System

Abstract

An object of a system according to an embodiment is to automatically generate a menu on the basis of position information and a budget of a user and reduce a burden of housework.SOLUTION: A system includes a position information acquisition part, a budget setting part, a menu generation part, and a notification part. The position information acquisition unit acquires position information of a user. The budget setting unit sets a budget of the user. The menu generation part generates a menu on the basis of the position information acquired by the position information acquisition part and the budget set by the budget setting part. The notification unit notifies the user of the menu generated by the menu generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, planning daily menus can be a burden, and there is room for improvement, especially in dual-income households where time for housework is limited.

[0005] The system according to the embodiment aims to reduce the burden of housework by automatically generating menus based on the user's location information and budget. [Means for solving the problem]

[0006] The system according to the embodiment includes a location information acquisition unit, a budget setting unit, a menu generation unit, and a notification unit. The location information acquisition unit acquires the user's location information. The budget setting unit sets the user's budget. The menu generation unit generates a menu based on the location information acquired by the location information acquisition unit and the budget set by the budget setting unit. The notification unit notifies the user of the menu generated by the menu generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can automatically generate menus based on the user's location information and budget, thereby reducing the burden of housework. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) The automatic menu generation application according to an embodiment of the present invention is a system that automatically generates a menu using a user's location information and a set budget. This reduces the burden on the user and supports healthy and varied meals.

[0029] The automatic menu generation application according to the embodiment includes a location information acquisition unit, a budget setting unit, a menu generation unit, and a notification unit. The location information acquisition unit acquires user location information. For example, it acquires the user's current location using GPS. It can also acquire location information using Wi-Fi or cell towers. The location information acquisition unit can also acquire location information manually entered by the user. The budget setting unit sets the user's budget. For example, it sets the budget entered by the user into the app. The budget setting unit can also automatically set the budget based on a budget previously set by the user. The budget setting unit can also adjust the range of the budget set by the user. The menu generation unit generates a menu based on the location information acquired by the location information acquisition unit and the budget set by the budget setting unit. For example, the generation AI proposes an optimal menu based on the user's location information and budget. The generation AI can also generate a menu taking into account the user's preferences and allergy information. The generation AI can also propose a wide variety of menus based on the user's past menu history. The notification unit notifies the user of the menu generated by the menu generation unit. For example, the menu may be notified to the user using a push notification. The notification unit may also notify the user of the menu using an email notification. The notification unit may also notify the user of the menu using an in-app notification. This allows the automatic menu generation application according to the embodiment to reduce the burden on the user and support healthy and varied meals. For example, by using the app, the user can reduce the time spent thinking about menus and devote more time to other important activities. The app may also reduce food waste and support economical and environmentally friendly meal choices.

[0030] The menu generation unit can analyze the user's past purchasing history and suggest the optimal shopping route within a budget. In the menu generation unit, for example, the generation AI analyzes the user's past purchasing history and suggests the optimal shopping route within a budget. For example, the menu generation unit selects the store with the best cost performance based on price data of ingredients that the user has purchased in the past. The menu generation unit also lists stores where specific ingredients can be purchased cheaply based on the user's purchasing history and suggests a shopping route within a budget. For example, it analyzes past sale information and selects the optimal shopping destination. In addition, the menu generation unit analyzes the user's purchasing history and suggests the optimal shopping route within a budget. For example, it suggests the most efficient shopping route based on price data of stores that the user frequently visits. In this way, efficient shopping is possible by analyzing the user's past purchasing history and suggesting the optimal shopping route.

[0031] The menu generation unit can create an optimal shopping plan that takes into account transportation means and travel time based on location information. For example, the generation AI of the menu generation unit creates an optimal shopping plan that takes into account transportation means and travel time based on the user's location information. For example, it proposes an optimal route depending on the means of transportation, such as walking, cycling, or car. The menu generation unit also proposes a route that completes shopping in the shortest time based on the user's location information and transportation means. For example, it generates a route that allows for efficient visits to multiple stores. The menu generation unit also creates an optimal shopping plan that takes into account transportation means and travel time based on the user's location information. For example, it proposes a route that takes into account the use of public transportation. This allows for efficient shopping by creating an optimal shopping plan that takes into account transportation means and travel time.

[0032] The menu generation unit can analyze menus of nearby restaurants and cafes based on location information and propose dining out plans within a budget. For example, the menu generation unit uses a generation AI to analyze menus of nearby restaurants and cafes based on the user's location information and propose dining out plans within a budget. For example, it proposes menus based on lunch and dinner budgets. The menu generation unit also collects special menus and discount information of nearby restaurants and cafes based on the user's location information and budget and proposes the optimal dining out plan. For example, it selects the optimal dining out destination based on sale information. The menu generation unit also uses a generation AI to analyze menus of nearby restaurants and cafes based on the user's location information and propose dining out plans within a budget. For example, it selects a restaurant that suits the user's preferences. In this way, the menu generation unit analyzes menus of nearby restaurants and cafes based on the user's location information and proposes dining out plans within a budget, thereby expanding the user's dining out options.

[0033] The menu generation unit can collect information on nearby events and fairs based on location information and suggest food ingredient purchases within a budget. For example, the menu generation unit uses a generation AI to collect information on nearby events and fairs based on the user's location information and suggest food ingredient purchases within a budget. For example, it provides information on local food festivals and farm stands. The menu generation unit also lists food ingredients that are on sale at nearby events and fairs based on the user's location information and budget and suggests an optimal purchasing plan. For example, it selects the optimal purchasing source based on sale information. The menu generation unit also uses a generation AI to collect information on nearby events and fairs based on the user's location information and suggest food ingredient purchases within a budget. For example, it introduces events that offer seasonal ingredients. This expands the user's shopping options by collecting information on nearby events and fairs and suggesting food ingredient purchases within a budget.

[0034] The menu generation unit can monitor ingredients in real time and propose an optimal usage plan based on their expiration dates. In the menu generation unit, for example, the generation AI monitors ingredients in the user's refrigerator in real time and proposes an optimal usage plan based on their expiration dates. For example, it proposes recipes that prioritize the use of ingredients that are close to their expiration dates. The menu generation unit also generates a usage plan according to expiration dates based on the food data in the user's refrigerator. For example, it proposes dishes that use ingredients that are close to their expiration dates. In addition, the menu generation unit monitors ingredients in the user's refrigerator in real time and proposes an optimal usage plan based on their expiration dates. For example, it proposes a menu that uses ingredients that are close to their expiration dates. In this way, food waste is reduced by monitoring ingredients in the user's refrigerator in real time and proposing an optimal usage plan based on their expiration dates.

[0035] The menu generation unit can extend the lifespan of ingredients by analyzing how ingredients are stored and suggesting the optimal storage method. In the menu generation unit, for example, the generation AI analyzes how the user stores ingredients and suggests the optimal storage method. For example, it suggests appropriate methods for refrigerating or freezing them. The menu generation unit also analyzes storage methods based on the user's ingredient data and makes suggestions for extending the lifespan of ingredients. For example, it suggests the optimal conditions for storage temperature and humidity. In addition, the menu generation unit analyzes how the user stores ingredients and suggests the optimal storage method, thereby extending the lifespan of ingredients. For example, it suggests the optimal selection of storage containers and packaging methods. In this way, the generation AI analyzes how the user stores ingredients and suggests the optimal storage method, thereby extending the lifespan of ingredients.

[0036] The menu generation unit can propose plans for home gardening or balcony cultivation based on ingredients, promoting self-sufficiency in ingredients. In the menu generation unit, for example, the generation AI proposes plans for home gardening or balcony cultivation based on the user's ingredients. For example, it proposes methods for growing easy-to-grow vegetables and herbs. The menu generation unit also proposes plans for home gardening or balcony cultivation based on the user's ingredient data. For example, it proposes cultivation plans according to the season. In addition, the menu generation unit proposes plans for home gardening or balcony cultivation based on the user's ingredients, promoting self-sufficiency in ingredients. For example, it proposes recipes using the grown ingredients. In this way, proposing plans for home gardening or balcony cultivation promotes self-sufficiency in ingredients.

[0037] The menu generation unit can suggest how to make preserved foods and processed foods based on ingredients, enabling them to be stored for a long time. In the menu generation unit, for example, the generation AI suggests how to make preserved foods and processed foods based on the user's ingredients. For example, it suggests how to make jam and pickles. The menu generation unit also suggests how to make preserved foods and processed foods based on the user's ingredient data. For example, it suggests how to make dried foods and canned foods. In addition, the menu generation unit suggests how to make preserved foods and processed foods based on the user's ingredients, enabling them to be stored for a long time. For example, it suggests methods for freezing and vacuum packing. In this way, suggesting how to make preserved foods and processed foods enables ingredients to be stored for a long time.

[0038] The menu generation unit can analyze the purchase history and compare it with sale information to suggest the optimal shopping timing. In the menu generation unit, for example, the generation AI analyzes the user's purchase history and compares it with past sale information to suggest the optimal shopping timing. For example, it analyzes the sale cycle and suggests the optimal shopping day. The menu generation unit also suggests the optimal shopping timing based on the user's purchase history and sale information. For example, it analyzes sale trends and selects the shopping day with the best deals. In addition, the menu generation unit uses the generation AI to analyze the user's purchase history and compare it with past sale information to suggest the optimal shopping timing. For example, it suggests the optimal shopping day based on sale information. In this way, efficient shopping is possible by analyzing the user's purchase history and comparing it with past sale information to suggest the optimal shopping timing.

[0039] The menu generation unit can compare prices at multiple stores in real time based on location information and suggest the best shopping destination for the most affordable price. For example, the generation AI of the menu generation unit compares prices at multiple stores in real time based on the user's location information and suggests the best shopping destination for the most affordable price. For example, it compares prices at stores that sell the same product. The menu generation unit also suggests the best shopping destination for the most affordable price based on the user's location information and price data. For example, it collects sale information in real time and selects the most suitable store. The menu generation unit also suggests the best shopping destination for the most affordable price based on the user's location information using the generation AI. For example, it utilizes data from a price comparison site. This allows the generation AI to compare prices at multiple stores in real time based on the user's location information and suggest the best shopping destination for the most affordable price, enabling efficient shopping.

[0040] The menu generation unit can collect information on nearby flea markets and bazaars based on location information and suggest shopping within a budget. For example, the generation AI of the menu generation unit collects information on nearby flea markets and bazaars based on the user's location information and suggests shopping within a budget. For example, it provides information on local flea markets. The menu generation unit also lists products that can be purchased at nearby flea markets and bazaars based on the user's location information and budget and suggests an optimal shopping plan. For example, it introduces bazaars where specific products can be purchased cheaply. The menu generation unit can collect information on nearby flea markets and bazaars based on the user's location information and suggests shopping within a budget. For example, it introduces bazaars where specific products can be purchased cheaply. In this way, efficient shopping is possible by collecting information on nearby flea markets and bazaars based on the user's location information and suggesting shopping within a budget.

[0041] The menu generation unit can collect information on nearby farm shops and direct-from-producer markets based on location information and suggest fresh ingredients. For example, the generation AI of the menu generation unit collects information on nearby farm shops and direct-from-producer markets based on the user's location information and suggests fresh ingredients. For example, it provides business information for local farm shops. The menu generation unit also lists fresh ingredients that can be purchased at nearby farm shops and direct-from-producer markets based on the user's location information and budget and suggests an optimal shopping plan. For example, it introduces fresh seasonal vegetables and fruits. The menu generation unit also lists information on nearby farm shops and direct-from-producer markets based on the user's location information and suggests fresh ingredients. For example, it introduces fresh seasonal vegetables and fruits. In this way, the generation AI collects information on nearby farm shops and direct-from-producer markets based on the user's location information and suggests fresh ingredients, enabling efficient shopping.

[0042] The menu generation unit can suggest substitutes for ingredients containing allergens based on allergy information. In the menu generation unit, for example, the generation AI suggests substitutes for ingredients containing allergens based on the user's allergy information. For example, for a user with a nut allergy, it suggests ingredients that can be used in place of nuts. The menu generation unit also lists substitutes for ingredients containing allergens based on the user's allergy information and suggests an optimal menu. For example, for a user with a dairy allergy, it suggests recipes that do not use dairy products. In addition, the menu generation unit suggests substitutes for ingredients containing allergens based on the user's allergy information. For example, for a user with a wheat allergy, it suggests ingredients that can be used in place of wheat. In this way, by suggesting substitutes for ingredients containing allergens based on the user's allergy information, it supports the user's safe eating.

[0043] The menu generation unit can suggest allergen-free dining options based on the allergy information. For example, the generation AI of the menu generation unit suggests allergen-free dining options based on the user's allergy information. For example, it suggests restaurants that offer nut-free menus to a user with a nut allergy. The menu generation unit also lists allergen-free dining options based on the user's allergy information and suggests the most suitable dining option. For example, it suggests cafes that offer dairy-free menus to a user with a dairy allergy. The menu generation unit also suggests allergen-free dining options based on the user's allergy information. For example, it suggests restaurants that offer wheat-free menus to a user with a wheat allergy. In this way, the generation AI suggests allergen-free dining options based on the user's allergy information, supporting the user's safe dining out.

[0044] The menu generation unit can suggest where to purchase ingredients that meet the dietary constraints based on the constraints. For example, the generation AI of the menu generation unit suggests where to purchase ingredients that meet the constraints based on the user's dietary constraints. For example, it suggests stores that carry vegetarian ingredients to a vegetarian user. The menu generation unit also lists where to purchase ingredients that meet the constraints based on the user's dietary constraints and suggests the optimal place to purchase them. For example, it suggests stores that carry low-carbohydrate ingredients to a user on a low-carbohydrate diet. The menu generation unit also suggests where to purchase ingredients that meet the constraints based on the user's dietary constraints using the generation AI. For example, it suggests stores that carry gluten-free ingredients to a user who wishes to eat gluten-free. In this way, the user's health is supported by suggesting where to purchase ingredients that meet the constraints based on the user's dietary constraints.

[0045] The menu generation unit can analyze past menu history and suggest optimal menus according to seasons and events. In the menu generation unit, for example, the generation AI analyzes the user's past menu history and suggests optimal menus according to seasons and events. For example, it suggests cold dishes in summer and hot dishes in winter. The menu generation unit also suggests menus tailored to specific events and holidays based on the user's past menu history. For example, it suggests special dishes for Christmas and New Year. In addition, the menu generation unit analyzes the user's past menu history using the generation AI and suggests optimal menus according to seasons and events. For example, it suggests dishes using seasonal ingredients. In this way, by analyzing the user's past menu history and suggesting optimal menus according to seasons and events, the variety of the user's meals is increased.

[0046] The menu generation unit can analyze ingredient consumption patterns based on past menu history and propose an optimal shopping list. In the menu generation unit, for example, the generation AI analyzes ingredient consumption patterns based on the user's past menu history and proposes an optimal shopping list. For example, it lists ingredients that are frequently used. The menu generation unit also analyzes ingredient consumption patterns based on the user's past menu history and proposes a shopping list that does not result in waste. For example, it creates a list that avoids ingredients that tend to go to waste. In addition, the menu generation unit analyzes ingredient consumption patterns based on the user's past menu history and proposes an optimal shopping list. For example, it lists ingredients that are purchased regularly. In this way, efficient shopping can be achieved by analyzing ingredient consumption patterns based on the user's past menu history and proposing an optimal shopping list.

[0047] The menu generation unit can increase the variety of meals by suggesting dishes from different countries or regions based on the user's past menu history. For example, the generation AI of the menu generation unit suggests dishes from different countries or regions based on the user's past menu history. For example, it suggests dishes from different countries such as Italian or Chinese cuisine. The menu generation unit also suggests dishes from different regions based on the user's past menu history, increasing the variety of meals. For example, it suggests local regional dishes or traditional dishes. The menu generation unit also suggests dishes from different countries or regions based on the user's past menu history, increasing the variety of meals. For example, it suggests dishes that the user does not usually eat. In this way, the variety of meals for the user is increased by suggesting dishes from different countries or regions based on the user's past menu history.

[0048] The menu generation unit can suggest new recipes using specific ingredients based on past menu history. In the menu generation unit, for example, the generation AI suggests new recipes using specific ingredients based on the user's past menu history. For example, it suggests a new dish using chicken. The menu generation unit also suggests new recipes using specific ingredients based on the user's past menu history. For example, it suggests a new dish using tomatoes. The menu generation unit also suggests new recipes using specific ingredients based on the user's past menu history. For example, it suggests a new dish using tofu. In this way, by suggesting new recipes using specific ingredients based on the user's past menu history, the variety of the user's meals is increased.

[0049] The menu generation unit can propose a menu that takes into consideration individual nutritional balance based on the health data. For example, the generation AI of the menu generation unit proposes a menu that takes into consideration individual nutritional balance based on the user's health data. For example, for a user who is deficient in vitamin C, it proposes dishes that are high in vitamin C. The menu generation unit also proposes a menu that takes into consideration nutritional balance based on the user's health data. For example, for a user who is deficient in iron, it proposes dishes that are high in iron. The menu generation unit also proposes a menu that takes into consideration individual nutritional balance based on the user's health data. For example, for a user who is deficient in calcium, it proposes dishes that are high in calcium. In this way, the user's health is supported by proposing a menu that takes into consideration individual nutritional balance based on the user's health data.

[0050] The menu generation unit can analyze the dietary history and suggest ingredients to make up for any nutrient deficiency or excess. In the menu generation unit, for example, the generation AI analyzes the user's dietary history and suggests ingredients to make up for any nutrient deficiency or excess. For example, for a user who is deficient in vitamin D, ingredients that are high in vitamin D are suggested. The menu generation unit also suggests ingredients to make up for any nutrient deficiency or excess based on the user's dietary history. For example, for a user who is deficient in magnesium, ingredients that are high in magnesium are suggested. The menu generation unit also analyzes the user's dietary history and suggests ingredients to make up for any nutrient deficiency or excess. For example, for a user who is deficient in omega-3 fatty acids, ingredients that are high in omega-3 fatty acids are suggested. In this way, the user's health is supported by analyzing the user's dietary history and suggesting ingredients to make up for any nutrient deficiency or excess.

[0051] The menu generation unit can suggest supplements that are fortified with specific nutrients based on the health data. In the menu generation unit, for example, the generation AI suggests supplements that are fortified with specific nutrients based on the user's health data. For example, a supplement containing vitamin B12 is suggested for a user who is deficient in vitamin B12. The menu generation unit also suggests supplements that are fortified with specific nutrients based on the user's health data. For example, a supplement containing zinc is suggested for a user who is deficient in zinc. The menu generation unit also suggests supplements that are fortified with specific nutrients based on the user's health data. For example, a supplement containing vitamin E is suggested for a user who is deficient in vitamin E. In this way, the user's health is supported by suggesting supplements that are fortified with specific nutrients based on the user's health data.

[0052] The menu generation unit can suggest where to purchase ingredients that are rich in specific nutrients based on the user's diet history. For example, the generation AI of the menu generation unit suggests where to purchase ingredients that are rich in specific nutrients based on the user's diet history. For example, for a user who is deficient in calcium, it suggests stores that sell ingredients that are rich in calcium. The menu generation unit also suggests where to purchase ingredients that are rich in specific nutrients based on the user's diet history. For example, for a user who is deficient in vitamin A, it suggests stores that sell ingredients that are rich in vitamin A. The menu generation unit also suggests where to purchase ingredients that are rich in specific nutrients based on the user's diet history using the generation AI. For example, for a user who is deficient in iron, it suggests stores that sell ingredients that are rich in iron. In this way, the user's health is supported by suggesting where to purchase ingredients that are rich in specific nutrients based on the user's diet history.

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

[0054] The menu generation unit can suggest where to purchase ingredients that meet the user's dietary restrictions based on the user's restrictions. For example, it will suggest stores that carry vegetarian ingredients to a vegetarian user. The menu generation unit also lists where to purchase ingredients that meet the user's dietary restrictions and suggests the optimal place to purchase them. For example, it will suggest stores that carry low-carbohydrate ingredients to a user on a low-carbohydrate diet. The menu generation unit also uses the generation AI to suggest where to purchase ingredients that meet the user's dietary restrictions based on the user's restrictions. For example, it will suggest stores that carry gluten-free ingredients to a user who wishes to eat gluten-free. In this way, it supports the user's health by suggesting where to purchase ingredients that meet the user's dietary restrictions.

[0055] The menu generation unit can collect information on nearby flea markets and bazaars based on location information and suggest shopping within a budget. For example, it can provide information on local flea markets. The menu generation unit also lists products that can be purchased at nearby flea markets and bazaars based on the user's location information and budget, and suggests the optimal shopping plan. For example, it can introduce bazaars where specific products can be purchased cheaply. The menu generation unit also uses the generation AI to collect information on nearby flea markets and bazaars based on the user's location information and suggest shopping within a budget. For example, it can introduce bazaars where specific products can be purchased cheaply. This allows for efficient shopping by collecting information on nearby flea markets and bazaars based on the user's location information and suggesting shopping within a budget.

[0056] The menu generation unit can increase the variety of meals by suggesting dishes from different countries or regions based on the user's past menu history. For example, the generation AI can suggest dishes from different countries or regions based on the user's past menu history. For example, it can suggest dishes from different countries such as Italian or Chinese cuisine. The menu generation unit can also increase the variety of meals by suggesting dishes from different regions based on the user's past menu history. For example, it can suggest local regional dishes or traditional dishes. The menu generation unit can also increase the variety of meals by suggesting dishes from different countries or regions based on the user's past menu history. For example, it can suggest dishes that the user does not usually eat. In this way, the variety of meals for the user can be increased by suggesting dishes from different countries or regions based on the user's past menu history.

[0057] The menu generation unit can collect information on nearby farm shops and direct-from-producer markets based on location information and suggest fresh ingredients. For example, the generation AI can collect information on nearby farm shops and direct-from-producer markets based on the user's location information and suggest fresh ingredients. For example, it can provide business hours for local farm shops. The menu generation unit can also list fresh ingredients that can be purchased at nearby farm shops and direct-from-producer markets based on the user's location information and budget, and suggest an optimal shopping plan. For example, it can introduce fresh seasonal vegetables and fruits. The menu generation unit can also collect information on nearby farm shops and direct-from-producer markets based on the user's location information and suggest fresh ingredients. For example, it can introduce fresh seasonal vegetables and fruits. This allows for efficient shopping by collecting information on nearby farm shops and direct-from-producer markets based on the user's location information and suggesting fresh ingredients.

[0058] The menu generation unit can suggest new recipes using specific ingredients based on past menu history. For example, the generation AI may suggest new recipes using specific ingredients based on the user's past menu history. For example, it may suggest a new dish using chicken. The menu generation unit may also suggest new recipes using specific ingredients based on the user's past menu history. For example, it may suggest a new dish using tomatoes. The menu generation unit may also suggest new recipes using specific ingredients based on the user's past menu history. For example, it may suggest a new dish using tofu. In this way, by suggesting new recipes using specific ingredients based on the user's past menu history, the variety of the user's meals is increased.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The location information acquisition unit acquires the user's location information. For example, the user's current location is acquired using GPS. Location information can also be acquired using Wi-Fi or cell towers. Furthermore, location information manually entered by the user can also be acquired. Step 2: The budget setting unit sets the user's budget. For example, it sets the budget entered by the user in the app. It can also automatically set the budget based on budgets set by the user in the past. It can also adjust the range of the budget set by the user. Step 3: The menu generation unit generates a menu based on the location information acquired by the location information acquisition unit and the budget set by the budget setting unit. For example, the generation AI suggests an optimal menu based on the user's location information and budget. The generation AI can also generate a menu taking into account the user's preferences and allergy information. Furthermore, it can suggest a wide variety of menus based on the user's past menu history. Step 4: The notification unit notifies the user of the menu generated by the menu generation unit. For example, the notification unit notifies the user of the menu using a push notification. The notification unit can also notify the user of the menu using an email notification or an in-app notification.

[0061] (Example 2) The automatic menu generation application according to an embodiment of the present invention is a system that automatically generates a menu using a user's location information and a set budget. This reduces the burden on the user and supports healthy and varied meals.

[0062] The automatic menu generation application according to the embodiment includes a location information acquisition unit, a budget setting unit, a menu generation unit, and a notification unit. The location information acquisition unit acquires user location information. For example, it acquires the user's current location using GPS. It can also acquire location information using Wi-Fi or cell towers. The location information acquisition unit can also acquire location information manually entered by the user. The budget setting unit sets the user's budget. For example, it sets the budget entered by the user into the app. The budget setting unit can also automatically set the budget based on a budget previously set by the user. The budget setting unit can also adjust the range of the budget set by the user. The menu generation unit generates a menu based on the location information acquired by the location information acquisition unit and the budget set by the budget setting unit. For example, the generation AI proposes an optimal menu based on the user's location information and budget. The generation AI can also generate a menu taking into account the user's preferences and allergy information. The generation AI can also propose a wide variety of menus based on the user's past menu history. The notification unit notifies the user of the menu generated by the menu generation unit. For example, the menu may be notified to the user using a push notification. The notification unit may also notify the user of the menu using an email notification. The notification unit may also notify the user of the menu using an in-app notification. This allows the automatic menu generation application according to the embodiment to reduce the burden on the user and support healthy and varied meals. For example, by using the app, the user can reduce the time spent thinking about menus and devote more time to other important activities. The app may also reduce food waste and support economical and environmentally friendly meal choices.

[0063] The menu generation unit can analyze the user's past purchasing history and suggest the optimal shopping route within a budget. In the menu generation unit, for example, the generation AI analyzes the user's past purchasing history and suggests the optimal shopping route within a budget. For example, the menu generation unit selects the store with the best cost performance based on price data of ingredients that the user has purchased in the past. The menu generation unit also lists stores where specific ingredients can be purchased cheaply based on the user's purchasing history and suggests a shopping route within a budget. For example, it analyzes past sale information and selects the optimal shopping destination. In addition, the menu generation unit analyzes the user's purchasing history and suggests the optimal shopping route within a budget. For example, it suggests the most efficient shopping route based on price data of stores that the user frequently visits. In this way, efficient shopping is possible by analyzing the user's past purchasing history and suggesting the optimal shopping route.

[0064] The menu generation unit can create an optimal shopping plan that takes into account transportation means and travel time based on location information. For example, the generation AI of the menu generation unit creates an optimal shopping plan that takes into account transportation means and travel time based on the user's location information. For example, it proposes an optimal route depending on the means of transportation, such as walking, cycling, or car. The menu generation unit also proposes a route that completes shopping in the shortest time based on the user's location information and transportation means. For example, it generates a route that allows for efficient visits to multiple stores. The menu generation unit also creates an optimal shopping plan that takes into account transportation means and travel time based on the user's location information. For example, it proposes a route that takes into account the use of public transportation. This allows for efficient shopping by creating an optimal shopping plan that takes into account transportation means and travel time.

[0065] The menu generation unit can use the emotion estimation function to suggest routes and time periods that will reduce the stress the user feels while shopping. The menu generation unit, for example, uses the emotion estimation function to suggest routes and time periods that will reduce the stress the user feels while shopping. For example, it suggests time periods that will avoid crowds. The menu generation unit also suggests a shopping route that will reduce stress based on the user's emotion data. For example, it selects stores that are quiet or offer a relaxing environment. The menu generation unit also uses the emotion estimation function to suggest routes and time periods that will reduce the stress the user feels while shopping. For example, it analyzes the user's past emotion data and suggests the optimal shopping time. This allows the user to have a more comfortable shopping experience by suggesting routes and time periods that will reduce the stress they feel while shopping.

[0066] The menu generation unit can analyze menus of nearby restaurants and cafes based on location information and propose dining out plans within a budget. For example, the menu generation unit uses a generation AI to analyze menus of nearby restaurants and cafes based on the user's location information and propose dining out plans within a budget. For example, it proposes menus based on lunch and dinner budgets. The menu generation unit also collects special menus and discount information of nearby restaurants and cafes based on the user's location information and budget and proposes the optimal dining out plan. For example, it selects the optimal dining out destination based on sale information. The menu generation unit also uses a generation AI to analyze menus of nearby restaurants and cafes based on the user's location information and propose dining out plans within a budget. For example, it selects a restaurant that suits the user's preferences. In this way, the menu generation unit analyzes menus of nearby restaurants and cafes based on the user's location information and proposes dining out plans within a budget, thereby expanding the user's dining out options.

[0067] The menu generation unit can collect information on nearby events and fairs based on location information and suggest food ingredient purchases within a budget. For example, the menu generation unit uses a generation AI to collect information on nearby events and fairs based on the user's location information and suggest food ingredient purchases within a budget. For example, it provides information on local food festivals and farm stands. The menu generation unit also lists food ingredients that are on sale at nearby events and fairs based on the user's location information and budget and suggests an optimal purchasing plan. For example, it selects the optimal purchasing source based on sale information. The menu generation unit also uses a generation AI to collect information on nearby events and fairs based on the user's location information and suggest food ingredient purchases within a budget. For example, it introduces events that offer seasonal ingredients. This expands the user's shopping options by collecting information on nearby events and fairs and suggesting food ingredient purchases within a budget.

[0068] The menu generation unit can use the emotion estimation function to analyze the user's emotions regarding the atmosphere and service of stores visited and suggest the optimal shopping destination. The menu generation unit, for example, uses the emotion estimation function to analyze the user's emotions regarding the atmosphere and service of stores visited and suggest the optimal shopping destination. For example, it selects a store where the user can relax. The menu generation unit also evaluates the store's atmosphere and service based on the user's emotion data and suggests the optimal shopping destination. For example, it analyzes past emotion data and selects a store that satisfies the user. The menu generation unit also uses the emotion estimation function to analyze the user's emotions regarding the atmosphere and service of stores visited and suggest the optimal shopping destination. For example, it selects a store that suits the user's preferences. In this way, the user's emotions regarding the atmosphere and service of stores visited are analyzed and the optimal shopping destination is suggested, enabling a comfortable shopping experience.

[0069] The menu generation unit can monitor ingredients in real time and propose an optimal usage plan based on their expiration dates. In the menu generation unit, for example, the generation AI monitors ingredients in the user's refrigerator in real time and proposes an optimal usage plan based on their expiration dates. For example, it proposes recipes that prioritize the use of ingredients that are close to their expiration dates. The menu generation unit also generates a usage plan according to expiration dates based on the food data in the user's refrigerator. For example, it proposes dishes that use ingredients that are close to their expiration dates. In addition, the menu generation unit monitors ingredients in the user's refrigerator in real time and proposes an optimal usage plan based on their expiration dates. For example, it proposes a menu that uses ingredients that are close to their expiration dates. In this way, food waste is reduced by monitoring ingredients in the user's refrigerator in real time and proposing an optimal usage plan based on their expiration dates.

[0070] The menu generation unit can extend the lifespan of ingredients by analyzing how ingredients are stored and suggesting the optimal storage method. In the menu generation unit, for example, the generation AI analyzes how the user stores ingredients and suggests the optimal storage method. For example, it suggests appropriate methods for refrigerating or freezing them. The menu generation unit also analyzes storage methods based on the user's ingredient data and makes suggestions for extending the lifespan of ingredients. For example, it suggests the optimal conditions for storage temperature and humidity. In addition, the menu generation unit analyzes how the user stores ingredients and suggests the optimal storage method, thereby extending the lifespan of ingredients. For example, it suggests the optimal selection of storage containers and packaging methods. In this way, the generation AI analyzes how the user stores ingredients and suggests the optimal storage method, thereby extending the lifespan of ingredients.

[0071] The menu generation unit can propose plans for home gardening or balcony cultivation based on ingredients, promoting self-sufficiency in ingredients. In the menu generation unit, for example, the generation AI proposes plans for home gardening or balcony cultivation based on the user's ingredients. For example, it proposes methods for growing easy-to-grow vegetables and herbs. The menu generation unit also proposes plans for home gardening or balcony cultivation based on the user's ingredient data. For example, it proposes cultivation plans according to the season. In addition, the menu generation unit proposes plans for home gardening or balcony cultivation based on the user's ingredients, promoting self-sufficiency in ingredients. For example, it proposes recipes using the grown ingredients. In this way, proposing plans for home gardening or balcony cultivation promotes self-sufficiency in ingredients.

[0072] The menu generation unit can suggest how to make preserved foods and processed foods based on ingredients, enabling them to be stored for a long time. In the menu generation unit, for example, the generation AI suggests how to make preserved foods and processed foods based on the user's ingredients. For example, it suggests how to make jam and pickles. The menu generation unit also suggests how to make preserved foods and processed foods based on the user's ingredient data. For example, it suggests how to make dried foods and canned foods. In addition, the menu generation unit suggests how to make preserved foods and processed foods based on the user's ingredients, enabling them to be stored for a long time. For example, it suggests methods for freezing and vacuum packing. In this way, suggesting how to make preserved foods and processed foods enables ingredients to be stored for a long time.

[0073] The menu generation unit can use the emotion estimation function to suggest challenges and game elements to enhance the user's sense of accomplishment when using up ingredients. The menu generation unit, for example, uses the emotion estimation function to suggest challenges and game elements to enhance the user's sense of accomplishment when using up ingredients. For example, it sets a mission to use up ingredients. The menu generation unit also suggests challenges and game elements to enhance the user's sense of accomplishment when using up ingredients based on the user's emotion data. For example, it introduces a point system or rankings. The menu generation unit also uses the emotion estimation function to suggest challenges and game elements to enhance the user's sense of accomplishment when using up ingredients. For example, it provides rewards or badges that give the user a sense of accomplishment. In this way, food waste is reduced by suggesting challenges and game elements to enhance the user's sense of accomplishment when using up ingredients.

[0074] The menu generation unit can analyze the purchase history and compare it with sale information to suggest the optimal shopping timing. In the menu generation unit, for example, the generation AI analyzes the user's purchase history and compares it with past sale information to suggest the optimal shopping timing. For example, it analyzes the sale cycle and suggests the optimal shopping day. The menu generation unit also suggests the optimal shopping timing based on the user's purchase history and sale information. For example, it analyzes sale trends and selects the shopping day with the best deals. In addition, the menu generation unit uses the generation AI to analyze the user's purchase history and compare it with past sale information to suggest the optimal shopping timing. For example, it suggests the optimal shopping day based on sale information. In this way, efficient shopping is possible by analyzing the user's purchase history and comparing it with past sale information to suggest the optimal shopping timing.

[0075] The menu generation unit can compare prices at multiple stores in real time based on location information and suggest the best shopping destination for the most affordable price. For example, the generation AI of the menu generation unit compares prices at multiple stores in real time based on the user's location information and suggests the best shopping destination for the most affordable price. For example, it compares prices at stores that sell the same product. The menu generation unit also suggests the best shopping destination for the most affordable price based on the user's location information and price data. For example, it collects sale information in real time and selects the most suitable store. The menu generation unit also suggests the best shopping destination for the most affordable price based on the user's location information using the generation AI. For example, it utilizes data from a price comparison site. This allows the generation AI to compare prices at multiple stores in real time based on the user's location information and suggest the best shopping destination for the most affordable price, enabling efficient shopping.

[0076] The menu generation unit uses the emotion estimation function to analyze the level of excitement the user has when viewing sale information and can notify the user at the optimal timing. The menu generation unit, for example, uses the emotion estimation function to analyze the level of excitement the user has when viewing sale information and can notify the user at the optimal timing. For example, sale information that excites the user is given priority. The menu generation unit also optimizes the timing of notifying the user of sale information based on the user's emotion data. For example, it analyzes past emotion data and notifies the user at the timing that makes the user most excited. The menu generation unit also uses the emotion estimation function to analyze the level of excitement the user has when viewing sale information and can notify the user at the optimal timing. For example, it notifies the user of sale information that suits the user's preferences. In this way, by analyzing the level of excitement the user has when viewing sale information and notifying the user at the optimal timing, the user's desire to purchase is increased.

[0077] The menu generation unit can collect information on nearby flea markets and bazaars based on location information and suggest shopping within a budget. For example, the generation AI of the menu generation unit collects information on nearby flea markets and bazaars based on the user's location information and suggests shopping within a budget. For example, it provides information on local flea markets. The menu generation unit also lists products that can be purchased at nearby flea markets and bazaars based on the user's location information and budget and suggests an optimal shopping plan. For example, it introduces bazaars where specific products can be purchased cheaply. The menu generation unit can collect information on nearby flea markets and bazaars based on the user's location information and suggests shopping within a budget. For example, it introduces bazaars where specific products can be purchased cheaply. In this way, efficient shopping is possible by collecting information on nearby flea markets and bazaars based on the user's location information and suggesting shopping within a budget.

[0078] The menu generation unit can collect information on nearby farm shops and direct-from-producer markets based on location information and suggest fresh ingredients. For example, the generation AI of the menu generation unit collects information on nearby farm shops and direct-from-producer markets based on the user's location information and suggests fresh ingredients. For example, it provides business information for local farm shops. The menu generation unit also lists fresh ingredients that can be purchased at nearby farm shops and direct-from-producer markets based on the user's location information and budget and suggests an optimal shopping plan. For example, it introduces fresh seasonal vegetables and fruits. The menu generation unit also lists information on nearby farm shops and direct-from-producer markets based on the user's location information and suggests fresh ingredients. For example, it introduces fresh seasonal vegetables and fruits. In this way, the generation AI collects information on nearby farm shops and direct-from-producer markets based on the user's location information and suggests fresh ingredients, enabling efficient shopping.

[0079] The menu generation unit can use the emotion estimation function to analyze the user's level of satisfaction when viewing sale information and prioritize notifying the user of the most suitable sale information. The menu generation unit, for example, uses the emotion estimation function to analyze the user's level of satisfaction when viewing sale information and prioritize notifying the user of the most suitable sale information. For example, it selects sale information that satisfies the user. The menu generation unit also optimizes the content of the sale information notification based on the user's emotion data. For example, it analyzes past emotion data and notifies the user of the most satisfying sale information. The menu generation unit also uses the emotion estimation function to analyze the user's level of satisfaction when viewing sale information and prioritize notifying the user of the most suitable sale information. For example, it notifies the user of sale information that matches the user's preferences. In this way, the user's desire to purchase is increased by analyzing the user's level of satisfaction when viewing sale information and prioritize notifying the user of the most suitable sale information.

[0080] The menu generation unit can suggest substitutes for ingredients containing allergens based on allergy information. In the menu generation unit, for example, the generation AI suggests substitutes for ingredients containing allergens based on the user's allergy information. For example, for a user with a nut allergy, it suggests ingredients that can be used in place of nuts. The menu generation unit also lists substitutes for ingredients containing allergens based on the user's allergy information and suggests an optimal menu. For example, for a user with a dairy allergy, it suggests recipes that do not use dairy products. In addition, the menu generation unit suggests substitutes for ingredients containing allergens based on the user's allergy information. For example, for a user with a wheat allergy, it suggests ingredients that can be used in place of wheat. In this way, by suggesting substitutes for ingredients containing allergens based on the user's allergy information, it supports the user's safe eating.

[0081] The menu generation unit can use the emotion estimation function to suggest recipes that will increase the user's sense of security when eating allergy-friendly foods. The menu generation unit, for example, uses the emotion estimation function to suggest recipes that will increase the user's sense of security when eating allergy-friendly foods. For example, it selects dishes that make the user feel safe. The menu generation unit also suggests recipes that will increase the user's sense of security when eating allergy-friendly foods based on the user's emotion data. For example, it analyzes past emotion data and selects dishes that make the user feel safe. The menu generation unit also uses the emotion estimation function to suggest recipes that will increase the user's sense of security when eating allergy-friendly foods. For example, it selects dishes that suit the user's preferences. In this way, recipes that will increase the user's sense of security when eating allergy-friendly foods are suggested, thereby supporting the user's safe eating.

[0082] The menu generation unit can suggest allergen-free dining options based on the allergy information. For example, the generation AI of the menu generation unit suggests allergen-free dining options based on the user's allergy information. For example, it suggests restaurants that offer nut-free menus to a user with a nut allergy. The menu generation unit also lists allergen-free dining options based on the user's allergy information and suggests the most suitable dining option. For example, it suggests cafes that offer dairy-free menus to a user with a dairy allergy. The menu generation unit also suggests allergen-free dining options based on the user's allergy information. For example, it suggests restaurants that offer wheat-free menus to a user with a wheat allergy. In this way, the generation AI suggests allergen-free dining options based on the user's allergy information, supporting the user's safe dining out.

[0083] The menu generation unit can suggest where to purchase ingredients that meet the dietary constraints based on the constraints. For example, the generation AI of the menu generation unit suggests where to purchase ingredients that meet the constraints based on the user's dietary constraints. For example, it suggests stores that carry vegetarian ingredients to a vegetarian user. The menu generation unit also lists where to purchase ingredients that meet the constraints based on the user's dietary constraints and suggests the optimal place to purchase them. For example, it suggests stores that carry low-carbohydrate ingredients to a user on a low-carbohydrate diet. The menu generation unit also suggests where to purchase ingredients that meet the constraints based on the user's dietary constraints using the generation AI. For example, it suggests stores that carry gluten-free ingredients to a user who wishes to eat gluten-free. In this way, the user's health is supported by suggesting where to purchase ingredients that meet the constraints based on the user's dietary constraints.

[0084] The menu generation unit can use the emotion estimation function to analyze the user's level of satisfaction when eating allergy-friendly meals and suggest optimal recipes. The menu generation unit, for example, uses the emotion estimation function to analyze the user's level of satisfaction when eating allergy-friendly meals and suggest optimal recipes. For example, it selects an allergy-friendly meal recipe that satisfies the user. The menu generation unit also analyzes the user's level of satisfaction when eating allergy-friendly meals based on the user's emotion data and suggests optimal recipes. For example, it analyzes past emotion data and selects recipes that satisfy the user. The menu generation unit also uses the emotion estimation function to analyze the user's level of satisfaction when eating allergy-friendly meals and suggest optimal recipes. For example, it selects an allergy-friendly meal recipe that suits the user's preferences. In this way, the menu generation unit supports the user's safe eating by analyzing the user's level of satisfaction when eating allergy-friendly meals and suggesting optimal recipes.

[0085] The menu generation unit can analyze past menu history and suggest optimal menus according to seasons and events. In the menu generation unit, for example, the generation AI analyzes the user's past menu history and suggests optimal menus according to seasons and events. For example, it suggests cold dishes in summer and hot dishes in winter. The menu generation unit also suggests menus tailored to specific events and holidays based on the user's past menu history. For example, it suggests special dishes for Christmas and New Year. In addition, the menu generation unit analyzes the user's past menu history using the generation AI and suggests optimal menus according to seasons and events. For example, it suggests dishes using seasonal ingredients. In this way, by analyzing the user's past menu history and suggesting optimal menus according to seasons and events, the variety of the user's meals is increased.

[0086] The menu generation unit can analyze ingredient consumption patterns based on past menu history and propose an optimal shopping list. In the menu generation unit, for example, the generation AI analyzes ingredient consumption patterns based on the user's past menu history and proposes an optimal shopping list. For example, it lists ingredients that are frequently used. The menu generation unit also analyzes ingredient consumption patterns based on the user's past menu history and proposes a shopping list that does not result in waste. For example, it creates a list that avoids ingredients that tend to go to waste. In addition, the menu generation unit analyzes ingredient consumption patterns based on the user's past menu history and proposes an optimal shopping list. For example, it lists ingredients that are purchased regularly. In this way, efficient shopping can be achieved by analyzing ingredient consumption patterns based on the user's past menu history and proposing an optimal shopping list.

[0087] The menu generation unit can use the emotion estimation function to analyze the user's level of satisfaction with past menus and propose an optimal menu. The menu generation unit, for example, uses the emotion estimation function to analyze the user's level of satisfaction with past menus and propose an optimal menu. For example, it re-proposes dishes that the user was satisfied with. The menu generation unit also analyzes the user's level of satisfaction with past menus based on the user's emotion data and proposes an optimal menu. For example, it analyzes past emotion data and selects dishes that will satisfy the user. The menu generation unit also uses the emotion estimation function to analyze the user's level of satisfaction with past menus and propose an optimal menu. For example, it selects dishes that match the user's preferences. In this way, the user's satisfaction with meals is increased by analyzing the user's level of satisfaction with past menus and proposing an optimal menu.

[0088] The menu generation unit can increase the variety of meals by suggesting dishes from different countries or regions based on the user's past menu history. For example, the generation AI of the menu generation unit suggests dishes from different countries or regions based on the user's past menu history. For example, it suggests dishes from different countries such as Italian or Chinese cuisine. The menu generation unit also suggests dishes from different regions based on the user's past menu history, increasing the variety of meals. For example, it suggests local regional dishes or traditional dishes. The menu generation unit also suggests dishes from different countries or regions based on the user's past menu history, increasing the variety of meals. For example, it suggests dishes that the user does not usually eat. In this way, the variety of meals for the user is increased by suggesting dishes from different countries or regions based on the user's past menu history.

[0089] The menu generation unit can suggest new recipes using specific ingredients based on past menu history. In the menu generation unit, for example, the generation AI suggests new recipes using specific ingredients based on the user's past menu history. For example, it suggests a new dish using chicken. The menu generation unit also suggests new recipes using specific ingredients based on the user's past menu history. For example, it suggests a new dish using tomatoes. The menu generation unit also suggests new recipes using specific ingredients based on the user's past menu history. For example, it suggests a new dish using tofu. In this way, by suggesting new recipes using specific ingredients based on the user's past menu history, the variety of the user's meals is increased.

[0090] The menu generation unit can propose a menu that takes into consideration individual nutritional balance based on the health data. For example, the generation AI of the menu generation unit proposes a menu that takes into consideration individual nutritional balance based on the user's health data. For example, for a user who is deficient in vitamin C, it proposes dishes that are high in vitamin C. The menu generation unit also proposes a menu that takes into consideration nutritional balance based on the user's health data. For example, for a user who is deficient in iron, it proposes dishes that are high in iron. The menu generation unit also proposes a menu that takes into consideration individual nutritional balance based on the user's health data. For example, for a user who is deficient in calcium, it proposes dishes that are high in calcium. In this way, the user's health is supported by proposing a menu that takes into consideration individual nutritional balance based on the user's health data.

[0091] The menu generation unit can analyze the dietary history and suggest ingredients to make up for any nutrient deficiency or excess. In the menu generation unit, for example, the generation AI analyzes the user's dietary history and suggests ingredients to make up for any nutrient deficiency or excess. For example, for a user who is deficient in vitamin D, ingredients that are high in vitamin D are suggested. The menu generation unit also suggests ingredients to make up for any nutrient deficiency or excess based on the user's dietary history. For example, for a user who is deficient in magnesium, ingredients that are high in magnesium are suggested. The menu generation unit also analyzes the user's dietary history and suggests ingredients to make up for any nutrient deficiency or excess. For example, for a user who is deficient in omega-3 fatty acids, ingredients that are high in omega-3 fatty acids are suggested. In this way, the user's health is supported by analyzing the user's dietary history and suggesting ingredients to make up for any nutrient deficiency or excess.

[0092] The menu generation unit can use the emotion estimation function to suggest recipes that will increase the user's sense of satisfaction when eating a healthy meal. The menu generation unit, for example, uses the emotion estimation function to suggest recipes that will increase the user's sense of satisfaction when eating a healthy meal. For example, it selects healthy dishes that will make the user feel satisfied. The menu generation unit also suggests recipes that will increase the user's sense of satisfaction when eating a healthy meal based on the user's emotion data. For example, it analyzes past emotion data and selects healthy dishes that will satisfy the user. The menu generation unit also uses the emotion estimation function to suggest recipes that will increase the user's sense of satisfaction when eating a healthy meal. For example, it selects healthy dishes that suit the user's preferences. In this way, the user's health is supported by suggesting recipes that will increase the user's sense of satisfaction when eating a healthy meal.

[0093] The menu generation unit can suggest supplements that are fortified with specific nutrients based on the health data. In the menu generation unit, for example, the generation AI suggests supplements that are fortified with specific nutrients based on the user's health data. For example, a supplement containing vitamin B12 is suggested for a user who is deficient in vitamin B12. The menu generation unit also suggests supplements that are fortified with specific nutrients based on the user's health data. For example, a supplement containing zinc is suggested for a user who is deficient in zinc. The menu generation unit also suggests supplements that are fortified with specific nutrients based on the user's health data. For example, a supplement containing vitamin E is suggested for a user who is deficient in vitamin E. In this way, the user's health is supported by suggesting supplements that are fortified with specific nutrients based on the user's health data.

[0094] The menu generation unit can suggest where to purchase ingredients that are rich in specific nutrients based on the user's diet history. For example, the generation AI of the menu generation unit suggests where to purchase ingredients that are rich in specific nutrients based on the user's diet history. For example, for a user who is deficient in calcium, it suggests stores that sell ingredients that are rich in calcium. The menu generation unit also suggests where to purchase ingredients that are rich in specific nutrients based on the user's diet history. For example, for a user who is deficient in vitamin A, it suggests stores that sell ingredients that are rich in vitamin A. The menu generation unit also suggests where to purchase ingredients that are rich in specific nutrients based on the user's diet history using the generation AI. For example, for a user who is deficient in iron, it suggests stores that sell ingredients that are rich in iron. In this way, the user's health is supported by suggesting where to purchase ingredients that are rich in specific nutrients based on the user's diet history.

[0095] The menu generation unit can use the emotion estimation function to analyze the emotions the user feels when eating a healthy meal and propose an optimal nutritional management plan. The menu generation unit, for example, uses the emotion estimation function to analyze the emotions the user feels when eating a healthy meal and propose an optimal nutritional management plan. For example, it selects a nutritional management plan that makes the user feel satisfied. The menu generation unit also analyzes the emotions the user feels when eating a healthy meal based on the user's emotion data and proposes an optimal nutritional management plan. For example, it analyzes past emotion data and selects a nutritional management plan that satisfies the user. The menu generation unit also uses the emotion estimation function to analyze the emotions the user feels when eating a healthy meal and proposes an optimal nutritional management plan. For example, it selects a nutritional management plan that suits the user's preferences. In this way, the user's health is supported by analyzing the emotions the user feels when eating a healthy meal and proposing an optimal nutritional management plan.

[0096] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0097] The menu generation unit can suggest where to purchase ingredients that meet the user's dietary restrictions based on the user's restrictions. For example, it will suggest stores that carry vegetarian ingredients to a vegetarian user. The menu generation unit also lists where to purchase ingredients that meet the user's dietary restrictions and suggests the optimal place to purchase them. For example, it will suggest stores that carry low-carbohydrate ingredients to a user on a low-carbohydrate diet. The menu generation unit also uses the generation AI to suggest where to purchase ingredients that meet the user's dietary restrictions based on the user's restrictions. For example, it will suggest stores that carry gluten-free ingredients to a user who wishes to eat gluten-free. In this way, it supports the user's health by suggesting where to purchase ingredients that meet the user's dietary restrictions.

[0098] The menu generation unit uses the emotion estimation function to analyze the excitement level of the user when viewing sale information and can notify the user at the optimal timing. For example, it prioritizes notifying the user of sale information that excites the user. The menu generation unit also optimizes the timing of notifying the user of sale information based on the user's emotion data. For example, it analyzes past emotion data and notifies the user at the timing that makes the user most excited. The menu generation unit also uses the emotion estimation function to analyze the excitement level of the user when viewing sale information and can notify the user at the optimal timing. For example, it notifies the user of sale information that suits the user's preferences. In this way, by analyzing the excitement level of the user when viewing sale information and notifying the user at the optimal timing, the user's desire to purchase is increased.

[0099] The menu generation unit can collect information on nearby flea markets and bazaars based on location information and suggest shopping within a budget. For example, it can provide information on local flea markets. The menu generation unit also lists products that can be purchased at nearby flea markets and bazaars based on the user's location information and budget, and suggests the optimal shopping plan. For example, it can introduce bazaars where specific products can be purchased cheaply. The menu generation unit also uses the generation AI to collect information on nearby flea markets and bazaars based on the user's location information and suggest shopping within a budget. For example, it can introduce bazaars where specific products can be purchased cheaply. This allows for efficient shopping by collecting information on nearby flea markets and bazaars based on the user's location information and suggesting shopping within a budget.

[0100] The menu generation unit can use the emotion estimation function to suggest recipes that will increase the user's sense of satisfaction when eating a healthy meal. For example, it selects healthy dishes that will make the user feel satisfied. The menu generation unit also suggests recipes that will increase the user's sense of satisfaction when eating a healthy meal based on the user's emotion data. For example, it analyzes past emotion data and selects healthy dishes that will make the user feel satisfied. The menu generation unit also uses the emotion estimation function to suggest recipes that will increase the user's sense of satisfaction when eating a healthy meal. For example, it selects healthy dishes that suit the user's preferences. In this way, the menu generation unit supports the user's health by suggesting recipes that will increase the user's sense of satisfaction when eating a healthy meal.

[0101] The menu generation unit can increase the variety of meals by suggesting dishes from different countries or regions based on the user's past menu history. For example, the generation AI can suggest dishes from different countries or regions based on the user's past menu history. For example, it can suggest dishes from different countries such as Italian or Chinese cuisine. The menu generation unit can also increase the variety of meals by suggesting dishes from different regions based on the user's past menu history. For example, it can suggest local regional dishes or traditional dishes. The menu generation unit can also increase the variety of meals by suggesting dishes from different countries or regions based on the user's past menu history. For example, it can suggest dishes that the user does not usually eat. In this way, the variety of meals for the user can be increased by suggesting dishes from different countries or regions based on the user's past menu history.

[0102] The menu generation unit can use the emotion estimation function to suggest recipes that will increase the user's sense of security when eating allergy-friendly foods. For example, it selects dishes that the user feels comfortable eating. The menu generation unit also suggests recipes that will increase the user's sense of security when eating allergy-friendly foods based on the user's emotion data. For example, it analyzes past emotion data and selects dishes that the user feels comfortable eating. The menu generation unit also uses the emotion estimation function to suggest recipes that will increase the user's sense of security when eating allergy-friendly foods. For example, it selects dishes that match the user's preferences. In this way, it supports the user's safe eating by suggesting recipes that will increase the user's sense of security when eating allergy-friendly foods.

[0103] The menu generation unit can collect information on nearby farm shops and direct-from-producer markets based on location information and suggest fresh ingredients. For example, the generation AI can collect information on nearby farm shops and direct-from-producer markets based on the user's location information and suggest fresh ingredients. For example, it can provide business hours for local farm shops. The menu generation unit can also list fresh ingredients that can be purchased at nearby farm shops and direct-from-producer markets based on the user's location information and budget, and suggest an optimal shopping plan. For example, it can introduce fresh seasonal vegetables and fruits. The menu generation unit can also collect information on nearby farm shops and direct-from-producer markets based on the user's location information and suggest fresh ingredients. For example, it can introduce fresh seasonal vegetables and fruits. This allows for efficient shopping by collecting information on nearby farm shops and direct-from-producer markets based on the user's location information and suggesting fresh ingredients.

[0104] The menu generation unit can use the emotion estimation function to analyze the user's level of satisfaction with past menus and propose an optimal menu. For example, it re-proposes dishes that satisfied the user. The menu generation unit also analyzes the user's level of satisfaction with past menus based on the user's emotion data and proposes an optimal menu. For example, it analyzes the past emotion data and selects dishes that will satisfy the user. The menu generation unit also uses the emotion estimation function to analyze the user's level of satisfaction with past menus and proposes an optimal menu. For example, it selects dishes that match the user's preferences. In this way, the user's satisfaction with meals is increased by analyzing the user's level of satisfaction with past menus and proposing an optimal menu.

[0105] The menu generation unit can suggest new recipes using specific ingredients based on past menu history. For example, the generation AI may suggest new recipes using specific ingredients based on the user's past menu history. For example, it may suggest a new dish using chicken. The menu generation unit may also suggest new recipes using specific ingredients based on the user's past menu history. For example, it may suggest a new dish using tomatoes. The menu generation unit may also suggest new recipes using specific ingredients based on the user's past menu history. For example, it may suggest a new dish using tofu. In this way, by suggesting new recipes using specific ingredients based on the user's past menu history, the variety of the user's meals is increased.

[0106] The menu generation unit can use the emotion estimation function to analyze the emotions felt by the user when eating a healthy meal and propose an optimal nutritional management plan. For example, it selects a nutritional management plan that will satisfy the user. The menu generation unit also analyzes the emotions felt by the user when eating a healthy meal based on the user's emotion data and proposes an optimal nutritional management plan. For example, it analyzes past emotion data and selects a nutritional management plan that will satisfy the user. The menu generation unit also uses the emotion estimation function to analyze the emotions felt by the user when eating a healthy meal and proposes an optimal nutritional management plan. For example, it selects a nutritional management plan that suits the user's preferences. In this way, the user's health is supported by analyzing the emotions felt by the user when eating a healthy meal and proposing an optimal nutritional management plan.

[0107] The processing flow of the second embodiment will be briefly explained below.

[0108] Step 1: The location information acquisition unit acquires the user's location information. For example, the user's current location is acquired using GPS. Location information can also be acquired using Wi-Fi or cell towers. Furthermore, location information manually entered by the user can also be acquired. Step 2: The budget setting unit sets the user's budget. For example, it sets the budget entered by the user in the app. It can also automatically set the budget based on budgets set by the user in the past. It can also adjust the range of the budget set by the user. Step 3: The menu generation unit generates a menu based on the location information acquired by the location information acquisition unit and the budget set by the budget setting unit. For example, the generation AI suggests an optimal menu based on the user's location information and budget. The generation AI can also generate a menu taking into account the user's preferences and allergy information. Furthermore, it can suggest a wide variety of menus based on the user's past menu history. Step 4: The notification unit notifies the user of the menu generated by the menu generation unit. For example, the notification unit notifies the user of the menu using a push notification. The notification unit can also notify the user of the menu using an email notification or an in-app notification.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0122] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0128] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0137] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0153] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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."

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0175] 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]

[0176] 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 location information acquisition unit that acquires location information of a user; a budget setting unit for setting a user's budget; a menu creation unit that creates a menu based on the location information acquired by the location information acquisition unit and the budget set by the budget setting unit; a notification unit that notifies the user of the menu generated by the menu generation unit. A system characterized by:

2. The menu generation unit Analyze the user's past purchase history and suggest the optimal shopping route within the budget.

2. The system of claim 1.

3. The menu generation unit Based on the location information, the system creates an optimal shopping plan that takes into account transportation methods and travel time.

2. The system of claim 1.

4. The menu generation unit Suggest routes and time slots to reduce the stress the user feels while shopping 2. The system of claim 1.

5. The menu generation unit Based on the location information, the menus of nearby restaurants and cafes are analyzed and dining out plans are proposed within the budget.

2. The system of claim 1.

6. The menu generation unit Based on the location information, information on nearby events and fairs is collected and food purchasing suggestions are made within the budget.

2. The system of claim 1.

7. The menu generation unit Analyze the user's feelings about the atmosphere and services of the stores they visit and suggest the best shopping destinations 2. The system of claim 1.

8. The menu generation unit Monitor ingredients in real time and suggest optimal usage plans based on expiration dates 2. The system of claim 1.

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