System

Abstract

An object of a system according to an embodiment is to efficiently propose a menu in consideration of likes and dislikes of a family member or a person scheduled to provide the menu.SOLUTION: A system includes a menu proposal part, a history management part, and an interaction part. The menu proposal part proposes a menu in consideration of likes and dislikes of the family and the person scheduled to be provided. The history management part manages a past menu history. The communicator interacts with the user through the messaging app.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] With conventional technology, it was difficult to propose menus that took into account the likes and dislikes of family members and the intended recipients, making it a burden to plan a menu every day.

[0005] The system according to the embodiment aims to efficiently propose a menu, taking into consideration the likes and dislikes of family members and those who will be receiving the meal. [Means for solving the problem]

[0006] The system according to the embodiment includes a menu suggestion unit, a history management unit, and a dialogue unit. The menu suggestion unit proposes a menu that takes into account the likes and dislikes of family members and the person who will be providing the meal. The history management unit manages the history of past menus. The dialogue unit communicates with the user via a messaging app. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently propose a menu, taking into consideration the likes and dislikes of family members and those who will be receiving the meal. [Brief explanation of the drawings]

[0008] [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. DETAILED DESCRIPTION OF THE INVENTION

[0009] 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.

[0010] First, the terms used in the following description will be explained.

[0011] 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, the 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), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] 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.

[0013] 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.

[0014] 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), and Bluetooth (registered trademark).

[0015] 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."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 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.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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).

[0019] 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.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. 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 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. 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.

[0022] 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.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 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.

[0025] 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. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The AI ​​chat system according to an embodiment of the present invention is a system that reduces the effort required to plan a menu, which is the biggest problem faced by people who prepare meals. This system interacts with users through messaging apps such as LINE, and makes menu suggestions that take into account the likes and dislikes of family members and those who will be hosting, suggestions that take into account past menu history, automatically generates daily menus, and makes suggestions through a dialogue format on LINE. This reduces the burden on users and enables efficient menu suggestions.

[0029] An AI chat system according to an embodiment includes a menu suggestion unit, a history management unit, and a dialogue unit. The menu suggestion unit proposes a menu that takes into account the likes and dislikes of the family member or the person who will be hosting the meal. For example, the generation AI proposes an appropriate menu based on the likes, dislikes, and allergy information of the family member or the person who will be hosting the meal provided by the user. The generation AI makes a suggestion such as, "Your child doesn't like vegetables, so how about a hamburger steak with vegetables as a secret ingredient?" The history management unit manages the history of past menus. For example, the generation AI records the history of previously prepared menus and adjusts the menu so that the same dish is not served frequently. For example, to avoid complaints such as, "I ate this the other day," the generation AI refers to menus from the past month and proposes a new menu that does not overlap. The dialogue unit communicates with the user through a messaging app such as LINE. For example, when the user asks, "What should we make today?" via LINE, the generation AI responds with, "It's cold today, so how about some warm soup and bread?" This allows the AI ​​chat system according to an embodiment to reduce the burden on the user and efficiently propose menus.

[0030] The menu suggestion unit can suggest menus that are tailored to individual health goals, taking into account the health status and nutritional balance of family members. For example, if a specific family member is on a diet, the unit suggests a low-calorie, nutritionally balanced menu. This makes it possible to suggest optimal menus that are tailored to the health status of family members.

[0031] The menu suggestion unit can select ingredients according to the season and weather and suggest a menu that incorporates a sense of the season. The menu suggestion unit, for example, selects ingredients according to the season and weather and suggests a menu that incorporates a sense of the season. For example, it suggests cold dishes and refreshing dishes in the summer and hot soups and stews in the winter. This makes it possible to suggest an optimal menu that incorporates a sense of the season.

[0032] The menu suggestion unit can learn the food preferences of family members and suggest special menus tailored to specific events or anniversaries. For example, the menu suggestion unit can learn the food preferences of family members and suggest special menus tailored to specific events or anniversaries. For example, it can suggest dishes that family members like for birthdays or wedding anniversaries. This makes it possible to suggest special menus tailored to specific events or anniversaries.

[0033] The menu suggestion unit suggests dishes from different cultures and countries based on the family's food preferences, allowing the family to enjoy a variety of foods. The menu suggestion unit suggests dishes from different cultures and countries based on the family's food preferences, for example. If the family likes Italian food, for example, it suggests pasta or pizza, and if the family likes Japanese food, it suggests sushi or tempura. This allows the family to suggest dishes from different cultures and countries and enjoy a variety of foods.

[0034] The history management unit can analyze consumption patterns of specific ingredients based on the menu history and make suggestions to reduce waste. The history management unit can analyze consumption patterns of specific ingredients based on the menu history and make suggestions to reduce waste. For example, frequently used ingredients can be given priority in suggestions to reduce waste. This can reduce food waste.

[0035] The history management unit can identify popular seasonal menu items based on the menu history and make suggestions for each season. The history management unit can identify popular seasonal menu items based on the menu history and make suggestions for each season. For example, the history management unit can suggest cold and refreshing dishes in the summer and hot soups and stews in the winter. This allows the system to suggest popular seasonal menu items.

[0036] The history management unit can evaluate the satisfaction of family members with meals based on the menu history and prioritize suggesting menus that provide high satisfaction. The history management unit, for example, evaluates the satisfaction of family members with meals based on the menu history and prioritize suggesting menus that provide high satisfaction. For example, it prioritizes suggesting dishes that have been highly rated by family members. This allows menus that provide high satisfaction to be prioritized.

[0037] The history management unit can share the menu history with other users and make suggestions based on popular menus. For example, the history management unit can share the menu history with other users and make suggestions based on popular menus. For example, it can suggest menus that other users have given high ratings. This allows users to refer to popular menus of other users.

[0038] The history management unit can analyze the purchase frequency of specific ingredients based on the menu history and generate an efficient shopping list. The history management unit can, for example, analyze the purchase frequency of specific ingredients based on the menu history and generate an efficient shopping list. For example, frequently used ingredients can be added to the list with priority. This allows for the generation of an efficient shopping list.

[0039] The generation unit can propose a menu that suits the time of day, taking into account the family's schedules and plans. For example, the generation unit can propose a menu that suits the time of day, taking into account the family's schedules and plans. For example, it can propose dishes that are easy to make on busy weekday nights, and dishes that take more time to make on weekends. This makes it possible to propose a menu that suits the family's schedule.

[0040] The generation unit can consider the seasons of specific ingredients and propose menus that utilize seasonal ingredients. For example, the generation unit considers the seasons of specific ingredients and proposes menus that utilize seasonal ingredients. For example, in spring, it proposes dishes using fresh vegetables and fish, and in autumn, it proposes dishes using mushrooms and root vegetables. In this way, it is possible to propose menus that utilize seasonal ingredients.

[0041] The generation unit updates the family's food preferences and allergy information in real time and can make suggestions based on the latest information. The generation unit, for example, updates the family's food preferences and allergy information in real time and can make suggestions based on the latest information. For example, if a family member's preferences change, the changes are reflected immediately. This allows menu suggestions based on the latest information.

[0042] The generation unit can suggest different combinations of dishes to encourage the discovery of new flavors. For example, the generation unit can suggest different combinations of dishes to encourage the discovery of new flavors. For example, the generation unit can suggest a menu that combines Japanese food and Western food. This can encourage the discovery of new flavors.

[0043] The generation unit can make suggestions based on a specific theme. The generation unit can make suggestions based on a specific theme, for example. For example, the generation unit can suggest healthy dishes, time-saving dishes, and luxurious dishes. This makes it possible to suggest menus based on a specific theme.

[0044] The dialogue unit can refer to the user's past dialogue history and make suggestions tailored to individual preferences. For example, in a dialogue format on LINE, the dialogue unit can refer to the user's past dialogue history and make suggestions tailored to individual preferences. For example, new suggestions can be made based on dishes that the user has previously liked. This makes it possible to make suggestions tailored to individual preferences based on the user's past dialogue history.

[0045] The dialogue unit can present multiple options in response to a user's question and allow the user to select the optimal menu from the options. For example, in a dialogue format on LINE, the dialogue unit can present multiple options in response to a user's question and allow the user to select the optimal menu from the options. For example, it can suggest, "Please choose from teriyaki chicken, stir-fried vegetables, or pasta." This allows the user to be presented with multiple options and allow the user to select the optimal menu.

[0046] The dialogue unit adds a function that allows the user to share with other family members, allowing the whole family to decide on a menu together. For example, the dialogue unit adds a function that allows the user to share with other family members in a dialogue format on LINE, allowing the whole family to decide on a menu together. For example, a group chat that all family members can join can be created. This allows the whole family to decide on a menu together.

[0047] The dialogue unit can add a function that allows a user to request a recipe for a specific dish. For example, in a dialogue format on LINE, the dialogue unit adds a function that allows a user to request a recipe for a specific dish. For example, a user may request, "Tell me the recipe for curry." This allows the user to request a recipe for a specific dish.

[0048] The inventory management unit can consider the expiration dates of ingredients and propose menus that prioritize the use of ingredients that are close to their expiration dates. For example, in managing ingredient inventory, the inventory management unit considers the expiration dates of ingredients and proposes menus that prioritize the use of ingredients that are close to their expiration dates. For example, it proposes dishes that use ingredients that are close to their expiration dates. This makes it possible to propose menus that prioritize the use of ingredients that are close to their expiration dates.

[0049] The inventory management unit analyzes the frequency of use of specific ingredients and can perform efficient inventory management. For example, in inventory management of ingredients, the inventory management unit analyzes the frequency of use of specific ingredients and can perform efficient inventory management. For example, frequently used ingredients are given priority in management. This allows for efficient inventory management.

[0050] The inventory management unit adds a function that allows the user to manually update inventory information, thereby enabling accurate inventory management. For example, in the inventory management of ingredients, the inventory management unit adds a function that allows the user to manually update inventory information, thereby enabling accurate inventory management. For example, the user manually inputs inventory information. This allows the user to manually update inventory information, enabling accurate inventory management.

[0051] The inventory management unit can share inventory information with other users and jointly generate shopping lists. For example, in food inventory management, the inventory management unit builds a system in which inventory information is shared with other users and shopping lists are jointly generated. For example, all family members share inventory information and create shopping lists. This allows inventory information to be shared with other users and shopping lists to be jointly generated.

[0052] The inventory management unit can suggest substitutes for specific ingredients to support flexible menu planning. For example, in managing ingredient inventory, the inventory management unit can suggest substitutes for specific ingredients to support flexible menu planning. For example, if a specific ingredient is in short supply, a substitute can be suggested. This makes it possible to suggest substitutes for specific ingredients and support flexible menu planning.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The menu suggestion unit can also suggest menus that reduce waste based on the user's ingredient inventory information. For example, it can suggest a menu that prioritizes the use of ingredients that are left over in the refrigerator, thereby reducing ingredient waste. This allows the user to use up ingredients efficiently. The menu suggestion unit can also suggest recipes that allow the user to use up specific ingredients. For example, it can suggest recipes for soups or stir-fries that use leftover vegetables. Furthermore, the menu suggestion unit can take into account the expiration dates of ingredients purchased by the user and suggest menus that prioritize the use of ingredients that are close to their expiration date. This allows ingredient waste to be minimized.

[0055] The menu suggestion unit can also suggest menus that match individual health goals, taking into account the user's health condition and nutritional balance. For example, if a specific family member is on a diet, it can suggest a low-calorie, nutritionally balanced menu. This makes it possible to suggest optimal menus that suit the family's health conditions. The menu suggestion unit can also suggest menus that use ingredients that are rich in specific nutrients. For example, it can suggest dishes that use ingredients that are rich in vitamin C. Furthermore, the menu suggestion unit can also suggest menus that avoid specific ingredients, depending on the user's health condition. For example, if a family member has high blood pressure, it can suggest dishes that are low in salt.

[0056] The menu suggestion unit can select ingredients according to the season and weather, and propose a menu that incorporates a sense of the season. For example, it can propose cold and refreshing dishes in the summer, and hot soups and stews in the winter. This makes it possible to propose an optimal menu that incorporates a sense of the season. The menu suggestion unit can also propose a menu that uses ingredients that are in season for a particular season. For example, it can propose dishes using fresh vegetables and fish in the spring, and dishes using mushrooms and root vegetables in the fall. Furthermore, the menu suggestion unit can also propose special menus that match seasonal events and occasions. For example, it can propose a luxurious dinner for Christmas, and street food-style dishes for a summer festival.

[0057] The menu suggestion unit can learn the user's food preferences and suggest special menus tailored to specific events or anniversaries. For example, it can suggest family favorite dishes for birthdays or wedding anniversaries. This makes it possible to suggest special menus tailored to specific events or anniversaries. The menu suggestion unit can also suggest special menus based on data on the user's past events and anniversaries. For example, it can suggest dishes that were popular on past birthdays. Furthermore, the menu suggestion unit can customize and suggest special dishes tailored to specific events or anniversaries in response to the user's request. For example, it can suggest a special dessert requested by the user.

[0058] The menu suggestion unit can suggest dishes from different cultures and countries based on the family's food preferences, allowing the user to enjoy a variety of foods. For example, if the family likes Italian food, it can suggest pasta or pizza, and if they like Japanese food, it can suggest sushi or tempura. This allows the user to enjoy a variety of foods by suggesting dishes from different cultures and countries. The menu suggestion unit can also make suggestions to encourage the user to try new dishes. For example, it can suggest ethnic dishes that the user does not usually eat. Furthermore, the menu suggestion unit can also provide information and recipes about dishes from specific cultures or countries. For example, it can introduce the history and cooking methods of dishes that interest the user.

[0059] The history management unit can also analyze consumption patterns of specific ingredients based on the menu history and make suggestions to reduce waste. For example, it can prioritize suggestions for frequently used ingredients to reduce waste. This can reduce food waste. The history management unit can also analyze consumption patterns of ingredients purchased by the user in the past and generate efficient shopping lists. For example, it can add frequently used ingredients to the list. Furthermore, the history management unit can take into account the expiration dates of specific ingredients and suggest menus that prioritize the use of ingredients with upcoming expiration dates. This can minimize food waste.

[0060] The history management unit can also identify popular seasonal menu items based on the menu history and make seasonal suggestions. For example, cold and refreshing dishes are suggested in the summer, and hot soups and stews are suggested in the winter. This allows popular seasonal menu items to be suggested. The history management unit can also suggest dishes that are popular in specific seasons based on the user's past menu history. For example, it can suggest dishes that were popular in past summers again. Furthermore, the history management unit can also suggest special menus tailored to seasonal events and occasions. For example, it can suggest a luxurious dinner for Christmas and street food-style dishes for a summer festival.

[0061] The generation unit can also propose menus that fit the time of day, taking into account the family's schedule and plans. For example, it can propose easy-to-prepare dishes for busy weekday nights and dishes that take more time to prepare on weekends. This allows the system to propose menus that fit the family's schedule. The generation unit can also propose special menus for specific events or anniversaries. For example, it can propose dishes that the family likes for birthdays or wedding anniversaries. Furthermore, the generation unit can customize and propose special dishes that fit specific events or anniversaries at the user's request. For example, it can propose a special dessert requested by the user.

[0062] The generation unit can also take into account the seasons of specific ingredients and propose menus that utilize seasonal ingredients. For example, in spring, it proposes dishes using fresh vegetables and fish, and in autumn, it proposes dishes using mushrooms and root vegetables. This makes it possible to propose menus that utilize seasonal ingredients. The generation unit can also propose special dishes that use seasonal ingredients in a specific season. For example, it proposes cold dishes and refreshing dishes in summer, and hot soups and stews in winter. Furthermore, the generation unit can customize and propose special dishes that use seasonal ingredients in response to a user's request. For example, it proposes seasonal desserts requested by the user.

[0063] The dialogue unit can also refer to the user's past dialogue history and make suggestions tailored to individual preferences. For example, in a dialogue format on LINE, the dialogue unit can refer to the user's past dialogue history and make suggestions tailored to individual preferences. For example, new suggestions can be made based on dishes that the user has previously liked. This makes it possible to make suggestions tailored to individual preferences based on the user's past dialogue history. The dialogue unit can also analyze whether a particular dish will elicit a positive response from the user based on the user's past dialogue history and make optimal suggestions. Furthermore, the dialogue unit can make special suggestions tailored to specific events or anniversaries based on the user's past dialogue history. For example, it can suggest special dishes for the user's birthday.

[0064] The dialogue unit can also present multiple options in response to a user's question and allow the user to select the optimal menu from the options. For example, in a dialogue format on LINE, multiple options can be presented in response to a user's question and the user can select the optimal menu from the options. For example, the dialogue unit can suggest, "Please choose from teriyaki chicken, stir-fried vegetables, or pasta." This allows the user to be presented with multiple options and select the optimal menu. The dialogue unit can also customize the options according to the user's preferences. For example, if the user prefers a particular ingredient, dishes using that ingredient can be included in the options. Furthermore, the dialogue unit can suggest optimal options based on the user's past selection history. For example, dishes that were popular in the past can be included again as options.

[0065] The dialogue unit can add a function that allows the user to share with other family members, allowing the whole family to decide on a menu together. For example, in a dialogue format on LINE, a function that allows the user to share with other family members can be added, allowing the whole family to decide on a menu together. For example, a group chat that all family members can join can be created. This allows the whole family to decide on a menu together. The dialogue unit can also suggest menus that reflect the opinions of all family members. For example, it can suggest dishes that all family members like. Furthermore, the dialogue unit can take into account the schedules of all family members and suggest menus that are suited to meal times that everyone can attend. For example, it can suggest special dishes for a weekend dinner when everyone is together.

[0066] The dialogue unit can also add a function that allows a user to request a recipe for a specific dish. For example, a function that allows a user to request a recipe for a specific dish in a dialogue format on LINE can be added. For example, a user may request, "Tell me a curry recipe." This allows the user to request a recipe for a specific dish. The dialogue unit can also customize and provide a recipe for a specific dish in response to a user's request. For example, it can provide a curry recipe using ingredients that the user prefers. Furthermore, the dialogue unit can provide a video on how to make a specific dish in response to a user's request. For example, it can provide a video explaining how to make curry.

[0067] The processing flow of the first embodiment will be briefly explained below.

[0068] Step 1: The menu suggestion unit proposes a menu that takes into account the likes and dislikes of family members and potential guests. For example, the generation AI proposes an appropriate menu based on the likes, dislikes, and allergy information of family members and potential guests provided by the user. For example, the generation AI might suggest, "Your child doesn't like vegetables, so how about a hamburger steak with vegetables as a secret ingredient?" Step 2: The history management unit manages the history of past menus. For example, the generation AI records the history of menus that have been made in the past and adjusts the menu to avoid serving the same dishes too frequently. For example, to avoid complaints such as "I ate this the other day," the generation AI refers to the menus from the past month and suggests new menus that do not overlap. Step 3: The dialogue unit interacts with the user through messaging apps such as LINE. For example, if the user asks "What should we make today?" via LINE, the generation AI will respond with something like, "It's cold today, so how about some hot soup and bread?"

[0069] (Example 2) The AI ​​chat system according to an embodiment of the present invention is a system that reduces the effort required to plan a menu, which is the biggest problem faced by people who prepare meals. This system interacts with users through messaging apps such as LINE, and makes menu suggestions that take into account the likes and dislikes of family members and those who will be hosting, suggestions that take into account past menu history, automatically generates daily menus, and makes suggestions through a dialogue format on LINE. This reduces the burden on users and enables efficient menu suggestions.

[0070] An AI chat system according to an embodiment includes a menu suggestion unit, a history management unit, and a dialogue unit. The menu suggestion unit proposes a menu that takes into account the likes and dislikes of the family member or the person who will be hosting the meal. For example, the generation AI proposes an appropriate menu based on the likes, dislikes, and allergy information of the family member or the person who will be hosting the meal provided by the user. The generation AI makes a suggestion such as, "Your child doesn't like vegetables, so how about a hamburger steak with vegetables as a secret ingredient?" The history management unit manages the history of past menus. For example, the generation AI records the history of previously prepared menus and adjusts the menu so that the same dish is not served frequently. For example, to avoid complaints such as, "I ate this the other day," the generation AI refers to menus from the past month and proposes a new menu that does not overlap. The dialogue unit communicates with the user through a messaging app such as LINE. For example, when the user asks, "What should we make today?" via LINE, the generation AI responds with, "It's cold today, so how about some warm soup and bread?" This allows the AI ​​chat system according to an embodiment to reduce the burden on the user and efficiently propose menus.

[0071] The menu suggestion unit can use the family's emotion estimation function to analyze the emotional reactions to specific dishes and suggest menus that elicit positive reactions. The menu suggestion unit, for example, uses the family's emotion estimation function to analyze the emotional reactions to specific dishes in real time. For example, it collects emotional data about dishes that the family has eaten in the past and preferentially suggests dishes that elicit a large number of positive reactions. This makes it possible to suggest optimal menus based on the family's emotions.

[0072] The menu suggestion unit can suggest menus that are tailored to individual health goals, taking into account the health status and nutritional balance of family members. For example, if a specific family member is on a diet, the unit suggests a low-calorie, nutritionally balanced menu. This makes it possible to suggest optimal menus that are tailored to the health status of family members.

[0073] The menu suggestion unit can select ingredients according to the season and weather and suggest a menu that incorporates a sense of the season. The menu suggestion unit, for example, selects ingredients according to the season and weather and suggests a menu that incorporates a sense of the season. For example, it suggests cold dishes and refreshing dishes in the summer and hot soups and stews in the winter. This makes it possible to suggest an optimal menu that incorporates a sense of the season.

[0074] The menu suggestion unit can learn the food preferences of family members and suggest special menus tailored to specific events or anniversaries. For example, the menu suggestion unit can learn the food preferences of family members and suggest special menus tailored to specific events or anniversaries. For example, it can suggest dishes that family members like for birthdays or wedding anniversaries. This makes it possible to suggest special menus tailored to specific events or anniversaries.

[0075] The menu suggestion unit suggests dishes from different cultures and countries based on the family's food preferences, allowing the family to enjoy a variety of foods. The menu suggestion unit suggests dishes from different cultures and countries based on the family's food preferences, for example. If the family likes Italian food, for example, it suggests pasta or pizza, and if the family likes Japanese food, it suggests sushi or tempura. This allows the family to suggest dishes from different cultures and countries and enjoy a variety of foods.

[0076] The menu suggestion unit can use the emotion estimation function to suggest dishes that match the mood of the family members, thereby improving meal satisfaction. The menu suggestion unit, for example, uses the emotion estimation function to suggest dishes that match the mood of the family members. For example, if a family member is tired, the unit suggests dishes that will help them relax, and if they are feeling down, the unit suggests dishes that will cheer them up. This makes it possible to suggest dishes that match the mood of the family members, thereby improving meal satisfaction.

[0077] The history management unit can analyze consumption patterns of specific ingredients based on the menu history and make suggestions to reduce waste. The history management unit can analyze consumption patterns of specific ingredients based on the menu history and make suggestions to reduce waste. For example, frequently used ingredients can be given priority in suggestions to reduce waste. This can reduce food waste.

[0078] The history management unit can identify popular seasonal menu items based on the menu history and make suggestions for each season. The history management unit can identify popular seasonal menu items based on the menu history and make suggestions for each season. For example, the history management unit can suggest cold and refreshing dishes in the summer and hot soups and stews in the winter. This allows the system to suggest popular seasonal menu items.

[0079] The history management unit can evaluate the satisfaction of family members with meals based on the menu history and prioritize suggesting menus that provide high satisfaction. The history management unit, for example, evaluates the satisfaction of family members with meals based on the menu history and prioritize suggesting menus that provide high satisfaction. For example, it prioritizes suggesting dishes that have been highly rated by family members. This allows menus that provide high satisfaction to be prioritized.

[0080] The history management unit can share the menu history with other users and make suggestions based on popular menus. For example, the history management unit can share the menu history with other users and make suggestions based on popular menus. For example, it can suggest menus that other users have given high ratings. This allows users to refer to popular menus of other users.

[0081] The history management unit can analyze the purchase frequency of specific ingredients based on the menu history and generate an efficient shopping list. The history management unit can, for example, analyze the purchase frequency of specific ingredients based on the menu history and generate an efficient shopping list. For example, frequently used ingredients can be added to the list with priority. This allows for the generation of an efficient shopping list.

[0082] The history management unit can use the emotion estimation function to analyze the family's emotional reactions to past menus and suggest new menus that will elicit a positive reaction. The history management unit, for example, uses the emotion estimation function to analyze the family's emotional reactions to past menus and suggest new menus that will elicit a positive reaction. For example, it can suggest new menus based on dishes that the family has given high ratings. This makes it possible to suggest new menus based on the family's emotional reactions.

[0083] The generation unit can propose a menu that suits the time of day, taking into account the family's schedules and plans. For example, the generation unit can propose a menu that suits the time of day, taking into account the family's schedules and plans. For example, it can propose dishes that are easy to make on busy weekday nights, and dishes that take more time to make on weekends. This makes it possible to propose a menu that suits the family's schedule.

[0084] The generation unit can consider the seasons of specific ingredients and propose menus that utilize seasonal ingredients. For example, the generation unit considers the seasons of specific ingredients and proposes menus that utilize seasonal ingredients. For example, in spring, it proposes dishes using fresh vegetables and fish, and in autumn, it proposes dishes using mushrooms and root vegetables. In this way, it is possible to propose menus that utilize seasonal ingredients.

[0085] The generation unit updates the family's food preferences and allergy information in real time and can make suggestions based on the latest information. The generation unit, for example, updates the family's food preferences and allergy information in real time and can make suggestions based on the latest information. For example, if a family member's preferences change, the changes are reflected immediately. This allows menu suggestions based on the latest information.

[0086] The generation unit can suggest different combinations of dishes to encourage the discovery of new flavors. For example, the generation unit can suggest different combinations of dishes to encourage the discovery of new flavors. For example, the generation unit can suggest a menu that combines Japanese food and Western food. This can encourage the discovery of new flavors.

[0087] The generation unit can make suggestions based on a specific theme. The generation unit can make suggestions based on a specific theme, for example. For example, the generation unit can suggest healthy dishes, time-saving dishes, and luxurious dishes. This makes it possible to suggest menus based on a specific theme.

[0088] The generation unit uses the emotion estimation function to suggest a menu that matches the mood of the family members, thereby improving meal satisfaction. The generation unit, for example, uses the emotion estimation function to suggest a menu that matches the mood of the family members. For example, if a family member is tired, the generation unit suggests dishes that will help them relax, and if they are feeling down, the generation unit suggests dishes that will cheer them up. In this way, the generation unit can suggest a menu that matches the mood of the family members, thereby improving meal satisfaction.

[0089] The dialogue unit can analyze the user's emotions in real time and make suggestions that will elicit a positive response. For example, in a dialogue format on LINE, the dialogue unit can analyze the user's emotions in real time and make suggestions that will elicit a positive response. For example, if the user is tired, it can suggest dishes that will help them relax. This makes it possible to make optimal suggestions based on the user's emotions.

[0090] The dialogue unit can refer to the user's past dialogue history and make suggestions tailored to individual preferences. For example, in a dialogue format on LINE, the dialogue unit can refer to the user's past dialogue history and make suggestions tailored to individual preferences. For example, new suggestions can be made based on dishes that the user has previously liked. This makes it possible to make suggestions tailored to individual preferences based on the user's past dialogue history.

[0091] The dialogue unit can present multiple options in response to a user's question and allow the user to select the optimal menu from the options. For example, in a dialogue format on LINE, the dialogue unit can present multiple options in response to a user's question and allow the user to select the optimal menu from the options. For example, it can suggest, "Please choose from teriyaki chicken, stir-fried vegetables, or pasta." This allows the user to be presented with multiple options and allow the user to select the optimal menu.

[0092] The dialogue unit adds a function that allows the user to share with other family members, allowing the whole family to decide on a menu together. For example, the dialogue unit adds a function that allows the user to share with other family members in a dialogue format on LINE, allowing the whole family to decide on a menu together. For example, a group chat that all family members can join can be created. This allows the whole family to decide on a menu together.

[0093] The dialogue unit can add a function that allows a user to request a recipe for a specific dish. For example, in a dialogue format on LINE, the dialogue unit adds a function that allows a user to request a recipe for a specific dish. For example, a user may request, "Tell me the recipe for curry." This allows the user to request a recipe for a specific dish.

[0094] The dialogue unit uses the emotion estimation function to conduct a dialogue that matches the user's mood, thereby improving meal satisfaction. The dialogue unit, for example, uses the emotion estimation function to conduct a dialogue that matches the user's mood. For example, if the user is tired, the dialogue unit suggests dishes that will help the user relax. This allows the dialogue to be conducted in accordance with the user's mood, improving meal satisfaction.

[0095] The inventory management unit can consider the expiration dates of ingredients and propose menus that prioritize the use of ingredients that are close to their expiration dates. For example, in managing ingredient inventory, the inventory management unit considers the expiration dates of ingredients and proposes menus that prioritize the use of ingredients that are close to their expiration dates. For example, it proposes dishes that use ingredients that are close to their expiration dates. This makes it possible to propose menus that prioritize the use of ingredients that are close to their expiration dates.

[0096] The inventory management unit analyzes the frequency of use of specific ingredients and can perform efficient inventory management. For example, in inventory management of ingredients, the inventory management unit analyzes the frequency of use of specific ingredients and can perform efficient inventory management. For example, frequently used ingredients are given priority in management. This allows for efficient inventory management.

[0097] The inventory management unit adds a function that allows the user to manually update inventory information, thereby enabling accurate inventory management. For example, in the inventory management of ingredients, the inventory management unit adds a function that allows the user to manually update inventory information, thereby enabling accurate inventory management. For example, the user manually inputs inventory information. This allows the user to manually update inventory information, enabling accurate inventory management.

[0098] The inventory management unit can share inventory information with other users and jointly generate shopping lists. For example, in food inventory management, the inventory management unit builds a system in which inventory information is shared with other users and shopping lists are jointly generated. For example, all family members share inventory information and create shopping lists. This allows inventory information to be shared with other users and shopping lists to be jointly generated.

[0099] The inventory management unit can suggest substitutes for specific ingredients to support flexible menu planning. For example, in managing ingredient inventory, the inventory management unit can suggest substitutes for specific ingredients to support flexible menu planning. For example, if a specific ingredient is in short supply, a substitute can be suggested. This makes it possible to suggest substitutes for specific ingredients and support flexible menu planning.

[0100] The inventory management unit uses the emotion estimation function to generate a shopping list that matches the user's mood, thereby improving shopping satisfaction. The inventory management unit, for example, uses the emotion estimation function to generate a shopping list that matches the user's mood. For example, if the user is tired, ingredients that are easy to cook are added to the list. In this way, a shopping list that matches the user's mood can be generated, improving shopping satisfaction.

[0101] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0102] The menu suggestion unit can also suggest menus that reduce waste based on the user's ingredient inventory information. For example, it can suggest a menu that prioritizes the use of ingredients that are left over in the refrigerator, thereby reducing ingredient waste. This allows the user to use up ingredients efficiently. The menu suggestion unit can also suggest recipes that allow the user to use up specific ingredients. For example, it can suggest recipes for soups or stir-fries that use leftover vegetables. Furthermore, the menu suggestion unit can take into account the expiration dates of ingredients purchased by the user and suggest menus that prioritize the use of ingredients that are close to their expiration date. This allows ingredient waste to be minimized.

[0103] The menu suggestion unit can use the user's emotion estimation function to analyze the user's emotional response to specific dishes and suggest a menu that will elicit a positive response. For example, it can collect emotional data on dishes the user has eaten in the past and prioritize suggesting dishes that have many positive responses. This makes it possible to suggest an optimal menu based on the user's emotions. The menu suggestion unit can also suggest relaxing or energizing dishes according to the user's emotional state. For example, if the user is tired, it will suggest relaxing dishes, and if the user is feeling down, it will suggest energizing dishes. Furthermore, the menu suggestion unit can analyze whether a specific dish will elicit a positive response from all family members based on the user's emotional data and suggest an optimal menu.

[0104] The menu suggestion unit can also suggest menus that match individual health goals, taking into account the user's health condition and nutritional balance. For example, if a specific family member is on a diet, it can suggest a low-calorie, nutritionally balanced menu. This makes it possible to suggest optimal menus that suit the family's health conditions. The menu suggestion unit can also suggest menus that use ingredients that are rich in specific nutrients. For example, it can suggest dishes that use ingredients that are rich in vitamin C. Furthermore, the menu suggestion unit can also suggest menus that avoid specific ingredients, depending on the user's health condition. For example, if a family member has high blood pressure, it can suggest dishes that are low in salt.

[0105] The menu suggestion unit can select ingredients according to the season and weather, and propose a menu that incorporates a sense of the season. For example, it can propose cold and refreshing dishes in the summer, and hot soups and stews in the winter. This makes it possible to propose an optimal menu that incorporates a sense of the season. The menu suggestion unit can also propose a menu that uses ingredients that are in season for a particular season. For example, it can propose dishes using fresh vegetables and fish in the spring, and dishes using mushrooms and root vegetables in the fall. Furthermore, the menu suggestion unit can also propose special menus that match seasonal events and occasions. For example, it can propose a luxurious dinner for Christmas, and street food-style dishes for a summer festival.

[0106] The menu suggestion unit can learn the user's food preferences and suggest special menus tailored to specific events or anniversaries. For example, it can suggest family favorite dishes for birthdays or wedding anniversaries. This makes it possible to suggest special menus tailored to specific events or anniversaries. The menu suggestion unit can also suggest special menus based on data on the user's past events and anniversaries. For example, it can suggest dishes that were popular on past birthdays. Furthermore, the menu suggestion unit can customize and suggest special dishes tailored to specific events or anniversaries in response to the user's request. For example, it can suggest a special dessert requested by the user.

[0107] The menu suggestion unit can suggest dishes from different cultures and countries based on the family's food preferences, allowing the user to enjoy a variety of foods. For example, if the family likes Italian food, it can suggest pasta or pizza, and if they like Japanese food, it can suggest sushi or tempura. This allows the user to enjoy a variety of foods by suggesting dishes from different cultures and countries. The menu suggestion unit can also make suggestions to encourage the user to try new dishes. For example, it can suggest ethnic dishes that the user does not usually eat. Furthermore, the menu suggestion unit can also provide information and recipes about dishes from specific cultures or countries. For example, it can introduce the history and cooking methods of dishes that interest the user.

[0108] The menu suggestion unit can use the emotion estimation function to suggest dishes that match the mood of the family members, thereby improving meal satisfaction. For example, if a family member is tired, it can suggest relaxing dishes, and if they are feeling down, it can suggest dishes that will cheer them up. This makes it possible to suggest dishes that match the family members' moods and improve meal satisfaction. The menu suggestion unit can also analyze whether a specific dish will evoke a positive response from all family members based on the family members' emotional data, and suggest an optimal menu. Furthermore, the menu suggestion unit can suggest dishes that use specific ingredients according to the emotional state of the family members. For example, it can suggest dishes that use herbs that have a relaxing effect.

[0109] The history management unit can also analyze consumption patterns of specific ingredients based on the menu history and make suggestions to reduce waste. For example, it can prioritize suggestions for frequently used ingredients to reduce waste. This can reduce food waste. The history management unit can also analyze consumption patterns of ingredients purchased by the user in the past and generate efficient shopping lists. For example, it can add frequently used ingredients to the list. Furthermore, the history management unit can take into account the expiration dates of specific ingredients and suggest menus that prioritize the use of ingredients with upcoming expiration dates. This can minimize food waste.

[0110] The history management unit can also identify popular seasonal menu items based on the menu history and make seasonal suggestions. For example, cold and refreshing dishes are suggested in the summer, and hot soups and stews are suggested in the winter. This allows popular seasonal menu items to be suggested. The history management unit can also suggest dishes that are popular in specific seasons based on the user's past menu history. For example, it can suggest dishes that were popular in past summers again. Furthermore, the history management unit can also suggest special menus tailored to seasonal events and occasions. For example, it can suggest a luxurious dinner for Christmas and street food-style dishes for a summer festival.

[0111] The history management unit can use the emotion estimation function to analyze the emotional reactions of family members to past menus and suggest new menus that will elicit positive reactions. For example, it can suggest new menus based on dishes that family members have given high ratings. This makes it possible to suggest new menus based on the emotional reactions of family members. The history management unit can also analyze whether a particular dish will elicit a positive reaction from all family members based on the family's emotional data and suggest the optimal menu. Furthermore, the history management unit can suggest dishes that use specific ingredients depending on the emotional state of the family members. For example, it can suggest dishes that use herbs that have a relaxing effect.

[0112] The generation unit can also propose menus that fit the time of day, taking into account the family's schedule and plans. For example, it can propose easy-to-prepare dishes for busy weekday nights and dishes that take more time to prepare on weekends. This allows the system to propose menus that fit the family's schedule. The generation unit can also propose special menus for specific events or anniversaries. For example, it can propose dishes that the family likes for birthdays or wedding anniversaries. Furthermore, the generation unit can customize and propose special dishes that fit specific events or anniversaries at the user's request. For example, it can propose a special dessert requested by the user.

[0113] The generation unit can also take into account the seasons of specific ingredients and propose menus that utilize seasonal ingredients. For example, in spring, it proposes dishes using fresh vegetables and fish, and in autumn, it proposes dishes using mushrooms and root vegetables. This makes it possible to propose menus that utilize seasonal ingredients. The generation unit can also propose special dishes that use seasonal ingredients in a specific season. For example, it proposes cold dishes and refreshing dishes in summer, and hot soups and stews in winter. Furthermore, the generation unit can customize and propose special dishes that use seasonal ingredients in response to a user's request. For example, it proposes seasonal desserts requested by the user.

[0114] The generation unit can use the emotion estimation function to suggest menus that match the moods of family members, improving meal satisfaction. For example, if a family member is tired, it can suggest relaxing dishes, and if they are feeling down, it can suggest dishes that will cheer them up. This makes it possible to suggest menus that match the moods of family members and improve meal satisfaction. The generation unit can also analyze whether a specific dish will evoke a positive response from all family members based on the family's emotional data, and suggest an optimal menu. Furthermore, the generation unit can suggest dishes that use specific ingredients depending on the emotional state of the family members. For example, it can suggest dishes that use herbs that have a relaxing effect.

[0115] The dialogue unit can analyze the user's emotions in real time and make suggestions that will elicit a positive response. For example, in a dialogue format on LINE, the dialogue unit can analyze the user's emotions in real time and make suggestions that will elicit a positive response. For example, if the user is tired, it can suggest dishes that will help them relax. This makes it possible to make optimal suggestions based on the user's emotions. The dialogue unit can also suggest specific dishes depending on the user's emotional state. For example, if the user is feeling stressed, it can suggest dishes that use herbs that have a relaxing effect. Furthermore, the dialogue unit can analyze whether a specific dish will elicit a positive response from the user based on the user's emotional data and make optimal suggestions.

[0116] The dialogue unit can also refer to the user's past dialogue history and make suggestions tailored to individual preferences. For example, in a dialogue format on LINE, the dialogue unit can refer to the user's past dialogue history and make suggestions tailored to individual preferences. For example, new suggestions can be made based on dishes that the user has previously liked. This makes it possible to make suggestions tailored to individual preferences based on the user's past dialogue history. The dialogue unit can also analyze whether a particular dish will elicit a positive response from the user based on the user's past dialogue history and make optimal suggestions. Furthermore, the dialogue unit can make special suggestions tailored to specific events or anniversaries based on the user's past dialogue history. For example, it can suggest special dishes for the user's birthday.

[0117] The dialogue unit can also present multiple options in response to a user's question and allow the user to select the optimal menu from the options. For example, in a dialogue format on LINE, multiple options can be presented in response to a user's question and the user can select the optimal menu from the options. For example, the dialogue unit can suggest, "Please choose from teriyaki chicken, stir-fried vegetables, or pasta." This allows the user to be presented with multiple options and select the optimal menu. The dialogue unit can also customize the options according to the user's preferences. For example, if the user prefers a particular ingredient, dishes using that ingredient can be included in the options. Furthermore, the dialogue unit can suggest optimal options based on the user's past selection history. For example, dishes that were popular in the past can be included again as options.

[0118] The dialogue unit can add a function that allows the user to share with other family members, allowing the whole family to decide on a menu together. For example, in a dialogue format on LINE, a function that allows the user to share with other family members can be added, allowing the whole family to decide on a menu together. For example, a group chat that all family members can join can be created. This allows the whole family to decide on a menu together. The dialogue unit can also suggest menus that reflect the opinions of all family members. For example, it can suggest dishes that all family members like. Furthermore, the dialogue unit can take into account the schedules of all family members and suggest menus that are suited to meal times that everyone can attend. For example, it can suggest special dishes for a weekend dinner when everyone is together.

[0119] The dialogue unit can also add a function that allows a user to request a recipe for a specific dish. For example, a function that allows a user to request a recipe for a specific dish in a dialogue format on LINE can be added. For example, a user may request, "Tell me a curry recipe." This allows the user to request a recipe for a specific dish. The dialogue unit can also customize and provide a recipe for a specific dish in response to a user's request. For example, it can provide a curry recipe using ingredients that the user prefers. Furthermore, the dialogue unit can provide a video on how to make a specific dish in response to a user's request. For example, it can provide a video explaining how to make curry.

[0120] The dialogue unit can use the emotion estimation function to engage in dialogue that matches the user's mood, thereby improving meal satisfaction. For example, the emotion estimation function can be used to engage in dialogue that matches the user's mood. For example, if the user is tired, a dish that will help them relax can be suggested. This allows dialogue that matches the user's mood, improving meal satisfaction. The dialogue unit can also analyze whether a particular dish will elicit a positive response from the user based on the user's emotion data, and make an optimal suggestion. Furthermore, the dialogue unit can suggest dishes that use specific ingredients according to the user's emotional state. For example, it can suggest dishes that use herbs that have a relaxing effect.

[0121] The processing flow of the second embodiment will be briefly explained below.

[0122] Step 1: The menu suggestion unit proposes a menu that takes into account the likes and dislikes of family members and potential guests. For example, the generation AI proposes an appropriate menu based on the likes, dislikes, and allergy information of family members and potential guests provided by the user. For example, the generation AI might suggest, "Your child doesn't like vegetables, so how about a hamburger steak with vegetables as a secret ingredient?" Step 2: The history management unit manages the history of past menus. For example, the generation AI records the history of menus that have been made in the past and adjusts the menu to avoid serving the same dishes too frequently. For example, to avoid complaints such as "I ate this the other day," the generation AI refers to the menus from the past month and suggests new menus that do not overlap. Step 3: The dialogue unit interacts with the user through messaging apps such as LINE. For example, if the user asks "What should we make today?" via LINE, the generation AI will respond with something like, "It's cold today, so how about some hot soup and bread?"

[0123] 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.

[0124] 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> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). 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 speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. 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. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0125] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0126] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0127] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0128] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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 and / or a LAN.

[0129] 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.

[0130] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0131] 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 user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0136] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0137] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0138] 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.

[0139] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0140] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0141] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0142] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0143] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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 and / or a LAN.

[0144] 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.

[0145] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0146] 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 user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0151] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0152] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0153] 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.

[0154] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0155] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0156] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0157] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0158] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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 and / or a LAN.

[0159] 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.

[0160] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0161] 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 image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0162] 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.

[0163] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the 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.

[0164] 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.

[0165] 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.

[0166] 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. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0167] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0168] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0169] 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.

[0170] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0171] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0172] 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.

[0173] FIG. 9 illustrates 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 behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions 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.

[0174] 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.

[0175] 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).

[0176] 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 expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, 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 expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0177] 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."

[0178] 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.

[0179] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0184] The hardware resource that executes the specific process 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 process may be a single processor.

[0185] 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.

[0186] 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.

[0187] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0188] 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.

[0189] 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. [Explanation of symbols]

[0190] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. The menu suggestion department proposes menus that take into consideration the likes and dislikes of family members and those who will be providing the meals. A history management section that manages past menu history; A dialogue unit that dialogues with a user through a messaging app. A system characterized by:

2. The menu suggestion unit Using the family's emotion estimation function, the family analyzes their emotional reactions to specific dishes and suggests menus that will elicit positive reactions. The system of claim 1 .

3. The history management unit Based on the menu history, analyze consumption patterns of specific ingredients and make suggestions to reduce waste. The system of claim 1 .

4. The generation part is To propose the menu according to the time, taking into consideration the schedule and plans of the family. The system of claim 1 .

5. The dialogue unit Analyzing the user's emotions in real time and making the suggestions that elicit a positive response The system of claim 1 .

6. The menu suggestion unit Using an emotion estimation function, the system suggests dishes that match the mood of the family members, improving meal satisfaction. The system of claim 1 .

7. The history management unit Using an emotion estimation function, the emotional reaction of the family to the menu history is analyzed, and the new menu that elicits a positive reaction is proposed. The system of claim 1 .

8. The dialogue unit Using an emotion estimation function, the dialogue is conducted in accordance with the mood of the user, thereby improving satisfaction with the meal. The system of claim 1 .

Images