System

Abstract

An object of a system according to an embodiment is to make meal planning and creation of a shopping list efficient and prevent duplicate procurement.SOLUTION: A system according to an embodiment includes a meal plan suggestion unit, a shopping list creation unit, a sale information provision unit, and an ingredient registration unit. The meal plan suggestion unit suggests a meal plan. The shopping list creation unit automatically creates a shopping list based on the meal plan suggested by the meal plan suggestion unit. The sale information providing unit provides sale information based on the shopping list created by the shopping list creating unit. A food material registration part registers food materials at home to prevent duplicate procurement.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] Conventional technology requires time-consuming meal planning and creating shopping lists, leading to problems such as duplicate purchases and food waste.

[0005] The system according to the embodiment aims to streamline meal planning and shopping list creation and prevent duplicate purchases. [Means for solving the problem]

[0006] The system according to the embodiment includes a meal plan proposal unit, a shopping list creation unit, a sale information provision unit, and an ingredient registration unit. The meal plan proposal unit proposes a meal plan. The shopping list creation unit automatically creates a shopping list based on the meal plan proposed by the meal plan proposal unit. The sale information provision unit provides sale information based on the shopping list created by the shopping list creation unit. The ingredient registration unit registers ingredients available at home to prevent duplicate purchases. [Effects of the Invention]

[0007] The system according to the embodiment can streamline meal planning and shopping list creation and prevent duplicate purchases. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10]1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The Smart Meals system according to an embodiment of the present invention automates meal planning and shopping list creation. This system proposes customized meal plans taking into account the number of meals served and nutritional balance. This allows the Smart Meals system to perform a comprehensive process, from proposing meal plans to creating shopping lists, providing sales information, and preventing duplicate food procurement.

[0029] The Smart Meals system according to the embodiment includes a meal plan suggestion unit, a shopping list creation unit, a sale information provision unit, and an ingredient registration unit. The meal plan suggestion unit proposes a meal plan. For example, the generation AI receives input information such as a user's preferences, health condition, and allergy information, and generates an optimal meal plan based on the input information. Furthermore, if a user inputs, "I would like suggestions for dinner three times a week," the generation AI can suggest menus that meet the user's request. The shopping list creation unit automatically creates a shopping list based on the meal plan proposed by the meal plan suggestion unit. For example, the generation AI automatically adds necessary products to the shopping list based on the proposed meal plan. Furthermore, if the generation AI adds "chicken, broccoli, and tomatoes" to the list, the user can take the list to the supermarket. The sale information provision unit provides sale information based on the shopping list created by the shopping list creation unit. For example, the generation AI provides sale information for nearby supermarkets based on the user's shopping list. Furthermore, the generation AI notifies the user, "Chicken is on sale at a nearby supermarket," allowing the user to utilize the information. The ingredient registration unit registers ingredients at home to prevent duplicate purchases. For example, if a user registers "I have three tomatoes in the refrigerator," the generation AI will not add tomatoes to the shopping list. The generation AI also monitors the user's ingredient usage and makes suggestions to reduce food waste. For example, the generation AI could notify the user that "You should soon use up all the tomatoes in your refrigerator," allowing the user to use up ingredients without wasting them. This allows the Smart Meals system to perform a consistent process, from proposing meal plans to creating shopping lists, providing sale information, and preventing duplicate ingredient purchases.

[0030] The meal plan suggestion unit analyzes the user's past meal history, learns their preferences and allergy information, and can propose more personalized meal plans. For example, the generation AI stores the user's past meal history in a database and learns the user's preferences and allergy information based on that data. For example, the generation AI analyzes the user's past meal history and the ingredients the user has avoided in the past, and reflects these in the next meal suggestion. The meal plan suggestion unit also analyzes the user's preference trends based on the user's past meal history and proposes a meal plan that takes allergy information into account. For example, if the user has a dairy allergy, the generation AI prioritizes suggesting menus that do not contain dairy products. The meal plan suggestion unit also analyzes the user's past meal history and learns preferences for specific ingredients and dishes. For example, the generation AI includes the user's frequently selected ingredients and dishes in the next meal plan. This allows the generation AI to propose more personalized meal plans based on the user's past meal history.

[0031] The meal plan proposal unit can propose seasonal menus based on ingredients that correspond to the season and weather. For example, the generation AI of the meal plan proposal unit refers to a seasonal ingredient database and proposes seasonal menus. For example, it proposes salads made with fresh vegetables in spring and hot soups in winter. The meal plan proposal unit also obtains weather data in real time, and the generation AI adjusts the meal plan based on that information. For example, it proposes hot dishes on cold days and cold dishes on hot days. The meal plan proposal unit also takes into account the nutritional value of ingredients according to the season and weather, and the generation AI proposes balanced menus. For example, it proposes menus that include many ingredients that are hydrating in summer. This makes it possible to propose seasonal menus that take into account ingredients according to the season and weather.

[0032] The shopping list creation unit can analyze the user's purchasing history and automatically add frequently purchased items to the list. For example, the generation AI stores the user's past purchasing history in a database, and automatically adds frequently purchased items to the list based on that data. For example, ingredients that the user purchases weekly are added to the list. The shopping list creation unit also analyzes the user's purchasing history, and the generation AI identifies frequently purchased items. For example, seasonings that the user purchases monthly are added to the list. The shopping list creation unit also learns the user's purchasing patterns, and automatically adds frequently purchased items to the list. For example, daily necessities that the user purchases regularly are added to the list. In this way, frequently purchased items can be automatically added to the list based on the user's purchasing history.

[0033] The shopping list creation unit can prioritize adding ingredients that need to be used up quickly to the list based on the shelf life of the ingredients. In the shopping list creation unit, for example, the generation AI references shelf life data for ingredients and prioritizes adding ingredients that need to be used up quickly to the list. For example, ingredients that are close to their expiration date are added to the list. The shopping list creation unit also takes into account the shelf life of ingredients and identifies ingredients that need to be used up quickly. For example, the list is created based on the shelf life of ingredients in the refrigerator. In addition, the shopping list creation unit tracks the shelf life of ingredients and prioritizes adding ingredients that need to be used up quickly to the list. For example, the list is created based on the shelf life of frozen foods. This allows ingredients that need to be used up quickly to be prioritized added to the list based on the shelf life of ingredients.

[0034] The sale information providing unit can analyze the user's purchasing history and prioritize providing sale information for items purchased in the past. For example, the generation AI stores the user's past purchasing history in a database, and based on that data, the sale information providing unit prioritizes providing sale information for items purchased in the past. For example, the sale information providing unit notifies the user of sale information for food ingredients that the user frequently purchases. The sale information providing unit can also analyze the user's purchasing history, and the generation AI can identify sale information for items purchased in the past. For example, the sale information providing unit can provide sale information for daily necessities that the user regularly purchases. The generation AI can also learn the user's purchasing patterns, and prioritize providing sale information for items purchased in the past. For example, the generation AI can notify the user of sale information for seasonings that the user purchases monthly. This allows the sale information for items purchased in the past to be prioritized based on the user's purchasing history.

[0035] The sale information providing unit can compare sale information from multiple nearby supermarkets and suggest the most cost-effective shopping route. For example, the sale information providing unit constructs a system in which the generation AI collects and compares sale information from multiple nearby supermarkets. For example, the sale information from each supermarket is obtained in real time and the most cost-effective shopping route is suggested. The sale information providing unit also compares sale information from nearby supermarkets based on the user's shopping list and suggests the most cost-effective route. For example, it suggests the supermarket where a specific ingredient can be purchased at the lowest price. The sale information providing unit also analyzes sale information from nearby supermarkets and suggests the optimal shopping route for the user. For example, it suggests the cheapest route by visiting multiple supermarkets. This makes it possible to compare sale information from multiple nearby supermarkets and suggest the most cost-effective shopping route.

[0036] The ingredient registration unit tracks the expiration dates of ingredients and can suggest recipes that prioritize using ingredients that are close to their expiration date. For example, the ingredient registration unit has a generation AI that tracks the expiration dates of ingredients in the user's refrigerator or pantry and suggests recipes that prioritize using ingredients that are close to their expiration date. For example, it suggests a menu using ingredients that are close to their expiration date. The ingredient registration unit also takes the expiration dates of ingredients into consideration and the generation AI identifies ingredients that are close to their expiration date. For example, it creates recipes based on the expiration dates of ingredients in the refrigerator. The ingredient registration unit also has a generation AI that tracks the expiration dates of ingredients and suggests recipes that prioritize using ingredients that are close to their expiration date. For example, it creates recipes based on the expiration dates of frozen foods. This makes it possible to track the expiration dates of ingredients and suggest recipes that prioritize using ingredients that are close to their expiration date.

[0037] The ingredient registration unit can analyze the frequency of ingredient use and automatically add frequently used ingredients to the list. For example, the generation AI stores the user's past ingredient use history in a database and automatically adds frequently used ingredients to the list based on that data. For example, ingredients that the user uses weekly are added to the list. The ingredient registration unit also analyzes the frequency of ingredient use by the user and identifies frequently used ingredients by the generation AI. For example, seasonings that the user uses monthly are added to the list. The ingredient registration unit also learns the user's ingredient use patterns and automatically adds frequently used ingredients to the list. For example, daily necessities that the user uses regularly are added to the list. This allows the frequency of ingredient use to be analyzed and frequently used ingredients to be automatically added to the list.

[0038] The ingredient registration unit integrates the ingredient registration information of other household members, enabling ingredient management for the entire household. For example, the generation AI integrates the ingredient registration information of all household members to build a system for ingredient management for the entire household. For example, it centrally manages the inventory of ingredients used by all family members. The ingredient registration unit also analyzes the ingredient registration information of all household members, and the generation AI manages ingredients for the entire household. For example, it tracks the expiration dates of ingredients used by all family members. The ingredient registration unit also manages ingredients for the entire household based on the ingredient registration information of all household members. For example, it prevents duplicate procurement of ingredients used by all family members. This enables ingredient management for the entire household.

[0039] The ingredient registration unit can learn the user's ingredient preferences and suggest substitute ingredients. For example, the generation AI of the ingredient registration unit analyzes the user's past ingredient usage history and learns the user's preferred ingredients. For example, it suggests substitute ingredients based on ingredients the user uses frequently. The ingredient registration unit also learns the user's ingredient preferences and the generation AI suggests substitute ingredients. For example, if the user avoids a particular ingredient, the generation AI adds that substitute ingredient to the list. The ingredient registration unit also suggests substitute ingredients based on the user's preferences. For example, if the user likes a particular ingredient, the generation AI suggests substitute ingredients if that ingredient is unavailable. In this way, the user's ingredient preferences can be learned and substitute ingredients can be suggested.

[0040] The ingredient registration unit monitors the user's ingredient usage in real time and makes suggestions to prevent food waste. For example, the generation AI in the ingredient registration unit monitors the ingredient usage in the user's refrigerator or pantry in real time and makes suggestions to prevent food waste. For example, it suggests menus using ingredients that are close to their expiration date. The ingredient registration unit also monitors the ingredient usage in real time and the generation AI makes suggestions to prevent food waste. For example, it creates recipes based on the expiration dates of ingredients in the refrigerator. The ingredient registration unit also monitors the ingredient usage in real time and makes suggestions to prevent food waste. For example, it creates recipes based on the expiration dates of frozen foods. This allows the generation AI to monitor the user's ingredient usage in real time and make suggestions to prevent food waste.

[0041] The ingredient registration unit can suggest methods for storing ingredients and provide advice for keeping the ingredients fresh. For example, the generation AI of the ingredient registration unit can suggest methods for storing ingredients and provide advice for keeping the ingredients fresh. For example, the unit can suggest the best way to store vegetables and fruits. The ingredient registration unit can also suggest methods for storing ingredients and provide advice for keeping the ingredients fresh. For example, the unit can suggest appropriate temperature settings for refrigerators and freezers. The ingredient registration unit can also suggest methods for storing ingredients and provide advice for keeping the ingredients fresh. For example, the unit can suggest food storage containers and packaging methods. This allows the generation AI to suggest methods for storing ingredients and provide advice for keeping the ingredients fresh.

[0042] The ingredient registration unit can work with local food banks to make suggestions for donating surplus ingredients. For example, the generation AI works with local food banks to make suggestions for users to donate surplus ingredients. For example, it suggests ways to donate ingredients that are close to their expiration date to food banks. The ingredient registration unit also works with local food banks to make suggestions for users to donate surplus ingredients. For example, it adds ingredients that the user can donate to a list and guides the user through the donation procedure. The ingredient registration unit also works with local food banks to make suggestions for users to donate surplus ingredients. For example, it notifies the user of the location and time for picking up donated ingredients. This allows the generation AI to work with local food banks to make suggestions for users to donate surplus ingredients.

[0043] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0044] The Smart Meals system may further include a satisfaction evaluation unit that evaluates the user's satisfaction with the meal. The satisfaction evaluation unit collects feedback provided by the user after the meal and uses it to improve the meal plan. For example, if the user evaluates the menu as "delicious," the menu may be prioritized for inclusion in future suggestions. Alternatively, if the user evaluates the menu as "not very liked," the menu may be excluded from suggestions. Furthermore, the satisfaction evaluation unit may analyze the user's feedback and make suggestions to improve the variety and balance of the meal plan. This allows the system to provide a meal plan that enhances the user's satisfaction.

[0045] The Smart Meals system can also include an image analysis unit that analyzes photos of a user's meals. When a user uploads a photo of their meal, the image analysis unit analyzes the content and reflects it in meal plan suggestions. For example, the image analysis unit can analyze a photo of a salad taken by the user and identify the ingredients contained in it. The image analysis unit can also evaluate the nutritional balance of a meal and suggest areas for improvement when a user uploads a photo of their meal. Furthermore, the image analysis unit can encourage interaction with other users by allowing users to share meal photos and share meal plan ideas. This allows the system to more accurately understand the content of a user's meal and provide personalized meal plans.

[0046] The Smart Meals system can further include an exercise data acquisition unit that acquires the user's exercise data. The exercise data acquisition unit monitors the user's exercise volume and activity level and adjusts the meal plan based on that information. For example, on days when the user exercises a lot, it suggests a menu that will replenish energy. The exercise data acquisition unit also uses the user's exercise data to allow the generation AI to propose a balanced meal plan. For example, on days when the user does not exercise enough, it can suggest a lighter meal. Furthermore, the exercise data acquisition unit can analyze the user's exercise habits and make suggestions to support a healthy lifestyle. This allows the system to provide a meal plan that takes the user's exercise data into consideration.

[0047] The Smart Meals system can also be equipped with a preference learning unit that learns the user's food preferences. The preference learning unit analyzes the user's past menu choices and avoided ingredients and reflects this in the next meal suggestions. For example, if the user frequently chooses a particular dish, that dish will be suggested preferentially. The preference learning unit can also learn the ingredients the user avoids and suggest menus that do not include them. Furthermore, the preference learning unit can suggest new menus based on the user's food preferences. For example, it can suggest new recipes using the user's favorite ingredients. This makes it possible to provide a personalized meal plan tailored to the user's preferences.

[0048] The Smart Meals system can further include a nutritional evaluation unit that evaluates the nutritional balance of the user's meals. The nutritional evaluation unit analyzes the nutritional balance of the menu selected by the user and makes suggestions for supplementing necessary nutrients. For example, it suggests ingredients to supplement vitamins and minerals that are lacking in the menu selected by the user. The nutritional evaluation unit can also take the user's health condition into consideration and use the generative AI to propose a balanced meal plan. For example, if the user needs to consume more of a specific nutrient, it will suggest a menu containing that nutrient. Furthermore, the nutritional evaluation unit can provide advice for improving the nutritional balance of the user's meals. This makes it possible to provide a nutritionally balanced meal plan to support the user's health.

[0049] The Smart Meals system can also be equipped with a time-saving suggestion unit to shorten the user's meal preparation time. The time-saving suggestion unit suggests menus that can be prepared in a short time when the user is busy. For example, if the user is busy at work, it suggests simple menus that can be made in 15 minutes or less. The time-saving suggestion unit can also take the user's schedule into consideration and use the generation AI to suggest efficient meal preparation methods. For example, it suggests preparing meals the night before to ensure that the next day's meal goes smoothly. Furthermore, the time-saving suggestion unit can also take the user's cooking skills into consideration and suggest menus that are easy to make. This makes it possible to provide time-saving menus that suit the user's busy lifestyle.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The meal plan suggestion unit proposes a meal plan. For example, the generation AI receives input information such as the user's preferences, health condition, and allergy information, and generates an optimal meal plan based on that information. Furthermore, if the user inputs, "I'd like suggestions for dinner three times a week," the generation AI can also suggest menus that meet that request. Step 2: The shopping list creation unit automatically creates a shopping list based on the meal plan proposed by the meal plan suggestion unit. For example, the generation AI automatically adds necessary products to the shopping list based on the proposed meal plan. If the generation AI adds "chicken, broccoli, and tomatoes" to the list, the user can take that list with them to the supermarket. Step 3: The sales information provider provides sales information based on the shopping list created by the shopping list creator. For example, the generation AI provides sales information for nearby supermarkets based on the user's shopping list. The generation AI can also notify the user that "chicken is on sale at a nearby supermarket," allowing the user to take advantage of that information. Step 4: The ingredient registration unit registers ingredients available at home to prevent duplicate purchases. For example, if a user registers "I have three tomatoes in the refrigerator," the generation AI will not add tomatoes to the shopping list. The generation AI also monitors the user's ingredient usage and makes suggestions for reducing food waste. For example, the generation AI can notify the user that "You should soon use up all the tomatoes in your refrigerator," allowing the user to use up ingredients without wasting them.

[0052] (Example 2) The Smart Meals system according to an embodiment of the present invention automates meal planning and shopping list creation. This system proposes customized meal plans taking into account the number of meals served and nutritional balance. This allows the Smart Meals system to perform a comprehensive process, from proposing meal plans to creating shopping lists, providing sales information, and preventing duplicate food procurement.

[0053] The Smart Meals system according to the embodiment includes a meal plan suggestion unit, a shopping list creation unit, a sale information provision unit, and an ingredient registration unit. The meal plan suggestion unit proposes a meal plan. For example, the generation AI receives input information such as a user's preferences, health condition, and allergy information, and generates an optimal meal plan based on the input information. Furthermore, if a user inputs, "I would like suggestions for dinner three times a week," the generation AI can suggest menus that meet the user's request. The shopping list creation unit automatically creates a shopping list based on the meal plan proposed by the meal plan suggestion unit. For example, the generation AI automatically adds necessary products to the shopping list based on the proposed meal plan. Furthermore, if the generation AI adds "chicken, broccoli, and tomatoes" to the list, the user can take the list to the supermarket. The sale information provision unit provides sale information based on the shopping list created by the shopping list creation unit. For example, the generation AI provides sale information for nearby supermarkets based on the user's shopping list. Furthermore, the generation AI notifies the user, "Chicken is on sale at a nearby supermarket," allowing the user to utilize the information. The ingredient registration unit registers ingredients at home to prevent duplicate purchases. For example, if a user registers "I have three tomatoes in the refrigerator," the generation AI will not add tomatoes to the shopping list. The generation AI also monitors the user's ingredient usage and makes suggestions to reduce food waste. For example, the generation AI could notify the user that "You should soon use up all the tomatoes in your refrigerator," allowing the user to use up ingredients without wasting them. This allows the Smart Meals system to perform a consistent process, from proposing meal plans to creating shopping lists, providing sale information, and preventing duplicate ingredient purchases.

[0054] The meal plan suggestion unit analyzes the user's past meal history, learns their preferences and allergy information, and can propose more personalized meal plans. For example, the generation AI stores the user's past meal history in a database and learns the user's preferences and allergy information based on that data. For example, the generation AI analyzes the user's past meal history and the ingredients the user has avoided in the past, and reflects these in the next meal suggestion. The meal plan suggestion unit also analyzes the user's preference trends based on the user's past meal history and proposes a meal plan that takes allergy information into account. For example, if the user has a dairy allergy, the generation AI prioritizes suggesting menus that do not contain dairy products. The meal plan suggestion unit also analyzes the user's past meal history and learns preferences for specific ingredients and dishes. For example, the generation AI includes the user's frequently selected ingredients and dishes in the next meal plan. This allows the generation AI to propose more personalized meal plans based on the user's past meal history.

[0055] The meal plan proposal unit can propose seasonal menus based on ingredients that correspond to the season and weather. For example, the generation AI of the meal plan proposal unit refers to a seasonal ingredient database and proposes seasonal menus. For example, it proposes salads made with fresh vegetables in spring and hot soups in winter. The meal plan proposal unit also obtains weather data in real time, and the generation AI adjusts the meal plan based on that information. For example, it proposes hot dishes on cold days and cold dishes on hot days. The meal plan proposal unit also takes into account the nutritional value of ingredients according to the season and weather, and the generation AI proposes balanced menus. For example, it proposes menus that include many ingredients that are hydrating in summer. This makes it possible to propose seasonal menus that take into account ingredients according to the season and weather.

[0056] The meal plan suggestion unit can use the emotion estimation function to analyze the user's current emotional state and suggest a meal plan that matches the mood. For example, the meal plan suggestion unit uses the emotion estimation function to analyze the user's facial expressions and voice to identify the user's current emotional state. For example, if the user is feeling stressed, the meal plan suggestion unit suggests a meal plan that will help them relax. Furthermore, the meal plan suggestion unit uses the generation AI to suggest a meal plan that matches the user's mood based on the user's emotional state. For example, if the user is tired, the meal plan suggestion unit suggests a menu that will replenish energy. Furthermore, the meal plan suggestion unit suggests ingredients and dishes that match the user's mood based on the emotion estimation data. For example, if the user is in a happy mood, the meal plan suggestion unit suggests a menu that includes a special dessert. This makes it possible to suggest a meal plan that matches the user's emotional state.

[0057] The shopping list creation unit can analyze the user's purchasing history and automatically add frequently purchased items to the list. For example, the generation AI stores the user's past purchasing history in a database, and automatically adds frequently purchased items to the list based on that data. For example, ingredients that the user purchases weekly are added to the list. The shopping list creation unit also analyzes the user's purchasing history, and the generation AI identifies frequently purchased items. For example, seasonings that the user purchases monthly are added to the list. The shopping list creation unit also learns the user's purchasing patterns, and automatically adds frequently purchased items to the list. For example, daily necessities that the user purchases regularly are added to the list. In this way, frequently purchased items can be automatically added to the list based on the user's purchasing history.

[0058] The shopping list creation unit can prioritize adding ingredients that need to be used up quickly to the list based on the shelf life of the ingredients. In the shopping list creation unit, for example, the generation AI references shelf life data for ingredients and prioritizes adding ingredients that need to be used up quickly to the list. For example, ingredients that are close to their expiration date are added to the list. The shopping list creation unit also takes into account the shelf life of ingredients and identifies ingredients that need to be used up quickly. For example, the list is created based on the shelf life of ingredients in the refrigerator. In addition, the shopping list creation unit tracks the shelf life of ingredients and prioritizes adding ingredients that need to be used up quickly to the list. For example, the list is created based on the shelf life of frozen foods. This allows ingredients that need to be used up quickly to be prioritized added to the list based on the shelf life of ingredients.

[0059] The shopping list creation unit can use the emotion estimation function to analyze the user's stress level and add ingredients that are useful for reducing stress to the list. For example, the shopping list creation unit can use the emotion estimation function to analyze the user's facial expressions and voice to identify the user's current stress level. For example, if the user is feeling stressed, ingredients that have a relaxing effect are added to the list. The shopping list creation unit also uses the generation AI to add ingredients that are useful for reducing stress to the list based on the user's stress level. For example, if the user is tired, herbal tea that has a relaxing effect is added to the list. The shopping list creation unit also analyzes the user's stress level based on the emotion estimation data and adds ingredients that are useful for reducing stress to the list. For example, if the user is tense, ingredients that have a relaxing effect are added to the list. This makes it possible to add ingredients to the list that correspond to the user's stress level.

[0060] The sale information providing unit can analyze the user's purchasing history and prioritize providing sale information for items purchased in the past. For example, the generation AI stores the user's past purchasing history in a database, and based on that data, the sale information providing unit prioritizes providing sale information for items purchased in the past. For example, the sale information providing unit notifies the user of sale information for food ingredients that the user frequently purchases. The sale information providing unit can also analyze the user's purchasing history, and the generation AI can identify sale information for items purchased in the past. For example, the sale information providing unit can provide sale information for daily necessities that the user regularly purchases. The generation AI can also learn the user's purchasing patterns, and prioritize providing sale information for items purchased in the past. For example, the generation AI can notify the user of sale information for seasonings that the user purchases monthly. This allows the sale information for items purchased in the past to be prioritized based on the user's purchasing history.

[0061] The sale information providing unit can compare sale information from multiple nearby supermarkets and suggest the most cost-effective shopping route. For example, the sale information providing unit constructs a system in which the generation AI collects and compares sale information from multiple nearby supermarkets. For example, the sale information from each supermarket is obtained in real time and the most cost-effective shopping route is suggested. The sale information providing unit also compares sale information from nearby supermarkets based on the user's shopping list and suggests the most cost-effective route. For example, it suggests the supermarket where a specific ingredient can be purchased at the lowest price. The sale information providing unit also analyzes sale information from nearby supermarkets and suggests the optimal shopping route for the user. For example, it suggests the cheapest route by visiting multiple supermarkets. This makes it possible to compare sale information from multiple nearby supermarkets and suggest the most cost-effective shopping route.

[0062] The ingredient registration unit tracks the expiration dates of ingredients and can suggest recipes that prioritize using ingredients that are close to their expiration date. For example, the ingredient registration unit has a generation AI that tracks the expiration dates of ingredients in the user's refrigerator or pantry and suggests recipes that prioritize using ingredients that are close to their expiration date. For example, it suggests a menu using ingredients that are close to their expiration date. The ingredient registration unit also takes the expiration dates of ingredients into consideration and the generation AI identifies ingredients that are close to their expiration date. For example, it creates recipes based on the expiration dates of ingredients in the refrigerator. The ingredient registration unit also has a generation AI that tracks the expiration dates of ingredients and suggests recipes that prioritize using ingredients that are close to their expiration date. For example, it creates recipes based on the expiration dates of frozen foods. This makes it possible to track the expiration dates of ingredients and suggest recipes that prioritize using ingredients that are close to their expiration date.

[0063] The ingredient registration unit can analyze the frequency of ingredient use and automatically add frequently used ingredients to the list. For example, the generation AI stores the user's past ingredient use history in a database and automatically adds frequently used ingredients to the list based on that data. For example, ingredients that the user uses weekly are added to the list. The ingredient registration unit also analyzes the frequency of ingredient use by the user and identifies frequently used ingredients by the generation AI. For example, seasonings that the user uses monthly are added to the list. The ingredient registration unit also learns the user's ingredient use patterns and automatically adds frequently used ingredients to the list. For example, daily necessities that the user uses regularly are added to the list. This allows the frequency of ingredient use to be analyzed and frequently used ingredients to be automatically added to the list.

[0064] The ingredient registration unit can use the emotion estimation function to suggest ingredients to be used according to the user's emotional state. For example, the ingredient registration unit uses the emotion estimation function to analyze the user's facial expressions and voice to identify the user's current emotional state. For example, if the user is feeling stressed, the ingredient registration unit suggests recipes that use ingredients that have a relaxing effect. Furthermore, the ingredient registration unit uses the generation AI to suggest ingredients that match the user's emotional state based on the user's emotional state. For example, if the user is tired, the ingredient registration unit suggests recipes that use ingredients that will help them relax. Furthermore, the ingredient registration unit suggests ingredients to be used according to the user's emotional state based on the emotion estimation data. For example, if the user is feeling happy, the ingredient registration unit suggests a recipe that includes a special dessert. This makes it possible to suggest ingredients to be used according to the user's emotional state.

[0065] The ingredient registration unit integrates the ingredient registration information of other household members, enabling ingredient management for the entire household. For example, the generation AI integrates the ingredient registration information of all household members to build a system for ingredient management for the entire household. For example, it centrally manages the inventory of ingredients used by all family members. The ingredient registration unit also analyzes the ingredient registration information of all household members, and the generation AI manages ingredients for the entire household. For example, it tracks the expiration dates of ingredients used by all family members. The ingredient registration unit also manages ingredients for the entire household based on the ingredient registration information of all household members. For example, it prevents duplicate procurement of ingredients used by all family members. This enables ingredient management for the entire household.

[0066] The ingredient registration unit can learn the user's ingredient preferences and suggest substitute ingredients. For example, the generation AI of the ingredient registration unit analyzes the user's past ingredient usage history and learns the user's preferred ingredients. For example, it suggests substitute ingredients based on ingredients the user uses frequently. The ingredient registration unit also learns the user's ingredient preferences and the generation AI suggests substitute ingredients. For example, if the user avoids a particular ingredient, the generation AI adds that substitute ingredient to the list. The ingredient registration unit also suggests substitute ingredients based on the user's preferences. For example, if the user likes a particular ingredient, the generation AI suggests substitute ingredients if that ingredient is unavailable. In this way, the user's ingredient preferences can be learned and substitute ingredients can be suggested.

[0067] The ingredient registration unit can use the emotion estimation function to suggest ingredients to be used according to the user's emotional state. For example, the ingredient registration unit uses the emotion estimation function to analyze the user's facial expressions and voice to identify the user's current emotional state. For example, if the user is feeling stressed, the ingredient registration unit suggests recipes that use ingredients that have a relaxing effect. Furthermore, the ingredient registration unit uses the generation AI to suggest ingredients that match the user's emotional state based on the user's emotional state. For example, if the user is tired, the ingredient registration unit suggests recipes that use ingredients that will help them relax. Furthermore, the ingredient registration unit suggests ingredients to be used according to the user's emotional state based on the emotion estimation data. For example, if the user is feeling happy, the ingredient registration unit suggests a recipe that includes a special dessert. This makes it possible to suggest ingredients to be used according to the user's emotional state.

[0068] The ingredient registration unit monitors the user's ingredient usage in real time and makes suggestions to prevent food waste. For example, the generation AI in the ingredient registration unit monitors the ingredient usage in the user's refrigerator or pantry in real time and makes suggestions to prevent food waste. For example, it suggests menus using ingredients that are close to their expiration date. The ingredient registration unit also monitors the ingredient usage in real time and the generation AI makes suggestions to prevent food waste. For example, it creates recipes based on the expiration dates of ingredients in the refrigerator. The ingredient registration unit also monitors the ingredient usage in real time and makes suggestions to prevent food waste. For example, it creates recipes based on the expiration dates of frozen foods. This allows the generation AI to monitor the user's ingredient usage in real time and make suggestions to prevent food waste.

[0069] The ingredient registration unit can suggest methods for storing ingredients and provide advice for keeping the ingredients fresh. For example, the generation AI of the ingredient registration unit can suggest methods for storing ingredients and provide advice for keeping the ingredients fresh. For example, the unit can suggest the best way to store vegetables and fruits. The ingredient registration unit can also suggest methods for storing ingredients and provide advice for keeping the ingredients fresh. For example, the unit can suggest appropriate temperature settings for refrigerators and freezers. The ingredient registration unit can also suggest methods for storing ingredients and provide advice for keeping the ingredients fresh. For example, the unit can suggest food storage containers and packaging methods. This allows the generation AI to suggest methods for storing ingredients and provide advice for keeping the ingredients fresh.

[0070] The ingredient registration unit can use the emotion estimation function to make suggestions for reducing food waste according to the user's emotional state. For example, the ingredient registration unit uses the emotion estimation function to analyze the user's facial expressions and voice to identify the user's current emotional state. For example, if the user is feeling stressed, the ingredient registration unit makes suggestions for reducing food waste that have a relaxing effect. Furthermore, the ingredient registration unit uses the generation AI to make suggestions for reducing food waste that match the user's emotional state based on the user's emotional state. For example, if the user is tired, the ingredient registration unit makes suggestions for reducing food waste that are easy to implement. Furthermore, the ingredient registration unit makes suggestions for reducing food waste according to the user's emotional state based on the emotion estimation data. For example, if the user is feeling happy, the ingredient registration unit makes suggestions for reducing food waste that are fun to implement. This makes it possible to make suggestions for reducing food waste according to the user's emotional state.

[0071] The ingredient registration unit can work with local food banks to make suggestions for donating surplus ingredients. For example, the generation AI works with local food banks to make suggestions for users to donate surplus ingredients. For example, it suggests ways to donate ingredients that are close to their expiration date to food banks. The ingredient registration unit also works with local food banks to make suggestions for users to donate surplus ingredients. For example, it adds ingredients that the user can donate to a list and guides the user through the donation procedure. The ingredient registration unit also works with local food banks to make suggestions for users to donate surplus ingredients. For example, it notifies the user of the location and time for picking up donated ingredients. This allows the generation AI to work with local food banks to make suggestions for users to donate surplus ingredients.

[0072] The ingredient registration unit can use the emotion estimation function to make suggestions for reducing food waste according to the user's emotional state. For example, the ingredient registration unit uses the emotion estimation function to analyze the user's facial expressions and voice to identify the user's current emotional state. For example, if the user is feeling stressed, the ingredient registration unit makes suggestions for reducing food waste that have a relaxing effect. Furthermore, the ingredient registration unit uses the generation AI to make suggestions for reducing food waste that match the user's emotional state based on the user's emotional state. For example, if the user is tired, the ingredient registration unit makes suggestions for reducing food waste that are easy to implement. Furthermore, the ingredient registration unit makes suggestions for reducing food waste according to the user's emotional state based on the emotion estimation data. For example, if the user is feeling happy, the ingredient registration unit makes suggestions for reducing food waste that are fun to implement. This makes it possible to make suggestions for reducing food waste according to the user's emotional state.

[0073] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0074] The Smart Meals system may further include a satisfaction evaluation unit that evaluates the user's satisfaction with the meal. The satisfaction evaluation unit collects feedback provided by the user after the meal and uses it to improve the meal plan. For example, if the user evaluates the menu as "delicious," the menu may be prioritized for inclusion in future suggestions. Alternatively, if the user evaluates the menu as "not very liked," the menu may be excluded from suggestions. Furthermore, the satisfaction evaluation unit may analyze the user's feedback and make suggestions to improve the variety and balance of the meal plan. This allows the system to provide a meal plan that enhances the user's satisfaction.

[0075] The Smart Meals system can also include an image analysis unit that analyzes photos of a user's meals. When a user uploads a photo of their meal, the image analysis unit analyzes the content and reflects it in meal plan suggestions. For example, the image analysis unit can analyze a photo of a salad taken by the user and identify the ingredients contained in it. The image analysis unit can also evaluate the nutritional balance of a meal and suggest areas for improvement when a user uploads a photo of their meal. Furthermore, the image analysis unit can encourage interaction with other users by allowing users to share meal photos and share meal plan ideas. This allows the system to more accurately understand the content of a user's meal and provide personalized meal plans.

[0076] The Smart Meals system can further include an exercise data acquisition unit that acquires the user's exercise data. The exercise data acquisition unit monitors the user's exercise volume and activity level and adjusts the meal plan based on that information. For example, on days when the user exercises a lot, it suggests a menu that will replenish energy. The exercise data acquisition unit also uses the user's exercise data to allow the generation AI to propose a balanced meal plan. For example, on days when the user does not exercise enough, it can suggest a lighter meal. Furthermore, the exercise data acquisition unit can analyze the user's exercise habits and make suggestions to support a healthy lifestyle. This allows the system to provide a meal plan that takes the user's exercise data into consideration.

[0077] The Smart Meals system can also suggest meal times based on the user's emotional state. For example, using the emotion estimation function, if the user is feeling stressed, it can suggest a meal time that will help them relax. Or, if the user is tired, it can suggest a meal time that will help them replenish their energy. Furthermore, it can adjust meal times based on the emotion estimation data according to the user's emotional state. For example, if the user is feeling happy, it can suggest a time to enjoy a special meal. This makes it possible to provide optimal meal times based on the user's emotional state.

[0078] The Smart Meals system can also be equipped with a preference learning unit that learns the user's food preferences. The preference learning unit analyzes the user's past menu choices and avoided ingredients and reflects this in the next meal suggestions. For example, if the user frequently chooses a particular dish, that dish will be suggested preferentially. The preference learning unit can also learn the ingredients the user avoids and suggest menus that do not include them. Furthermore, the preference learning unit can suggest new menus based on the user's food preferences. For example, it can suggest new recipes using the user's favorite ingredients. This makes it possible to provide a personalized meal plan tailored to the user's preferences.

[0079] The Smart Meals system can also suggest meal presentations based on the user's emotional state. For example, using the emotion estimation function, if the user is feeling stressed, the system can suggest meal presentations and table settings that will help them relax. Or, if the user is tired, the system can suggest meal presentations that are simple and easy to enjoy. Furthermore, the emotion estimation data can be used to tailor meal presentations to the user's emotional state. For example, if the user is feeling happy, the system can suggest colorful food presentations and special table settings. This allows the system to provide the optimal meal presentation based on the user's emotional state.

[0080] The Smart Meals system can further include a nutritional evaluation unit that evaluates the nutritional balance of the user's meals. The nutritional evaluation unit analyzes the nutritional balance of the menu selected by the user and makes suggestions for supplementing necessary nutrients. For example, it suggests ingredients to supplement vitamins and minerals that are lacking in the menu selected by the user. The nutritional evaluation unit can also take the user's health condition into consideration and use the generative AI to propose a balanced meal plan. For example, if the user needs to consume more of a specific nutrient, it will suggest a menu containing that nutrient. Furthermore, the nutritional evaluation unit can provide advice for improving the nutritional balance of the user's meals. This makes it possible to provide a nutritionally balanced meal plan to support the user's health.

[0081] The Smart Meals system can also adjust meal portions based on the user's emotional state. For example, using the emotion estimation function, if the user is feeling stressed, the system can suggest an appropriate amount of meal. Also, if the user is tired, the system can suggest an appropriate amount of meal to replenish energy. Furthermore, the emotion estimation data can be used to adjust meal portions according to the user's emotional state. For example, if the user is feeling happy, the system can suggest a slightly larger meal. This allows the system to provide the optimal amount of meal according to the user's emotional state.

[0082] The Smart Meals system can also be equipped with a time-saving suggestion unit to shorten the user's meal preparation time. The time-saving suggestion unit suggests menus that can be prepared in a short time when the user is busy. For example, if the user is busy at work, it suggests simple menus that can be made in 15 minutes or less. The time-saving suggestion unit can also take the user's schedule into consideration and use the generation AI to suggest efficient meal preparation methods. For example, it suggests preparing meals the night before to ensure that the next day's meal goes smoothly. Furthermore, the time-saving suggestion unit can also take the user's cooking skills into consideration and suggest menus that are easy to make. This makes it possible to provide time-saving menus that suit the user's busy lifestyle.

[0083] The Smart Meals system can also suggest mealtime music based on the user's emotional state. For example, using the emotion estimation function, if the user is feeling stressed, it can suggest relaxing music. If the user is tired, it can suggest uplifting music to replenish energy. Furthermore, based on the emotion estimation data, it can also adjust the music according to the user's emotional state. For example, if the user is feeling happy, it can suggest fun music. This makes it possible to provide optimal mealtime music according to the user's emotional state.

[0084] The processing flow of the second embodiment will be briefly explained below.

[0085] Step 1: The meal plan suggestion unit proposes a meal plan. For example, the generation AI receives input information such as the user's preferences, health condition, and allergy information, and generates an optimal meal plan based on that information. Furthermore, if the user inputs, "I'd like suggestions for dinner three times a week," the generation AI can also suggest menus that meet that request. Step 2: The shopping list creation unit automatically creates a shopping list based on the meal plan proposed by the meal plan suggestion unit. For example, the generation AI automatically adds necessary products to the shopping list based on the proposed meal plan. If the generation AI adds "chicken, broccoli, and tomatoes" to the list, the user can take that list with them to the supermarket. Step 3: The sales information provider provides sales information based on the shopping list created by the shopping list creator. For example, the generation AI provides sales information for nearby supermarkets based on the user's shopping list. The generation AI can also notify the user that "chicken is on sale at a nearby supermarket," allowing the user to take advantage of that information. Step 4: The ingredient registration unit registers ingredients available at home to prevent duplicate purchases. For example, if a user registers "I have three tomatoes in the refrigerator," the generation AI will not add tomatoes to the shopping list. The generation AI also monitors the user's ingredient usage and makes suggestions for reducing food waste. For example, the generation AI can notify the user that "You should soon use up all the tomatoes in your refrigerator," allowing the user to use up ingredients without wasting them.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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).

[0095] 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.

[0096] 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.

[0097] 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.

[0098] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0099] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0114] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0130] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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."

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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, in order to avoid confusion and to 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.

[0152] 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]

[0153] 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 meal plan proposal department that proposes meal plans; a shopping list creation unit that automatically creates a shopping list based on the meal plan proposed by the meal plan proposal unit; a sales information providing unit that provides sales information based on the shopping list created by the shopping list creating unit; It has an ingredient registration section that registers ingredients at home and prevents duplicate purchases. A system characterized by:

2. The meal plan proposal unit Analyzing the user's past meal history, learning their preferences and allergies, and proposing a more personalized meal plan 2. The system of claim 1.

3. The shopping list creation unit Analyzes user purchasing history and automatically adds frequently purchased items to the list 2. The system of claim 1.

4. The sales information providing unit Analyze users' purchasing history and prioritize sales information for previously purchased items 2. The system of claim 1.

5. The ingredient registration unit Track the expiration dates of the ingredients and suggest recipes that prioritize the use of ingredients that are close to their expiration date 2. The system of claim 1.

6. The meal plan proposal unit Analyze the user's current emotional state and suggest a meal plan that matches their mood 2. The system of claim 1.

7. The shopping list creation unit Analyze the user's stress level and add the ingredients that help reduce stress to the list 2. The system of claim 1.

8. The sales information providing unit To provide the sale information according to the emotional state of the user, thereby improving the enjoyment of shopping.

2. The system of claim 1.

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