system

Abstract

An object of a system according to an embodiment is to provide a recipe personalized based on a preference of a user and an inventory status of an ingredient and to manage a best-before date and a use-by date of the ingredient.SOLUTION: A system includes a recipe generation part, an advice provision part, a combination proposal part, and a time limit management part. The recipe generation unit generates a personalized recipe on the basis of the user's preference and the inventory status of food ingredients. The advice providing unit provides advice according to the progress of cooking based on the recipe generated by the recipe generating unit. The combination proposal unit proposes an optimal combination of ingredients on the basis of ingredients that the user has. The time limit management unit manages the best-before date and the use-by date of the food material using the QR code at the time of purchase.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem that it is difficult to provide personalized recipes based on user preferences and ingredient availability, and that managing the expiration dates and best-before dates of ingredients is cumbersome.

[0005] The system according to the embodiment aims to provide personalized recipes based on the user's preferences and the availability of ingredients, and to manage the expiration dates and best-before dates of ingredients. [Means for solving the problem]

[0006] The system according to the embodiment includes a recipe generation unit, an advice providing unit, a combination suggestion unit, and an expiration date management unit. The recipe generation unit generates personalized recipes based on the user's preferences and ingredient availability. The advice providing unit provides advice according to the cooking progress based on the recipe generated by the recipe generation unit. The combination suggestion unit suggests optimal ingredient combinations based on the ingredients the user has. The expiration date management unit manages best-before dates and expiration dates of ingredients using QR codes at the time of purchase. [Effects of the Invention]

[0007] The system according to the embodiment can provide personalized recipes based on the user's preferences and the availability of ingredients, and can manage the expiration dates and best-before dates of ingredients. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The FlavorChef Guide system according to an embodiment of the present invention creates personalized recipes based on the user's preferences and ingredient availability, provides advice as the cooking progresses, suggests optimal ingredient combinations, and manages the expiration dates and best-before dates of ingredients, allowing users to enjoy cooking more effectively.

[0029] The FlavorChef Guide system according to an embodiment includes a recipe generation unit, an advice providing unit, a combination suggestion unit, and an expiration date management unit. The recipe generation unit generates personalized recipes based on the user's preferences and the availability of ingredients. For example, if a user requests an "easy dinner recipe using chicken," the recipe generation unit generates an appropriate recipe using the generation AI, which analyzes the prompt containing the instruction. The recipe generation unit can also generate recipes taking into account the user's allergy information. For example, the generation AI can suggest recipes that avoid ingredients to which the user is allergic. The recipe generation unit can also analyze the user's past cooking history and generate recipes that reflect changes in preferences or attempts at new ingredients. For example, if the user has recently developed a preference for spicy food, the recipe generation unit can suggest spicy recipes. The advice providing unit provides advice based on the progress of cooking based on the recipe generated by the recipe generation unit. For example, in response to a question such as "What should I add to this sauce to make it even more delicious?", the advice providing unit analyzes the question and provides appropriate advice. The advice providing unit can also monitor the progress of cooking in real time and provide advice at the appropriate time. For example, it provides real-time instructions on the heat level and cooking time for stir-frying. The combination suggestion unit suggests optimal ingredient combinations based on the ingredients the user has on hand. For example, when the combination suggestion unit asks, "What can I make with the tomatoes, cheese, and basil in the fridge?", the generation AI analyzes the question and suggests an appropriate dish. The combination suggestion unit can also suggest combinations that take into account the nutritional value of ingredients. For example, it could combine ingredients rich in vitamins and minerals. Furthermore, the combination suggestion unit can analyze the user's past ingredient usage history and suggest new combinations. For example, it could combine ingredients that have never been used before. The expiration date management unit uses QR codes at the time of purchase to manage the expiration dates and best-before dates of ingredients. For example, when a user scans the QR code of ingredients purchased at the supermarket, the information is registered on the platform, and the generation AI manages the expiration dates and best-before dates.The expiration date management unit can also automatically generate recipes that prioritize ingredients with upcoming expiration dates. For example, it can suggest dishes using ingredients with upcoming expiration dates. Furthermore, the expiration date management unit can also monitor the storage conditions of ingredients and suggest optimal storage methods. For example, it can suggest storage methods based on temperature and humidity. This allows the FlavorChef Guide system according to the embodiment to allow users to enjoy cooking more effectively. For example, when a beginner cook tries a new recipe, the generation AI can provide real-time advice, increasing the success rate. Furthermore, managing the expiration dates of ingredients can reduce food waste and is expected to have an economical effect. Furthermore, personalized recipes and suggested ingredient combinations can give users new cooking ideas and expand the enjoyment of eating.

[0030] The recipe generation unit can analyze the user's past cooking history and generate recipes that reflect changes in preferences and the trial of new ingredients. The recipe generation unit, for example, analyzes the history of dishes the user has made in the past and generates recipes that reflect changes in preferences. For example, if the user has recently developed a preference for spicy food, the recipe generation unit will suggest spicy recipes. The recipe generation unit will also suggest recipes that use new ingredients that the user has not tried before. For example, the recipe generation unit may generate recipes that use ingredients the user has not tried yet, providing a new cooking experience. The recipe generation unit will also generate recipes that reflect changes in preferences with the seasons based on the user's cooking history. For example, the recipe generation unit may suggest light salads in the summer and warm soups in the winter. In this way, by analyzing the user's past cooking history and generating recipes that reflect changes in preferences and the trial of new ingredients, the recipe generation unit provides the user with a more personalized cooking experience.

[0031] The recipe generation unit generates recipes according to the season and weather, and can suggest dishes that incorporate a sense of the season. The recipe generation unit generates recipes that incorporate a sense of the season, for example, by taking into account seasonal ingredients. For example, it suggests a salad using fresh vegetables in spring, and a dish using mushrooms in autumn. The recipe generation unit also generates recipes that match the weather based on weather data. For example, it suggests a hot soup on a cold day, and a cold dessert on a hot day. The recipe generation unit also generates recipes that match seasonal events and occasions. For example, it suggests a special dinner for Christmas, and street food-style dishes for a summer festival. In this way, by generating recipes according to the season and weather, and suggesting dishes that incorporate a sense of the season, it is possible to provide the user with a cooking experience that is sensitive to the season.

[0032] The recipe generation unit generates recipes that take into account the user's health condition and nutritional balance, and can support health management. The recipe generation unit generates nutritionally balanced recipes, for example, based on the user's health data. For example, if a specific nutrient is lacking, it suggests recipes that supplement that nutrient. The recipe generation unit also generates recipes that match the user's health goals (for example, weight loss or muscle building). For example, it suggests low-calorie dishes or high-protein dishes. The recipe generation unit also generates recipes that take into account the user's allergy information and dietary restrictions. For example, it suggests gluten-free or vegan recipes. In this way, the recipe generation unit generates recipes that take into account the user's health condition and nutritional balance, and supports health management, thereby maintaining the user's health.

[0033] The recipe generation unit generates recipes for multiple people that take into account the preferences of family and friends, thereby promoting a collaborative cooking experience. The recipe generation unit, for example, generates recipes that everyone can enjoy by taking into account the preferences of family and friends. For example, it suggests dishes that reflect the preferences of each member. The recipe generation unit also generates recipes that can be made together to promote a cooking experience for multiple people. For example, it suggests dishes that everyone can make and enjoy, such as pizza or tacos. The recipe generation unit also generates recipes that take into account the dietary restrictions and allergy information of family and friends. For example, it suggests dishes that are allergy-friendly or that accommodate specific dietary restrictions. In this way, by generating recipes for multiple people that take into account the preferences of family and friends and promoting a collaborative cooking experience, the user can enjoy cooking with family and friends.

[0034] The advice providing unit can monitor the progress of cooking in real time and provide advice at the appropriate time. The advice providing unit, for example, monitors the progress of cooking using a sensor and provides advice at the appropriate time. For example, it gives instructions on the heat level and simmering time for stir-frying in real time. The advice providing unit also builds a system that provides advice in real time as the user cooks. For example, it gives instructions on the next step or when to add seasonings. The advice providing unit also monitors the progress of cooking using a camera and provides advice at the appropriate time. For example, it gives advice based on the degree of doneness or changes in color. In this way, the progress of cooking can be monitored in real time and advice can be provided at the appropriate time, allowing the user to cook smoothly.

[0035] The advice providing unit can provide advice according to the user's cooking skill level and support skill improvement. The advice providing unit, for example, analyzes the user's cooking skill level and provides advice accordingly. For example, it provides detailed explanations of basic procedures to beginners and suggests advanced techniques to advanced cooks. The advice providing unit also provides advice to support skill improvement based on the user's past cooking history. For example, it points out areas for improvement in dishes that failed in the past. The advice providing unit also suggests recipes according to the user's cooking skill level and supports skill improvement. For example, it provides easy recipes for beginners and challenging recipes for advanced cooks. In this way, by providing advice according to the user's cooking skill level and supporting skill improvement, the user can improve their cooking techniques.

[0036] The advice providing unit can provide advice corresponding to different cooking styles. For example, the advice providing unit provides advice according to the cooking style selected by the user. For example, in the case of Japanese cuisine, it suggests tips on how to make dashi stock and plating. The advice providing unit also builds a system that provides advice corresponding to different cooking styles. For example, in the case of Western cuisine, it suggests how to make a sauce and how to adjust the degree of doneness. The advice providing unit also suggests recipes according to the cooking style selected by the user and provides advice based on them. For example, in the case of Chinese cuisine, it suggests stir-frying techniques and how to use seasonings. In this way, by providing advice corresponding to different cooking styles, the user can enjoy a variety of cooking styles.

[0037] The advice providing unit can troubleshoot during cooking and provide advice to prevent failure. For example, the advice providing unit analyzes problems that occur during cooking in real time and provides advice to prevent failure. For example, it suggests measures to prevent food from burning or excessive salt. The advice providing unit also builds a system that provides advice to solve problems that users face while cooking. For example, it suggests what to do if the sauce separates. The advice providing unit also troubleshoots during cooking and provides advice to prevent failure. For example, it suggests how to adjust if the food is not cooked evenly. In this way, troubleshooting during cooking and providing advice to prevent failure allows the user to cook successfully.

[0038] The combination suggestion unit can suggest combinations that take into account the nutritional value of ingredients and support a balanced diet. The combination suggestion unit, for example, analyzes the nutritional value of ingredients and suggests combinations that support a balanced diet. For example, it combines ingredients that are rich in vitamins and minerals. The combination suggestion unit also suggests combinations of ingredients that meet the user's nutritional needs. For example, if a specific nutrient is lacking, it suggests ingredients that supplement that nutrient. The combination suggestion unit also takes into account the nutritional value of ingredients and suggests combinations that support a healthy diet. For example, it combines ingredients that are low in calories and high in protein. This allows the user to enjoy a healthy diet by suggesting combinations that take into account the nutritional value of ingredients and supporting a balanced diet.

[0039] The combination suggestion unit can analyze the user's past ingredient use history and suggest new combinations. The combination suggestion unit, for example, analyzes the user's past ingredient use history and suggests new combinations. For example, combining ingredients that have not been used before. The combination suggestion unit also suggests new cooking ideas based on the user's ingredient use history. For example, it generates recipes that combine ingredients that have been used in the past with new ingredients. The combination suggestion unit also analyzes the user's past ingredient use history and suggests ingredient combinations. For example, it provides new cooking ideas based on combinations of ingredients that have been used in the past. In this way, the user can obtain new cooking ideas by analyzing the user's past ingredient use history and suggesting new combinations.

[0040] The combination suggestion unit can propose new fusion dishes by combining ingredients from different cultures and regions. The combination suggestion unit, for example, proposes new fusion dishes by combining ingredients from different cultures and regions. For example, it generates a recipe that combines Japanese cuisine with Italian cuisine. The combination suggestion unit also proposes new cooking ideas by combining ingredients from different regions. For example, it generates a recipe that combines Asian and European ingredients. The combination suggestion unit also proposes new fusion dishes by combining ingredients from different cultures. For example, it generates a recipe that combines Mexican and Chinese cuisine. This allows users to enjoy a variety of cuisines by combining ingredients from different cultures and regions to propose new fusion dishes.

[0041] The combination suggestion unit can suggest optimal combinations based on the storage method and cooking method of ingredients. The combination suggestion unit, for example, considers the storage method of ingredients and suggests optimal combinations. For example, it generates recipes that combine ingredients that can be frozen. The combination suggestion unit also suggests optimal combinations of ingredients based on the cooking method. For example, it generates recipes that combine ingredients that are suitable for grilling. The combination suggestion unit also suggests optimal combinations based on the storage method and cooking method of ingredients. For example, it generates recipes that combine ingredients that can be stored for a long time. This allows the user to use ingredients without waste by suggesting optimal combinations based on the storage method and cooking method of ingredients.

[0042] The expiration management unit can automatically generate recipes that prioritize the use of ingredients that are close to their expiration date or best-before date. The expiration management unit, for example, automatically generates recipes that prioritize the use of ingredients that are close to their expiration date or best-before date. For example, it suggests dishes that use ingredients that are close to their expiration date. The expiration management unit also generates recipes to reduce waste based on ingredients that are close to their expiration date or best-before date. For example, it suggests dishes that use ingredients that are close to their expiration date. The expiration management unit also generates recipes that prioritize the use of ingredients that are close to their expiration date or best-before date. For example, it suggests dishes that use ingredients that are close to their expiration date. In this way, by automatically generating recipes that prioritize the use of ingredients that are close to their expiration date or best-before date, it is possible to reduce ingredient waste.

[0043] The expiration management unit can monitor the storage conditions of ingredients and suggest the optimal storage method. The expiration management unit, for example, monitors the storage conditions of ingredients using a sensor and suggests the optimal storage method. For example, it suggests a storage method based on temperature and humidity. The expiration management unit also monitors the storage conditions of ingredients using a camera and suggests the optimal storage method. For example, it suggests a storage method based on changes in color or shape. The expiration management unit also analyzes the storage conditions of ingredients and suggests the optimal storage method. For example, it suggests a storage method based on the storage period and storage environment. In this way, by monitoring the storage conditions of ingredients and suggesting the optimal storage method, it is possible to maintain the quality of ingredients and reduce waste.

[0044] The expiration date management unit allows all family members to share information about best-before dates and expiration dates, promoting collaborative food management. The expiration date management unit, for example, builds a system that allows all family members to share information about best-before dates and expiration dates. For example, the information is shared using a smartphone app. The expiration date management unit also allows all family members to check information about best-before dates and expiration dates, promoting collaborative food management. For example, the information is managed using a shared calendar. The expiration date management unit also shares information about best-before dates and expiration dates with all family members, promoting food management to reduce waste. For example, the information is managed using a shared list. In this way, all family members can share information about best-before dates and expiration dates, promoting collaborative food management, reducing food waste and allowing all family members to manage food efficiently.

[0045] The expiration management unit can analyze the food ingredient purchase history and propose a purchasing plan to reduce waste. The expiration management unit, for example, analyzes the food ingredient purchase history and proposes a purchasing plan to reduce waste. For example, it proposes the optimal purchase amount based on past purchase data. The expiration management unit also proposes a purchasing plan to reduce waste based on the user's purchase history. For example, it proposes the optimal purchase amount based on past consumption data. The expiration management unit also analyzes the food ingredient purchase history and proposes a purchasing plan to reduce waste. For example, it proposes the optimal purchase amount based on past purchase data. In this way, by analyzing the food ingredient purchase history and proposing a purchasing plan to reduce waste, the user can purchase food ingredients efficiently and reduce waste.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The FlavorChef Guide system can also generate recipes that take into account the user's dietary preferences and allergy information. For example, if a user is allergic to a particular ingredient, it will suggest recipes that avoid that ingredient. It can also analyze the user's past cooking history and generate recipes that reflect changes in preferences and attempts at new ingredients. For example, if the user has recently developed a preference for spicy food, it will suggest spicy recipes. This allows the FlavorChef Guide system to help users enjoy cooking more effectively.

[0048] The FlavorChef Guide system can also generate recipes that take into account the user's health condition and nutritional balance. For example, if a user is lacking in a particular nutrient, it will suggest recipes that supplement that nutrient. It can also generate recipes that match the user's health goals (such as weight loss or muscle building). For example, it will suggest low-calorie dishes or high-protein dishes. In this way, the FlavorChef Guide system can support the user's health management.

[0049] The FlavorChef Guide system can also generate recipes according to the season and weather. For example, it can suggest salads using fresh vegetables in spring and dishes using mushrooms in autumn. It can also suggest hot soups on cold days and cold desserts on hot days based on weather data. This allows the FlavorChef Guide system to provide users with a seasonal cooking experience.

[0050] The FlavorChef Guide system can also generate recipes for multiple people, taking into account the preferences of family and friends. For example, it can suggest dishes that reflect the preferences of each member. It can also generate recipes that can be made collaboratively. For example, it can suggest dishes that can be made by everyone, such as pizza or tacos. This allows the FlavorChef Guide system to allow users to enjoy cooking together with family and friends.

[0051] The FlavorChef Guide system can also monitor the progress of cooking in real time and provide advice at the appropriate time. For example, it can give real-time instructions on the heat level for stir-frying or the cooking time. It can also give instructions on the next step or when to add seasonings. This allows the FlavorChef Guide system to help users cook smoothly.

[0052] The FlavorChef Guide system can also provide advice based on the user's cooking skill level. For example, it can provide detailed explanations of basic steps for beginners and suggest advanced techniques for advanced cooks. It can also point out areas for improvement in dishes that have failed in the past. In this way, the FlavorChef Guide system can help users improve their cooking skills.

[0053] The FlavorChef Guide system can also provide advice for different cooking styles. For example, for Japanese cuisine, it can suggest tips on how to make dashi stock and how to arrange the food. For Western cuisine, it can also suggest how to make sauces and how to adjust the doneness of food. This allows users to enjoy a variety of cooking styles.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The recipe generator generates personalized recipes based on the user's preferences and ingredient availability. For example, if a user requests an "easy chicken dinner recipe," the generator AI analyzes the prompt containing that instruction and generates an appropriate recipe. It can also generate recipes taking into account the user's allergy information. Furthermore, it can analyze the user's past cooking history and generate recipes that reflect changes in preferences and attempts at new ingredients. Step 2: The advice provider provides advice based on the recipe generated by the recipe generator as the cooking progresses. For example, in response to a question such as "What can I add to this sauce to make it even more delicious?", the AI ​​analyzes the question and provides appropriate advice. It can also monitor the cooking progress in real time and provide advice at the appropriate time. Step 3: The combination suggestion unit suggests optimal combinations of ingredients based on the ingredients the user has. For example, if the question is, "What can I make with the tomatoes, cheese, and basil in my refrigerator?", the generative AI will analyze the question and suggest an appropriate dish. It can also suggest combinations that take into account the nutritional value of ingredients. It can also analyze the user's past ingredient usage history and suggest new combinations. Step 4: The expiration date management unit uses QR codes at the time of purchase to manage the expiration dates and best-before dates of ingredients. For example, when a user scans the QR code of ingredients purchased at a supermarket, the information is registered on the platform, and the generation AI manages the expiration dates and best-before dates. It can also suggest dishes that use ingredients that are close to their expiration date. It can also monitor the storage conditions of ingredients and suggest optimal storage methods.

[0056] (Example 2) The FlavorChef Guide system according to an embodiment of the present invention creates personalized recipes based on the user's preferences and ingredient availability, provides advice as the cooking progresses, suggests optimal ingredient combinations, and manages the expiration dates and best-before dates of ingredients, allowing users to enjoy cooking more effectively.

[0057] The FlavorChef Guide system according to an embodiment includes a recipe generation unit, an advice providing unit, a combination suggestion unit, and an expiration date management unit. The recipe generation unit generates personalized recipes based on the user's preferences and the availability of ingredients. For example, if a user requests an "easy dinner recipe using chicken," the recipe generation unit generates an appropriate recipe using the generation AI, which analyzes the prompt containing the instruction. The recipe generation unit can also generate recipes taking into account the user's allergy information. For example, the generation AI can suggest recipes that avoid ingredients to which the user is allergic. The recipe generation unit can also analyze the user's past cooking history and generate recipes that reflect changes in preferences or attempts at new ingredients. For example, if the user has recently developed a preference for spicy food, the recipe generation unit can suggest spicy recipes. The advice providing unit provides advice based on the progress of cooking based on the recipe generated by the recipe generation unit. For example, in response to a question such as "What should I add to this sauce to make it even more delicious?", the advice providing unit analyzes the question and provides appropriate advice. The advice providing unit can also monitor the progress of cooking in real time and provide advice at the appropriate time. For example, it provides real-time instructions on the heat level and cooking time for stir-frying. The combination suggestion unit suggests optimal ingredient combinations based on the ingredients the user has on hand. For example, when the combination suggestion unit asks, "What can I make with the tomatoes, cheese, and basil in the fridge?", the generation AI analyzes the question and suggests an appropriate dish. The combination suggestion unit can also suggest combinations that take into account the nutritional value of ingredients. For example, it could combine ingredients rich in vitamins and minerals. Furthermore, the combination suggestion unit can analyze the user's past ingredient usage history and suggest new combinations. For example, it could combine ingredients that have never been used before. The expiration date management unit uses QR codes at the time of purchase to manage the expiration dates and best-before dates of ingredients. For example, when a user scans the QR code of ingredients purchased at the supermarket, the information is registered on the platform, and the generation AI manages the expiration dates and best-before dates.The expiration date management unit can also automatically generate recipes that prioritize ingredients with upcoming expiration dates. For example, it can suggest dishes using ingredients with upcoming expiration dates. Furthermore, the expiration date management unit can also monitor the storage conditions of ingredients and suggest optimal storage methods. For example, it can suggest storage methods based on temperature and humidity. This allows the FlavorChef Guide system according to the embodiment to allow users to enjoy cooking more effectively. For example, when a beginner cook tries a new recipe, the generation AI can provide real-time advice, increasing the success rate. Furthermore, managing the expiration dates of ingredients can reduce food waste and is expected to have an economical effect. Furthermore, personalized recipes and suggested ingredient combinations can give users new cooking ideas and expand the enjoyment of eating.

[0058] The recipe generation unit can analyze the user's past cooking history and generate recipes that reflect changes in preferences and the trial of new ingredients. The recipe generation unit, for example, analyzes the history of dishes the user has made in the past and generates recipes that reflect changes in preferences. For example, if the user has recently developed a preference for spicy food, the recipe generation unit will suggest spicy recipes. The recipe generation unit will also suggest recipes that use new ingredients that the user has not tried before. For example, the recipe generation unit may generate recipes that use ingredients the user has not tried yet, providing a new cooking experience. The recipe generation unit will also generate recipes that reflect changes in preferences with the seasons based on the user's cooking history. For example, the recipe generation unit may suggest light salads in the summer and warm soups in the winter. In this way, by analyzing the user's past cooking history and generating recipes that reflect changes in preferences and the trial of new ingredients, the recipe generation unit provides the user with a more personalized cooking experience.

[0059] The recipe generation unit generates recipes according to the season and weather, and can suggest dishes that incorporate a sense of the season. The recipe generation unit generates recipes that incorporate a sense of the season, for example, by taking into account seasonal ingredients. For example, it suggests a salad using fresh vegetables in spring, and a dish using mushrooms in autumn. The recipe generation unit also generates recipes that match the weather based on weather data. For example, it suggests a hot soup on a cold day, and a cold dessert on a hot day. The recipe generation unit also generates recipes that match seasonal events and occasions. For example, it suggests a special dinner for Christmas, and street food-style dishes for a summer festival. In this way, by generating recipes according to the season and weather, and suggesting dishes that incorporate a sense of the season, it is possible to provide the user with a cooking experience that is sensitive to the season.

[0060] The recipe generation unit can use the emotion estimation function to suggest recipes that match the user's current mood. The recipe generation unit, for example, analyzes the user's emotional state in real time and suggests recipes that match the mood. For example, if the user is feeling stressed, the recipe generation unit suggests dishes that will relax them. The recipe generation unit also uses the emotion estimation function to suggest special dishes or desserts if the user is feeling happy or excited. For example, it provides recipes that are perfect for celebrations. The recipe generation unit also suggests recipes to improve the user's mood based on the user's emotion data. For example, if the user is feeling down, the recipe generation unit suggests dishes that will brighten the mood. In this way, the emotion estimation function is used to suggest recipes that match the user's current mood, providing a cooking experience that matches the user's mood.

[0061] The recipe generation unit generates recipes that take into account the user's health condition and nutritional balance, and can support health management. The recipe generation unit generates nutritionally balanced recipes, for example, based on the user's health data. For example, if a specific nutrient is lacking, it suggests recipes that supplement that nutrient. The recipe generation unit also generates recipes that match the user's health goals (for example, weight loss or muscle building). For example, it suggests low-calorie dishes or high-protein dishes. The recipe generation unit also generates recipes that take into account the user's allergy information and dietary restrictions. For example, it suggests gluten-free or vegan recipes. In this way, the recipe generation unit generates recipes that take into account the user's health condition and nutritional balance, and supports health management, thereby maintaining the user's health.

[0062] The recipe generation unit generates recipes for multiple people that take into account the preferences of family and friends, thereby promoting a collaborative cooking experience. The recipe generation unit, for example, generates recipes that everyone can enjoy by taking into account the preferences of family and friends. For example, it suggests dishes that reflect the preferences of each member. The recipe generation unit also generates recipes that can be made together to promote a cooking experience for multiple people. For example, it suggests dishes that everyone can make and enjoy, such as pizza or tacos. The recipe generation unit also generates recipes that take into account the dietary restrictions and allergy information of family and friends. For example, it suggests dishes that are allergy-friendly or that accommodate specific dietary restrictions. In this way, by generating recipes for multiple people that take into account the preferences of family and friends and promoting a collaborative cooking experience, the user can enjoy cooking with family and friends.

[0063] The recipe generation unit uses the emotion estimation function to suggest recipes that take into account the emotions of all family members, thereby improving the atmosphere at the dinner table. The recipe generation unit, for example, analyzes the emotional states of all family members and suggests recipes that everyone can enjoy. For example, it suggests dishes that will relax everyone. The recipe generation unit also uses the emotion estimation function to suggest recipes that will improve the mood of all family members. For example, it suggests dishes that will make everyone happy. The recipe generation unit also suggests recipes that will improve the atmosphere at the dinner table, based on the emotional data of all family members. For example, it suggests special dishes that everyone can enjoy. In this way, the emotion estimation function is used to suggest recipes that take into account the emotions of all family members, improving the atmosphere at the dinner table and providing a cooking experience that the whole family can enjoy.

[0064] The advice providing unit can monitor the progress of cooking in real time and provide advice at the appropriate time. The advice providing unit, for example, monitors the progress of cooking using a sensor and provides advice at the appropriate time. For example, it gives instructions on the heat level and simmering time for stir-frying in real time. The advice providing unit also builds a system that provides advice in real time as the user cooks. For example, it gives instructions on the next step or when to add seasonings. The advice providing unit also monitors the progress of cooking using a camera and provides advice at the appropriate time. For example, it gives advice based on the degree of doneness or changes in color. In this way, the progress of cooking can be monitored in real time and advice can be provided at the appropriate time, allowing the user to cook smoothly.

[0065] The advice providing unit can provide advice according to the user's cooking skill level and support skill improvement. The advice providing unit, for example, analyzes the user's cooking skill level and provides advice accordingly. For example, it provides detailed explanations of basic procedures to beginners and suggests advanced techniques to advanced cooks. The advice providing unit also provides advice to support skill improvement based on the user's past cooking history. For example, it points out areas for improvement in dishes that failed in the past. The advice providing unit also suggests recipes according to the user's cooking skill level and supports skill improvement. For example, it provides easy recipes for beginners and challenging recipes for advanced cooks. In this way, by providing advice according to the user's cooking skill level and supporting skill improvement, the user can improve their cooking techniques.

[0066] The advice providing unit can use the emotion estimation function to provide advice to reduce the user's stress level. For example, the advice providing unit uses the emotion estimation function to analyze the user's stress level and provide advice to reduce stress. For example, it suggests relaxing dishes or simple steps. The advice providing unit also analyzes the user's emotional state in real time and, if the user is feeling stressed, provides advice to reduce stress. For example, it suggests relaxing music. The advice providing unit also provides advice to reduce the user's stress level based on the emotion estimation data. For example, if the user is feeling stressed, it suggests an easy and fun dish. In this way, by using the emotion estimation function to provide advice to reduce the user's stress level, the user can relax and enjoy cooking.

[0067] The advice providing unit can provide advice corresponding to different cooking styles. For example, the advice providing unit provides advice according to the cooking style selected by the user. For example, in the case of Japanese cuisine, it suggests tips on how to make dashi stock and plating. The advice providing unit also builds a system that provides advice corresponding to different cooking styles. For example, in the case of Western cuisine, it suggests how to make a sauce and how to adjust the degree of doneness. The advice providing unit also suggests recipes according to the cooking style selected by the user and provides advice based on them. For example, in the case of Chinese cuisine, it suggests stir-frying techniques and how to use seasonings. In this way, by providing advice corresponding to different cooking styles, the user can enjoy a variety of cooking styles.

[0068] The advice providing unit can troubleshoot during cooking and provide advice to prevent failure. For example, the advice providing unit analyzes problems that occur during cooking in real time and provides advice to prevent failure. For example, it suggests measures to prevent food from burning or excessive salt. The advice providing unit also builds a system that provides advice to solve problems that users face while cooking. For example, it suggests what to do if the sauce separates. The advice providing unit also troubleshoots during cooking and provides advice to prevent failure. For example, it suggests how to adjust if the food is not cooked evenly. In this way, troubleshooting during cooking and providing advice to prevent failure allows the user to cook successfully.

[0069] The advice providing unit can use the emotion estimation function to analyze the user's emotions while cooking and provide advice to elicit positive emotions. The advice providing unit, for example, uses the emotion estimation function to analyze the user's emotions while cooking in real time and provide advice to elicit positive emotions. For example, it makes suggestions to provide a fun cooking experience. The advice providing unit also analyzes the user's emotional state and provides advice to elicit positive emotions. For example, it suggests simple dishes to increase successful experiences. The advice providing unit also analyzes the user's emotions while cooking based on the emotion estimation data and provides advice to elicit positive emotions. For example, it makes suggestions to play fun music. In this way, the emotion estimation function can be used to analyze the user's emotions while cooking and provide advice to elicit positive emotions, allowing the user to enjoy cooking.

[0070] The combination suggestion unit can suggest combinations that take into account the nutritional value of ingredients and support a balanced diet. The combination suggestion unit, for example, analyzes the nutritional value of ingredients and suggests combinations that support a balanced diet. For example, it combines ingredients that are rich in vitamins and minerals. The combination suggestion unit also suggests combinations of ingredients that meet the user's nutritional needs. For example, if a specific nutrient is lacking, it suggests ingredients that supplement that nutrient. The combination suggestion unit also takes into account the nutritional value of ingredients and suggests combinations that support a healthy diet. For example, it combines ingredients that are low in calories and high in protein. This allows the user to enjoy a healthy diet by suggesting combinations that take into account the nutritional value of ingredients and supporting a balanced diet.

[0071] The combination suggestion unit can analyze the user's past ingredient use history and suggest new combinations. The combination suggestion unit, for example, analyzes the user's past ingredient use history and suggests new combinations. For example, combining ingredients that have not been used before. The combination suggestion unit also suggests new cooking ideas based on the user's ingredient use history. For example, it generates recipes that combine ingredients that have been used in the past with new ingredients. The combination suggestion unit also analyzes the user's past ingredient use history and suggests ingredient combinations. For example, it provides new cooking ideas based on combinations of ingredients that have been used in the past. In this way, the user can obtain new cooking ideas by analyzing the user's past ingredient use history and suggesting new combinations.

[0072] The combination suggestion unit can use the emotion estimation function to suggest ingredient combinations that match the user's mood. The combination suggestion unit, for example, uses the emotion estimation function to suggest ingredient combinations that match the user's mood. For example, if the user wants to relax, the combination suggestion unit will combine ingredients that have a relaxing effect. The combination suggestion unit also analyzes the user's emotional state in real time to suggest ingredient combinations that match the user's mood. For example, if the user wants to feel energized, the combination suggestion unit will combine ingredients that will replenish energy. The combination suggestion unit also suggests ingredient combinations that match the user's mood based on the emotion estimation data. For example, if the user is feeling stressed, the combination suggestion unit will combine ingredients that have a stress-reducing effect. In this way, the emotion estimation function can be used to suggest ingredient combinations that match the user's mood, allowing the user to enjoy cooking that suits their mood.

[0073] The combination suggestion unit can propose new fusion dishes by combining ingredients from different cultures and regions. The combination suggestion unit, for example, proposes new fusion dishes by combining ingredients from different cultures and regions. For example, it generates a recipe that combines Japanese cuisine with Italian cuisine. The combination suggestion unit also proposes new cooking ideas by combining ingredients from different regions. For example, it generates a recipe that combines Asian and European ingredients. The combination suggestion unit also proposes new fusion dishes by combining ingredients from different cultures. For example, it generates a recipe that combines Mexican and Chinese cuisine. This allows users to enjoy a variety of cuisines by combining ingredients from different cultures and regions to propose new fusion dishes.

[0074] The combination suggestion unit can suggest optimal combinations based on the storage method and cooking method of ingredients. The combination suggestion unit, for example, considers the storage method of ingredients and suggests optimal combinations. For example, it generates recipes that combine ingredients that can be frozen. The combination suggestion unit also suggests optimal combinations of ingredients based on the cooking method. For example, it generates recipes that combine ingredients that are suitable for grilling. The combination suggestion unit also suggests optimal combinations based on the storage method and cooking method of ingredients. For example, it generates recipes that combine ingredients that can be stored for a long time. This allows the user to use ingredients without waste by suggesting optimal combinations based on the storage method and cooking method of ingredients.

[0075] The combination suggestion unit can use the emotion estimation function to suggest ingredient combinations that take into account the emotions of all family members. The combination suggestion unit, for example, uses the emotion estimation function to suggest ingredient combinations that take into account the emotions of all family members. For example, it generates recipes that combine ingredients that will relax everyone. The combination suggestion unit also analyzes the emotional states of all family members to suggest ingredient combinations that can be enjoyed by all. For example, it generates recipes that combine ingredients that will bring joy to all. The combination suggestion unit also suggests ingredient combinations that take into account the emotions of all family members based on the emotion estimation data. For example, it generates recipes that combine ingredients that will relax everyone. In this way, by using the emotion estimation function to suggest ingredient combinations that take into account the emotions of all family members, a cooking experience that can be enjoyed by the whole family is provided.

[0076] The expiration management unit can automatically generate recipes that prioritize the use of ingredients that are close to their expiration date or best-before date. The expiration management unit, for example, automatically generates recipes that prioritize the use of ingredients that are close to their expiration date or best-before date. For example, it suggests dishes that use ingredients that are close to their expiration date. The expiration management unit also generates recipes to reduce waste based on ingredients that are close to their expiration date or best-before date. For example, it suggests dishes that use ingredients that are close to their expiration date. The expiration management unit also generates recipes that prioritize the use of ingredients that are close to their expiration date or best-before date. For example, it suggests dishes that use ingredients that are close to their expiration date. In this way, by automatically generating recipes that prioritize the use of ingredients that are close to their expiration date or best-before date, it is possible to reduce ingredient waste.

[0077] The expiration management unit can monitor the storage conditions of ingredients and suggest the optimal storage method. The expiration management unit, for example, monitors the storage conditions of ingredients using a sensor and suggests the optimal storage method. For example, it suggests a storage method based on temperature and humidity. The expiration management unit also monitors the storage conditions of ingredients using a camera and suggests the optimal storage method. For example, it suggests a storage method based on changes in color or shape. The expiration management unit also analyzes the storage conditions of ingredients and suggests the optimal storage method. For example, it suggests a storage method based on the storage period and storage environment. In this way, by monitoring the storage conditions of ingredients and suggesting the optimal storage method, it is possible to maintain the quality of ingredients and reduce waste.

[0078] The expiration management unit can use the emotion estimation function to suggest an ingredient management method to reduce stress for the user. The expiration management unit, for example, uses the emotion estimation function to suggest an ingredient management method to reduce stress for the user. For example, it suggests a simple and efficient storage method. The expiration management unit also analyzes the user's emotional state in real time to suggest an ingredient management method to reduce stress. For example, it suggests ingredients with a long storage period. The expiration management unit also suggests an ingredient management method to reduce stress for the user based on the emotion estimation data. For example, it suggests ingredients that are easy to store. In this way, by using the emotion estimation function to suggest an ingredient management method to reduce stress for the user, the user can manage ingredients without stress.

[0079] The expiration date management unit allows all family members to share information about best-before dates and expiration dates, promoting collaborative food management. The expiration date management unit, for example, builds a system that allows all family members to share information about best-before dates and expiration dates. For example, the information is shared using a smartphone app. The expiration date management unit also allows all family members to check information about best-before dates and expiration dates, promoting collaborative food management. For example, the information is managed using a shared calendar. The expiration date management unit also shares information about best-before dates and expiration dates with all family members, promoting food management to reduce waste. For example, the information is managed using a shared list. In this way, all family members can share information about best-before dates and expiration dates, promoting collaborative food management, reducing food waste and allowing all family members to manage food efficiently.

[0080] The expiration management unit can analyze the food ingredient purchase history and propose a purchasing plan to reduce waste. The expiration management unit, for example, analyzes the food ingredient purchase history and proposes a purchasing plan to reduce waste. For example, it proposes the optimal purchase amount based on past purchase data. The expiration management unit also proposes a purchasing plan to reduce waste based on the user's purchase history. For example, it proposes the optimal purchase amount based on past consumption data. The expiration management unit also analyzes the food ingredient purchase history and proposes a purchasing plan to reduce waste. For example, it proposes the optimal purchase amount based on past purchase data. In this way, by analyzing the food ingredient purchase history and proposing a purchasing plan to reduce waste, the user can purchase food ingredients efficiently and reduce waste.

[0081] The expiration management unit can use the emotion estimation function to propose a food management method that takes into account the emotions of all family members. The expiration management unit, for example, uses the emotion estimation function to propose a food management method that takes into account the emotions of all family members. For example, it proposes a storage method that allows everyone to relax. The expiration management unit also analyzes the emotional states of all family members to propose a food management method that everyone can enjoy. For example, it proposes a storage method that makes everyone happy. The expiration management unit also proposes a food management method that takes into account the emotions of all family members based on the emotion estimation data. For example, it proposes a storage method that allows everyone to relax. In this way, by using the emotion estimation function to propose a food management method that takes into account the emotions of all family members, all family members can manage food without stress.

[0082] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0083] The FlavorChef Guide system can also generate recipes that take into account the user's dietary preferences and allergy information. For example, if a user is allergic to a particular ingredient, it will suggest recipes that avoid that ingredient. It can also analyze the user's past cooking history and generate recipes that reflect changes in preferences and attempts at new ingredients. For example, if the user has recently developed a preference for spicy food, it will suggest spicy recipes. This allows the FlavorChef Guide system to help users enjoy cooking more effectively.

[0084] The FlavorChef Guide system can also generate recipes that take into account the user's health condition and nutritional balance. For example, if a user is lacking in a particular nutrient, it will suggest recipes that supplement that nutrient. It can also generate recipes that match the user's health goals (such as weight loss or muscle building). For example, it will suggest low-calorie dishes or high-protein dishes. In this way, the FlavorChef Guide system can support the user's health management.

[0085] The FlavorChef Guide system can also generate recipes according to the season and weather. For example, it can suggest salads using fresh vegetables in spring and dishes using mushrooms in autumn. It can also suggest hot soups on cold days and cold desserts on hot days based on weather data. This allows the FlavorChef Guide system to provide users with a seasonal cooking experience.

[0086] The FlavorChef Guide system also uses emotion estimation to suggest recipes that match the user's current mood. For example, if the user is feeling stressed, it can suggest relaxing dishes. If the user is feeling happy or excited, it can suggest special dishes or desserts. This allows the FlavorChef Guide system to provide a cooking experience that suits the user's mood.

[0087] The FlavorChef Guide system can also generate recipes for multiple people, taking into account the preferences of family and friends. For example, it can suggest dishes that reflect the preferences of each member. It can also generate recipes that can be made collaboratively. For example, it can suggest dishes that can be made by everyone, such as pizza or tacos. This allows the FlavorChef Guide system to allow users to enjoy cooking together with family and friends.

[0088] The FlavorChef Guide system also uses emotion estimation to suggest recipes that take into account the emotions of each family member. For example, it can suggest dishes that will relax everyone. It can also suggest dishes that will bring joy to everyone. This allows the FlavorChef Guide system to provide a cooking experience that the whole family can enjoy.

[0089] The FlavorChef Guide system can also monitor the progress of cooking in real time and provide advice at the appropriate time. For example, it can give real-time instructions on the heat level for stir-frying or the cooking time. It can also give instructions on the next step or when to add seasonings. This allows the FlavorChef Guide system to help users cook smoothly.

[0090] The FlavorChef Guide system can also provide advice based on the user's cooking skill level. For example, it can provide detailed explanations of basic steps for beginners and suggest advanced techniques for advanced cooks. It can also point out areas for improvement in dishes that have failed in the past. In this way, the FlavorChef Guide system can help users improve their cooking skills.

[0091] The FlavorChef Guide system can also use its emotion estimation function to provide advice to reduce the user's stress level. For example, it can suggest relaxing dishes and simple steps. It can also suggest relaxing music. This allows the FlavorChef Guide system to help users relax and enjoy cooking.

[0092] The FlavorChef Guide system can also provide advice for different cooking styles. For example, for Japanese cuisine, it can suggest tips on how to make dashi stock and how to arrange the food. For Western cuisine, it can also suggest how to make sauces and how to adjust the doneness of food. This allows users to enjoy a variety of cooking styles.

[0093] The processing flow of the second embodiment will be briefly explained below.

[0094] Step 1: The recipe generator generates personalized recipes based on the user's preferences and ingredient availability. For example, if a user requests an "easy chicken dinner recipe," the generator AI analyzes the prompt containing that instruction and generates an appropriate recipe. It can also generate recipes taking into account the user's allergy information. Furthermore, it can analyze the user's past cooking history and generate recipes that reflect changes in preferences and attempts at new ingredients. Step 2: The advice provider provides advice based on the recipe generated by the recipe generator as the cooking progresses. For example, in response to a question such as "What can I add to this sauce to make it even more delicious?", the AI ​​analyzes the question and provides appropriate advice. It can also monitor the cooking progress in real time and provide advice at the appropriate time. Step 3: The combination suggestion unit suggests optimal combinations of ingredients based on the ingredients the user has. For example, if the question is, "What can I make with the tomatoes, cheese, and basil in my refrigerator?", the generative AI will analyze the question and suggest an appropriate dish. It can also suggest combinations that take into account the nutritional value of ingredients. It can also analyze the user's past ingredient usage history and suggest new combinations. Step 4: The expiration date management unit uses QR codes at the time of purchase to manage the expiration dates and best-before dates of ingredients. For example, when a user scans the QR code of ingredients purchased at a supermarket, the information is registered on the platform, and the generation AI manages the expiration dates and best-before dates. It can also suggest dishes that use ingredients that are close to their expiration date. It can also monitor the storage conditions of ingredients and suggest optimal storage methods.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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).

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0114] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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."

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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]

[0162] 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 recipe generator that generates personalized recipes based on user preferences and ingredient availability; an advice providing unit that provides advice according to the progress of cooking based on the recipe created by the recipe creating unit; a combination suggestion unit that suggests optimal combinations of ingredients based on ingredients that the user has; An expiration date management unit that manages the expiration dates and expiry dates of ingredients using QR codes at the time of purchase. A system characterized by:

2. The recipe generation unit Analyze the user's cooking history and generate recipes that reflect changes in preferences and attempts at new ingredients.

2. The system of claim 1.

3. The recipe generation unit Generates recipes according to the season and weather, and suggests dishes that incorporate a seasonal feel 2. The system of claim 1.

4. The recipe generation unit Suggest recipes that match the user's current mood 2. The system of claim 1.

5. The recipe generation unit Generates recipes that take into consideration the user's health condition and nutritional balance, and supports health management 2. The system of claim 1.

Images