System

Abstract

An object of a system according to an embodiment is to enable a user to efficiently perform self-cooking.SOLUTION: A system includes a user information input part, a menu generation part, a cooking time scheduling part, and a reminder part. The user information input unit inputs user information. The menu generation part generates a menu on the basis of the user information inputted by the user information input part. A cooking time scheduling part schedules the cooking time based on the menu generated by the menu generation part. The reminding unit reminds the user of the recipe and the necessary ingredients at the time of purchase.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, when users cook at home, planning menus, managing cooking times, and reminding themselves of necessary ingredients are complicated and difficult to do efficiently.

[0005] The system according to the embodiment aims to enable users to efficiently prepare meals at home. [Means for solving the problem]

[0006] The system according to the embodiment includes a user information input unit, a menu creation unit, a cooking time scheduling unit, and a reminder unit. The user information input unit inputs user information. The menu creation unit creates a menu based on the user information input by the user information input unit. The cooking time scheduling unit schedules cooking times based on the menu created by the menu creation unit. The reminder unit reminds the user of recipes and necessary ingredients when shopping. [Effects of the Invention]

[0007] The system according to the embodiment can enable users to efficiently prepare meals at home. [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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[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 home cooking support system according to an embodiment of the present invention is a system that reduces the time users spend thinking about cooking at home and improves daily typing. This home cooking support system inputs user information, and a generation AI generates monthly menus, schedules cooking times, and displays pop-up reminders of recipes and necessary ingredients when shopping. As a result, the home cooking support system reduces the time users spend thinking about cooking at home and improves daily quality of life (QOL).

[0029] The home cooking support system according to the embodiment includes a user information input unit, a menu generation unit, a cooking time scheduling unit, and a reminder unit. The user information input unit inputs user information. For example, information such as the user's preferences, allergy information, budget limit, number of calories per meal, residential area, family composition, and frequency of eating out can be input. The menu generation unit generates a menu using a generation AI based on the user information input by the user information input unit. For example, the generation AI uses a text generation AI (e.g., LLM) to generate a menu that meets the user's requirements. The generation AI can also use a multimodal generation AI to generate a menu tailored to the user's preferences and health condition. The cooking time scheduling unit schedules cooking times based on the menu generated by the menu generation unit. For example, the generation AI sets cooking times according to the user's schedule. The reminder unit reminds the user of recipes and necessary ingredients when shopping. For example, the generation AI displays a pop-up list of necessary ingredients when the user goes shopping. As a result, the home cooking support system according to the embodiment can reduce the time users spend thinking about cooking and improve their daily typing skills. For example, users can use the system to cook their meals efficiently and improve their quality of life.

[0030] The user information input unit can analyze the user's past meal history and suggest changes in preferences and new ingredients. The user information input unit, for example, stores the user's past meal history in a database and analyzes the data to understand changes in preferences. For example, if an ingredient that was frequently eaten in the past has not been eaten recently, a new ingredient can be suggested. The user's meal history can also be analyzed to detect changes in preferences for specific ingredients or dishes. For example, if a dish that was previously a favorite has not been eaten recently, that dish can be suggested again. The unit can also suggest new ingredients and dishes based on the user's meal history. For example, recipes using ingredients that have not been eaten before can be suggested to increase the variety of the user's meals. This makes it possible to suggest new ingredients and dishes that suit the user's preferences.

[0031] The user information input unit can link the user's health data and generate a menu according to the user's health condition. The user information input unit, for example, analyzes data obtained from the user's fitness tracker and suggests a menu according to the user's health condition. For example, on days when the user exercises a lot, it suggests a high-protein meal. Furthermore, if the user's health data indicates a deficiency in a specific nutrient, it suggests a menu using ingredients that supplement that nutrient. For example, if there is a vitamin D deficiency, it suggests recipes using fish or mushrooms. Furthermore, the user's health data is linked to generate a menu according to fluctuations in weight and body fat percentage. For example, if the user's weight is increasing, it suggests a low-calorie meal. This makes it possible to provide an optimal menu according to the user's health condition.

[0032] The user information input unit can integrate information on all members of the user's family and generate a menu that takes into account everyone's preferences and allergies. The user information input unit, for example, inputs the preferences and allergy information of all members of the user's family and integrates it to generate a menu that will satisfy everyone. For example, if there is a member of the family who has a nut allergy, it will suggest recipes that do not contain nuts. It also analyzes the dietary history of all family members and suggests a menu that takes everyone's preferences into consideration. For example, it will include a dish that everyone likes once a week. It also links the health data of all family members and generates a menu that suits everyone's health condition. For example, if there is a member of the family with high blood pressure, it will suggest low-salt recipes. This makes it possible to provide a menu that will satisfy the whole family.

[0033] The user information input unit can analyze the user's social media posts about food and incorporate trends and popular recipes. The user information input unit, for example, analyzes the user's social media posts to understand the latest food trends and popular recipes. For example, it can incorporate dishes that are trending on social media into menus. It can also identify ingredients and dishes that the user is interested in based on the user's social media posts and reflect these in menus. For example, it can suggest recipes using ingredients that the user frequently posts about. It can also analyze food trends on social media to provide the user with new cooking ideas. For example, it can suggest dishes that are popular on social media as special weekend menus. This makes it possible to provide menus that match the user's interests and trends.

[0034] The menu generation unit can propose optimal menus by taking into account the seasonality of ingredients. For example, the menu generation unit registers the seasonality of ingredients for each season in a database and proposes optimal menus based on that data. For example, in spring, recipes using asparagus or bamboo shoots are proposed. Also, nutritious menus are proposed by taking into account the seasonality of ingredients for each season. For example, in winter, recipes using citrus fruits rich in vitamin C are proposed. Also, cost-effective menus are proposed by taking into account the seasonality of ingredients for each season. For example, in summer, recipes using inexpensive ingredients such as tomatoes and cucumbers are proposed. In this way, it is possible to provide menus that make use of seasonal ingredients for each season.

[0035] The menu generation unit can grasp the user's ingredient inventory status in real time and generate menus that reduce waste. For example, the menu generation unit grasps the inventory status of the user's refrigerator or pantry in real time and suggests menus that reduce waste based on that data. For example, it suggests recipes that prioritize using ingredients that are in high stock. It also analyzes the user's ingredient inventory status and suggests menus that prioritize using ingredients that are close to their expiration date. For example, it suggests recipes that use ingredients that are close to their expiration date. It also grasps the user's ingredient inventory status in real time and automatically generates a shopping list to reduce waste. For example, it adds ingredients that are low in stock to the list. This reduces ingredient waste and provides efficient menus.

[0036] The menu generation unit can generate a multinational menu that incorporates cuisine from different cultures and countries. For example, the menu generation unit registers cuisine from different cultures and countries in a database and proposes a multinational menu based on that data. For example, it proposes a menu that incorporates Italian cuisine or Chinese cuisine. It also proposes a menu that incorporates cuisine from different cultures and countries taking into account the user's preferences and allergy information. For example, it proposes multinational recipes that accommodate allergies. It also proposes menus that incorporate cuisine from different cultures and countries, increasing the variety of the user's meals. For example, it proposes a menu that incorporates cuisine from a different country once a week. This makes it possible to provide multinational menus and increase the variety of the user's meals.

[0037] The menu generation unit can analyze photos of the user's meals and suggest visually beautiful menus. The menu generation unit, for example, analyzes photos of meals taken by the user and suggests visually beautiful menus. For example, it suggests recipes that take into consideration the balance of colors and presentation. It also suggests visually beautiful menus based on the user's photos of meals. For example, it suggests dishes that look beautiful based on the results of analyzing the photos. It also analyzes photos of the user's meals and suggests visually beautiful menus. For example, it analyzes the colors and composition of the photos and suggests recipes with beautiful presentation. This makes it possible to provide visually beautiful menus and improve the user's satisfaction with their meals.

[0038] The cooking time scheduling unit can analyze the user's past cooking time data and propose an optimal cooking schedule. The cooking time scheduling unit, for example, stores the user's past cooking time data in a database and analyzes the data to propose an optimal cooking schedule. For example, recipes with short cooking times are proposed based on past data. The cooking time scheduling unit can also analyze the user's past cooking time data and propose an efficient cooking schedule. For example, recipes are proposed in order of shortest cooking times based on past data. The cooking time scheduling unit can also propose an optimal cooking schedule based on the user's past cooking time data. For example, recipes with shortest cooking times are proposed preferentially based on past data. This makes it possible to provide an optimal cooking schedule based on past data.

[0039] The cooking time scheduling unit can analyze the user's lifestyle rhythm and suggest the most efficient cooking time. The cooking time scheduling unit, for example, stores the user's lifestyle rhythm in a database and analyzes the data to suggest the most efficient cooking time. For example, the cooking time is set to match the time the user returns home from work. The cooking time scheduling unit also analyzes the user's lifestyle rhythm and suggests efficient cooking times. For example, an easy-to-make breakfast recipe is suggested to match the time the user eats breakfast. The cooking time scheduling unit also suggests an optimal cooking schedule based on the user's lifestyle rhythm. For example, a nutritious recipe is suggested to match the time the user eats a meal after exercising. This makes it possible to provide the optimal cooking time to match the user's lifestyle rhythm.

[0040] The reminding unit can analyze the user's shopping history and provide reminders at the optimal timing. The reminding unit, for example, stores the user's shopping history in a database, analyzes the data, and provides reminders at the optimal timing. For example, it predicts the timing of shopping based on past shopping patterns. It also analyzes the user's shopping history and provides efficient reminders. For example, it predicts the timing of shopping based on past data and provides reminders for necessary ingredients. It also provides reminders at the optimal timing based on the user's shopping history. For example, it predicts the timing of shopping based on past data and provides reminders for necessary ingredients. This allows reminders to be provided at the optimal timing, preventing users from forgetting to buy things.

[0041] The reminding unit can grasp the inventory status of the user's refrigerator in real time and remind the user of necessary ingredients. The reminding unit, for example, grasps the inventory status of the user's refrigerator in real time and reminds the user of necessary ingredients based on that data. For example, it adds ingredients that are low in stock to a list. It also analyzes the inventory status of the user's refrigerator and provides efficient reminders. For example, it adds ingredients that are low in stock to a list and predicts the timing of shopping. It also provides reminders at the optimal timing based on the inventory status of the user's refrigerator. For example, it adds ingredients that are low in stock to a list and predicts the timing of shopping. This makes it possible to grasp the inventory status of the refrigerator in real time and provide reminders of necessary ingredients.

[0042] The reminding unit can automatically generate a shopping list for a user and send it to a smartphone. The reminding unit, for example, builds a system that automatically generates a shopping list for a user and sends it to a smartphone. For example, it adds necessary ingredients to the list and sends it to a smartphone. The reminding unit also automatically generates a shopping list for a user to support efficient shopping. For example, it adds necessary ingredients to the list and sends it to a smartphone. The reminding unit also builds a system that automatically generates a shopping list for a user and sends it to a smartphone. For example, it adds necessary ingredients to the list and sends it to a smartphone. This allows the automatically generated shopping list to be sent to a smartphone, making shopping more efficient.

[0043] The reminding unit can analyze the user's shopping frequency and patterns and suggest an optimal shopping route. The reminding unit, for example, stores the user's shopping frequency and patterns in a database and analyzes the data to suggest an optimal shopping route. For example, it suggests an efficient shopping route. It also analyzes the user's shopping frequency and patterns and suggests an efficient shopping route. For example, it suggests an optimal shopping route based on the shopping frequency and patterns. It also suggests an optimal shopping route based on the user's shopping frequency and patterns. For example, it suggests an optimal shopping route based on the shopping frequency and patterns. This makes it possible to suggest an optimal shopping route and improve shopping efficiency.

[0044] The subscription management unit can analyze a user's frequency of use and satisfaction level and propose the most suitable pricing plan. For example, the subscription management unit stores the user's frequency of use and satisfaction level in a database and analyzes that data to propose the most suitable pricing plan. For example, it proposes a discount plan for users who use frequently. It also analyzes the user's frequency of use and satisfaction level to propose an efficient pricing plan. For example, it proposes a pricing plan based on the number of times a user uses the service infrequently. It also proposes the most suitable pricing plan based on the user's frequency of use and satisfaction level. For example, it proposes a discount plan for users who use frequently. This makes it possible to provide the most suitable pricing plan according to the user's usage status.

[0045] The subscription management unit can analyze the usage status of all family members of a user and propose a family plan. For example, the subscription management unit stores the usage status of all family members of a user in a database and analyzes that data to propose a family plan. For example, if all family members use the service, a discount plan is proposed. The subscription management unit also analyzes the usage status of all family members and proposes an efficient family plan. For example, if all family members use the service, a fee plan based on the number of times the service is used is proposed. The subscription management unit also proposes the optimal family plan based on the usage status of all family members of a user. For example, if all family members use the service, a discount plan is proposed. This makes it possible to provide the optimal family plan according to the usage status of all family members.

[0046] The subscription management unit can provide points and benefits according to the user's usage. For example, the subscription management unit stores the user's usage in a database, analyzes the data, and provides points and benefits. For example, points are awarded to users who use the service frequently. The subscription management unit also analyzes the user's usage and provides efficient points and benefits. For example, a plan with benefits is proposed for users who use the service infrequently. The subscription management unit also provides optimal points and benefits based on the user's usage. For example, points are awarded to users who use the service frequently. This makes it possible to provide points and benefits according to the user's usage.

[0047] The subscription management unit can customize the service content based on user feedback. For example, the subscription management unit stores user feedback in a database and analyzes the data to customize the service content. For example, new functions are added in response to user requests. The subscription management unit also analyzes user feedback to customize efficient service content. For example, service content is changed in response to user requests. The subscription management unit also customizes optimal service content based on user feedback. For example, new functions are added in response to user requests. In this way, the service content can be customized based on user feedback.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The user information input unit can analyze the user's social media posts about food and incorporate trends and popular recipes. For example, it can analyze the user's social media posts to understand the latest food trends and popular recipes. For example, it can incorporate dishes that are trending on social media into the menu. It can also identify ingredients and dishes that the user is interested in based on the user's social media posts and reflect these in the menu. For example, it can suggest recipes that use ingredients that the user frequently posts about. It can also analyze food trends on social media to provide the user with new cooking ideas. For example, it can suggest dishes that are popular on social media as special weekend menus. This makes it possible to provide menus that match the user's interests and trends.

[0050] The menu generation unit can propose optimal menus by taking into account the seasonality of ingredients. For example, the seasonality of ingredients for each season can be registered in a database, and optimal menus can be proposed based on that data. For example, in spring, recipes using asparagus or bamboo shoots can be proposed. Also, nutritious menus can be proposed by taking into account the seasonality of ingredients for each season. For example, in winter, recipes using citrus fruits rich in vitamin C can be proposed. Also, cost-effective menus can be proposed by taking into account the seasonality of ingredients for each season. For example, in summer, recipes using inexpensive ingredients such as tomatoes and cucumbers can be proposed. This makes it possible to provide menus that make use of seasonal ingredients for each season.

[0051] The menu generation unit can grasp the user's ingredient inventory status in real time and generate menus that reduce waste. For example, it grasps the inventory status of the user's refrigerator or pantry in real time and suggests menus that reduce waste based on that data. For example, it suggests recipes that prioritize using ingredients that are in high stock. It also analyzes the user's ingredient inventory status and suggests menus that prioritize using ingredients that are close to their expiration date. For example, it suggests recipes that use ingredients that are close to their expiration date. It also grasps the user's ingredient inventory status in real time and automatically generates a shopping list to reduce waste. For example, it adds ingredients that are low in stock to the list. This reduces ingredient waste and provides efficient menus.

[0052] The menu generation unit can generate multinational menus that incorporate cuisine from different cultures and countries. For example, it registers cuisine from different cultures and countries in a database and proposes multinational menus based on that data. For example, it proposes menus that incorporate Italian and Chinese cuisine. It also proposes menus that incorporate cuisine from different cultures and countries taking into account the user's preferences and allergy information. For example, it proposes multinational recipes that accommodate allergies. It also proposes menus that incorporate cuisine from different cultures and countries, increasing the variety of the user's meals. For example, it proposes a menu that includes cuisine from a different country once a week. This makes it possible to provide multinational menus and increase the variety of the user's meals.

[0053] The menu generation unit can analyze photos of the user's meals and suggest visually beautiful menus. For example, it can analyze photos of meals taken by the user and suggest visually beautiful menus. For example, it can suggest recipes that take into consideration the balance of colors and presentation. It can also suggest visually beautiful menus based on photos of the user's meals. For example, it can suggest dishes that look beautiful based on the results of analyzing the photos. It can also analyze photos of the user's meals and suggest visually beautiful menus. For example, it can analyze the colors and composition of the photos and suggest recipes with beautiful presentation. This can provide visually beautiful menus and improve the user's satisfaction with their meals.

[0054] The cooking time scheduling unit can analyze the user's past cooking time data and propose an optimal cooking schedule. For example, the user's past cooking time data is stored in a database, and the data is analyzed to propose an optimal cooking schedule. For example, recipes with short cooking times are proposed based on past data. The cooking time scheduling unit can also analyze the user's past cooking time data and propose an efficient cooking schedule. For example, recipes are proposed in order of shortest cooking times based on past data. The cooking time scheduling unit can also propose an optimal cooking schedule based on the user's past cooking time data. For example, recipes with shortest cooking times are proposed preferentially based on past data. This makes it possible to provide an optimal cooking schedule based on past data.

[0055] The cooking time scheduling unit can analyze the user's lifestyle rhythm and suggest the most efficient cooking time. For example, the user's lifestyle rhythm is stored in a database, and the data is analyzed to suggest the most efficient cooking time. For example, the cooking time is set to match the time the user returns home from work. The cooking time scheduling unit can also analyze the user's lifestyle rhythm and suggest efficient cooking times. For example, an easy-to-make breakfast recipe can be suggested to match the time the user eats breakfast. The cooking time scheduling unit can also suggest an optimal cooking schedule based on the user's lifestyle rhythm. For example, a nutritious recipe can be suggested to match the time the user eats a meal after exercising. This makes it possible to provide the optimal cooking time to match the user's lifestyle rhythm.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The user information input unit inputs user information, such as user preferences, allergy information, budget limit, number of calories per meal, residential area, family composition, frequency of eating out, etc. Step 2: In the menu generation unit, the generation AI generates a menu based on the user information input by the user information input unit. For example, the generation AI uses a text generation AI (e.g., LLM) to generate a menu that meets the user's requirements. The generation AI can also use a multimodal generation AI to generate a menu that meets the user's preferences and health condition. Step 3: The cooking time scheduling unit schedules cooking times based on the menu generated by the menu generation unit. For example, the generation AI sets cooking times according to the user's schedule. Step 4: The reminder section reminds the user of the recipe and the ingredients they need when they go shopping. For example, the AI ​​generation system displays a pop-up with a list of ingredients when the user goes shopping.

[0058] (Example 2) The home cooking support system according to an embodiment of the present invention is a system that reduces the time users spend thinking about cooking at home and improves daily typing. This home cooking support system inputs user information, and a generation AI generates monthly menus, schedules cooking times, and displays pop-up reminders of recipes and necessary ingredients when shopping. As a result, the home cooking support system reduces the time users spend thinking about cooking at home and improves daily quality of life (QOL).

[0059] The home cooking support system according to the embodiment includes a user information input unit, a menu generation unit, a cooking time scheduling unit, and a reminder unit. The user information input unit inputs user information. For example, information such as the user's preferences, allergy information, budget limit, number of calories per meal, residential area, family composition, and frequency of eating out can be input. The menu generation unit generates a menu using a generation AI based on the user information input by the user information input unit. For example, the generation AI uses a text generation AI (e.g., LLM) to generate a menu that meets the user's requirements. The generation AI can also use a multimodal generation AI to generate a menu tailored to the user's preferences and health condition. The cooking time scheduling unit schedules cooking times based on the menu generated by the menu generation unit. For example, the generation AI sets cooking times according to the user's schedule. The reminder unit reminds the user of recipes and necessary ingredients when shopping. For example, the generation AI displays a pop-up list of necessary ingredients when the user goes shopping. As a result, the home cooking support system according to the embodiment can reduce the time users spend thinking about cooking and improve their daily typing skills. For example, users can use the system to cook their meals efficiently and improve their quality of life.

[0060] The user information input unit can analyze the user's emotional state and suggest low-stress meal plans. For example, when the user inputs information, the user information input unit uses a camera or microphone to analyze facial expressions and voice and estimate the user's emotional state in real time. For example, if it is determined that the user is under high stress, it suggests a meal plan using ingredients with a relaxing effect. Furthermore, the emotional state of the user at the time of input is analyzed, and if it is determined that the user is under low stress, it suggests a meal plan that is a little more elaborate than usual. For example, if the user is relaxed, it suggests a meal plan that encourages the user to try a new recipe. Furthermore, if it is determined that the user is under high stress, it suggests a meal plan that requires a short cooking time and is easy to make. For example, if the user is under high stress, it suggests frozen foods or simple stir-fries. This reduces the user's stress and provides a more comfortable home cooking experience.

[0061] The user information input unit can analyze the user's past meal history and suggest changes in preferences and new ingredients. The user information input unit, for example, stores the user's past meal history in a database and analyzes the data to understand changes in preferences. For example, if an ingredient that was frequently eaten in the past has not been eaten recently, a new ingredient can be suggested. The user's meal history can also be analyzed to detect changes in preferences for specific ingredients or dishes. For example, if a dish that was previously a favorite has not been eaten recently, that dish can be suggested again. The unit can also suggest new ingredients and dishes based on the user's meal history. For example, recipes using ingredients that have not been eaten before can be suggested to increase the variety of the user's meals. This makes it possible to suggest new ingredients and dishes that suit the user's preferences.

[0062] The user information input unit can link the user's health data and generate a menu according to the user's health condition. The user information input unit, for example, analyzes data obtained from the user's fitness tracker and suggests a menu according to the user's health condition. For example, on days when the user exercises a lot, it suggests a high-protein meal. Furthermore, if the user's health data indicates a deficiency in a specific nutrient, it suggests a menu using ingredients that supplement that nutrient. For example, if there is a vitamin D deficiency, it suggests recipes using fish or mushrooms. Furthermore, the user's health data is linked to generate a menu according to fluctuations in weight and body fat percentage. For example, if the user's weight is increasing, it suggests a low-calorie meal. This makes it possible to provide an optimal menu according to the user's health condition.

[0063] The user information input unit can integrate information on all members of the user's family and generate a menu that takes into account everyone's preferences and allergies. The user information input unit, for example, inputs the preferences and allergy information of all members of the user's family and integrates it to generate a menu that will satisfy everyone. For example, if there is a member of the family who has a nut allergy, it will suggest recipes that do not contain nuts. It also analyzes the dietary history of all family members and suggests a menu that takes everyone's preferences into consideration. For example, it will include a dish that everyone likes once a week. It also links the health data of all family members and generates a menu that suits everyone's health condition. For example, if there is a member of the family with high blood pressure, it will suggest low-salt recipes. This makes it possible to provide a menu that will satisfy the whole family.

[0064] The user information input unit can analyze the user's social media posts about food and incorporate trends and popular recipes. The user information input unit, for example, analyzes the user's social media posts to understand the latest food trends and popular recipes. For example, it can incorporate dishes that are trending on social media into menus. It can also identify ingredients and dishes that the user is interested in based on the user's social media posts and reflect these in menus. For example, it can suggest recipes using ingredients that the user frequently posts about. It can also analyze food trends on social media to provide the user with new cooking ideas. For example, it can suggest dishes that are popular on social media as special weekend menus. This makes it possible to provide menus that match the user's interests and trends.

[0065] The user information input unit can analyze the emotional state of the user and provide an interface that elicits positive emotions. For example, when the user inputs information, the user information input unit uses an emotion estimation function to analyze the emotional state in real time and provide an interface that elicits positive emotions. For example, an encouraging message is displayed during input. The unit also provides an interface that analyzes the emotional state of the user and elicits positive emotions. For example, relaxing music is played during input. The unit also provides an interface that analyzes the emotional state of the user and elicits positive emotions. For example, success stories and positive feedback are displayed during input. This makes it possible to provide an interface that keeps the user's emotions positive.

[0066] The menu generation unit can propose optimal menus by taking into account the seasonality of ingredients. For example, the menu generation unit registers the seasonality of ingredients for each season in a database and proposes optimal menus based on that data. For example, in spring, recipes using asparagus or bamboo shoots are proposed. Also, nutritious menus are proposed by taking into account the seasonality of ingredients for each season. For example, in winter, recipes using citrus fruits rich in vitamin C are proposed. Also, cost-effective menus are proposed by taking into account the seasonality of ingredients for each season. For example, in summer, recipes using inexpensive ingredients such as tomatoes and cucumbers are proposed. In this way, it is possible to provide menus that make use of seasonal ingredients for each season.

[0067] The menu generation unit can grasp the user's ingredient inventory status in real time and generate menus that reduce waste. For example, the menu generation unit grasps the inventory status of the user's refrigerator or pantry in real time and suggests menus that reduce waste based on that data. For example, it suggests recipes that prioritize using ingredients that are in high stock. It also analyzes the user's ingredient inventory status and suggests menus that prioritize using ingredients that are close to their expiration date. For example, it suggests recipes that use ingredients that are close to their expiration date. It also grasps the user's ingredient inventory status in real time and automatically generates a shopping list to reduce waste. For example, it adds ingredients that are low in stock to the list. This reduces ingredient waste and provides efficient menus.

[0068] The menu generation unit can use the user's emotion estimation function to suggest a menu that corresponds to monthly mood fluctuations. The menu generation unit suggests a menu that corresponds to monthly mood fluctuations, for example, based on the user's emotion estimation data. For example, when the user is feeling down, it suggests recipes using ingredients that have a mood-boosting effect. The unit also analyzes the user's emotional state and suggests a menu that corresponds to monthly mood fluctuations. For example, when the user is feeling high, it suggests recipes using ingredients that have a relaxing effect. The unit also suggests a menu that corresponds to monthly mood fluctuations, based on the user's emotion estimation data. For example, when the user is feeling good, it suggests a menu that encourages the user to try a new recipe. This makes it possible to provide an optimal menu that corresponds to the user's mood.

[0069] The menu generation unit can generate a multinational menu that incorporates cuisine from different cultures and countries. For example, the menu generation unit registers cuisine from different cultures and countries in a database and proposes a multinational menu based on that data. For example, it proposes a menu that incorporates Italian cuisine or Chinese cuisine. It also proposes a menu that incorporates cuisine from different cultures and countries taking into account the user's preferences and allergy information. For example, it proposes multinational recipes that accommodate allergies. It also proposes menus that incorporate cuisine from different cultures and countries, increasing the variety of the user's meals. For example, it proposes a menu that incorporates cuisine from a different country once a week. This makes it possible to provide multinational menus and increase the variety of the user's meals.

[0070] The menu generation unit can analyze photos of the user's meals and suggest visually beautiful menus. The menu generation unit, for example, analyzes photos of meals taken by the user and suggests visually beautiful menus. For example, it suggests recipes that take into consideration the balance of colors and presentation. It also suggests visually beautiful menus based on the user's photos of meals. For example, it suggests dishes that look beautiful based on the results of analyzing the photos. It also analyzes photos of the user's meals and suggests visually beautiful menus. For example, it analyzes the colors and composition of the photos and suggests recipes with beautiful presentation. This makes it possible to provide visually beautiful menus and improve the user's satisfaction with their meals.

[0071] The cooking time scheduling unit can analyze the user's past cooking time data and propose an optimal cooking schedule. The cooking time scheduling unit, for example, stores the user's past cooking time data in a database and analyzes the data to propose an optimal cooking schedule. For example, recipes with short cooking times are proposed based on past data. The cooking time scheduling unit can also analyze the user's past cooking time data and propose an efficient cooking schedule. For example, recipes are proposed in order of shortest cooking times based on past data. The cooking time scheduling unit can also propose an optimal cooking schedule based on the user's past cooking time data. For example, recipes with shortest cooking times are proposed preferentially based on past data. This makes it possible to provide an optimal cooking schedule based on past data.

[0072] The cooking time scheduling unit can analyze the user's lifestyle rhythm and suggest the most efficient cooking time. The cooking time scheduling unit, for example, stores the user's lifestyle rhythm in a database and analyzes the data to suggest the most efficient cooking time. For example, the cooking time is set to match the time the user returns home from work. The cooking time scheduling unit also analyzes the user's lifestyle rhythm and suggests efficient cooking times. For example, an easy-to-make breakfast recipe is suggested to match the time the user eats breakfast. The cooking time scheduling unit also suggests an optimal cooking schedule based on the user's lifestyle rhythm. For example, a nutritious recipe is suggested to match the time the user eats a meal after exercising. This makes it possible to provide the optimal cooking time to match the user's lifestyle rhythm.

[0073] The cooking time scheduling unit can use the user's emotion estimation function to suggest a low-stress cooking schedule. The cooking time scheduling unit suggests a low-stress cooking schedule, for example, based on the user's emotion estimation data. For example, when stress is high, it suggests simple recipes with short cooking times. It also analyzes the user's emotional state to suggest a low-stress cooking schedule. For example, when stress is low, it suggests slightly more elaborate dishes. It also suggests a low-stress cooking schedule based on the user's emotion estimation data. For example, when stress is high, it suggests frozen foods or simple stir-fries. In this way, it is possible to provide a cooking schedule that reduces the user's stress.

[0074] The reminding unit can analyze the user's shopping history and provide reminders at the optimal timing. The reminding unit, for example, stores the user's shopping history in a database, analyzes the data, and provides reminders at the optimal timing. For example, it predicts the timing of shopping based on past shopping patterns. It also analyzes the user's shopping history and provides efficient reminders. For example, it predicts the timing of shopping based on past data and provides reminders for necessary ingredients. It also provides reminders at the optimal timing based on the user's shopping history. For example, it predicts the timing of shopping based on past data and provides reminders for necessary ingredients. This allows reminders to be provided at the optimal timing, preventing users from forgetting to buy things.

[0075] The reminding unit can grasp the inventory status of the user's refrigerator in real time and remind the user of necessary ingredients. The reminding unit, for example, grasps the inventory status of the user's refrigerator in real time and reminds the user of necessary ingredients based on that data. For example, it adds ingredients that are low in stock to a list. It also analyzes the inventory status of the user's refrigerator and provides efficient reminders. For example, it adds ingredients that are low in stock to a list and predicts the timing of shopping. It also provides reminders at the optimal timing based on the inventory status of the user's refrigerator. For example, it adds ingredients that are low in stock to a list and predicts the timing of shopping. This makes it possible to grasp the inventory status of the refrigerator in real time and provide reminders of necessary ingredients.

[0076] The reminding unit can use the user's emotion estimation function to suggest a less stressful reminding method. The reminding unit, for example, suggests a less stressful reminding method based on the user's emotion estimation data. For example, when stress is high, the frequency of reminders is reduced. The reminding unit also analyzes the user's emotional state and suggests a less stressful reminding method. For example, when stress is low, the frequency of reminders is increased. The reminding unit also suggests a less stressful reminding method based on the user's emotion estimation data. For example, when stress is high, the frequency of reminders is reduced. This makes it possible to provide a reminding method that reduces the user's stress.

[0077] The reminding unit can automatically generate a shopping list for a user and send it to a smartphone. The reminding unit, for example, builds a system that automatically generates a shopping list for a user and sends it to a smartphone. For example, it adds necessary ingredients to the list and sends it to a smartphone. The reminding unit also automatically generates a shopping list for a user to support efficient shopping. For example, it adds necessary ingredients to the list and sends it to a smartphone. The reminding unit also builds a system that automatically generates a shopping list for a user and sends it to a smartphone. For example, it adds necessary ingredients to the list and sends it to a smartphone. This allows the automatically generated shopping list to be sent to a smartphone, making shopping more efficient.

[0078] The reminding unit can analyze the user's shopping frequency and patterns and suggest an optimal shopping route. The reminding unit, for example, stores the user's shopping frequency and patterns in a database and analyzes the data to suggest an optimal shopping route. For example, it suggests an efficient shopping route. It also analyzes the user's shopping frequency and patterns and suggests an efficient shopping route. For example, it suggests an optimal shopping route based on the shopping frequency and patterns. It also suggests an optimal shopping route based on the user's shopping frequency and patterns. For example, it suggests an optimal shopping route based on the shopping frequency and patterns. This makes it possible to suggest an optimal shopping route and improve shopping efficiency.

[0079] The reminding unit can use the user's emotion estimation function to suggest a less stressful reminding method. The reminding unit, for example, suggests a less stressful reminding method based on the user's emotion estimation data. For example, when stress is high, the frequency of reminders is reduced. The reminding unit also analyzes the user's emotional state and suggests a less stressful reminding method. For example, when stress is low, the frequency of reminders is increased. The reminding unit also suggests a less stressful reminding method based on the user's emotion estimation data. For example, when stress is high, the frequency of reminders is reduced. This makes it possible to provide a reminding method that reduces the user's stress.

[0080] The subscription management unit can analyze a user's frequency of use and satisfaction level and propose the most suitable pricing plan. For example, the subscription management unit stores the user's frequency of use and satisfaction level in a database and analyzes that data to propose the most suitable pricing plan. For example, it proposes a discount plan for users who use frequently. It also analyzes the user's frequency of use and satisfaction level to propose an efficient pricing plan. For example, it proposes a pricing plan based on the number of times a user uses the service infrequently. It also proposes the most suitable pricing plan based on the user's frequency of use and satisfaction level. For example, it proposes a discount plan for users who use frequently. This makes it possible to provide the most suitable pricing plan according to the user's usage status.

[0081] The subscription management unit can analyze the usage status of all family members of a user and propose a family plan. For example, the subscription management unit stores the usage status of all family members of a user in a database and analyzes that data to propose a family plan. For example, if all family members use the service, a discount plan is proposed. The subscription management unit also analyzes the usage status of all family members and proposes an efficient family plan. For example, if all family members use the service, a fee plan based on the number of times the service is used is proposed. The subscription management unit also proposes the optimal family plan based on the usage status of all family members of a user. For example, if all family members use the service, a discount plan is proposed. This makes it possible to provide the optimal family plan according to the usage status of all family members.

[0082] The subscription management unit can use the user emotion estimation function to propose a pricing plan that provides high satisfaction. The subscription management unit proposes a pricing plan that provides high satisfaction based on, for example, the user emotion estimation data. For example, a pricing plan with benefits is proposed for a user with a high emotion score. The subscription management unit also analyzes the user's emotional state and proposes a pricing plan that provides high satisfaction. For example, a discount plan is proposed for a user with a low emotion score. The subscription management unit also proposes a pricing plan that provides high satisfaction based on the user emotion estimation data. For example, a pricing plan with benefits is proposed for a user with a high emotion score. In this way, it is possible to provide a pricing plan that provides high satisfaction based on the user's emotions.

[0083] The subscription management unit can provide points and benefits according to the user's usage. For example, the subscription management unit stores the user's usage in a database, analyzes the data, and provides points and benefits. For example, points are awarded to users who use the service frequently. The subscription management unit also analyzes the user's usage and provides efficient points and benefits. For example, a plan with benefits is proposed for users who use the service infrequently. The subscription management unit also provides optimal points and benefits based on the user's usage. For example, points are awarded to users who use the service frequently. This makes it possible to provide points and benefits according to the user's usage.

[0084] The subscription management unit can customize the service content based on user feedback. For example, the subscription management unit stores user feedback in a database and analyzes the data to customize the service content. For example, new functions are added in response to user requests. The subscription management unit also analyzes user feedback to customize efficient service content. For example, service content is changed in response to user requests. The subscription management unit also customizes optimal service content based on user feedback. For example, new functions are added in response to user requests. In this way, the service content can be customized based on user feedback.

[0085] The subscription management unit can use the user emotion estimation function to propose a pricing plan that provides high satisfaction. The subscription management unit proposes a pricing plan that provides high satisfaction based on, for example, the user emotion estimation data. For example, a pricing plan with benefits is proposed for a user with a high emotion score. The subscription management unit also analyzes the user's emotional state and proposes a pricing plan that provides high satisfaction. For example, a discount plan is proposed for a user with a low emotion score. The subscription management unit also proposes a pricing plan that provides high satisfaction based on the user emotion estimation data. For example, a pricing plan with benefits is proposed for a user with a high emotion score. In this way, it is possible to provide a pricing plan that provides high satisfaction based on the user's emotions.

[0086] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0087] The user information input unit can analyze the user's social media posts about food and incorporate trends and popular recipes. For example, it can analyze the user's social media posts to understand the latest food trends and popular recipes. For example, it can incorporate dishes that are trending on social media into the menu. It can also identify ingredients and dishes that the user is interested in based on the user's social media posts and reflect these in the menu. For example, it can suggest recipes that use ingredients that the user frequently posts about. It can also analyze food trends on social media to provide the user with new cooking ideas. For example, it can suggest dishes that are popular on social media as special weekend menus. This makes it possible to provide menus that match the user's interests and trends.

[0088] The user information input unit can analyze the emotional state of the user and provide an interface that draws out positive emotions. For example, when the user inputs information, the emotional state is analyzed in real time using an emotion estimation function, and an interface that draws out positive emotions is provided. For example, an encouraging message is displayed during input. Also, an interface is provided that analyzes the emotional state of the user and draws out positive emotions. For example, relaxing music is played during input. Also, an interface is provided that analyzes the emotional state of the user and draws out positive emotions. For example, success stories and positive feedback are displayed during input. In this way, an interface that keeps the user's emotions positive can be provided.

[0089] The menu generation unit can propose optimal menus by taking into account the seasonality of ingredients. For example, the seasonality of ingredients for each season can be registered in a database, and optimal menus can be proposed based on that data. For example, in spring, recipes using asparagus or bamboo shoots can be proposed. Also, nutritious menus can be proposed by taking into account the seasonality of ingredients for each season. For example, in winter, recipes using citrus fruits rich in vitamin C can be proposed. Also, cost-effective menus can be proposed by taking into account the seasonality of ingredients for each season. For example, in summer, recipes using inexpensive ingredients such as tomatoes and cucumbers can be proposed. This makes it possible to provide menus that make use of seasonal ingredients for each season.

[0090] The menu generation unit can grasp the user's ingredient inventory status in real time and generate menus that reduce waste. For example, it grasps the inventory status of the user's refrigerator or pantry in real time and suggests menus that reduce waste based on that data. For example, it suggests recipes that prioritize using ingredients that are in high stock. It also analyzes the user's ingredient inventory status and suggests menus that prioritize using ingredients that are close to their expiration date. For example, it suggests recipes that use ingredients that are close to their expiration date. It also grasps the user's ingredient inventory status in real time and automatically generates a shopping list to reduce waste. For example, it adds ingredients that are low in stock to the list. This reduces ingredient waste and provides efficient menus.

[0091] The menu generation unit can use the user's emotion estimation function to suggest menus that correspond to monthly mood fluctuations. For example, based on the user's emotion estimation data, it suggests menus that correspond to monthly mood fluctuations. For example, when the user is feeling down, it suggests recipes that use ingredients that have a mood-boosting effect. It also analyzes the user's emotional state and suggests menus that correspond to monthly mood fluctuations. For example, when the user is feeling high, it suggests recipes that use ingredients that have a relaxing effect. It also suggests menus that correspond to monthly mood fluctuations based on the user's emotion estimation data. For example, when the user is feeling good, it suggests a menu that encourages them to try a new recipe. This makes it possible to provide the optimal menu that corresponds to the user's mood.

[0092] The menu generation unit can generate multinational menus that incorporate cuisine from different cultures and countries. For example, it registers cuisine from different cultures and countries in a database and proposes multinational menus based on that data. For example, it proposes menus that incorporate Italian and Chinese cuisine. It also proposes menus that incorporate cuisine from different cultures and countries taking into account the user's preferences and allergy information. For example, it proposes multinational recipes that accommodate allergies. It also proposes menus that incorporate cuisine from different cultures and countries, increasing the variety of the user's meals. For example, it proposes a menu that includes cuisine from a different country once a week. This makes it possible to provide multinational menus and increase the variety of the user's meals.

[0093] The menu generation unit can analyze photos of the user's meals and suggest visually beautiful menus. For example, it can analyze photos of meals taken by the user and suggest visually beautiful menus. For example, it can suggest recipes that take into consideration the balance of colors and presentation. It can also suggest visually beautiful menus based on photos of the user's meals. For example, it can suggest dishes that look beautiful based on the results of analyzing the photos. It can also analyze photos of the user's meals and suggest visually beautiful menus. For example, it can analyze the colors and composition of the photos and suggest recipes with beautiful presentation. This can provide visually beautiful menus and improve the user's satisfaction with their meals.

[0094] The cooking time scheduling unit can analyze the user's past cooking time data and propose an optimal cooking schedule. For example, the user's past cooking time data is stored in a database, and the data is analyzed to propose an optimal cooking schedule. For example, recipes with short cooking times are proposed based on past data. The cooking time scheduling unit can also analyze the user's past cooking time data and propose an efficient cooking schedule. For example, recipes are proposed in order of shortest cooking times based on past data. The cooking time scheduling unit can also propose an optimal cooking schedule based on the user's past cooking time data. For example, recipes with shortest cooking times are proposed preferentially based on past data. This makes it possible to provide an optimal cooking schedule based on past data.

[0095] The cooking time scheduling unit can analyze the user's lifestyle rhythm and suggest the most efficient cooking time. For example, the user's lifestyle rhythm is stored in a database, and the data is analyzed to suggest the most efficient cooking time. For example, the cooking time is set to match the time the user returns home from work. The cooking time scheduling unit can also analyze the user's lifestyle rhythm and suggest efficient cooking times. For example, an easy-to-make breakfast recipe can be suggested to match the time the user eats breakfast. The cooking time scheduling unit can also suggest an optimal cooking schedule based on the user's lifestyle rhythm. For example, a nutritious recipe can be suggested to match the time the user eats a meal after exercising. This makes it possible to provide the optimal cooking time to match the user's lifestyle rhythm.

[0096] The cooking time scheduling unit can use the user's emotion estimation function to suggest a low-stress cooking schedule. For example, a low-stress cooking schedule is suggested based on the user's emotion estimation data. For example, when stress is high, simple recipes with short cooking times are suggested. The unit also analyzes the user's emotional state to suggest a low-stress cooking schedule. For example, when stress is low, slightly more elaborate dishes are suggested. The unit also suggests a low-stress cooking schedule based on the user's emotion estimation data. For example, when stress is high, frozen foods or simple stir-fries are suggested. This makes it possible to provide a cooking schedule that reduces the user's stress.

[0097] The processing flow of the second embodiment will be briefly explained below.

[0098] Step 1: The user information input unit inputs user information, such as user preferences, allergy information, budget limit, number of calories per meal, residential area, family composition, frequency of eating out, etc. Step 2: In the menu generation unit, the generation AI generates a menu based on the user information input by the user information input unit. For example, the generation AI uses a text generation AI (e.g., LLM) to generate a menu that meets the user's requirements. The generation AI can also use a multimodal generation AI to generate a menu that meets the user's preferences and health condition. Step 3: The cooking time scheduling unit schedules cooking times based on the menu generated by the menu generation unit. For example, the generation AI sets cooking times according to the user's schedule. Step 4: The reminder section reminds the user of the recipe and the ingredients they need when they go shopping. For example, the AI ​​generation system displays a pop-up with a list of ingredients when the user goes shopping.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0103] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0112] 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. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0127] In the headset type terminal 314, 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 headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0143] In the robot 414, 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. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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."

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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]

[0166] 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. a user information input unit for inputting user information; a menu creation unit that creates a menu based on the user information input by the user information input unit; a cooking time scheduling unit that schedules cooking times based on the menu generated by the menu generating unit; A reminder unit that reminds you of recipes and necessary ingredients when shopping. A system characterized by:

2. The user information input unit Analyzing the user's emotional state and suggesting stress-free meals 2. The system of claim 1.

3. The user information input unit Analyze the user's past eating history to detect changes in preferences and suggest new ingredients 2. The system of claim 1.

4. The user information input unit Linking user health data to generate menus based on health status 2. The system of claim 1.

5. The user information input unit Integrates information about the user's entire family and generates menus that take into account everyone's preferences and allergies 2. The system of claim 1.

6. The user information input unit Analyze users' social media posts about food and incorporate trends and popular recipes 2. The system of claim 1.

Images