System

Abstract

An object of a system according to an embodiment is to utilize image recognition of ingredients and seasonings to propose an optimal dish to a user and give advice by voice during cooking.SOLUTION: A system includes an analysis unit, a proposal unit, a provision unit, and an advice unit. The analysis unit analyzes an image of an ingredient or a seasoning. The proposal unit proposes a dish on the basis of the ingredients or seasoning analyzed by the analysis unit. The providing unit provides the cooking procedure or recipe proposed by the proposal unit. The advice unit gives advice by voice during cooking.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 does not adequately utilize image recognition of ingredients and seasonings to suggest dishes or provide voice advice during cooking, so there is room for improvement.

[0005] The system according to the embodiment aims to utilize image recognition of ingredients and seasonings to suggest the best dish for the user and provide audio advice during cooking. [Means for solving the problem]

[0006] The system according to the embodiment includes an analysis unit, a suggestion unit, a provision unit, and an advice unit. The analysis unit analyzes images of ingredients or seasonings. The suggestion unit suggests dishes based on the ingredients or seasonings analyzed by the analysis unit. The provision unit provides cooking procedures or recipes suggested by the suggestion unit. The advice unit provides advice by voice while cooking. [Effects of the Invention]

[0007] The system according to the embodiment utilizes image recognition of ingredients and seasonings to suggest the best dish for the user and provide audio advice while cooking. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A cooking assistance system according to an embodiment of the present invention analyzes images of ingredients and seasonings, recommends dishes to the user, and provides audio advice during cooking. The cooking assistance system analyzes images of ingredients and seasonings, identifies them using a generation AI, and recommends dishes to the user. Furthermore, the cooking assistance system provides cooking steps and recipes and provides audio advice during cooking. For example, a user inputs images of ingredients and seasonings into the cooking assistance system. For example, the user takes a photo of vegetables and seasonings in the refrigerator and uploads it to the system. The image is analyzed by the generation AI to identify the ingredients and seasonings. The cooking assistance system then recommends dishes based on the identified ingredients and seasonings. For example, if the generation AI recognizes tomatoes, cheese, and basil in the refrigerator, the generation AI suggests a pasta dish using these ingredients. These suggestions may be customized based on the user's preferences and past cooking history. Furthermore, when the user selects a suggested dish, the generation AI provides the cooking steps and recipe. This explanation is visually easy to understand using photographs and illustrations. For example, the instructions show the steps for cutting tomatoes and the time to boil pasta in detail. While cooking, the generation AI provides advice via voice. For example, it provides specific instructions such as "Next, cut the tomatoes" or "Boil the pasta for 10 minutes." This makes it possible to realize a cooking support service that can be used by a wide range of people, from beginners to advanced cooks. The cooking support system can recognize images of ingredients and seasonings, suggest recommended dishes to the user, and provide voice advice while cooking, making it possible to provide a cooking support service that can be used by a wide range of people, from beginners to advanced cooks. For example, by simply inputting images of ingredients and seasonings, users can easily find recommended dishes and receive support while cooking.

[0029] A cooking assistance system according to an embodiment includes an analysis unit, a suggestion unit, a providing unit, and an advice unit. The analysis unit analyzes images of ingredients or seasonings. The images of ingredients or seasonings include, but are not limited to, still images, videos, and resolutions. The analysis unit analyzes and identifies the characteristics of the ingredients or seasonings using, for example, an image recognition algorithm. The analysis unit can also analyze the characteristics of the ingredients or seasonings using a feature extraction method. For example, the analysis unit analyzes characteristics such as color, shape, and texture to identify the ingredients or seasonings. The suggestion unit suggests a dish based on the ingredients or seasonings analyzed by the analysis unit. The suggestion is based on, for example, but is not limited to, criteria such as the user's preferences, nutritional balance, and cooking time. For example, the suggestion unit can suggest a customized dish based on the user's preferences and past cooking history. The providing unit provides the cooking steps or recipe suggested by the suggestion unit. The suggestion is provided in, for example, but is not limited to, a format such as text, images, or videos. For example, the providing unit provides cooking steps and recipes in a visually easy-to-understand manner using photographs and illustrations. The advising unit gives advice by voice while cooking. The audio advice is given based on, for example, points to note while cooking, instructions for the next step, and other content and timing, but is not limited to such examples. For example, the advising unit gives specific instructions by voice, such as "Next, please cut the tomatoes" or "Boil the pasta for 10 minutes." As a result, the cooking assistance system according to the embodiment performs image recognition of ingredients and seasonings, suggests recommended dishes to the user, and gives audio advice while cooking, thereby providing a cooking assistance service that can be used by a wide range of people, from beginners to advanced cooks.

[0030] The cooking assistance system includes a history storage unit that stores a user's preferences and past history. The history storage unit stores the user's preferences and past history. The preferences and past history include, but are not limited to, past cooking history and the user's taste preferences. For example, the history storage unit stores information about dishes the user has made in the past and the ingredients used. The history storage unit can also store the user's taste preferences and allergy information. For example, the history storage unit stores the types of dishes the user likes and their seasoning tendencies. By storing the user's preferences and past history, the accuracy of suggestions can be improved. Some or all of the above-described processing in the history storage unit may be performed using, or without, a generation AI. For example, the history storage unit can input the user's past cooking history into the generation AI and have the generation AI manage the stored data.

[0031] The cooking assistance system includes a customization unit that customizes suggestions based on data stored by the history storage unit. The customization unit customizes suggestions based on the data stored by the history storage unit. Customization of suggestions is performed based on criteria such as, for example, the user's past selections and current situation, but is not limited to these examples. For example, the customization unit suggests optimal dishes based on the user's past cooking history and taste preferences. The customization unit can also customize suggestions based on the user's current health condition and dietary restrictions. For example, the customization unit suggests nutritionally balanced dishes based on the user's health condition. By customizing suggestions based on the stored data, optimal suggestions can be made to the user. Some or all of the above-described processing in the customization unit may be performed using, or without, a generation AI. For example, the customization unit may input data stored by the history storage unit into the generation AI and cause the generation AI to customize the suggestions.

[0032] The advice unit can provide specific audio advice. The advice unit provides specific audio advice, such as important points to note during cooking or instructions for the next step. For example, the advice unit provides specific audio instructions such as "Next, cut the tomatoes" or "Boil the pasta for 10 minutes." The advice unit can also provide important points to note during cooking by audio. For example, the advice unit provides advice such as "Pay attention to the heat" or "Stir carefully to prevent burning." By providing specific audio advice, the user can receive appropriate support while cooking. Some or all of the above-described processing in the advice unit may be performed using, or without, a generation AI. For example, the advice unit can input the cooking situation into the generation AI and cause the generation AI to generate specific audio advice.

[0033] The analysis unit can analyze and identify the characteristics of ingredients and seasonings. The analysis unit can analyze and identify, for example, the color, shape, texture, and other characteristics of ingredients and seasonings. For example, the analysis unit can analyze and identify the characteristics of ingredients and seasonings using an image recognition algorithm. The analysis unit can also analyze the characteristics of ingredients and seasonings using a feature extraction method. For example, the analysis unit can analyze and identify the color and shape of ingredients. The analysis unit can also analyze and identify the texture and packaging characteristics of seasonings. This enables accurate dish suggestions by analyzing and identifying the characteristics of ingredients and seasonings. Some or all of the above-described processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input image data of ingredients and seasonings into the generation AI and have the generation AI analyze and identify the characteristics.

[0034] The providing unit can provide cooking steps or recipes in a visually easy-to-understand manner using photographs or illustrations. The providing unit, for example, provides cooking steps or recipes in a visually easy-to-understand manner using photographs or illustrations. For example, the providing unit uses photographs or illustrations to show in detail the steps for cutting tomatoes or the time it takes to boil pasta. The providing unit can also visually explain cooking steps using videos. For example, the providing unit shows each cooking step in a video and provides it in a format that is visually easy for the user to understand. This allows the user to smoothly proceed with cooking by providing visually easy-to-understand steps and recipes. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the providing unit can input data on cooking steps or recipes into the generation AI and have the generation AI provide the data in a visually easy-to-understand format.

[0035] The analysis unit can analyze the freshness of ingredients and seasonings and suggest optimal dishes based on the freshness. The analysis unit, for example, analyzes the color and shape of ingredients to evaluate freshness. For example, the analysis unit has the generation AI analyze changes in the color and shape of ingredients to evaluate freshness. The analysis unit can also analyze the expiration date of seasonings to evaluate freshness. For example, the analysis unit has the generation AI analyze the expiration date printed on the seasoning package to evaluate freshness. The analysis unit can also analyze the smell of ingredients to evaluate freshness. For example, the analysis unit has the generation AI analyze the smell of ingredients to evaluate freshness. This allows optimal dishes to be suggested based on the freshness of ingredients and seasonings, thereby providing more delicious dishes. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input freshness data of ingredients and seasonings into the generation AI and have the generation AI evaluate freshness and suggest dishes.

[0036] The analysis unit can analyze the nutritional value of ingredients and seasonings and suggest dishes based on their nutritional balance. The analysis unit, for example, analyzes the nutritional components of ingredients and evaluates their nutritional balance. For example, the analysis unit causes the generation AI to analyze the nutritional components of ingredients and evaluate their nutritional balance. The analysis unit can also analyze the nutritional components of seasonings and evaluate their nutritional balance. For example, the analysis unit causes the generation AI to analyze the nutritional components of seasonings and evaluate their nutritional balance. The analysis unit can also analyze the calories of ingredients and evaluate their nutritional balance. For example, the analysis unit causes the generation AI to analyze the calories of ingredients and evaluate their nutritional balance. This makes it possible to suggest dishes based on their nutritional balance, thereby providing a healthy diet. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input nutritional value data of ingredients and seasonings into the generation AI and cause the generation AI to evaluate the nutritional balance and suggest dishes.

[0037] The analysis unit can analyze the origin information of ingredients and seasonings and suggest dishes based on the origin. The analysis unit, for example, analyzes the origin information of ingredients and suggests dishes unique to the region. For example, the analysis unit causes the generation AI to analyze the origin information of ingredients and suggest dishes unique to the region. The analysis unit can also analyze the origin information of seasonings and suggest dishes unique to the region. For example, the analysis unit causes the generation AI to analyze the origin information of seasonings and suggest dishes unique to the region. The analysis unit can also analyze the origin information of ingredients and suggest dishes based on the origin. For example, the analysis unit causes the generation AI to analyze the origin information of ingredients and suggest dishes based on the origin. In this way, dishes unique to the region can be provided by suggesting dishes based on the origin. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the origin information of ingredients and seasonings to the generation AI and cause the generation AI to suggest dishes based on the origin.

[0038] The analysis unit can analyze allergen information of ingredients and seasonings and suggest dishes that do not contain allergens. The analysis unit, for example, analyzes allergen information of ingredients and suggests dishes that do not contain allergens. For example, the analysis unit uses a generation AI to analyze allergen information of ingredients and suggest dishes that do not contain allergens. The analysis unit can also analyze allergen information of seasonings and suggest dishes that do not contain allergens. For example, the analysis unit uses a generation AI to analyze allergen information of seasonings and suggest dishes that do not contain allergens. The analysis unit can also analyze allergen information of ingredients and suggest dishes that do not contain allergens. For example, the analysis unit uses a generation AI to analyze allergen information of ingredients and suggest dishes that do not contain allergens. In this way, by suggesting dishes that do not contain allergens, it is possible to provide dishes that are safe for users with allergies. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input allergen information about ingredients and seasonings into the generation AI and have the generation AI suggest dishes that do not contain allergens.

[0039] The analysis unit can analyze storage methods for ingredients and seasonings and suggest dishes based on their storage conditions. The analysis unit, for example, analyzes storage methods for ingredients and suggests dishes based on their storage conditions. For example, the analysis unit allows the generation AI to analyze storage methods for ingredients and suggest dishes based on their storage conditions. The analysis unit can also analyze storage methods for seasonings and suggest dishes based on their storage conditions. For example, the analysis unit allows the generation AI to analyze storage methods for seasonings and suggest dishes based on their storage conditions. The analysis unit can also analyze storage methods for ingredients and suggest dishes based on their storage conditions. For example, the analysis unit allows the generation AI to analyze storage methods for ingredients and suggest dishes based on their storage conditions. In this way, by suggesting dishes based on their storage conditions, ingredients and seasonings can be used without waste. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input storage methods for ingredients and seasonings into the generation AI and cause the generation AI to suggest dishes based on their storage conditions.

[0040] The analysis unit can analyze price information on ingredients and seasonings and suggest dishes with high cost performance. The analysis unit, for example, analyzes price information on ingredients and suggests dishes with high cost performance. For example, the analysis unit allows the generation AI to analyze price information on ingredients and suggest dishes with high cost performance. The analysis unit can also analyze price information on seasonings and suggest dishes with high cost performance. For example, the analysis unit allows the generation AI to analyze price information on seasonings and suggest dishes with high cost performance. The analysis unit can also analyze price information on ingredients and suggest dishes with high cost performance. For example, the analysis unit allows the generation AI to analyze price information on ingredients and suggest dishes with high cost performance. In this way, economical dishes can be provided by suggesting dishes with high cost performance. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input price information on ingredients and seasonings to the generation AI and cause the generation AI to suggest dishes with high cost performance.

[0041] The suggestion unit can suggest optimal dishes based on the user's health condition. The suggestion unit, for example, analyzes the user's health condition and suggests optimal dishes. For example, the suggestion unit uses a generation AI to analyze the user's health condition and suggest optimal dishes. The suggestion unit can also suggest optimal dishes based on the user's health condition. For example, the suggestion unit uses a generation AI to analyze the user's health condition and suggest optimal dishes. The suggestion unit can also suggest optimal dishes based on the user's health condition. For example, the suggestion unit uses a generation AI to analyze the user's health condition and suggest optimal dishes. In this way, a healthy diet can be provided by suggesting optimal dishes based on the user's health condition. Some or all of the above-described processing in the suggestion unit may be performed using, or without, the generation AI. For example, the suggestion unit can input the user's health condition data into the generation AI and cause the generation AI to suggest optimal dishes.

[0042] The suggestion unit can suggest dishes based on the user's dietary restrictions. The suggestion unit, for example, analyzes the user's dietary restrictions and suggests optimal dishes. For example, the suggestion unit uses a generation AI to analyze the user's dietary restrictions and suggest optimal dishes. The suggestion unit can also suggest dishes based on the user's dietary restrictions. For example, the suggestion unit uses a generation AI to analyze the user's dietary restrictions and suggest optimal dishes. The suggestion unit can also suggest dishes based on the user's dietary restrictions. For example, the suggestion unit uses a generation AI to analyze the user's dietary restrictions and suggest optimal dishes. In this way, by suggesting dishes based on the user's dietary restrictions, a meal suitable for the user can be provided. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, the generation AI. For example, the suggestion unit can input the user's dietary restriction data into the generation AI and cause the generation AI to suggest optimal dishes.

[0043] The suggestion unit can improve the accuracy of suggestions by referring to the user's past cooking history. The suggestion unit, for example, analyzes the user's past cooking history and suggests the optimal dish. For example, the suggestion unit uses a generation AI to analyze the user's past cooking history and suggest the optimal dish. The suggestion unit can also improve the accuracy of suggestions by referring to the user's past cooking history. For example, the suggestion unit uses a generation AI to analyze the user's past cooking history and suggest the optimal dish. The suggestion unit can also improve the accuracy of suggestions by referring to the user's past cooking history. For example, the suggestion unit uses a generation AI to analyze the user's past cooking history and suggest the optimal dish. In this way, the accuracy of suggestions is improved by referring to the user's past cooking history. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, the generation AI. For example, the suggestion unit can input the user's past cooking history data into the generation AI and cause the generation AI to improve the accuracy of suggestions.

[0044] The suggestion unit can suggest dishes based on the user's ingredient inventory. The suggestion unit, for example, analyzes the user's ingredient inventory and suggests the optimal dish. For example, the suggestion unit uses a generation AI to analyze the user's ingredient inventory and suggest the optimal dish. The suggestion unit can also suggest dishes based on the user's ingredient inventory. For example, the suggestion unit uses a generation AI to analyze the user's ingredient inventory and suggest the optimal dish. The suggestion unit can also suggest dishes based on the user's ingredient inventory. For example, the suggestion unit uses a generation AI to analyze the user's ingredient inventory and suggest the optimal dish. In this way, by suggesting dishes based on the user's ingredient inventory, ingredients can be used without waste. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the suggestion unit can input the user's ingredient inventory data into the generation AI and cause the generation AI to suggest the optimal dish.

[0045] The suggestion unit can suggest dishes based on the user's mealtimes. The suggestion unit, for example, analyzes the user's mealtimes and suggests optimal dishes. For example, the suggestion unit uses a generation AI to analyze the user's mealtimes and suggest optimal dishes. The suggestion unit can also suggest dishes based on the user's mealtimes. For example, the suggestion unit uses a generation AI to analyze the user's mealtimes and suggest optimal dishes. The suggestion unit can also suggest dishes based on the user's mealtimes. For example, the suggestion unit uses a generation AI to analyze the user's mealtimes and suggest optimal dishes. In this way, meals can be provided at appropriate times by suggesting dishes based on the user's mealtimes. Some or all of the above-described processing in the suggestion unit may be performed using, or without, the generation AI. For example, the suggestion unit can input the user's mealtime period data into the generation AI and cause the generation AI to suggest optimal dishes.

[0046] The suggestion unit can suggest dishes based on the user's family composition. The suggestion unit, for example, analyzes the user's family composition and suggests optimal dishes. For example, the suggestion unit uses a generation AI to analyze the user's family composition and suggest optimal dishes. The suggestion unit can also suggest dishes based on the user's family composition. For example, the suggestion unit uses a generation AI to analyze the user's family composition and suggest optimal dishes. The suggestion unit can also suggest dishes based on the user's family composition. For example, the suggestion unit uses a generation AI to analyze the user's family composition and suggest optimal dishes. In this way, by suggesting dishes based on the user's family composition, it is possible to provide meals that satisfy the entire family. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, the generation AI. For example, the suggestion unit can input the user's family composition data into the generation AI and cause the generation AI to suggest optimal dishes.

[0047] The providing unit can provide detailed instructions according to the difficulty of the cooking. The providing unit, for example, analyzes the difficulty of the cooking and provides the detailed instructions. For example, the providing unit has the generation AI analyze the difficulty of the cooking and provide the detailed instructions. The providing unit can also provide detailed instructions according to the difficulty of the cooking. For example, the providing unit has the generation AI analyze the difficulty of the cooking and provide the detailed instructions. The providing unit can also provide detailed instructions according to the difficulty of the cooking. For example, the providing unit has the generation AI analyze the difficulty of the cooking and provide the detailed instructions. In this way, by providing detailed instructions according to the difficulty of the cooking, the user can proceed with the cooking appropriately. Some or all of the above-mentioned processing in the providing unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the providing unit can input cooking difficulty data into the generation AI and cause the generation AI to provide detailed instructions.

[0048] The providing unit can adjust the steps based on the cooking time of the dish. The providing unit, for example, analyzes the cooking time of the dish and adjusts the steps. For example, the providing unit has the generation AI analyze the cooking time of the dish and adjust the steps. The providing unit can also adjust the steps based on the cooking time of the dish. For example, the providing unit has the generation AI analyze the cooking time of the dish and adjust the steps. The providing unit can also adjust the steps based on the cooking time of the dish. For example, the providing unit has the generation AI analyze the cooking time of the dish and adjust the steps. In this way, by adjusting the steps based on the cooking time of the dish, cooking can be progressed efficiently. Some or all of the above-mentioned processing in the providing unit may be performed using the generation AI, for example, or may be performed without using the generation AI. For example, the providing unit can input cooking time data of the dish to the generation AI and have the generation AI adjust the steps.

[0049] The providing unit can customize the steps according to the type of cooking utensils used by the user. The providing unit, for example, analyzes the user's cooking utensils and customizes the steps. For example, the providing unit has a generation AI analyze the user's cooking utensils and customize the steps. The providing unit can also customize the steps according to the type of cooking utensils used by the user. For example, the providing unit has a generation AI analyze the user's cooking utensils and customize the steps. The providing unit can also customize the steps according to the type of cooking utensils used by the user. For example, the providing unit has a generation AI analyze the user's cooking utensils and customize the steps. This allows the user to make maximum use of the utensils they own by customizing the steps according to the type of cooking utensils used by the user. Some or all of the above-described processing by the providing unit may be performed using the generation AI, for example, or may be performed without using the generation AI. For example, the providing unit can input the user's cooking utensil data into the generation AI and cause the generation AI to customize the steps.

[0050] The providing unit can provide the information including nutritional information of the dish. For example, the providing unit analyzes the nutritional components of the dish and provides the nutritional information. For example, the providing unit has the generation AI analyze the nutritional components of the dish and provide the nutritional information. The providing unit can also analyze the calories of the dish and provide the nutritional information. For example, the providing unit has the generation AI analyze the calories of the dish and provide the nutritional information. The providing unit can also analyze the vitamin and mineral content of the dish and provide the nutritional information. For example, the providing unit has the generation AI analyze the vitamin and mineral content of the dish and provide the nutritional information. In this way, by providing the nutritional information of the dish, the user can select a healthy meal. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, the generation AI. For example, the providing unit can input nutritional information data of the dish to the generation AI and cause the generation AI to provide the nutritional information.

[0051] The providing unit can provide information including a storage method for the dish. The providing unit, for example, analyzes the storage method for the dish and provides the storage method. For example, the providing unit has the generation AI analyze the storage method for the dish and provide the storage method. The providing unit can also analyze the storage period for the dish and provide the storage method. For example, the providing unit has the generation AI analyze the storage period for the dish and provide the storage method. The providing unit can also analyze the storage temperature for the dish and provide the storage method. For example, the providing unit has the generation AI analyze the storage temperature for the dish and provide the storage method. In this way, by providing the storage method for the dish, the user can keep the dish fresh for a longer period of time. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, the generation AI. For example, the providing unit can input data on the storage method for the dish into the generation AI and have the generation AI provide the storage method.

[0052] The providing unit can provide information including a method for arranging a dish. The providing unit, for example, analyzes a method for arranging a dish and provides the method. For example, the providing unit has the generation AI analyze a method for arranging a dish and provide the method. The providing unit can also analyze examples of dish arrangements and provide the method. For example, the providing unit has the generation AI analyze examples of dish arrangements and provide the method. The providing unit can also analyze tips for arranging a dish and provide the method. For example, the providing unit has the generation AI analyze tips for arranging a dish and provide the method. By providing methods for arranging a dish, the user can enjoy cooking in a variety of ways. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, the generation AI. For example, the providing unit can input data on a method for arranging a dish into the generation AI and have the generation AI provide the method for arranging the dish.

[0053] The advice unit can adjust the level of detail of the advice according to the user's cooking skill. The advice unit, for example, analyzes the user's cooking skill and provides detailed advice. For example, the advice unit uses a generation AI to analyze the user's cooking skill and provide detailed advice. The advice unit can also adjust the level of detail of the advice according to the user's cooking skill. For example, the advice unit uses a generation AI to analyze the user's cooking skill and provide detailed advice. The advice unit can also adjust the level of detail of the advice according to the user's cooking skill. For example, the advice unit uses a generation AI to analyze the user's cooking skill and provide detailed advice. In this way, by adjusting the level of detail of the advice according to the user's cooking skill, appropriate advice can be provided to the user. Some or all of the above-described processing in the advice unit may be performed using, or without, the generation AI. For example, the advice unit can input the user's cooking skill data into the generation AI and cause the generation AI to adjust the level of detail of the advice.

[0054] The advice unit can provide timely advice based on the cooking progress. The advice unit, for example, analyzes the cooking progress and provides timely advice. For example, the advice unit uses a generation AI to analyze the cooking progress and provide timely advice. The advice unit can also provide timely advice based on the cooking progress. For example, the advice unit uses a generation AI to analyze the cooking progress and provide timely advice. The advice unit can also provide timely advice based on the cooking progress. For example, the advice unit uses a generation AI to analyze the cooking progress and provide timely advice. This allows the user to cook appropriately by providing timely advice based on the cooking progress. Some or all of the above-described processing in the advice unit may be performed using, or without, the generation AI. For example, the advice unit can input cooking progress data into the generation AI and cause the generation AI to provide timely advice.

[0055] The advice unit can customize advice based on the user's cooking environment. The advice unit, for example, analyzes the user's cooking environment and provides optimal advice. For example, the advice unit uses a generation AI to analyze the user's cooking environment and provide optimal advice. The advice unit can also customize advice based on the user's cooking environment. For example, the advice unit uses a generation AI to analyze the user's cooking environment and provide optimal advice. The advice unit can also customize advice based on the user's cooking environment. For example, the advice unit uses a generation AI to analyze the user's cooking environment and provide optimal advice. In this way, by customizing advice based on the user's cooking environment, optimal advice for the user can be provided. Some or all of the above-described processing in the advice unit may be performed using, or without, the generation AI. For example, the advice unit can input the user's cooking environment data into the generation AI and cause the generation AI to customize the advice.

[0056] The advice unit can provide advice regarding the safety of cooking. For example, the advice unit analyzes the safety of cooking and provides safety advice. For example, the advice unit uses a generation AI to analyze the safety of cooking and provide safety advice. The advice unit can also provide advice regarding the safety of cooking. For example, the advice unit uses a generation AI to analyze the safety of cooking and provide safety advice. The advice unit can also provide advice regarding the safety of cooking. For example, the advice unit uses a generation AI to analyze the safety of cooking and provide safety advice. By providing advice regarding the safety of cooking, the user can proceed with cooking safely. Some or all of the above-described processing in the advice unit may be performed using, or without, the generation AI. For example, the advice unit can input safety data about cooking to the generation AI and cause the generation AI to provide safety advice.

[0057] The advice unit can provide advice on how to present food. The advice unit, for example, analyzes the presentation method of food and provides optimal advice. For example, the advice unit uses the generation AI to analyze the presentation method of food and provides optimal advice. The advice unit can also provide advice on how to present food. For example, the advice unit uses the generation AI to analyze the presentation method of food and provides optimal advice. The advice unit can also provide advice on how to present food. For example, the advice unit uses the generation AI to analyze the presentation method of food and provides optimal advice. By providing advice on how to present food, the user can achieve beautiful presentation. Some or all of the above-described processing in the advice unit may be performed using, or without, the generation AI. For example, the advice unit can input food presentation method data into the generation AI and cause the generation AI to provide optimal advice.

[0058] The advice unit can provide advice regarding clearing up after cooking. The advice unit, for example, analyzes a method for clearing up after cooking and provides optimal advice. For example, the advice unit uses a generation AI to analyze a method for clearing up after cooking and provides optimal advice. The advice unit can also provide advice regarding clearing up after cooking. For example, the advice unit uses a generation AI to analyze a method for clearing up after cooking and provides optimal advice. The advice unit can also provide advice regarding clearing up after cooking. For example, the advice unit uses a generation AI to analyze a method for clearing up after cooking and provides optimal advice. By providing advice regarding clearing up after cooking, the user can clean up efficiently. Some or all of the above-described processing in the advice unit may be performed using, or without, the generation AI. For example, the advice unit can input data regarding a method for clearing up after cooking into the generation AI and cause the generation AI to provide optimal advice.

[0059] The history storage unit can manage the stored data based on the user's privacy settings when saving the history. The history storage unit, for example, analyzes the user's privacy settings and provides the optimal storage method. For example, the generation AI analyzes the user's privacy settings in the history storage unit and provides the optimal storage method. The history storage unit can also manage the stored data based on the user's privacy settings when saving the history. For example, the generation AI analyzes the user's privacy settings in the history storage unit and provides the optimal storage method. The history storage unit can also manage the stored data based on the user's privacy settings when saving the history. For example, the generation AI analyzes the user's privacy settings in the history storage unit and provides the optimal storage method. This makes it possible to protect the user's privacy by managing the stored data based on the user's privacy settings. Some or all of the above-described processing in the history storage unit may be performed using, or without, the generation AI. For example, the history storage unit can input the user's privacy setting data into the generation AI and have the generation AI manage the stored data.

[0060] The history storage unit can compress the stored data according to the user's data capacity when saving the history. The history storage unit, for example, analyzes the user's data capacity and provides the optimal compression method. For example, the history storage unit has a generation AI analyze the user's data capacity and provide the optimal compression method. The history storage unit can also compress the stored data according to the user's data capacity when saving the history. For example, the history storage unit has a generation AI analyze the user's data capacity and provide the optimal compression method. The history storage unit can also compress the stored data according to the user's data capacity when saving the history. For example, the history storage unit has a generation AI analyze the user's data capacity and provide the optimal compression method. This enables efficient data management by compressing the stored data according to the user's data capacity. Some or all of the above-mentioned processing in the history storage unit may be performed using, or without, the generation AI. For example, the history storage unit can input user's data capacity data to the generation AI and have the generation AI compress the stored data.

[0061] The history storage unit can select a storage method by taking into account the user's device information when saving the history. The history storage unit, for example, analyzes the user's device information and provides the optimal storage method. For example, the history storage unit has a generation AI analyze the user's device information and provide the optimal storage method. The history storage unit can also select a storage method by taking into account the user's device information when saving the history. For example, the history storage unit has a generation AI analyze the user's device information and provide the optimal storage method. The history storage unit can also select a storage method by taking into account the user's device information when saving the history. For example, the history storage unit has a generation AI analyze the user's device information and provide the optimal storage method. In this way, the optimal storage method can be provided by selecting a storage method by taking into account the user's device information. Some or all of the above-described processing in the history storage unit can be performed using, or without, the generation AI. For example, the history storage unit can input the user's device information data into the generation AI and have the generation AI select a storage method.

[0062] The history storage unit can use the user's cloud storage to save data when saving history. The history storage unit, for example, analyzes the user's cloud storage and provides the optimal storage method. For example, the history storage unit has a generation AI analyze the user's cloud storage and provide the optimal storage method. The history storage unit can also use the user's cloud storage to save data when saving history. For example, the history storage unit has a generation AI analyze the user's cloud storage and provide the optimal storage method. The history storage unit can also use the user's cloud storage to save data when saving history. For example, the history storage unit has a generation AI analyze the user's cloud storage and provide the optimal storage method. This enables efficient data management by saving data using the user's cloud storage. Some or all of the above-mentioned processing in the history storage unit can be performed using, or without, the generation AI. For example, the history storage unit can input the user's cloud storage data into the generation AI and have the generation AI select a storage method.

[0063] The customization unit can analyze the user's past history during customization to provide optimal customization. The customization unit, for example, analyzes the user's past history and provides optimal customization. For example, the customization unit has a generation AI analyze the user's past history and provide optimal customization. The customization unit can also analyze the user's past history during customization to provide optimal customization. For example, the customization unit has a generation AI analyze the user's past history and provide optimal customization. The customization unit can also analyze the user's past history during customization to provide optimal customization. For example, the customization unit has a generation AI analyze the user's past history and provide optimal customization. In this way, by analyzing the user's past history and providing optimal customization, it is possible to provide optimal suggestions for the user. Some or all of the above-described processing in the customization unit may be performed using, or without, the generation AI. For example, the customization unit can input the user's past history data into the generation AI and cause the generation AI to provide optimal customization.

[0064] The customization unit can perform customization based on the user's current health condition at the time of customization. The customization unit, for example, analyzes the user's health condition and provides optimal customization. For example, the customization unit uses a generation AI to analyze the user's health condition and provide optimal customization. The customization unit can also perform customization based on the user's current health condition at the time of customization. For example, the customization unit uses a generation AI to analyze the user's health condition and provide optimal customization. The customization unit can also perform customization based on the user's current health condition at the time of customization. For example, the customization unit uses a generation AI to analyze the user's health condition and provide optimal customization. This makes it possible to provide optimal suggestions for the user by customizing based on the user's current health condition. Some or all of the above-described processing in the customization unit can be performed using, or without, the generation AI. For example, the customization unit can input the user's health condition data into the generation AI and cause the generation AI to provide optimal customization.

[0065] The customization unit can perform customization taking into account the user's device information during customization. The customization unit, for example, analyzes the user's device information and provides optimal customization. For example, the customization unit uses a generation AI to analyze the user's device information and provide optimal customization. The customization unit can also perform customization taking into account the user's device information during customization. For example, the customization unit uses a generation AI to analyze the user's device information and provide optimal customization. The customization unit can also perform customization taking into account the user's device information during customization. For example, the customization unit uses a generation AI to analyze the user's device information and provide optimal customization. In this way, by performing customization taking into account the user's device information, it is possible to provide optimal suggestions for the user. Some or all of the above-described processing in the customization unit may be performed using, or without, the generation AI. For example, the customization unit can input the user's device information data into the generation AI and cause the generation AI to provide optimal customization.

[0066] The customization unit can perform customization by reflecting user feedback during customization. The customization unit, for example, analyzes user feedback and provides optimal customization. For example, the customization unit has a generation AI analyze user feedback and provide optimal customization. The customization unit can also perform customization by reflecting user feedback during customization. For example, the customization unit has a generation AI analyze user feedback and provide optimal customization. The customization unit can also perform customization by reflecting user feedback during customization. For example, the customization unit has a generation AI analyze user feedback and provide optimal customization. In this way, by performing customization by reflecting user feedback, it is possible to provide optimal suggestions for the user. Some or all of the above-described processing in the customization unit may be performed using, or without, the generation AI. For example, the customization unit can input user feedback data into the generation AI and cause the generation AI to provide optimal customization.

[0067] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0068] The analysis unit can not only analyze images of ingredients and seasonings, but also analyze the user's cooking environment. For example, the analysis unit can analyze the layout of the user's kitchen and the available cooking utensils to suggest the optimal cooking method. This allows the user to know the cooking method that is best suited to their environment and cook efficiently. The analysis unit can also analyze the user's cooking skills and provide advice based on their skills. For example, it can suggest basic cooking methods for beginners and advanced techniques for advanced cooks. Furthermore, the analysis unit can analyze the user's health condition and suggest healthy dishes. For example, it can suggest dishes that use ingredients that are rich in specific nutrients.

[0069] The analysis unit can not only analyze and identify the characteristics of ingredients and seasonings, but also analyze the freshness of the ingredients and seasonings and suggest optimal dishes based on the freshness. For example, the analysis unit can analyze the color and shape of ingredients to evaluate freshness. It can also analyze the expiration date of seasonings to evaluate freshness. This makes it possible to suggest optimal dishes based on the freshness of ingredients and seasonings, thereby providing more delicious dishes. The analysis unit can also analyze the nutritional value of ingredients and seasonings to suggest dishes based on nutritional balance. For example, it can suggest dishes using ingredients that are rich in specific nutrients. Furthermore, the analysis unit can analyze information on the origin of ingredients and seasonings to suggest dishes based on the origin. For example, it can suggest dishes unique to a region.

[0070] The providing unit can not only provide cooking steps and recipes that are visually easy to understand using photographs and illustrations, but also provide detailed steps according to the difficulty level of the cooking. For example, detailed steps can be provided for beginners and simple steps for advanced cooks. This makes it possible to provide optimal support according to the user's skill level. The providing unit can also adjust the steps based on the cooking time of the dish. For example, it can prioritize providing steps that allow cooking in a short time. Furthermore, the providing unit can customize the steps according to the type of cooking utensils used by the user. For example, it can provide steps that use specific cooking utensils.

[0071] The analysis unit can analyze the freshness of ingredients and seasonings and suggest optimal dishes based on the freshness, as well as analyze price information on ingredients and seasonings to suggest dishes with high cost performance. For example, the analysis unit can analyze price information on ingredients and suggest dishes with high cost performance. It can also analyze price information on seasonings and suggest dishes with high cost performance. This makes it possible to provide economical dishes. The analysis unit can also analyze the nutritional value of ingredients and seasonings and suggest dishes based on nutritional balance. For example, it can suggest dishes that use ingredients that are high in specific nutrients. Furthermore, the analysis unit can analyze information on the origin of ingredients and seasonings and suggest dishes based on the origin. For example, it can suggest dishes that are unique to a region.

[0072] The analysis unit can not only analyze the nutritional value of ingredients and seasonings and suggest dishes based on nutritional balance, but can also analyze allergen information on ingredients and seasonings and suggest dishes that do not contain allergens. For example, the analysis unit can analyze allergen information on ingredients and suggest dishes that do not contain allergens. It can also analyze allergen information on seasonings and suggest dishes that do not contain allergens. This makes it possible to provide safe dishes to users with allergies. The analysis unit can also analyze storage methods for ingredients and seasonings and suggest dishes based on their storage conditions. For example, it can suggest dishes that use ingredients that require refrigeration. Furthermore, the analysis unit can analyze the expiration dates of ingredients and seasonings and suggest dishes based on the expiration dates. For example, it can suggest dishes that prioritize ingredients that are close to their expiration date.

[0073] The analysis unit can not only analyze the origin information of ingredients and seasonings and suggest dishes based on their origin, but can also analyze the storage method of ingredients and seasonings and suggest dishes based on their storage conditions. For example, the analysis unit can analyze the storage method of ingredients and suggest dishes based on their storage conditions. It can also analyze the storage method of seasonings and suggest dishes based on their storage conditions. This allows ingredients and seasonings to be used without waste. The analysis unit can also analyze price information of ingredients and seasonings and suggest dishes with high cost performance. For example, it can analyze price information of ingredients and suggest dishes with high cost performance. Furthermore, the analysis unit can analyze the nutritional value of ingredients and seasonings and suggest dishes based on nutritional balance. For example, it can suggest dishes using ingredients that are high in specific nutrients.

[0074] The processing flow of the first embodiment will be briefly explained below.

[0075] Step 1: The analysis unit analyzes images of ingredients or seasonings. The images of ingredients or seasonings include still images, videos, and resolutions. The analysis unit uses image recognition algorithms and feature extraction methods to analyze features such as color, shape, and texture to identify the ingredients or seasonings. Step 2: The suggestion unit suggests dishes based on the ingredients or seasonings analyzed by the analysis unit. The suggestions are made based on criteria such as the user's preferences, nutritional balance, and cooking time. The suggestion unit can suggest customized dishes based on the user's preferences and past history. Step 3: The provision unit provides the cooking steps or recipes proposed by the suggestion unit. The provision is done in the form of text, images, videos, etc. The provision unit uses photos and illustrations to provide the cooking steps or recipes in a visually easy-to-understand manner. Step 4: The advice module provides audio advice while cooking. Audio advice is based on the content and timing of cooking instructions, such as things to be careful of while cooking and instructions for the next step. For example, specific instructions such as "Next, cut the tomatoes" or "Boil the pasta for 10 minutes" are provided.

[0076] (Example 2) A cooking assistance system according to an embodiment of the present invention analyzes images of ingredients and seasonings, recommends dishes to the user, and provides audio advice during cooking. The cooking assistance system analyzes images of ingredients and seasonings, identifies them using a generation AI, and recommends dishes to the user. Furthermore, the cooking assistance system provides cooking steps and recipes and provides audio advice during cooking. For example, a user inputs images of ingredients and seasonings into the cooking assistance system. For example, the user takes a photo of vegetables and seasonings in the refrigerator and uploads it to the system. The image is analyzed by the generation AI to identify the ingredients and seasonings. The cooking assistance system then recommends dishes based on the identified ingredients and seasonings. For example, if the generation AI recognizes tomatoes, cheese, and basil in the refrigerator, the generation AI suggests a pasta dish using these ingredients. These suggestions may be customized based on the user's preferences and past cooking history. Furthermore, when the user selects a suggested dish, the generation AI provides the cooking steps and recipe. This explanation is visually easy to understand using photographs and illustrations. For example, the instructions show the steps for cutting tomatoes and the time to boil pasta in detail. While cooking, the generation AI provides advice via voice. For example, it provides specific instructions such as "Next, cut the tomatoes" or "Boil the pasta for 10 minutes." This makes it possible to realize a cooking support service that can be used by a wide range of people, from beginners to advanced cooks. The cooking support system can recognize images of ingredients and seasonings, suggest recommended dishes to the user, and provide voice advice while cooking, making it possible to provide a cooking support service that can be used by a wide range of people, from beginners to advanced cooks. For example, by simply inputting images of ingredients and seasonings, users can easily find recommended dishes and receive support while cooking.

[0077] A cooking assistance system according to an embodiment includes an analysis unit, a suggestion unit, a providing unit, and an advice unit. The analysis unit analyzes images of ingredients or seasonings. The images of ingredients or seasonings include, but are not limited to, still images, videos, and resolutions. The analysis unit analyzes and identifies the characteristics of the ingredients or seasonings using, for example, an image recognition algorithm. The analysis unit can also analyze the characteristics of the ingredients or seasonings using a feature extraction method. For example, the analysis unit analyzes characteristics such as color, shape, and texture to identify the ingredients or seasonings. The suggestion unit suggests a dish based on the ingredients or seasonings analyzed by the analysis unit. The suggestion is based on, for example, but is not limited to, criteria such as the user's preferences, nutritional balance, and cooking time. For example, the suggestion unit can suggest a customized dish based on the user's preferences and past cooking history. The providing unit provides the cooking steps or recipe suggested by the suggestion unit. The suggestion is provided in, for example, but is not limited to, a format such as text, images, or videos. For example, the providing unit provides cooking steps and recipes in a visually easy-to-understand manner using photographs and illustrations. The advising unit gives advice by voice while cooking. The audio advice is given based on, for example, points to note while cooking, instructions for the next step, and other content and timing, but is not limited to such examples. For example, the advising unit gives specific instructions by voice, such as "Next, please cut the tomatoes" or "Boil the pasta for 10 minutes." As a result, the cooking assistance system according to the embodiment performs image recognition of ingredients and seasonings, suggests recommended dishes to the user, and gives audio advice while cooking, thereby providing a cooking assistance service that can be used by a wide range of people, from beginners to advanced cooks.

[0078] The cooking assistance system includes a history storage unit that stores a user's preferences and past history. The history storage unit stores the user's preferences and past history. The preferences and past history include, but are not limited to, past cooking history and the user's taste preferences. For example, the history storage unit stores information about dishes the user has made in the past and the ingredients used. The history storage unit can also store the user's taste preferences and allergy information. For example, the history storage unit stores the types of dishes the user likes and their seasoning tendencies. By storing the user's preferences and past history, the accuracy of suggestions can be improved. Some or all of the above-described processing in the history storage unit may be performed using, or without, a generation AI. For example, the history storage unit can input the user's past cooking history into the generation AI and have the generation AI manage the stored data.

[0079] The cooking assistance system includes a customization unit that customizes suggestions based on data stored by the history storage unit. The customization unit customizes suggestions based on the data stored by the history storage unit. Customization of suggestions is performed based on criteria such as, for example, the user's past selections and current situation, but is not limited to these examples. For example, the customization unit suggests optimal dishes based on the user's past cooking history and taste preferences. The customization unit can also customize suggestions based on the user's current health condition and dietary restrictions. For example, the customization unit suggests nutritionally balanced dishes based on the user's health condition. By customizing suggestions based on the stored data, optimal suggestions can be made to the user. Some or all of the above-described processing in the customization unit may be performed using, or without, a generation AI. For example, the customization unit may input data stored by the history storage unit into the generation AI and cause the generation AI to customize the suggestions.

[0080] The advice unit can provide specific audio advice. The advice unit provides specific audio advice, such as important points to note during cooking or instructions for the next step. For example, the advice unit provides specific audio instructions such as "Next, cut the tomatoes" or "Boil the pasta for 10 minutes." The advice unit can also provide important points to note during cooking by audio. For example, the advice unit provides advice such as "Pay attention to the heat" or "Stir carefully to prevent burning." By providing specific audio advice, the user can receive appropriate support while cooking. Some or all of the above-described processing in the advice unit may be performed using, or without, a generation AI. For example, the advice unit can input the cooking situation into the generation AI and cause the generation AI to generate specific audio advice.

[0081] The analysis unit can analyze and identify the characteristics of ingredients and seasonings. The analysis unit can analyze and identify, for example, the color, shape, texture, and other characteristics of ingredients and seasonings. For example, the analysis unit can analyze and identify the characteristics of ingredients and seasonings using an image recognition algorithm. The analysis unit can also analyze the characteristics of ingredients and seasonings using a feature extraction method. For example, the analysis unit can analyze and identify the color and shape of ingredients. The analysis unit can also analyze and identify the texture and packaging characteristics of seasonings. This enables accurate dish suggestions by analyzing and identifying the characteristics of ingredients and seasonings. Some or all of the above-described processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input image data of ingredients and seasonings into the generation AI and have the generation AI analyze and identify the characteristics.

[0082] The providing unit can provide cooking steps or recipes in a visually easy-to-understand manner using photographs or illustrations. The providing unit, for example, provides cooking steps or recipes in a visually easy-to-understand manner using photographs or illustrations. For example, the providing unit uses photographs or illustrations to show in detail the steps for cutting tomatoes or the time it takes to boil pasta. The providing unit can also visually explain cooking steps using videos. For example, the providing unit shows each cooking step in a video and provides it in a format that is visually easy for the user to understand. This allows the user to smoothly proceed with cooking by providing visually easy-to-understand steps and recipes. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the providing unit can input data on cooking steps or recipes into the generation AI and have the generation AI provide the data in a visually easy-to-understand format.

[0083] The analysis unit can estimate the user's emotions and adjust the analysis accuracy of ingredients and seasonings based on the estimated user emotions. The analysis unit can estimate emotions using, for example, facial expression recognition or voice analysis of the user. For example, the analysis unit can analyze changes in the user's facial expressions and tone of voice to estimate emotions. The analysis unit can also adjust the analysis accuracy of ingredients and seasonings based on the user's emotions. For example, if the user is stressed, the analysis unit can cause the generation AI to increase analysis accuracy and reduce misrecognition. If the user is relaxed, the analysis unit can cause the generation AI to maintain normal analysis accuracy and prioritize processing speed. If the user is in a hurry, the analysis unit can increase analysis accuracy and quickly identify ingredients and seasonings. This allows for more accurate analysis results by adjusting the analysis accuracy according to the user's emotions. Some or all of the above-described processing in the analysis unit can be performed using, or without, the generation AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust its analysis accuracy.

[0084] The analysis unit can analyze the freshness of ingredients and seasonings and suggest optimal dishes based on the freshness. The analysis unit, for example, analyzes the color and shape of ingredients to evaluate freshness. For example, the analysis unit has the generation AI analyze changes in the color and shape of ingredients to evaluate freshness. The analysis unit can also analyze the expiration date of seasonings to evaluate freshness. For example, the analysis unit has the generation AI analyze the expiration date printed on the seasoning package to evaluate freshness. The analysis unit can also analyze the smell of ingredients to evaluate freshness. For example, the analysis unit has the generation AI analyze the smell of ingredients to evaluate freshness. This allows optimal dishes to be suggested based on the freshness of ingredients and seasonings, thereby providing more delicious dishes. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input freshness data of ingredients and seasonings into the generation AI and have the generation AI evaluate freshness and suggest dishes.

[0085] The analysis unit can analyze the nutritional value of ingredients and seasonings and suggest dishes based on their nutritional balance. The analysis unit, for example, analyzes the nutritional components of ingredients and evaluates their nutritional balance. For example, the analysis unit causes the generation AI to analyze the nutritional components of ingredients and evaluate their nutritional balance. The analysis unit can also analyze the nutritional components of seasonings and evaluate their nutritional balance. For example, the analysis unit causes the generation AI to analyze the nutritional components of seasonings and evaluate their nutritional balance. The analysis unit can also analyze the calories of ingredients and evaluate their nutritional balance. For example, the analysis unit causes the generation AI to analyze the calories of ingredients and evaluate their nutritional balance. This makes it possible to suggest dishes based on their nutritional balance, thereby providing a healthy diet. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input nutritional value data of ingredients and seasonings into the generation AI and cause the generation AI to evaluate the nutritional balance and suggest dishes.

[0086] The analysis unit can analyze the origin information of ingredients and seasonings and suggest dishes based on the origin. The analysis unit, for example, analyzes the origin information of ingredients and suggests dishes unique to the region. For example, the analysis unit causes the generation AI to analyze the origin information of ingredients and suggest dishes unique to the region. The analysis unit can also analyze the origin information of seasonings and suggest dishes unique to the region. For example, the analysis unit causes the generation AI to analyze the origin information of seasonings and suggest dishes unique to the region. The analysis unit can also analyze the origin information of ingredients and suggest dishes based on the origin. For example, the analysis unit causes the generation AI to analyze the origin information of ingredients and suggest dishes based on the origin. In this way, dishes unique to the region can be provided by suggesting dishes based on the origin. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the origin information of ingredients and seasonings to the generation AI and cause the generation AI to suggest dishes based on the origin.

[0087] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. The analysis unit can estimate emotions using, for example, facial expression recognition or voice analysis of the user. For example, the analysis unit can analyze changes in the user's facial expressions and tone of voice to estimate emotions. The analysis unit can also adjust the display method of the analysis results based on the user's emotions. For example, if the user is stressed, the analysis unit can provide a simple display method. If the user is relaxed, the analysis unit can provide a detailed display method. If the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. In this way, by adjusting the display method of the analysis results according to the user's emotions, it is possible to provide a display that is easy for the user to view. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method.

[0088] The analysis unit can analyze allergen information of ingredients and seasonings and suggest dishes that do not contain allergens. The analysis unit, for example, analyzes allergen information of ingredients and suggests dishes that do not contain allergens. For example, the analysis unit uses a generation AI to analyze allergen information of ingredients and suggest dishes that do not contain allergens. The analysis unit can also analyze allergen information of seasonings and suggest dishes that do not contain allergens. For example, the analysis unit uses a generation AI to analyze allergen information of seasonings and suggest dishes that do not contain allergens. The analysis unit can also analyze allergen information of ingredients and suggest dishes that do not contain allergens. For example, the analysis unit uses a generation AI to analyze allergen information of ingredients and suggest dishes that do not contain allergens. In this way, by suggesting dishes that do not contain allergens, it is possible to provide dishes that are safe for users with allergies. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input allergen information about ingredients and seasonings into the generation AI and have the generation AI suggest dishes that do not contain allergens.

[0089] The analysis unit can analyze storage methods for ingredients and seasonings and suggest dishes based on their storage conditions. The analysis unit, for example, analyzes storage methods for ingredients and suggests dishes based on their storage conditions. For example, the analysis unit allows the generation AI to analyze storage methods for ingredients and suggest dishes based on their storage conditions. The analysis unit can also analyze storage methods for seasonings and suggest dishes based on their storage conditions. For example, the analysis unit allows the generation AI to analyze storage methods for seasonings and suggest dishes based on their storage conditions. The analysis unit can also analyze storage methods for ingredients and suggest dishes based on their storage conditions. For example, the analysis unit allows the generation AI to analyze storage methods for ingredients and suggest dishes based on their storage conditions. In this way, by suggesting dishes based on their storage conditions, ingredients and seasonings can be used without waste. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input storage methods for ingredients and seasonings into the generation AI and cause the generation AI to suggest dishes based on their storage conditions.

[0090] The analysis unit can analyze price information on ingredients and seasonings and suggest dishes with high cost performance. The analysis unit, for example, analyzes price information on ingredients and suggests dishes with high cost performance. For example, the analysis unit allows the generation AI to analyze price information on ingredients and suggest dishes with high cost performance. The analysis unit can also analyze price information on seasonings and suggest dishes with high cost performance. For example, the analysis unit allows the generation AI to analyze price information on seasonings and suggest dishes with high cost performance. The analysis unit can also analyze price information on ingredients and suggest dishes with high cost performance. For example, the analysis unit allows the generation AI to analyze price information on ingredients and suggest dishes with high cost performance. In this way, economical dishes can be provided by suggesting dishes with high cost performance. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input price information on ingredients and seasonings to the generation AI and cause the generation AI to suggest dishes with high cost performance.

[0091] The suggestion unit can estimate the user's emotions and adjust the way the suggestions are expressed based on the estimated user emotions. The suggestion unit can estimate the emotions using, for example, facial expression recognition or voice analysis of the user. For example, the suggestion unit can analyze changes in the user's facial expressions and tone of voice to estimate the emotions. The suggestion unit can also adjust the way the suggestions are expressed based on the user's emotions. For example, if the user is stressed, the suggestion unit can provide a simple way of expressing the user's emotions. If the user is relaxed, the suggestion unit can provide a detailed way of expressing the user's emotions. If the user is in a hurry, the suggestion unit can provide a way of expressing the user's emotions that focuses on the main points. In this way, by adjusting the way the suggestions are expressed based on the user's emotions, it is possible to provide suggestions that are easy for the user to understand. Some or all of the above-described processing in the suggestion unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the way the suggestions are expressed.

[0092] The suggestion unit can suggest optimal dishes based on the user's health condition. The suggestion unit, for example, analyzes the user's health condition and suggests optimal dishes. For example, the suggestion unit uses a generation AI to analyze the user's health condition and suggest optimal dishes. The suggestion unit can also suggest optimal dishes based on the user's health condition. For example, the suggestion unit uses a generation AI to analyze the user's health condition and suggest optimal dishes. The suggestion unit can also suggest optimal dishes based on the user's health condition. For example, the suggestion unit uses a generation AI to analyze the user's health condition and suggest optimal dishes. In this way, a healthy diet can be provided by suggesting optimal dishes based on the user's health condition. Some or all of the above-described processing in the suggestion unit may be performed using, or without, the generation AI. For example, the suggestion unit can input the user's health condition data into the generation AI and cause the generation AI to suggest optimal dishes.

[0093] The suggestion unit can suggest dishes based on the user's dietary restrictions. The suggestion unit, for example, analyzes the user's dietary restrictions and suggests optimal dishes. For example, the suggestion unit uses a generation AI to analyze the user's dietary restrictions and suggest optimal dishes. The suggestion unit can also suggest dishes based on the user's dietary restrictions. For example, the suggestion unit uses a generation AI to analyze the user's dietary restrictions and suggest optimal dishes. The suggestion unit can also suggest dishes based on the user's dietary restrictions. For example, the suggestion unit uses a generation AI to analyze the user's dietary restrictions and suggest optimal dishes. In this way, by suggesting dishes based on the user's dietary restrictions, a meal suitable for the user can be provided. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, the generation AI. For example, the suggestion unit can input the user's dietary restriction data into the generation AI and cause the generation AI to suggest optimal dishes.

[0094] The suggestion unit can improve the accuracy of suggestions by referring to the user's past cooking history. The suggestion unit, for example, analyzes the user's past cooking history and suggests the optimal dish. For example, the suggestion unit uses a generation AI to analyze the user's past cooking history and suggest the optimal dish. The suggestion unit can also improve the accuracy of suggestions by referring to the user's past cooking history. For example, the suggestion unit uses a generation AI to analyze the user's past cooking history and suggest the optimal dish. The suggestion unit can also improve the accuracy of suggestions by referring to the user's past cooking history. For example, the suggestion unit uses a generation AI to analyze the user's past cooking history and suggest the optimal dish. In this way, the accuracy of suggestions is improved by referring to the user's past cooking history. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, the generation AI. For example, the suggestion unit can input the user's past cooking history data into the generation AI and cause the generation AI to improve the accuracy of suggestions.

[0095] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated user emotions. The suggestion unit can estimate the emotions using, for example, facial expression recognition or voice analysis of the user. For example, the suggestion unit can analyze changes in the user's facial expressions and tone of voice to estimate the emotions. The suggestion unit can also adjust the length of the suggestions based on the user's emotions. For example, if the user is feeling stressed, the suggestion unit can provide short suggestions. If the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is in a hurry, the suggestion unit can also provide suggestions that are concise. In this way, by adjusting the length of the suggestions according to the user's emotions, it is possible to provide suggestions that are appropriate for the user. Some or all of the above-described processing in the suggestion unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the suggestion unit can input user emotion data into the generation AI and cause the generation AI to adjust the length of the suggestions.

[0096] The suggestion unit can suggest dishes based on the user's ingredient inventory. The suggestion unit, for example, analyzes the user's ingredient inventory and suggests the optimal dish. For example, the suggestion unit uses a generation AI to analyze the user's ingredient inventory and suggest the optimal dish. The suggestion unit can also suggest dishes based on the user's ingredient inventory. For example, the suggestion unit uses a generation AI to analyze the user's ingredient inventory and suggest the optimal dish. The suggestion unit can also suggest dishes based on the user's ingredient inventory. For example, the suggestion unit uses a generation AI to analyze the user's ingredient inventory and suggest the optimal dish. In this way, by suggesting dishes based on the user's ingredient inventory, ingredients can be used without waste. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the suggestion unit can input the user's ingredient inventory data into the generation AI and cause the generation AI to suggest the optimal dish.

[0097] The suggestion unit can suggest dishes based on the user's mealtimes. The suggestion unit, for example, analyzes the user's mealtimes and suggests optimal dishes. For example, the suggestion unit uses a generation AI to analyze the user's mealtimes and suggest optimal dishes. The suggestion unit can also suggest dishes based on the user's mealtimes. For example, the suggestion unit uses a generation AI to analyze the user's mealtimes and suggest optimal dishes. The suggestion unit can also suggest dishes based on the user's mealtimes. For example, the suggestion unit uses a generation AI to analyze the user's mealtimes and suggest optimal dishes. In this way, meals can be provided at appropriate times by suggesting dishes based on the user's mealtimes. Some or all of the above-described processing in the suggestion unit may be performed using, or without, the generation AI. For example, the suggestion unit can input the user's mealtime period data into the generation AI and cause the generation AI to suggest optimal dishes.

[0098] The suggestion unit can suggest dishes based on the user's family composition. The suggestion unit, for example, analyzes the user's family composition and suggests optimal dishes. For example, the suggestion unit uses a generation AI to analyze the user's family composition and suggest optimal dishes. The suggestion unit can also suggest dishes based on the user's family composition. For example, the suggestion unit uses a generation AI to analyze the user's family composition and suggest optimal dishes. The suggestion unit can also suggest dishes based on the user's family composition. For example, the suggestion unit uses a generation AI to analyze the user's family composition and suggest optimal dishes. In this way, by suggesting dishes based on the user's family composition, it is possible to provide meals that satisfy the entire family. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, the generation AI. For example, the suggestion unit can input the user's family composition data into the generation AI and cause the generation AI to suggest optimal dishes.

[0099] The providing unit can estimate the user's emotions and adjust the presentation of the recipe to be provided based on the estimated user's emotions. The providing unit, for example, estimates the user's emotions using facial expression recognition or voice analysis. For example, the providing unit can analyze changes in the user's facial expressions and tone of voice to estimate the user's emotions. The providing unit can also adjust the presentation of the recipe to be provided based on the user's emotions. For example, if the user is stressed, the providing unit can provide a simple presentation. If the user is relaxed, the providing unit can provide a detailed presentation. If the user is in a hurry, the providing unit can provide a presentation that focuses on the main points. This allows the presentation of the recipe to be adjusted according to the user's emotions, making it easy for the user to understand. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the presentation of the recipe.

[0100] The providing unit can provide detailed instructions according to the difficulty of the cooking. The providing unit, for example, analyzes the difficulty of the cooking and provides the detailed instructions. For example, the providing unit has the generation AI analyze the difficulty of the cooking and provide the detailed instructions. The providing unit can also provide detailed instructions according to the difficulty of the cooking. For example, the providing unit has the generation AI analyze the difficulty of the cooking and provide the detailed instructions. The providing unit can also provide detailed instructions according to the difficulty of the cooking. For example, the providing unit has the generation AI analyze the difficulty of the cooking and provide the detailed instructions. In this way, by providing detailed instructions according to the difficulty of the cooking, the user can proceed with the cooking appropriately. Some or all of the above-mentioned processing in the providing unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the providing unit can input cooking difficulty data into the generation AI and cause the generation AI to provide detailed instructions.

[0101] The providing unit can adjust the steps based on the cooking time of the dish. The providing unit, for example, analyzes the cooking time of the dish and adjusts the steps. For example, the providing unit has the generation AI analyze the cooking time of the dish and adjust the steps. The providing unit can also adjust the steps based on the cooking time of the dish. For example, the providing unit has the generation AI analyze the cooking time of the dish and adjust the steps. The providing unit can also adjust the steps based on the cooking time of the dish. For example, the providing unit has the generation AI analyze the cooking time of the dish and adjust the steps. In this way, by adjusting the steps based on the cooking time of the dish, cooking can be progressed efficiently. Some or all of the above-mentioned processing in the providing unit may be performed using the generation AI, for example, or may be performed without using the generation AI. For example, the providing unit can input cooking time data of the dish to the generation AI and have the generation AI adjust the steps.

[0102] The providing unit can customize the steps according to the type of cooking utensils used by the user. The providing unit, for example, analyzes the user's cooking utensils and customizes the steps. For example, the providing unit has a generation AI analyze the user's cooking utensils and customize the steps. The providing unit can also customize the steps according to the type of cooking utensils used by the user. For example, the providing unit has a generation AI analyze the user's cooking utensils and customize the steps. The providing unit can also customize the steps according to the type of cooking utensils used by the user. For example, the providing unit has a generation AI analyze the user's cooking utensils and customize the steps. This allows the user to make maximum use of the utensils they own by customizing the steps according to the type of cooking utensils used by the user. Some or all of the above-described processing by the providing unit may be performed using the generation AI, for example, or may be performed without using the generation AI. For example, the providing unit can input the user's cooking utensil data into the generation AI and cause the generation AI to customize the steps.

[0103] The providing unit can estimate the user's emotions and adjust the length of the recipe to be provided based on the estimated user emotions. The providing unit, for example, estimates the user's emotions using facial expression recognition or voice analysis. For example, the providing unit analyzes changes in the user's facial expressions and tone of voice to estimate the user's emotions. The providing unit can also adjust the length of the recipe to be provided based on the user's emotions. For example, if the user is feeling stressed, the providing unit can provide a short recipe. If the user is relaxed, the providing unit can provide a detailed recipe. If the user is in a hurry, the providing unit can provide a recipe that focuses on the main points. In this way, by adjusting the length of the recipe according to the user's emotions, it is possible to provide a recipe that is appropriate for the user. Some or all of the above-described processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the length of the recipe.

[0104] The providing unit can provide the information including nutritional information of the dish. For example, the providing unit analyzes the nutritional components of the dish and provides the nutritional information. For example, the providing unit has the generation AI analyze the nutritional components of the dish and provide the nutritional information. The providing unit can also analyze the calories of the dish and provide the nutritional information. For example, the providing unit has the generation AI analyze the calories of the dish and provide the nutritional information. The providing unit can also analyze the vitamin and mineral content of the dish and provide the nutritional information. For example, the providing unit has the generation AI analyze the vitamin and mineral content of the dish and provide the nutritional information. In this way, by providing the nutritional information of the dish, the user can select a healthy meal. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, the generation AI. For example, the providing unit can input nutritional information data of the dish to the generation AI and cause the generation AI to provide the nutritional information.

[0105] The providing unit can provide information including a storage method for the dish. The providing unit, for example, analyzes the storage method for the dish and provides the storage method. For example, the providing unit has the generation AI analyze the storage method for the dish and provide the storage method. The providing unit can also analyze the storage period for the dish and provide the storage method. For example, the providing unit has the generation AI analyze the storage period for the dish and provide the storage method. The providing unit can also analyze the storage temperature for the dish and provide the storage method. For example, the providing unit has the generation AI analyze the storage temperature for the dish and provide the storage method. In this way, by providing the storage method for the dish, the user can keep the dish fresh for a longer period of time. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, the generation AI. For example, the providing unit can input data on the storage method for the dish into the generation AI and have the generation AI provide the storage method.

[0106] The providing unit can provide information including a method for arranging a dish. The providing unit, for example, analyzes a method for arranging a dish and provides the method. For example, the providing unit has the generation AI analyze a method for arranging a dish and provide the method. The providing unit can also analyze examples of dish arrangements and provide the method. For example, the providing unit has the generation AI analyze examples of dish arrangements and provide the method. The providing unit can also analyze tips for arranging a dish and provide the method. For example, the providing unit has the generation AI analyze tips for arranging a dish and provide the method. By providing methods for arranging a dish, the user can enjoy cooking in a variety of ways. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, the generation AI. For example, the providing unit can input data on a method for arranging a dish into the generation AI and have the generation AI provide the method for arranging the dish.

[0107] The advice unit can estimate the user's emotions and adjust the way the advice is expressed based on the estimated user's emotions. The advice unit estimates the user's emotions using, for example, facial expression recognition or voice analysis. For example, the advice unit can analyze changes in the user's facial expressions and tone of voice to estimate the user's emotions. The advice unit can also adjust the way the advice is expressed based on the user's emotions. For example, if the user is feeling stressed, the advice unit can provide a simple way of expressing the advice. If the user is relaxed, the advice unit can also provide a detailed way of expressing the advice. If the user is in a hurry, the advice unit can also provide a way of expressing the advice that focuses on the main points. In this way, by adjusting the way the advice is expressed based on the user's emotions, it is possible to provide advice that is easy for the user to understand. Some or all of the above-mentioned processing in the advice unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the advice unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the way the advice is expressed.

[0108] The advice unit can adjust the level of detail of the advice according to the user's cooking skill. The advice unit, for example, analyzes the user's cooking skill and provides detailed advice. For example, the advice unit uses a generation AI to analyze the user's cooking skill and provide detailed advice. The advice unit can also adjust the level of detail of the advice according to the user's cooking skill. For example, the advice unit uses a generation AI to analyze the user's cooking skill and provide detailed advice. The advice unit can also adjust the level of detail of the advice according to the user's cooking skill. For example, the advice unit uses a generation AI to analyze the user's cooking skill and provide detailed advice. In this way, by adjusting the level of detail of the advice according to the user's cooking skill, appropriate advice can be provided to the user. Some or all of the above-described processing in the advice unit may be performed using, or without, the generation AI. For example, the advice unit can input the user's cooking skill data into the generation AI and cause the generation AI to adjust the level of detail of the advice.

[0109] The advice unit can provide timely advice based on the cooking progress. The advice unit, for example, analyzes the cooking progress and provides timely advice. For example, the advice unit uses a generation AI to analyze the cooking progress and provide timely advice. The advice unit can also provide timely advice based on the cooking progress. For example, the advice unit uses a generation AI to analyze the cooking progress and provide timely advice. The advice unit can also provide timely advice based on the cooking progress. For example, the advice unit uses a generation AI to analyze the cooking progress and provide timely advice. This allows the user to cook appropriately by providing timely advice based on the cooking progress. Some or all of the above-described processing in the advice unit may be performed using, or without, the generation AI. For example, the advice unit can input cooking progress data into the generation AI and cause the generation AI to provide timely advice.

[0110] The advice unit can customize advice based on the user's cooking environment. The advice unit, for example, analyzes the user's cooking environment and provides optimal advice. For example, the advice unit uses a generation AI to analyze the user's cooking environment and provide optimal advice. The advice unit can also customize advice based on the user's cooking environment. For example, the advice unit uses a generation AI to analyze the user's cooking environment and provide optimal advice. The advice unit can also customize advice based on the user's cooking environment. For example, the advice unit uses a generation AI to analyze the user's cooking environment and provide optimal advice. In this way, by customizing advice based on the user's cooking environment, optimal advice for the user can be provided. Some or all of the above-described processing in the advice unit may be performed using, or without, the generation AI. For example, the advice unit can input the user's cooking environment data into the generation AI and cause the generation AI to customize the advice.

[0111] The advice unit can estimate the user's emotions and adjust the timing of advice based on the estimated user emotions. The advice unit estimates the user's emotions using, for example, facial expression recognition or voice analysis. For example, the advice unit analyzes changes in the user's facial expressions and tone of voice to estimate the user's emotions. The advice unit can also adjust the timing of advice based on the user's emotions. For example, if the user is feeling stressed, the advice unit can provide timely advice. If the user is relaxed, the advice unit can also provide detailed advice. If the user is in a hurry, the advice unit can also provide advice that focuses on the main points. In this way, by adjusting the timing of advice according to the user's emotions, advice can be provided at an appropriate time for the user. Some or all of the above-described processing in the advice unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the advice unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the timing of advice.

[0112] The advice unit can provide advice regarding the safety of cooking. For example, the advice unit analyzes the safety of cooking and provides safety advice. For example, the advice unit uses a generation AI to analyze the safety of cooking and provide safety advice. The advice unit can also provide advice regarding the safety of cooking. For example, the advice unit uses a generation AI to analyze the safety of cooking and provide safety advice. The advice unit can also provide advice regarding the safety of cooking. For example, the advice unit uses a generation AI to analyze the safety of cooking and provide safety advice. By providing advice regarding the safety of cooking, the user can proceed with cooking safely. Some or all of the above-described processing in the advice unit may be performed using, or without, the generation AI. For example, the advice unit can input safety data about cooking to the generation AI and cause the generation AI to provide safety advice.

[0113] The advice unit can provide advice on how to present food. The advice unit, for example, analyzes the presentation method of food and provides optimal advice. For example, the advice unit uses the generation AI to analyze the presentation method of food and provides optimal advice. The advice unit can also provide advice on how to present food. For example, the advice unit uses the generation AI to analyze the presentation method of food and provides optimal advice. The advice unit can also provide advice on how to present food. For example, the advice unit uses the generation AI to analyze the presentation method of food and provides optimal advice. By providing advice on how to present food, the user can achieve beautiful presentation. Some or all of the above-described processing in the advice unit may be performed using, or without, the generation AI. For example, the advice unit can input food presentation method data into the generation AI and cause the generation AI to provide optimal advice.

[0114] The advice unit can provide advice regarding clearing up after cooking. The advice unit, for example, analyzes a method for clearing up after cooking and provides optimal advice. For example, the advice unit uses a generation AI to analyze a method for clearing up after cooking and provides optimal advice. The advice unit can also provide advice regarding clearing up after cooking. For example, the advice unit uses a generation AI to analyze a method for clearing up after cooking and provides optimal advice. The advice unit can also provide advice regarding clearing up after cooking. For example, the advice unit uses a generation AI to analyze a method for clearing up after cooking and provides optimal advice. By providing advice regarding clearing up after cooking, the user can clean up efficiently. Some or all of the above-described processing in the advice unit may be performed using, or without, the generation AI. For example, the advice unit can input data regarding a method for clearing up after cooking into the generation AI and cause the generation AI to provide optimal advice.

[0115] The history storage unit can estimate a user's emotions and adjust the history storage method based on the estimated user emotions. The history storage unit can estimate emotions using, for example, facial expression recognition or voice analysis. For example, the history storage unit can analyze changes in the user's facial expressions and tone of voice to estimate emotions. The history storage unit can also adjust the history storage method based on the user's emotions. For example, if the user is stressed, the history storage unit can provide a simple storage method. If the user is relaxed, the history storage unit can provide a detailed storage method. If the user is in a hurry, the history storage unit can provide a storage method that focuses on the main points. In this way, by adjusting the history storage method according to the user's emotions, it is possible to provide a storage method that is appropriate for the user. Some or all of the above-mentioned processing in the history storage unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the history storage unit can input user emotion data into the generation AI and have the generation AI adjust the storage method.

[0116] The history storage unit can manage the stored data based on the user's privacy settings when saving the history. The history storage unit, for example, analyzes the user's privacy settings and provides the optimal storage method. For example, the generation AI analyzes the user's privacy settings in the history storage unit and provides the optimal storage method. The history storage unit can also manage the stored data based on the user's privacy settings when saving the history. For example, the generation AI analyzes the user's privacy settings in the history storage unit and provides the optimal storage method. The history storage unit can also manage the stored data based on the user's privacy settings when saving the history. For example, the generation AI analyzes the user's privacy settings in the history storage unit and provides the optimal storage method. This makes it possible to protect the user's privacy by managing the stored data based on the user's privacy settings. Some or all of the above-described processing in the history storage unit may be performed using, or without, the generation AI. For example, the history storage unit can input the user's privacy setting data into the generation AI and have the generation AI manage the stored data.

[0117] The history storage unit can compress the stored data according to the user's data capacity when saving the history. The history storage unit, for example, analyzes the user's data capacity and provides the optimal compression method. For example, the history storage unit has a generation AI analyze the user's data capacity and provide the optimal compression method. The history storage unit can also compress the stored data according to the user's data capacity when saving the history. For example, the history storage unit has a generation AI analyze the user's data capacity and provide the optimal compression method. The history storage unit can also compress the stored data according to the user's data capacity when saving the history. For example, the history storage unit has a generation AI analyze the user's data capacity and provide the optimal compression method. This enables efficient data management by compressing the stored data according to the user's data capacity. Some or all of the above-mentioned processing in the history storage unit may be performed using, or without, the generation AI. For example, the history storage unit can input user's data capacity data to the generation AI and have the generation AI compress the stored data.

[0118] The history storage unit can estimate a user's emotions and adjust the frequency of history storage based on the estimated user emotions. The history storage unit can estimate emotions using, for example, facial expression recognition or voice analysis. For example, the history storage unit can analyze changes in the user's facial expressions and tone of voice to estimate emotions. The history storage unit can also adjust the frequency of history storage based on the user's emotions. For example, if the user is stressed, the history storage unit can provide frequent storage. If the user is relaxed, the history storage unit can provide detailed storage. If the user is in a hurry, the history storage unit can provide storage that focuses on the main points. In this way, by adjusting the frequency of history storage according to the user's emotions, it is possible to provide a storage frequency appropriate for the user. Some or all of the above-described processing in the history storage unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the history storage unit can input user emotion data into the generation AI and have the generation AI adjust the storage frequency.

[0119] The history storage unit can select a storage method by taking into account the user's device information when saving the history. The history storage unit, for example, analyzes the user's device information and provides the optimal storage method. For example, the history storage unit has a generation AI analyze the user's device information and provide the optimal storage method. The history storage unit can also select a storage method by taking into account the user's device information when saving the history. For example, the history storage unit has a generation AI analyze the user's device information and provide the optimal storage method. The history storage unit can also select a storage method by taking into account the user's device information when saving the history. For example, the history storage unit has a generation AI analyze the user's device information and provide the optimal storage method. In this way, the optimal storage method can be provided by selecting a storage method by taking into account the user's device information. Some or all of the above-described processing in the history storage unit can be performed using, or without, the generation AI. For example, the history storage unit can input the user's device information data into the generation AI and have the generation AI select a storage method.

[0120] The history storage unit can use the user's cloud storage to save data when saving history. The history storage unit, for example, analyzes the user's cloud storage and provides the optimal storage method. For example, the history storage unit has a generation AI analyze the user's cloud storage and provide the optimal storage method. The history storage unit can also use the user's cloud storage to save data when saving history. For example, the history storage unit has a generation AI analyze the user's cloud storage and provide the optimal storage method. The history storage unit can also use the user's cloud storage to save data when saving history. For example, the history storage unit has a generation AI analyze the user's cloud storage and provide the optimal storage method. This enables efficient data management by saving data using the user's cloud storage. Some or all of the above-mentioned processing in the history storage unit can be performed using, or without, the generation AI. For example, the history storage unit can input the user's cloud storage data into the generation AI and have the generation AI select a storage method.

[0121] The customization unit can estimate the user's emotions and adjust the customization method based on the estimated user emotions. The customization unit can estimate the user's emotions using, for example, facial expression recognition or voice analysis. For example, the customization unit can analyze changes in the user's facial expressions and tone of voice to estimate the user's emotions. The customization unit can also adjust the customization method based on the user's emotions. For example, if the user is stressed, the customization unit can provide a simple customization method. If the user is relaxed, the customization unit can provide a detailed customization method. If the user is in a hurry, the customization unit can also provide a customization method that focuses on the main points. In this way, by adjusting the customization method according to the user's emotions, it is possible to provide optimal customization for the user. Some or all of the above-described processing in the customization unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the customization unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the customization method.

[0122] The customization unit can analyze the user's past history during customization to provide optimal customization. The customization unit, for example, analyzes the user's past history and provides optimal customization. For example, the customization unit has a generation AI analyze the user's past history and provide optimal customization. The customization unit can also analyze the user's past history during customization to provide optimal customization. For example, the customization unit has a generation AI analyze the user's past history and provide optimal customization. The customization unit can also analyze the user's past history during customization to provide optimal customization. For example, the customization unit has a generation AI analyze the user's past history and provide optimal customization. In this way, by analyzing the user's past history and providing optimal customization, it is possible to provide optimal suggestions for the user. Some or all of the above-described processing in the customization unit may be performed using, or without, the generation AI. For example, the customization unit can input the user's past history data into the generation AI and cause the generation AI to provide optimal customization.

[0123] The customization unit can perform customization based on the user's current health condition at the time of customization. The customization unit, for example, analyzes the user's health condition and provides optimal customization. For example, the customization unit uses a generation AI to analyze the user's health condition and provide optimal customization. The customization unit can also perform customization based on the user's current health condition at the time of customization. For example, the customization unit uses a generation AI to analyze the user's health condition and provide optimal customization. The customization unit can also perform customization based on the user's current health condition at the time of customization. For example, the customization unit uses a generation AI to analyze the user's health condition and provide optimal customization. This makes it possible to provide optimal suggestions for the user by customizing based on the user's current health condition. Some or all of the above-described processing in the customization unit can be performed using, or without, the generation AI. For example, the customization unit can input the user's health condition data into the generation AI and cause the generation AI to provide optimal customization.

[0124] The customization unit can estimate the user's emotions and adjust the frequency of customization based on the estimated user emotions. The customization unit estimates the user's emotions using, for example, facial expression recognition or voice analysis. For example, the customization unit analyzes changes in the user's facial expressions and tone of voice to estimate the user's emotions. The customization unit can also adjust the frequency of customization based on the user's emotions. For example, if the user is stressed, the customization unit can provide frequent customization. If the user is relaxed, the customization unit can provide detailed customization. If the user is in a hurry, the customization unit can provide customization that focuses on the main points. In this way, by adjusting the frequency of customization according to the user's emotions, it is possible to provide optimal customization for the user. Some or all of the above-described processing in the customization unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the customization unit can input user emotion data into the generation AI and cause the generation AI to adjust the customization frequency.

[0125] The customization unit can perform customization taking into account the user's device information during customization. The customization unit, for example, analyzes the user's device information and provides optimal customization. For example, the customization unit uses a generation AI to analyze the user's device information and provide optimal customization. The customization unit can also perform customization taking into account the user's device information during customization. For example, the customization unit uses a generation AI to analyze the user's device information and provide optimal customization. The customization unit can also perform customization taking into account the user's device information during customization. For example, the customization unit uses a generation AI to analyze the user's device information and provide optimal customization. In this way, by performing customization taking into account the user's device information, it is possible to provide optimal suggestions for the user. Some or all of the above-described processing in the customization unit may be performed using, or without, the generation AI. For example, the customization unit can input the user's device information data into the generation AI and cause the generation AI to provide optimal customization.

[0126] The customization unit can perform customization by reflecting user feedback during customization. The customization unit, for example, analyzes user feedback and provides optimal customization. For example, the customization unit has a generation AI analyze user feedback and provide optimal customization. The customization unit can also perform customization by reflecting user feedback during customization. For example, the customization unit has a generation AI analyze user feedback and provide optimal customization. The customization unit can also perform customization by reflecting user feedback during customization. For example, the customization unit has a generation AI analyze user feedback and provide optimal customization. In this way, by performing customization by reflecting user feedback, it is possible to provide optimal suggestions for the user. Some or all of the above-described processing in the customization unit may be performed using, or without, the generation AI. For example, the customization unit can input user feedback data into the generation AI and cause the generation AI to provide optimal customization. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned analysis unit, suggestion unit, provision unit, advice unit, history storage unit, and customization unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the analysis unit acquires images of ingredients and seasonings using the camera 42 of the smart device 14 and analyzes the images using the specific processing unit 290 of the data processing device 12. The suggestion unit suggests dishes based on the analysis results using the specific processing unit 290 of the data processing device 12. The provision unit displays cooking steps and recipes using the display 40A of the smart device 14. The advice unit gives advice by voice using the speaker 40B of the smart device 14. The history storage unit stores the user's preferences and past history in the database 24 of the data processing device 12. The customization unit customizes suggestions based on the data stored by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned analysis unit, suggestion unit, provision unit, advice unit, history storage unit, and customization unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the analysis unit acquires images of ingredients and seasonings using the camera 42 of the smart glasses 214 and analyzes the images using the specific processing unit 290 of the data processing device 12. The suggestion unit suggests dishes based on the analysis results using the specific processing unit 290 of the data processing device 12. The provision unit displays cooking steps and recipes using the display of the smart glasses 214. The advice unit gives advice by voice using the speaker 240 of the smart glasses 214. The history storage unit stores user preferences and past history in the database 24 of the data processing device 12. The customization unit customizes suggestions based on the data stored by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned analysis unit, suggestion unit, provision unit, advice unit, history storage unit, and customization unit is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the analysis unit acquires images of ingredients and seasonings using the camera 42 of the headset terminal 314 and analyzes the images using the specific processing unit 290 of the data processing device 12. The suggestion unit proposes dishes based on the analysis results using the specific processing unit 290 of the data processing device 12. The provision unit displays cooking steps and recipes using the display 343 of the headset terminal 314. The advice unit gives advice by voice using the speaker 240 of the headset terminal 314. The history storage unit stores the user's preferences and past history in the database 24 of the data processing device 12. The customization unit customizes suggestions based on the data stored by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned analysis unit, suggestion unit, provision unit, advice unit, history storage unit, and customization unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the analysis unit acquires images of ingredients and seasonings using the camera 42 of the robot 414 and analyzes the images using the specific processing unit 290 of the data processing device 12. The suggestion unit proposes dishes based on the analysis results using the specific processing unit 290 of the data processing device 12. The provision unit displays cooking steps and recipes using the display of the robot 414. The advice unit gives advice by voice using the speaker 240 of the robot 414. The history storage unit stores the user's preferences and past history in the database 24 of the data processing device 12. The customization unit customizes suggestions based on the data stored by the specific processing unit 290 of the data processing device 12.

[0127] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0128] The analysis unit can not only analyze images of ingredients and seasonings, but also analyze the user's cooking environment. For example, the analysis unit can analyze the layout of the user's kitchen and the available cooking utensils to suggest the optimal cooking method. This allows the user to know the cooking method that is best suited to their environment and cook efficiently. The analysis unit can also analyze the user's cooking skills and provide advice based on their skills. For example, it can suggest basic cooking methods for beginners and advanced techniques for advanced cooks. Furthermore, the analysis unit can analyze the user's health condition and suggest healthy dishes. For example, it can suggest dishes that use ingredients that are rich in specific nutrients.

[0129] The history storage unit can not only store the user's preferences and past history, but also estimate the user's emotions and adjust the history storage method based on the emotions. For example, if the user is feeling stressed, the history storage unit can provide a simple storage method. On the other hand, if the user is relaxed, it can provide a detailed storage method. This makes it possible to provide the optimal storage method according to the user's emotions. The history storage unit can also improve the storage method by reflecting user feedback. For example, if the user prefers a specific storage method, it can provide that method preferentially. Furthermore, the history storage unit can manage stored data based on the user's privacy settings. For example, it can encrypt and store specific data.

[0130] The customization unit not only customizes suggestions based on the user's preferences and past history, but also estimates the user's emotions and adjusts the way suggestions are expressed based on the emotions. For example, if the user is feeling stressed, the customization unit can provide a simple way of expressing the user's emotions. On the other hand, if the user is relaxed, the customization unit can provide a detailed way of expressing the user's emotions. This makes it possible to provide optimal suggestions according to the user's emotions. The customization unit can also customize suggestions based on the user's health condition. For example, it can suggest dishes that are rich in specific nutrients. Furthermore, the customization unit can improve the accuracy of suggestions by reflecting user feedback. For example, if the user likes a specific dish, it can preferentially suggest that dish.

[0131] The advice unit can not only provide specific audio advice, but also estimate the user's emotions and adjust the way the advice is expressed based on the emotions. For example, if the user is feeling stressed, the advice unit can provide a simple way of expressing the advice. On the other hand, if the user is relaxed, the advice unit can provide a detailed way of expressing the advice. This makes it possible to provide optimal advice according to the user's emotions. The advice unit can also adjust the level of detail of the advice according to the user's cooking skill. For example, basic advice can be provided to beginners and advanced advice to advanced cooks. Furthermore, the advice unit can provide timely advice based on the progress of cooking. For example, advice on the next step can be provided when a specific step is completed.

[0132] The analysis unit can not only analyze and identify the characteristics of ingredients and seasonings, but also analyze the freshness of the ingredients and seasonings and suggest optimal dishes based on the freshness. For example, the analysis unit can analyze the color and shape of ingredients to evaluate freshness. It can also analyze the expiration date of seasonings to evaluate freshness. This makes it possible to suggest optimal dishes based on the freshness of ingredients and seasonings, thereby providing more delicious dishes. The analysis unit can also analyze the nutritional value of ingredients and seasonings to suggest dishes based on nutritional balance. For example, it can suggest dishes using ingredients that are rich in specific nutrients. Furthermore, the analysis unit can analyze information on the origin of ingredients and seasonings to suggest dishes based on the origin. For example, it can suggest dishes unique to a region.

[0133] The providing unit can not only provide cooking steps and recipes that are visually easy to understand using photographs and illustrations, but also provide detailed steps according to the difficulty level of the cooking. For example, detailed steps can be provided for beginners and simple steps for advanced cooks. This makes it possible to provide optimal support according to the user's skill level. The providing unit can also adjust the steps based on the cooking time of the dish. For example, it can prioritize providing steps that allow cooking in a short time. Furthermore, the providing unit can customize the steps according to the type of cooking utensils used by the user. For example, it can provide steps that use specific cooking utensils.

[0134] The analysis unit can estimate the user's emotions and adjust the analysis accuracy of ingredients and seasonings based on the estimated user's emotions. It can also adjust the display method of the analysis results based on the user's emotions. For example, if the user is feeling stressed, the analysis unit can provide a simple display method. On the other hand, if the user is relaxed, the analysis unit can provide a detailed display method. This makes it possible to provide the optimal display method according to the user's emotions. The analysis unit can also adjust the timing of analysis based on the user's emotions. For example, if the user is in a hurry, the analysis can be performed quickly. Furthermore, the analysis unit can adjust the priority of analysis based on the user's emotions. For example, the analysis unit can prioritize important ingredients and seasonings.

[0135] The analysis unit can analyze the freshness of ingredients and seasonings and suggest optimal dishes based on the freshness, as well as analyze price information on ingredients and seasonings to suggest dishes with high cost performance. For example, the analysis unit can analyze price information on ingredients and suggest dishes with high cost performance. It can also analyze price information on seasonings and suggest dishes with high cost performance. This makes it possible to provide economical dishes. The analysis unit can also analyze the nutritional value of ingredients and seasonings and suggest dishes based on nutritional balance. For example, it can suggest dishes that use ingredients that are high in specific nutrients. Furthermore, the analysis unit can analyze information on the origin of ingredients and seasonings and suggest dishes based on the origin. For example, it can suggest dishes that are unique to a region.

[0136] The analysis unit can not only analyze the nutritional value of ingredients and seasonings and suggest dishes based on nutritional balance, but can also analyze allergen information on ingredients and seasonings and suggest dishes that do not contain allergens. For example, the analysis unit can analyze allergen information on ingredients and suggest dishes that do not contain allergens. It can also analyze allergen information on seasonings and suggest dishes that do not contain allergens. This makes it possible to provide safe dishes to users with allergies. The analysis unit can also analyze storage methods for ingredients and seasonings and suggest dishes based on their storage conditions. For example, it can suggest dishes that use ingredients that require refrigeration. Furthermore, the analysis unit can analyze the expiration dates of ingredients and seasonings and suggest dishes based on the expiration dates. For example, it can suggest dishes that prioritize ingredients that are close to their expiration date.

[0137] The analysis unit can not only analyze the origin information of ingredients and seasonings and suggest dishes based on their origin, but can also analyze the storage method of ingredients and seasonings and suggest dishes based on their storage conditions. For example, the analysis unit can analyze the storage method of ingredients and suggest dishes based on their storage conditions. It can also analyze the storage method of seasonings and suggest dishes based on their storage conditions. This allows ingredients and seasonings to be used without waste. The analysis unit can also analyze price information of ingredients and seasonings and suggest dishes with high cost performance. For example, it can analyze price information of ingredients and suggest dishes with high cost performance. Furthermore, the analysis unit can analyze the nutritional value of ingredients and seasonings and suggest dishes based on nutritional balance. For example, it can suggest dishes using ingredients that are high in specific nutrients.

[0138] The processing flow of the second embodiment will be briefly explained below.

[0139] Step 1: The analysis unit analyzes images of ingredients or seasonings. The images of ingredients or seasonings include still images, videos, and resolutions. The analysis unit uses image recognition algorithms and feature extraction methods to analyze features such as color, shape, and texture to identify the ingredients or seasonings. Step 2: The suggestion unit suggests dishes based on the ingredients or seasonings analyzed by the analysis unit. The suggestions are made based on criteria such as the user's preferences, nutritional balance, and cooking time. The suggestion unit can suggest customized dishes based on the user's preferences and past history. Step 3: The provision unit provides the cooking steps or recipes proposed by the suggestion unit. The provision is done in the form of text, images, videos, etc. The provision unit uses photos and illustrations to provide the cooking steps or recipes in a visually easy-to-understand manner. Step 4: The advice module provides audio advice while cooking. Audio advice is based on the content and timing of cooking instructions, such as things to be careful of while cooking and instructions for the next step. For example, specific instructions such as "Next, cut the tomatoes" or "Boil the pasta for 10 minutes" are provided.

[0140] 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.

[0141] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<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.

[0142] 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.

[0143] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0144] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0145] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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 AI 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.

[0158] 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.

[0159] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0160] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] 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.

[0167] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0168] 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.

[0169] 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.

[0170] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 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 identification processing unit 290 using these models.

[0171] 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.

[0172] 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.

[0173] 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 AI 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.

[0174] 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.

[0175] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0176] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0177] 7, a 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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).

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also 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 perform the same process as the identification processing unit 290 using these models.

[0188] 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.

[0189] 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.

[0190] 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 AI 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.

[0191] 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.

[0192] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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).

[0197] 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.

[0198] 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."

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0210] 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.

[0211] [Explanation of symbols]

[0212] 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. an analysis unit that analyzes an image of an ingredient or seasoning; a suggestion unit that suggests dishes based on the ingredients or seasonings analyzed by the analysis unit; a providing unit that provides cooking procedures or recipes suggested by the suggestion unit; An advice unit that gives advice by voice while cooking is provided. A system characterized by:

2. Equipped with a history storage unit that stores user preferences and past history 2. The system of claim 1.

3. A customization unit that customizes proposals based on the data stored by the history storage unit 3. The system of claim 2.

4. The advice unit Give specific audio advice 2. The system of claim 1.

5. The analysis unit Analyze and identify the characteristics of ingredients and seasonings 2. The system of claim 1.

6. The providing unit Provide visually easy-to-follow cooking instructions or recipes using photos or illustrations 2. The system of claim 1.

7. The analysis unit Estimate the user's emotions and adjust the analysis accuracy of ingredients and seasonings based on the estimated user emotions 2. The system of claim 1.

8. The analysis unit Analyzes the freshness of ingredients and seasonings and suggests the best dishes based on their freshness 2. The system of claim 1.

9. The analysis unit Analyzes the nutritional value of ingredients and seasonings and suggests dishes based on nutritional balance 2. The system of claim 1.

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