presented to the user visually or audibly.

[2050] Detailed System Description

[2051] User side

[2052] 1. The user launches the application on their device. The user inputs their own and their family's health status and preferences, and provides current weather information.

[2053] 2. The emotion engine recognizes emotions from the user's voice and facial expressions through the camera and microphone installed on the device.

[2054] 3. The device sends this information (family composition, physical condition, weather, past meals, favorite and disliked ingredients, and recognized emotions) to the server.

[2055] Server side

[2056] 1. The server analyzes the information it receives, including information about the user's family structure, physical condition, weather, past meals, favorite and disliked ingredients, and emotional information.

[2057] 2. The server generates an optimal menu by calling a generative AI model based on the received data. The generative AI model calculates necessary nutrients, takes into account recent meals, and customizes the menu to reflect the user's preferences and dislikes. It also uses recognized emotional information to adjust the menu to suit the user's mood.

[2058] Specific examples

[2059] For example, if the user has a cold on a cold winter day, the server might generate the following menu:

[2060] For adults and children: Chicken and vegetable hotpot, miso soup with lots of ingredients, and boiled spinach

[2061] For babies: soft boiled carrots and chicken, pumpkin puree

[2062] If the user is feeling tired, a menu containing ingredients that have a relaxing effect may be suggested.

[2063] Collect feedback (optional)

[2064] 1. After eating, the user enters their satisfaction level and comments.

[2065] 2. The device sends the feedback information to the server.

[2066] 3. The server analyzes the received feedback and uses it as training data for the generative AI model, thereby improving the accuracy of the next menu suggestion.

[2067] Prompt Sentence Examples

[2068] If the user is suffering from a cold on a cold winter day and looks tired, the system will suggest the following meal plans:

[2069] Family consists of 2 adults, 1 child, and 1 baby

[2070] Her favorite food is Japanese food, and her least favorite food is spicy food.

[2071] Users are feeling fatigued

[2072] My last meal was curry rice

[2073] Output:

[2074] For adults and children aged 5 and over: Chicken and vegetable hotpot, miso soup with lots of ingredients, and boiled spinach

[2075] For 8-month-old babies: soft-boiled carrots and chicken, pumpkin puree

[2076] In this way, the user can efficiently prepare healthy meals, and the satisfaction of the meal is improved because the menu is adjusted based on the user's emotional state.

[2077] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2078] Step 1:

[2079] The user launches the application on the device and inputs information such as family composition, physical condition, weather, past meals, and favorite and disliked ingredients. The necessary information is then saved as input data on the device.

[2080] Step 2:

[2081] The user uses the device's built-in camera and microphone to recognize their voice and facial expressions. The device then uses an emotion engine to recognize the user's emotions and generate emotion data. The emotion data is output in the form of, for example, "tired," "energetic," or "sad."

[2082] Step 3:

[2083] The device then sends the information entered in step 1, including family composition, physical condition, weather, past meals, and preferred and disliked ingredients, along with the emotional data generated in step 2, to the server.

[2084] Step 4:

[2085] The server analyzes the received information, obtaining information on family composition, physical condition, weather, past dietary habits, favorite and disliked ingredients, and the user's emotional state. Based on this information, the server preprocesses the data based on the relevant information in the database. Specifically, it checks the consistency of the data and begins calculating the necessary nutrients.

[2086] Step 5:

[2087] The server calls the generative AI model and passes the received data as input. The generative AI model performs the following calculations to generate a menu:

[2088] Calculate the nutrients you need based on your family composition, physical condition, and climate.

[2089] Consider what you have eaten recently and select a menu that does not overlap with your previous meals.

[2090] Customize menus to reflect the user's preferences and dislikes.

[2091] We suggest baby foods appropriate for the infant's age.

[2092] To adjust a menu to match a user's mood using emotional information.

[2093] Step 6:

[2094] The server transmits the generated menu to the terminal, which generates menu data that is visually displayed on the terminal.

[2095] Step 7:

[2096] The device presents the menu to the user, who can then view it on the device screen and make adjustments or changes as needed.

[2097] Step 8: (Optional)

[2098] After eating, users input their satisfaction level and comments, which generates feedback data.

[2099] Step 9: (Optional)

[2100] The device sends feedback information to the server, which analyzes the received feedback and uses it as training data for the generative AI model, thereby improving the accuracy of the next menu suggestion.

[2101] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[2102] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[2103] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[2104] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[2105] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[2106] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[2107] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[2108] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[2109] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[2110] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[2111] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[2112] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[2113] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[2114] 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.

[2115] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[2116] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[2117] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[2118] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[2119] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[2120] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[2121] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[2122] The following is further disclosed regarding the above embodiment.

[2123] (Claim 1)

[2124] A means for inputting the user's family structure, physical condition, weather, past meal contents, and preferences;

[2125] means for receiving the input information;

[2126] A generation AI model means for generating a nutritionally balanced menu using the received information;

[2127] means for presenting the generated menu to a user;

[2128] A system including:

[2129] (Claim 2)

[2130] 2. The system of claim 1, wherein the generative AI model means includes a function for generating baby food recipes according to the child's age.

[2131] (Claim 3)

[2132] The system according to claim 1, wherein the generating AI model means includes a function for selecting different ingredients and cooking methods based on past meal content.

[2133] "Example 1"

[2134] (Claim 1)

[2135] A means for inputting the user's family structure, physical condition, weather, past meal contents, and preferences;

[2136] means for receiving the input information;

[2137] means for analyzing the received information;

[2138] means for passing the analyzed information to a generative AI model;

[2139] A means for generating an optimal menu using the generative AI model;

[2140] means for presenting the generated menu to a user;

[2141] A system including:

[2142] (Claim 2)

[2143] 2. The system of claim 1, wherein the generative AI model means includes a function for generating baby food recipes according to the child's age.

[2144] (Claim 3)

[2145] The system according to claim 1, wherein the generating AI model means includes a function for selecting different ingredients and cooking methods based on past meal content.

[2146] (Claim 4)

[2147] The system of claim 1, further comprising a function for providing feedback information to the generating AI model means to improve the accuracy of the next menu suggestion.

[2148] (Claim 5)

[2149] The system of claim 1, wherein the generative AI model includes a function for receiving analytical information in the form of a prompt sentence and generating a menu.

[2150] "Application Example 1"

[2151] (Claim 1)

[2152] a means for inputting the user's household composition, health status, weather conditions, previous dietary history, and preferences;

[2153] means for receiving the input information;

[2154] A generation AI model means for generating a nutritionally balanced menu using the received information;

[2155] means for filtering and displaying information related to suggested meal options based on the generated menu;

[2156] a means for ordering the selected meal option;

[2157] A system including:

[2158] (Claim 2)

[2159] The system of claim 1, wherein the generative AI model means includes a function for generating baby food recipes according to the age of the infant.

[2160] (Claim 3)

[2161] 2. The system of claim 1, wherein the generative AI model means includes a function for selecting different foods and cooking methods based on past eating history.

[2162] "Example 2: Combining Emotion Engines"

[2163] (Claim 1)

[2164] A means for inputting the user's family structure, physical condition, weather, past meal contents, emotions, and preferences;

[2165] means for receiving the input information;

[2166] A generation AI model means for generating a nutritionally balanced menu using the received information;

[2167] means for presenting the generated menu to a user;

[2168] A system including:

[2169] (Claim 2)

[2170] The system of claim 1, wherein the generative AI model means includes a function for generating baby food recipes according to a child's developmental stage.

[2171] (Claim 3)

[2172] The system according to claim 1, wherein the generating AI model means includes a function for selecting different ingredients and cooking methods based on past meal content.

[2173] "Application example 2 when combining emotion engines"

[2174] (Claim 1)

[2175] A means for inputting the user's family structure, physical condition, weather, past meal contents, and preferences;

[2176] means for receiving the input information;

[2177] A generation AI model means for generating a nutritionally balanced menu using the received information;

[2178] means for recognizing a user's emotion;

[2179] A means for adjusting a menu based on the emotional information of the user;

[2180] means for presenting the generated menu to a user;

[2181] A system including:

[2182] (Claim 2)

[2183] 2. The system of claim 1, wherein the generative AI model means includes a function for generating baby food recipes according to the child's age.

[2184] (Claim 3)

[2185] The system according to claim 1, wherein the generating AI model means includes a function for selecting different ingredients and cooking methods based on past meal content. [Explanation of symbols]

[2186] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for inputting the user's family structure, physical condition, weather, past meal contents, and preferences; means for receiving the input information; A generation AI model means for generating a nutritionally balanced menu using the received information; means for presenting the generated menu to a user; A system including:

2. 2. The system according to claim 1, wherein the generative AI model means includes a function for generating baby food recipes according to the age of the child.

3. The system according to claim 1 , wherein the generating AI model means includes a function for selecting different ingredients and cooking methods based on past meal content.

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