System

Abstract

An object of a system according to an embodiment is to automatically record nutritional information of a meal, propose an appropriate recipe, and assist efficient purchase of ingredients.SOLUTION: A system includes a foodstuff identifier, a nutrient tracer, a recipe suggester, and a purchase of ingredients assister. The food identifier analyzes the picture of the meal taken by the user to identify the food. The nutrition tracking unit records nutrition information of the food identified by the food identification unit. The recipe suggesting unit suggests a recipe based on the nutrition information recorded by the nutrition tracking unit. The purchase of ingredients assisting part assists the online purchase of the necessary food material based on the recipe proposed by the recipe proposing part.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the drawback of requiring users to manually record nutritional information for meals and find suitable recipes, which is time-consuming and makes it difficult to purchase ingredients efficiently.

[0005] The system according to the embodiment aims to automatically record nutritional information of meals, suggest appropriate recipes, and assist in efficient food purchasing. [Means for solving the problem]

[0006] The system according to the embodiment includes a food identification unit, a nutrition tracking unit, a recipe suggestion unit, and an ingredient purchasing assistance unit. The food identification unit analyzes photos of meals taken by a user and identifies foods. The nutrition tracking unit records nutritional information for foods identified by the food identification unit. The recipe suggestion unit suggests recipes based on the nutritional information recorded by the nutrition tracking unit. The ingredient purchasing assistance unit assists with online purchase of ingredients required based on the recipe suggested by the recipe suggestion unit. [Effects of the Invention]

[0007] The system according to the embodiment can automatically record nutritional information of meals, suggest appropriate recipes, and assist with efficient food purchasing. [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) A food management system according to an embodiment of the present invention analyzes photos of meals taken by users, and a generative AI identifies the food, records its nutritional information, suggests recipes, and assists with online food purchasing. This allows the food management system to perform a variety of tasks, from identifying the food to recording its nutritional information, suggesting recipes, and purchasing ingredients, all in one place, simply by the user taking a photo of the meal.

[0029] A food management system according to an embodiment includes a food identification unit, a nutrition tracking unit, a recipe suggestion unit, and an ingredient purchasing assistance unit. The food identification unit analyzes photos of meals taken by a user and identifies the food. For example, the generation AI uses image recognition technology to identify the ingredients in the photos. The food identification unit can also identify the type of food by comparing the information with a database. For example, it can identify that the items in the photo are apples, bread, and salad. The nutrition tracking unit records nutritional information for the foods identified by the food identification unit. For example, it can record information such as "apples are rich in vitamin C," "bread is high in carbohydrates," and "salad is rich in dietary fiber." The nutrition tracking unit can also retrieve and record nutritional information for the identified foods from a database. For example, the generation AI can automatically update the nutritional information for the foods to provide the latest data. The recipe suggestion unit suggests recipes based on the nutritional information recorded by the nutrition tracking unit. For example, if a user wants to increase their vitamin C intake, the generation AI can suggest recipes using ingredients rich in vitamin C. The recipe suggestion unit can also suggest recipes taking into account the user's preferences and past eating history. For example, the generation AI analyzes the user's past meal history and suggests individually customized recipes. The ingredient purchasing assistance unit assists in online purchasing of ingredients needed based on the recipe suggested by the recipe suggestion unit. For example, ingredients needed for the suggested recipe are added to an online shopping cart. The ingredient purchasing assistance unit can also compare prices from multiple online stores and assist in purchasing ingredients at the best price. For example, the generation AI collects price information from multiple online stores and assists in purchasing ingredients at the best price. As a result, the food management system according to the embodiment can perform a variety of processes, from identifying food items to recording nutritional information, suggesting recipes, and purchasing ingredients, all in one place, simply by the user taking a photo of the meal. For example, the user can easily obtain the information needed to maintain a healthy diet and smoothly purchase ingredients. Furthermore, suggesting recipes tailored to the user's preferences can increase the enjoyment of eating.

[0030] The food identification unit can identify the freshness or quality of food and notify the user. For example, the food identification unit analyzes changes in color and shape to detect a decline in freshness so that the generation AI can identify the freshness of food. For example, if an apple starts to change color, it will determine that its freshness has declined. The food identification unit also analyzes scratches and discoloration on the surface to detect a decline in quality so that the generation AI can identify the quality of food. For example, if mold grows on the surface of bread, it will determine that the quality has declined. The food identification unit also displays an alert within the application to notify the user of the results of the generation AI's identification of food freshness and quality. For example, it will notify the user if salad leaves are wilted. In this way, food safety can be ensured by identifying the freshness and quality of food and notifying the user.

[0031] The food identification unit can identify the origin or producer information of food and provide it to the user. For example, the food identification unit analyzes package label information and extracts origin information so that the generation AI can identify the origin of the food. For example, it reads origin information written on an apple label. The food identification unit also analyzes QR codes or barcodes and obtains producer information so that the generation AI can identify the food's producer information. For example, it scans a QR code printed on a bread package. The food identification unit also displays detailed information within the application to provide the user with the origin and producer information identified by the generation AI. For example, it displays information about the origin and producer of a salad. In this way, the generation AI can identify the origin and producer information of food and provide it to the user, thereby improving the reliability of the food.

[0032] The food identification unit can identify food allergen information and issue warnings to users with allergies. For example, the food identification unit analyzes ingredient labels on packaging and extracts allergen information so that the generation AI can identify food allergen information. For example, it identifies wheat and dairy products included in the ingredient labeling of bread. The food identification unit also builds a system that issues warnings to users with allergies based on the results of the generation AI's identification of food allergen information. For example, it notifies the user of allergen information contained in apples. The food identification unit also identifies food allergen information using the generation AI and displays an alert within the application to users with allergies. For example, it notifies the user of allergen information contained in nuts in a salad. This allows the generation AI to identify allergen information and issue a warning to users with allergies, thereby preventing allergic reactions.

[0033] The food identification unit can identify food cooking methods and suggest the optimal cooking method to the user. For example, the food identification unit analyzes cooking instructions on packaging and extracts the optimal cooking method so that the generation AI can identify the food cooking method. For example, it identifies the toasting method written on a bread package. The food identification unit also builds a system in which the generation AI identifies food cooking methods and suggests the optimal cooking method to the user. For example, it suggests baked apples or apple pie as ways to cook apples. The food identification unit also identifies food cooking methods and displays the cooking method to the user within the application. For example, it suggests how to make dressing as a way to cook salad. In this way, the generation AI identifies food cooking methods and suggests the optimal cooking method to the user, improving cooking efficiency.

[0034] The nutrition tracking unit can update the nutritional information of foods in real time and provide the latest data. For example, the nutrition tracking unit retrieves the latest nutritional information from an online database and displays it within the application so that the generation AI can update the nutritional information of foods in real time. For example, the nutrition tracking unit retrieves the latest nutritional data for apples. The nutrition tracking unit also builds a system in which the generation AI updates the nutritional information of foods in real time and provides the latest nutritional data to the user. For example, the nutritional information for bread is updated regularly. The nutrition tracking unit also updates the nutritional information of foods in real time so that the generation AI can display the latest nutritional data to the user within the application. For example, nutritional information for salad is provided based on the latest data. In this way, the nutritional information of foods is updated in real time and the latest nutritional data is provided, allowing the user to always obtain the latest information.

[0035] The nutrition tracking unit can integrate nutritional information about food with other health data to provide comprehensive health management. For example, the generation AI of the nutrition tracking unit integrates nutritional information about food with exercise data to build a system for comprehensive health management. For example, it can provide nutritional intake advice based on the amount of exercise. The generation AI of the nutrition tracking unit also integrates nutritional information about food with sleep data to link data within the application for comprehensive health management. For example, it can provide nutritional intake advice based on sleep quality. The generation AI of the nutrition tracking unit also integrates nutritional information about food with other health data to build a system for comprehensive health management that comprehensively evaluates the user's health status. For example, it can provide health management that takes into account the balance of exercise, sleep, and nutrition. This allows the generation AI of the nutritional information about food to integrate with other health data to provide comprehensive health management and comprehensively evaluate the user's health status.

[0036] The nutrition tracking unit can suggest nutritional supplements to the user based on the nutritional information of food. For example, the nutrition tracking unit builds a system in which the generation AI suggests nutritional supplements to the user based on the nutritional information of food. For example, if there is a vitamin C deficiency, a vitamin C supplement is suggested. The nutrition tracking unit also analyzes the nutritional information of food and evaluates the user's nutritional balance, then suggests appropriate nutritional supplements. For example, if there is an iron deficiency, an iron supplement is suggested. The nutrition tracking unit also displays supplement information within the application so that the generation AI can suggest nutritional supplements to the user based on the nutritional information of food. For example, if there is a calcium deficiency, a calcium supplement is suggested. This allows the user's nutritional balance to be complemented by suggesting nutritional supplements based on the nutritional information of food.

[0037] The recipe suggestion unit can analyze a user's past eating history and suggest individually customized recipes. For example, the recipe suggestion unit uses a generation AI to analyze a user's past eating history and suggest customized recipes based on nutritional balance and preferences. For example, it suggests new recipes based on ingredients that the user has liked to eat in the past. The recipe suggestion unit also analyzes a user's past eating history and suggests recipes to supplement that nutrient if the user is deficient in a specific nutrient. For example, if the user is deficient in vitamin D, it suggests recipes using ingredients rich in vitamin D. The recipe suggestion unit also uses a generation AI to suggest recipes to increase meal variety based on the user's past eating history. For example, it suggests different cooking methods using the same ingredients. In this way, by analyzing a user's past eating history and suggesting individually customized recipes, it is possible to provide meals that suit the user's preferences.

[0038] The recipe suggestion unit can suggest recipes according to the season and weather, and provide meals that incorporate a seasonal feel. For example, the recipe suggestion unit's generation AI analyzes season and weather data and suggests recipes that incorporate a seasonal feel based on that information. For example, it suggests cold dishes in summer and hot dishes in winter. The recipe suggestion unit also identifies seasonal ingredients and suggests recipes using those ingredients. For example, it suggests salads using fresh vegetables in spring and dishes using mushrooms in autumn. The recipe suggestion unit also suggests recipes that match the weather based on weather data. For example, it suggests hot soup on rainy days and barbecue dishes on sunny days. In this way, the system can suggest recipes that correspond to the season and weather, and provide meals that incorporate a seasonal feel, enriching the user's dining experience.

[0039] The recipe suggestion unit manages the user's ingredient inventory information and can suggest recipes based on inventory. For example, the recipe suggestion unit builds a system in which the generation AI manages the user's ingredient inventory information and suggests recipes based on inventory. For example, it suggests recipes based on the ingredients in the refrigerator. The recipe suggestion unit also analyzes the user's ingredient inventory information and suggests recipes that prioritize ingredients that are close to their expiration date. For example, it suggests dishes that use vegetables that are close to their expiration date. The recipe suggestion unit also suggests recipes to reduce waste based on the user's ingredient inventory information. For example, it suggests recipes to use up leftover ingredients. In this way, by managing the user's ingredient inventory information and suggesting recipes based on inventory, it is possible to reduce ingredient waste.

[0040] The recipe suggestion unit can suggest recipes that meet the user's dietary restrictions. For example, the recipe suggestion unit constructs a system in which the generation AI analyzes the user's dietary restriction information and suggests recipes that meet those restrictions. For example, it suggests low-calorie diet recipes. The recipe suggestion unit also considers the user's religious restrictions and suggests recipes that meet those restrictions. For example, it suggests recipes that are halal or kosher. The recipe suggestion unit also suggests allergen-free recipes based on the user's allergy information. For example, it suggests gluten-free or nut-free recipes. This allows the system to suggest recipes that meet the user's dietary restrictions, thereby accommodating the user's health management and religious restrictions.

[0041] The food purchasing assistance unit can analyze a user's purchasing history and provide an individually customized food purchasing list. For example, the food purchasing assistance unit constructs a system in which a generation AI analyzes a user's purchasing history and provides an individually customized food purchasing list. For example, the list is created based on ingredients purchased in the past. The food purchasing assistance unit also analyzes the user's purchasing history and prioritizes frequently purchased ingredients in the list. For example, milk and bread that are purchased weekly are added to the list. The food purchasing assistance unit also provides a food purchasing list tailored to the season or event based on the user's purchasing history. For example, ingredients for Christmas or a barbecue are added to the list. In this way, the user's purchasing history is analyzed and an individually customized food purchasing list is provided, improving the user's purchasing experience.

[0042] The food purchasing assistance unit can compare multiple online stores and assist users in purchasing food ingredients at the optimal price. For example, the generation AI collects price information from multiple online stores and builds a system to assist users in purchasing food ingredients at the optimal price. For example, it compares prices for the same food ingredient and presents the lowest price. The food purchasing assistance unit also checks inventory information from multiple online stores based on the user's purchase list and prioritizes displaying stores with inventory. For example, it suggests alternative products if a specific food ingredient is out of stock. The food purchasing assistance unit also compares delivery conditions from multiple online stores and proposes the optimal delivery option. For example, it presents options with the shortest delivery time or free shipping. This allows users to compare multiple online stores and assists them in purchasing food ingredients at the optimal price, thereby reducing purchasing costs.

[0043] The food purchasing assistance unit manages the user's food inventory information and can automatically list food ingredients that are running low. For example, the food purchasing assistance unit builds a system in which a generation AI manages the user's food inventory information and automatically lists food ingredients that are running low. For example, the inventory status of food ingredients in the refrigerator is checked periodically. The food purchasing assistance unit also analyzes the user's food inventory information and prioritizes listing food ingredients that are close to their expiration date. For example, milk and vegetables that are close to their expiration date are added to the list. The food purchasing assistance unit also provides a food purchasing list to reduce waste based on the user's food inventory information. For example, it creates a list for using up leftover food ingredients. In this way, food waste can be reduced by managing the user's food inventory information and automatically listing food ingredients that are running low.

[0044] The food purchasing assistance unit can set up regular automatic food purchasing based on the user's purchasing history. For example, the generation AI builds a system that sets up regular automatic food purchasing based on the user's purchasing history. For example, it automatically adds ingredients to be purchased weekly to a list. The food purchasing assistance unit also analyzes the user's purchasing history and prioritizes frequently purchased ingredients to the automatic purchasing list. For example, it adds milk and bread that are purchased weekly to the list. The food purchasing assistance unit also sets up regular automatic food purchasing according to seasons and events based on the user's purchasing history. For example, it adds ingredients for Christmas and barbecues to the list. In this way, setting up regular automatic food purchasing based on the user's purchasing history improves the user's purchasing experience.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] The food management system can further include a storage suggestion unit that suggests food storage methods. In the storage suggestion unit, for example, the generative AI suggests the optimal storage method based on the type of food and its freshness. For example, it recommends storing apples in the refrigerator, and suggests that bread should be stored in the freezer. The storage suggestion unit can also predict the shelf life of food and notify the user. For example, it recommends that salad leaves be consumed within three days. This makes it possible to reduce food waste and maintain freshness by suggesting food storage methods.

[0047] The food management system can further include a calorie calculation unit that calculates the calorie consumption of food. For example, the calorie calculation unit uses a generation AI to acquire the user's exercise data and compare the calories of the food consumed with the calories consumed. For example, it calculates the calorie consumption when eating an apple followed by 30 minutes of jogging. The calorie calculation unit can also adjust the calorie intake based on the user's target weight. For example, it can suggest low-calorie foods to a user who is trying to lose weight. This allows the system to calculate the calorie consumption and support the user's health management.

[0048] The food management system can further include an allergen warning unit that identifies allergen information in food and issues a warning to users with allergies. The allergen warning unit, for example, uses a generation AI to analyze food ingredient labels and extract allergen information. For example, it identifies wheat and dairy products in the ingredient labels of bread. The allergen warning unit then displays an alert within the application to users with allergies based on the identified allergen information. For example, it notifies them of the allergen information of nuts in a salad. This allows the allergen information to be identified and allergic reactions to be prevented.

[0049] The food management system can further include a cooking suggestion unit that identifies food cooking methods and suggests optimal cooking methods to the user. The cooking suggestion unit, for example, analyzes cooking instructions on packaging to allow the generative AI to identify food cooking methods and extract optimal cooking methods. For example, it identifies toasting instructions written on bread packaging. The cooking suggestion unit can also suggest cooking methods taking into account the user's preferences and past cooking history. For example, it can suggest new recipes based on cooking methods that the user has previously preferred. This improves cooking efficiency by identifying food cooking methods and suggesting optimal cooking methods to the user.

[0050] The food management system can further include a place of origin information provision unit that identifies the place of origin and producer information of food and provides it to the user. The place of origin information provision unit, for example, analyzes label information on a package and extracts the place of origin information so that the generation AI can identify the place of origin of the food. For example, it reads the place of origin information written on an apple label. The place of origin information provision unit also displays detailed information within the application to provide the identified place of origin and producer information to the user. For example, it displays information about the place of origin and producer of a salad. In this way, the place of origin and producer information of food is identified and provided to the user, thereby improving the reliability of the food.

[0051] The processing flow of the first embodiment will be briefly explained below.

[0052] Step 1: The food identification unit analyzes photos of food taken by the user and identifies the food. For example, the generative AI uses image recognition technology to identify the ingredients in the photo. The food identification unit can also identify the type of food by comparing it with a database. For example, it can identify that the items in the photo are apples, bread, and salad. Step 2: The nutrition tracking unit records the nutritional information of the foods identified by the food identification unit. For example, it records information such as apples being rich in vitamin C, bread being high in carbohydrates, and salad being rich in dietary fiber. The nutrition tracking unit can also retrieve and record nutritional information of the identified foods from a database. For example, the generative AI can automatically update the nutritional information of the foods to provide the latest data. Step 3: The recipe suggestion unit suggests recipes based on the nutritional information recorded by the nutrition tracking unit. For example, if the user wants to consume a lot of vitamin C, the generation AI will suggest recipes using ingredients rich in vitamin C. The recipe suggestion unit can also suggest recipes taking into account the user's preferences and past eating history. For example, the generation AI may analyze the user's past eating history and suggest individually customized recipes. Step 4: The ingredient purchasing assistance unit assists with online purchasing of the necessary ingredients based on the recipe suggested by the recipe suggestion unit. For example, it adds the ingredients needed for the suggested recipe to an online shopping cart. The ingredient purchasing assistance unit can also assist with comparing prices from multiple online stores to purchase ingredients at the best price. For example, the generation AI collects price information from multiple online stores and assists with purchasing ingredients at the best price.

[0053] (Example 2) A food management system according to an embodiment of the present invention analyzes photos of meals taken by users, and a generative AI identifies the food, records its nutritional information, suggests recipes, and assists with online food purchasing. This allows the food management system to perform a variety of tasks, from identifying the food to recording its nutritional information, suggesting recipes, and purchasing ingredients, all in one place, simply by the user taking a photo of the meal.

[0054] A food management system according to an embodiment includes a food identification unit, a nutrition tracking unit, a recipe suggestion unit, and an ingredient purchasing assistance unit. The food identification unit analyzes photos of meals taken by a user and identifies the food. For example, the generation AI uses image recognition technology to identify the ingredients in the photos. The food identification unit can also identify the type of food by comparing the information with a database. For example, it can identify that the items in the photo are apples, bread, and salad. The nutrition tracking unit records nutritional information for the foods identified by the food identification unit. For example, it can record information such as "apples are rich in vitamin C," "bread is high in carbohydrates," and "salad is rich in dietary fiber." The nutrition tracking unit can also retrieve and record nutritional information for the identified foods from a database. For example, the generation AI can automatically update the nutritional information for the foods to provide the latest data. The recipe suggestion unit suggests recipes based on the nutritional information recorded by the nutrition tracking unit. For example, if a user wants to increase their vitamin C intake, the generation AI can suggest recipes using ingredients rich in vitamin C. The recipe suggestion unit can also suggest recipes taking into account the user's preferences and past eating history. For example, the generation AI analyzes the user's past meal history and suggests individually customized recipes. The ingredient purchasing assistance unit assists in online purchasing of ingredients needed based on the recipe suggested by the recipe suggestion unit. For example, ingredients needed for the suggested recipe are added to an online shopping cart. The ingredient purchasing assistance unit can also compare prices from multiple online stores and assist in purchasing ingredients at the best price. For example, the generation AI collects price information from multiple online stores and assists in purchasing ingredients at the best price. As a result, the food management system according to the embodiment can perform a variety of processes, from identifying food items to recording nutritional information, suggesting recipes, and purchasing ingredients, all in one place, simply by the user taking a photo of the meal. For example, the user can easily obtain the information needed to maintain a healthy diet and smoothly purchase ingredients. Furthermore, suggesting recipes tailored to the user's preferences can increase the enjoyment of eating.

[0055] The food identification unit can identify the freshness or quality of food and notify the user. For example, the food identification unit analyzes changes in color and shape to detect a decline in freshness so that the generation AI can identify the freshness of food. For example, if an apple starts to change color, it will determine that its freshness has declined. The food identification unit also analyzes scratches and discoloration on the surface to detect a decline in quality so that the generation AI can identify the quality of food. For example, if mold grows on the surface of bread, it will determine that the quality has declined. The food identification unit also displays an alert within the application to notify the user of the results of the generation AI's identification of food freshness and quality. For example, it will notify the user if salad leaves are wilted. In this way, food safety can be ensured by identifying the freshness and quality of food and notifying the user.

[0056] The food identification unit can identify the origin or producer information of food and provide it to the user. For example, the food identification unit analyzes package label information and extracts origin information so that the generation AI can identify the origin of the food. For example, it reads origin information written on an apple label. The food identification unit also analyzes QR codes or barcodes and obtains producer information so that the generation AI can identify the food's producer information. For example, it scans a QR code printed on a bread package. The food identification unit also displays detailed information within the application to provide the user with the origin and producer information identified by the generation AI. For example, it displays information about the origin and producer of a salad. In this way, the generation AI can identify the origin and producer information of food and provide it to the user, thereby improving the reliability of the food.

[0057] The food identification unit can use the emotion estimation function to analyze the user's emotion toward a photo taken and suggest foods that elicit positive emotions. For example, the food identification unit uses the emotion estimation function to analyze the user's emotion toward a photo taken in real time and identify foods that elicit positive emotions. For example, it suggests foods that elicit positive emotions for a photo taken with the user smiling. The food identification unit also uses the emotion estimation function to analyze the user's emotional state and suggest foods that elicit positive emotions. For example, it suggests foods that have a relaxing effect when the user is feeling stressed. The food identification unit also uses the emotion estimation function to build a system that suggests foods that elicit positive emotions based on the user's emotion data. For example, it analyzes the user's past emotion data and identifies foods that elicit positive emotions. In this way, by suggesting foods that elicit positive emotions based on the user's emotions, user satisfaction is improved.

[0058] The food identification unit can identify food allergen information and issue warnings to users with allergies. For example, the food identification unit analyzes ingredient labels on packaging and extracts allergen information so that the generation AI can identify food allergen information. For example, it identifies wheat and dairy products included in the ingredient labeling of bread. The food identification unit also builds a system that issues warnings to users with allergies based on the results of the generation AI's identification of food allergen information. For example, it notifies the user of allergen information contained in apples. The food identification unit also identifies food allergen information using the generation AI and displays an alert within the application to users with allergies. For example, it notifies the user of allergen information contained in nuts in a salad. This allows the generation AI to identify allergen information and issue a warning to users with allergies, thereby preventing allergic reactions.

[0059] The food identification unit can identify food cooking methods and suggest the optimal cooking method to the user. For example, the food identification unit analyzes cooking instructions on packaging and extracts the optimal cooking method so that the generation AI can identify the food cooking method. For example, it identifies the toasting method written on a bread package. The food identification unit also builds a system in which the generation AI identifies food cooking methods and suggests the optimal cooking method to the user. For example, it suggests baked apples or apple pie as ways to cook apples. The food identification unit also identifies food cooking methods and displays the cooking method to the user within the application. For example, it suggests how to make dressing as a way to cook salad. In this way, the generation AI identifies food cooking methods and suggests the optimal cooking method to the user, improving cooking efficiency.

[0060] The food identification unit can use the emotion estimation function to analyze the emotion of a user regarding a photograph taken by the user and provide a food identification result based on the emotion. For example, the food identification unit uses the emotion estimation function to analyze the emotion of a user regarding a photograph taken by the user in real time and provide a food identification result based on the emotion. For example, foods that evoke positive emotions are identified for a photograph taken by a user with a smile. The food identification unit also uses the emotion estimation function to analyze the emotional state of a user and build a system that provides a food identification result based on the emotion. For example, foods that have a relaxing effect are identified when the user is feeling stressed. The food identification unit also uses the emotion estimation function to provide a food identification result based on the emotion based on the user's emotion data. For example, past emotion data of the user is analyzed to identify foods that evoke positive emotions. In this way, by providing a food identification result based on the user's emotion, user satisfaction is improved.

[0061] The nutrition tracking unit can update the nutritional information of foods in real time and provide the latest data. For example, the nutrition tracking unit retrieves the latest nutritional information from an online database and displays it within the application so that the generation AI can update the nutritional information of foods in real time. For example, the nutrition tracking unit retrieves the latest nutritional data for apples. The nutrition tracking unit also builds a system in which the generation AI updates the nutritional information of foods in real time and provides the latest nutritional data to the user. For example, the nutritional information for bread is updated regularly. The nutrition tracking unit also updates the nutritional information of foods in real time so that the generation AI can display the latest nutritional data to the user within the application. For example, nutritional information for salad is provided based on the latest data. In this way, the nutritional information of foods is updated in real time and the latest nutritional data is provided, allowing the user to always obtain the latest information.

[0062] The nutrition tracking unit uses the emotion estimation function to provide nutritional information according to the user's emotional state and perform emotion-based nutritional management. For example, the nutrition tracking unit uses the emotion estimation function to analyze the user's emotional state in real time and provide emotion-based nutritional information. For example, when a user is feeling stressed, it provides nutritional information useful for stress reduction. The nutrition tracking unit also uses the emotion estimation function to build a system for emotion-based nutritional management based on the user's emotional data. For example, when a user is feeling positive, it provides nutritional information useful for energy replenishment. The nutrition tracking unit also uses the emotion estimation function to link emotion data and nutritional information within the application to provide nutritional information according to the user's emotional state and perform emotion-based nutritional management. For example, it displays nutritional balance according to the user's emotional state. This provides nutritional information according to the user's emotional state and performs emotion-based nutritional management, thereby supporting the user's health management.

[0063] The nutrition tracking unit can integrate nutritional information about food with other health data to provide comprehensive health management. For example, the generation AI of the nutrition tracking unit integrates nutritional information about food with exercise data to build a system for comprehensive health management. For example, it can provide nutritional intake advice based on the amount of exercise. The generation AI of the nutrition tracking unit also integrates nutritional information about food with sleep data to link data within the application for comprehensive health management. For example, it can provide nutritional intake advice based on sleep quality. The generation AI of the nutrition tracking unit also integrates nutritional information about food with other health data to build a system for comprehensive health management that comprehensively evaluates the user's health status. For example, it can provide health management that takes into account the balance of exercise, sleep, and nutrition. This allows the generation AI of the nutritional information about food to integrate with other health data to provide comprehensive health management and comprehensively evaluate the user's health status.

[0064] The nutrition tracking unit can suggest nutritional supplements to the user based on the nutritional information of food. For example, the nutrition tracking unit builds a system in which the generation AI suggests nutritional supplements to the user based on the nutritional information of food. For example, if there is a vitamin C deficiency, a vitamin C supplement is suggested. The nutrition tracking unit also analyzes the nutritional information of food and evaluates the user's nutritional balance, then suggests appropriate nutritional supplements. For example, if there is an iron deficiency, an iron supplement is suggested. The nutrition tracking unit also displays supplement information within the application so that the generation AI can suggest nutritional supplements to the user based on the nutritional information of food. For example, if there is a calcium deficiency, a calcium supplement is suggested. This allows the user's nutritional balance to be complemented by suggesting nutritional supplements based on the nutritional information of food.

[0065] The nutrition tracking unit uses the emotion estimation function to provide nutritional information according to the user's emotional state and perform emotion-based nutritional management. For example, the nutrition tracking unit uses the emotion estimation function to analyze the user's emotional state in real time and provide emotion-based nutritional information. For example, when a user is feeling stressed, it provides nutritional information useful for stress reduction. The nutrition tracking unit also uses the emotion estimation function to build a system for emotion-based nutritional management based on the user's emotional data. For example, when a user is feeling positive, it provides nutritional information useful for energy replenishment. The nutrition tracking unit also uses the emotion estimation function to link emotion data and nutritional information within the application to provide nutritional information according to the user's emotional state and perform emotion-based nutritional management. For example, it displays nutritional balance according to the user's emotional state. This provides nutritional information according to the user's emotional state and performs emotion-based nutritional management, thereby supporting the user's health management.

[0066] The recipe suggestion unit can analyze a user's past eating history and suggest individually customized recipes. For example, the recipe suggestion unit uses a generation AI to analyze a user's past eating history and suggest customized recipes based on nutritional balance and preferences. For example, it suggests new recipes based on ingredients that the user has liked to eat in the past. The recipe suggestion unit also analyzes a user's past eating history and suggests recipes to supplement that nutrient if the user is deficient in a specific nutrient. For example, if the user is deficient in vitamin D, it suggests recipes using ingredients rich in vitamin D. The recipe suggestion unit also uses a generation AI to suggest recipes to increase meal variety based on the user's past eating history. For example, it suggests different cooking methods using the same ingredients. In this way, by analyzing a user's past eating history and suggesting individually customized recipes, it is possible to provide meals that suit the user's preferences.

[0067] The recipe suggestion unit can suggest recipes according to the season and weather, and provide meals that incorporate a seasonal feel. For example, the recipe suggestion unit's generation AI analyzes season and weather data and suggests recipes that incorporate a seasonal feel based on that information. For example, it suggests cold dishes in summer and hot dishes in winter. The recipe suggestion unit also identifies seasonal ingredients and suggests recipes using those ingredients. For example, it suggests salads using fresh vegetables in spring and dishes using mushrooms in autumn. The recipe suggestion unit also suggests recipes that match the weather based on weather data. For example, it suggests hot soup on rainy days and barbecue dishes on sunny days. In this way, the system can suggest recipes that correspond to the season and weather, and provide meals that incorporate a seasonal feel, enriching the user's dining experience.

[0068] The recipe suggestion unit can use the emotion estimation function to suggest recipes based on the user's emotional state and provide meals based on the emotions. For example, the recipe suggestion unit uses the emotion estimation function to analyze the user's emotional state in real time and suggest recipes based on the emotions. For example, if the user is tired, the recipe suggestion unit suggests recipes using ingredients that have a relaxing effect. The recipe suggestion unit also uses the emotion estimation function to build a system that provides meals based on the user's emotional data. For example, if the user is feeling positive, the recipe suggestion unit suggests recipes that are useful for replenishing energy. The recipe suggestion unit also uses the emotion estimation function to link the emotion data and recipe information within the application to suggest recipes based on the user's emotional state and provide meals based on the emotions. For example, a recipe based on the user's emotional state is displayed. In this way, recipes based on the user's emotional state are suggested and meals based on the emotions are provided, thereby improving user satisfaction.

[0069] The recipe suggestion unit manages the user's ingredient inventory information and can suggest recipes based on inventory. For example, the recipe suggestion unit builds a system in which the generation AI manages the user's ingredient inventory information and suggests recipes based on inventory. For example, it suggests recipes based on the ingredients in the refrigerator. The recipe suggestion unit also analyzes the user's ingredient inventory information and suggests recipes that prioritize ingredients that are close to their expiration date. For example, it suggests dishes that use vegetables that are close to their expiration date. The recipe suggestion unit also suggests recipes to reduce waste based on the user's ingredient inventory information. For example, it suggests recipes to use up leftover ingredients. In this way, by managing the user's ingredient inventory information and suggesting recipes based on inventory, it is possible to reduce ingredient waste.

[0070] The recipe suggestion unit can suggest recipes that meet the user's dietary restrictions. For example, the recipe suggestion unit constructs a system in which the generation AI analyzes the user's dietary restriction information and suggests recipes that meet those restrictions. For example, it suggests low-calorie diet recipes. The recipe suggestion unit also considers the user's religious restrictions and suggests recipes that meet those restrictions. For example, it suggests recipes that are halal or kosher. The recipe suggestion unit also suggests allergen-free recipes based on the user's allergy information. For example, it suggests gluten-free or nut-free recipes. This allows the system to suggest recipes that meet the user's dietary restrictions, thereby accommodating the user's health management and religious restrictions.

[0071] The food purchasing assistance unit can analyze a user's purchasing history and provide an individually customized food purchasing list. For example, the food purchasing assistance unit constructs a system in which a generation AI analyzes a user's purchasing history and provides an individually customized food purchasing list. For example, the list is created based on ingredients purchased in the past. The food purchasing assistance unit also analyzes the user's purchasing history and prioritizes frequently purchased ingredients in the list. For example, milk and bread that are purchased weekly are added to the list. The food purchasing assistance unit also provides a food purchasing list tailored to the season or event based on the user's purchasing history. For example, ingredients for Christmas or a barbecue are added to the list. In this way, the user's purchasing history is analyzed and an individually customized food purchasing list is provided, improving the user's purchasing experience.

[0072] The food purchasing assistance unit can compare multiple online stores and assist users in purchasing food ingredients at the optimal price. For example, the generation AI collects price information from multiple online stores and builds a system to assist users in purchasing food ingredients at the optimal price. For example, it compares prices for the same food ingredient and presents the lowest price. The food purchasing assistance unit also checks inventory information from multiple online stores based on the user's purchase list and prioritizes displaying stores with inventory. For example, it suggests alternative products if a specific food ingredient is out of stock. The food purchasing assistance unit also compares delivery conditions from multiple online stores and proposes the optimal delivery option. For example, it presents options with the shortest delivery time or free shipping. This allows users to compare multiple online stores and assists them in purchasing food ingredients at the optimal price, thereby reducing purchasing costs.

[0073] The food purchasing assist unit can use the emotion estimation function to provide a food purchasing list that corresponds to the user's emotional state, thereby providing a purchasing experience based on emotions. For example, the food purchasing assist unit uses the emotion estimation function to analyze the user's emotional state in real time and provide a food purchasing list based on emotions. For example, if the user is feeling stressed, food ingredients that have a relaxing effect are added to the list. The food purchasing assist unit also uses the emotion estimation function to build a system that provides an emotion-based purchasing experience based on the user's emotional data. For example, if the user is feeling positive emotions, food ingredients that are useful for replenishing energy are added to the list. The food purchasing assist unit also uses the emotion estimation function to link the emotion data and the purchasing list within the application to provide a food purchasing list that corresponds to the user's emotional state and provide a purchasing experience based on emotions. For example, food ingredients that correspond to the user's emotional state are added to the list. In this way, a food purchasing list that corresponds to the user's emotional state is provided, providing a purchasing experience based on emotions and improving user satisfaction.

[0074] The food purchasing assistance unit manages the user's food inventory information and can automatically list food ingredients that are running low. For example, the food purchasing assistance unit builds a system in which a generation AI manages the user's food inventory information and automatically lists food ingredients that are running low. For example, the inventory status of food ingredients in the refrigerator is checked periodically. The food purchasing assistance unit also analyzes the user's food inventory information and prioritizes listing food ingredients that are close to their expiration date. For example, milk and vegetables that are close to their expiration date are added to the list. The food purchasing assistance unit also provides a food purchasing list to reduce waste based on the user's food inventory information. For example, it creates a list for using up leftover food ingredients. In this way, food waste can be reduced by managing the user's food inventory information and automatically listing food ingredients that are running low.

[0075] The food purchasing assistance unit can set up regular automatic food purchasing based on the user's purchasing history. For example, the generation AI builds a system that sets up regular automatic food purchasing based on the user's purchasing history. For example, it automatically adds ingredients to be purchased weekly to a list. The food purchasing assistance unit also analyzes the user's purchasing history and prioritizes frequently purchased ingredients to the automatic purchasing list. For example, it adds milk and bread that are purchased weekly to the list. The food purchasing assistance unit also sets up regular automatic food purchasing according to seasons and events based on the user's purchasing history. For example, it adds ingredients for Christmas and barbecues to the list. In this way, setting up regular automatic food purchasing based on the user's purchasing history improves the user's purchasing experience.

[0076] The food purchasing assist unit can use the emotion estimation function to provide a food purchasing list that corresponds to the user's emotional state, thereby providing a purchasing experience based on emotions. For example, the food purchasing assist unit uses the emotion estimation function to analyze the user's emotional state in real time and provide a food purchasing list based on emotions. For example, if the user is feeling stressed, food ingredients that have a relaxing effect are added to the list. The food purchasing assist unit also uses the emotion estimation function to build a system that provides an emotion-based purchasing experience based on the user's emotional data. For example, if the user is feeling positive emotions, food ingredients that are useful for replenishing energy are added to the list. The food purchasing assist unit also uses the emotion estimation function to link the emotion data and the purchasing list within the application to provide a food purchasing list that corresponds to the user's emotional state and provide a purchasing experience based on emotions. For example, food ingredients that correspond to the user's emotional state are added to the list. In this way, a food purchasing list that corresponds to the user's emotional state is provided, providing a purchasing experience based on emotions and improving user satisfaction.

[0077] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0078] The food management system can further include a storage suggestion unit that suggests food storage methods. In the storage suggestion unit, for example, the generative AI suggests the optimal storage method based on the type of food and its freshness. For example, it recommends storing apples in the refrigerator, and suggests that bread should be stored in the freezer. The storage suggestion unit can also predict the shelf life of food and notify the user. For example, it recommends that salad leaves be consumed within three days. This makes it possible to reduce food waste and maintain freshness by suggesting food storage methods.

[0079] The food management system can further include a calorie calculation unit that calculates the calorie consumption of food. For example, the calorie calculation unit uses a generation AI to acquire the user's exercise data and compare the calories of the food consumed with the calories consumed. For example, it calculates the calorie consumption when eating an apple followed by 30 minutes of jogging. The calorie calculation unit can also adjust the calorie intake based on the user's target weight. For example, it can suggest low-calorie foods to a user who is trying to lose weight. This allows the system to calculate the calorie consumption and support the user's health management.

[0080] The food management system can further include an allergen warning unit that identifies allergen information in food and issues a warning to users with allergies. The allergen warning unit, for example, uses a generation AI to analyze food ingredient labels and extract allergen information. For example, it identifies wheat and dairy products in the ingredient labels of bread. The allergen warning unit then displays an alert within the application to users with allergies based on the identified allergen information. For example, it notifies them of the allergen information of nuts in a salad. This allows the allergen information to be identified and allergic reactions to be prevented.

[0081] The food management system can further include a cooking suggestion unit that identifies food cooking methods and suggests optimal cooking methods to the user. The cooking suggestion unit, for example, analyzes cooking instructions on packaging to allow the generative AI to identify food cooking methods and extract optimal cooking methods. For example, it identifies toasting instructions written on bread packaging. The cooking suggestion unit can also suggest cooking methods taking into account the user's preferences and past cooking history. For example, it can suggest new recipes based on cooking methods that the user has previously preferred. This improves cooking efficiency by identifying food cooking methods and suggesting optimal cooking methods to the user.

[0082] The food management system can further include a place of origin information provision unit that identifies the place of origin and producer information of food and provides it to the user. The place of origin information provision unit, for example, analyzes label information on a package and extracts the place of origin information so that the generation AI can identify the place of origin of the food. For example, it reads the place of origin information written on an apple label. The place of origin information provision unit also displays detailed information within the application to provide the identified place of origin and producer information to the user. For example, it displays information about the place of origin and producer of a salad. In this way, the place of origin and producer information of food is identified and provided to the user, thereby improving the reliability of the food.

[0083] The food management system can further include a nutrition emotion management unit that uses an emotion estimation function to provide nutrition information according to the user's emotional state and perform emotion-based nutrition management. The nutrition emotion management unit, for example, uses the emotion estimation function to analyze the user's emotional state in real time and provide emotion-based nutrition information. For example, if the user is feeling stressed, it provides nutrition information that is useful for stress reduction. The nutrition emotion management unit also builds a system that performs emotion-based nutrition management based on the user's emotional data. For example, if the user is feeling positive, it provides nutrition information that is useful for replenishing energy. In this way, providing nutrition information according to the user's emotional state and performing emotion-based nutrition management can support the user's health management.

[0084] The food management system can further include a recipe emotion suggestion unit that uses an emotion estimation function to suggest recipes based on the user's emotional state and provide meals based on the emotions. The recipe emotion suggestion unit, for example, uses the emotion estimation function to analyze the user's emotional state in real time and suggest recipes based on the emotions. For example, if the user is tired, it suggests recipes using ingredients that have a relaxing effect. The recipe emotion suggestion unit also builds a system that provides meals based on the emotions based on the user's emotional data. For example, if the user is feeling positive, it suggests recipes that are useful for replenishing energy. In this way, by suggesting recipes based on the user's emotional state and providing meals based on the emotions, user satisfaction is improved.

[0085] The food management system can further include a purchasing emotion assist unit that uses an emotion estimation function to provide a food ingredient shopping list that corresponds to the user's emotional state, thereby providing a purchasing experience based on emotions. The purchasing emotion assist unit, for example, uses the emotion estimation function to analyze the user's emotional state in real time and provide a food ingredient shopping list based on emotions. For example, if the user is feeling stressed, ingredients that have a relaxing effect are added to the list. The purchasing emotion assist unit also builds a system that provides a purchasing experience based on emotions based on the user's emotional data. For example, if the user is feeling positive emotions, ingredients that are useful for replenishing energy are added to the list. In this way, a food ingredient shopping list that corresponds to the user's emotional state is provided, providing a purchasing experience based on emotions and improving user satisfaction.

[0086] The food management system may further include an emotion identification unit that uses an emotion estimation function to provide food identification results based on the user's emotional state. The emotion identification unit, for example, uses the emotion estimation function to analyze the user's emotional state in real time and provide food identification results based on the emotion. For example, it identifies foods that evoke positive emotions in a photo taken by a user with a smile. The emotion identification unit also builds a system that provides food identification results based on the user's emotion data. For example, it identifies foods that have a relaxing effect when the user is feeling stressed. This improves user satisfaction by providing food identification results based on the user's emotional state.

[0087] The food management system can further include a nutrition emotion management unit that uses an emotion estimation function to provide nutrition information according to the user's emotional state and perform emotion-based nutrition management. The nutrition emotion management unit, for example, uses the emotion estimation function to analyze the user's emotional state in real time and provide emotion-based nutrition information. For example, if the user is feeling stressed, it provides nutrition information that is useful for stress reduction. The nutrition emotion management unit also builds a system that performs emotion-based nutrition management based on the user's emotional data. For example, if the user is feeling positive, it provides nutrition information that is useful for replenishing energy. In this way, providing nutrition information according to the user's emotional state and performing emotion-based nutrition management can support the user's health management.

[0088] The processing flow of the second embodiment will be briefly explained below.

[0089] Step 1: The food identification unit analyzes photos of food taken by the user and identifies the food. For example, the generative AI uses image recognition technology to identify the ingredients in the photo. The food identification unit can also identify the type of food by comparing it with a database. For example, it can identify that the items in the photo are apples, bread, and salad. Step 2: The nutrition tracking unit records the nutritional information of the foods identified by the food identification unit. For example, it records information such as apples being rich in vitamin C, bread being high in carbohydrates, and salad being rich in dietary fiber. The nutrition tracking unit can also retrieve and record nutritional information of the identified foods from a database. For example, the generative AI can automatically update the nutritional information of the foods to provide the latest data. Step 3: The recipe suggestion unit suggests recipes based on the nutritional information recorded by the nutrition tracking unit. For example, if the user wants to consume a lot of vitamin C, the generation AI will suggest recipes using ingredients rich in vitamin C. The recipe suggestion unit can also suggest recipes taking into account the user's preferences and past eating history. For example, the generation AI may analyze the user's past eating history and suggest individually customized recipes. Step 4: The ingredient purchasing assistance unit assists with online purchasing of the necessary ingredients based on the recipe suggested by the recipe suggestion unit. For example, it adds the ingredients needed for the suggested recipe to an online shopping cart. The ingredient purchasing assistance unit can also assist with comparing prices from multiple online stores to purchase ingredients at the best price. For example, the generation AI collects price information from multiple online stores and assists with purchasing ingredients at the best price.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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).

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0109] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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."

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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]

[0157] 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 system comprising: a unit that analyzes photos of meals taken by a user and identifies foods; a nutrition tracking unit that records nutritional information of the foods identified by the food identification unit; a recipe suggestion unit that suggests recipes based on the nutritional information recorded by the nutrition tracking unit; and an ingredient purchase assistance unit that assists in the online purchase of ingredients required based on the recipes suggested by the recipe suggestion unit.

2. The system of claim 1 , wherein the food identification unit identifies the freshness or quality of the food and notifies the user.

3. The system according to claim 1 , wherein the food product identification unit identifies the origin or producer information of the food product and provides it to the user.

4. The system according to claim 1 , wherein the food identification unit analyzes the user's feelings toward the photograph taken by the user and suggests foods that elicit positive feelings.

5. The system according to claim 1 , wherein the food identification unit identifies allergen information of the food and issues a warning to a user who has an allergy.

6. The system according to claim 1 , wherein the food identification unit identifies a cooking method for the food and suggests an optimal cooking method to the user.

Images