System

Abstract

An object of a system according to an embodiment is to propose a dish that suits a user's preference.SOLUTION: A system includes a favorite component registration part, a dish proposal part, a component learning part, and an image analysis part. The favorite component registration unit registers a user's favorite umami component. The dish proposal unit proposes a dish based on the umami component registered by the favorite component registration unit. The ingredient learning unit learns ingredients of the dish proposed by the dish proposal unit. The image analysis unit analyzes the material on the basis of the component learned by the component learning unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of making it difficult for users to find dishes that suit their preferences.

[0005] The system according to the embodiment aims to suggest dishes that suit the preferences of the user. [Means for solving the problem]

[0006] The system according to the embodiment includes a preferred ingredient registration unit, a dish suggestion unit, an ingredient learning unit, and an image analysis unit. The preferred ingredient registration unit registers the user's preferred umami ingredients. The dish suggestion unit suggests dishes based on the umami ingredients registered by the preferred ingredient registration unit. The ingredient learning unit learns the ingredients of the dishes suggested by the dish suggestion unit. The image analysis unit analyzes ingredients based on the ingredients learned by the ingredient learning unit. [Effects of the Invention]

[0007] The system according to the embodiment can suggest dishes that match the preferences of the user. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The dish recommendation system according to an embodiment of the present invention allows a user to register their favorite umami components, and the system recommends dishes rich in umami components based on the registered umami components. This allows the system to recommend dishes that match the user's preferences, thereby improving the user's satisfaction with the meal.

[0029] A dish suggestion system according to an embodiment includes a preferred ingredient registration unit, a dish suggestion unit, an ingredient learning unit, and an image analysis unit. The preferred ingredient registration unit registers a user's preferred umami ingredients. For example, the user can select and register specific umami ingredients such as glutamic acid or inosinic acid. The dish suggestion unit suggests dishes based on the umami ingredients registered by the preferred ingredient registration unit. For example, if a user registers that they like glutamic acid, the dish suggestion unit lists dishes that are high in glutamic acid and suggests them to the user. The ingredient learning unit learns the ingredients of the dishes suggested by the dish suggestion unit. For example, the ingredient learning unit acquires ingredient information of food ingredients from a database and learns what umami ingredients each ingredient contains. The image analysis unit analyzes the ingredients based on the ingredients learned by the ingredient learning unit. For example, the image analysis unit analyzes an image of a dish, identifies the ingredients contained therein, and analyzes what umami ingredients the ingredients contain. As a result, the dish recommendation system according to the embodiment can suggest dishes based on the user's preferences, learn ingredients, and analyze materials, enabling more accurate suggestions.

[0030] The preferred component registration unit can analyze the user's history of past meals and automatically estimate the user's preferred umami components. For example, the preferred component registration unit inputs the user's history of past meals into the system and automatically estimates the user's preferred umami components based on the data. For example, it analyzes the ingredients of meals frequently ordered by the user and identifies common umami components. The preferred component registration unit also collects reviews and ratings of meals eaten by the user in the past and estimates the user's preferred umami components based on the data. For example, it analyzes the ingredients of meals given high ratings by the user and identifies the user's preferred umami components. The preferred component registration unit also uploads photos of meals eaten by the user in the past and uses image analysis technology to identify the ingredients of the meals and estimate the user's preferred umami components. For example, it extracts major ingredients from the meal images and identifies common umami components. This allows the user's preferred umami components to be automatically estimated based on the user's past meal history.

[0031] The preferred component registration unit creates a taste profile for the user and updates it periodically, thereby responding to changes in preferences. The preferred component registration unit, for example, builds a system that creates a taste profile for the user and updates it periodically. For example, the profile is updated every time the user tries a new dish, thereby responding to changes in preferences. The preferred component registration unit also periodically conducts a survey based on the user's taste profile to understand changes in preferences. For example, the user is asked to rate new dishes and the results are reflected in the profile. The preferred component registration unit also periodically suggests recommended dishes based on the user's taste profile and updates the profile based on that feedback. For example, the user's ratings after trying the suggested dishes are collected and reflected in the profile. In this way, the user's taste profile is created and updated periodically, thereby responding to changes in preferences.

[0032] The preferred component registration unit can enable an intuitive operation using voice input when a user registers a preferred umami component. For example, a system is developed in which the preferred component registration unit enables an intuitive operation using voice input when a user registers a preferred umami component. For example, a voice recognition technology is used to convert a user's instructions into text data. The preferred component registration unit also provides an interface using voice input that enables a user to easily register a preferred umami component. For example, when a user simply says, "I like glutamic acid," the system registers that umami component. The preferred component registration unit also uses voice input to simplify the operation when a user registers a preferred umami component. For example, when a user simply speaks the name of a dish, the umami components contained in that dish are automatically registered. This allows for intuitive operation using voice input.

[0033] The preferred component registration unit can share a user's preferred umami components with other users and suggest recommended dishes within the community. The preferred component registration unit, for example, develops a community function for sharing a user's preferred umami components with other users. For example, a user publishes his or her preferences and receives recommended dishes from other users. The preferred component registration unit also builds a system for sharing users' preferred umami components within a community and for users with common preferences to suggest recommended dishes to each other. For example, users who like the same umami components exchange recipes. The preferred component registration unit also develops a system for suggesting recommended dishes within a community based on the user's preferred umami components. For example, a list of recommended dishes from other users is created based on the umami components registered by the user. This allows a user's preferred umami components to be shared with other users and for recommended dishes to be suggested within the community.

[0034] The dish suggestion unit reflects users' past evaluation data in the dishes suggested by the generation AI, allowing for more accurate suggestions. For example, the dish suggestion unit builds a system that reflects users' past evaluation data in the dishes suggested by the generation AI. For example, it suggests similar dishes based on data on dishes that users have given high ratings. The dish suggestion unit also improves the accuracy of the dishes suggested by the generation AI based on users' past evaluation data. For example, it avoids ingredients in dishes that users have given low ratings. The dish suggestion unit also reflects users' past evaluation data in the dishes suggested by the generation AI, developing a system that allows for more accurate suggestions. For example, it suggests the optimal dish based on user evaluation data. This allows for more accurate dish suggestions by reflecting users' past evaluation data.

[0035] The dish suggestion unit can include nutritional balance and calorie information in the dishes proposed by the generation AI. For example, the dish suggestion unit will build a system that includes nutritional balance and calorie information in the dishes proposed by the generation AI. For example, nutritional information will be displayed to make it easier for users to choose health-conscious meals. The dish suggestion unit will also improve the accuracy of the dishes proposed by the generation AI based on nutritional balance and calorie information. For example, it will suggest dishes that suit the user's health condition. The dish suggestion unit will also develop a system that includes nutritional balance and calorie information in the dishes proposed by the generation AI. For example, if the user is on a diet, it will suggest low-calorie dishes. This makes it possible to suggest dishes that include nutritional balance and calorie information.

[0036] The dish suggestion unit can add recommended dishes according to the season and weather to the dishes suggested by the generation AI. For example, the dish suggestion unit will build a system that adds recommended dishes according to the season and weather to the dishes suggested by the generation AI. For example, it will suggest cold dishes in the summer and hot dishes in the winter. The dish suggestion unit will also improve the accuracy of the dishes suggested by the generation AI based on season and weather data. For example, it will suggest hot soup on a rainy day. The dish suggestion unit will also develop a system that adds recommended dishes according to the season and weather to the dishes suggested by the generation AI. For example, it will suggest dishes using seasonal ingredients. This will make it possible to add recommended dishes according to the season and weather.

[0037] The dish suggestion unit can incorporate regional ingredients and dishes into the dishes suggested by the generation AI, allowing users to enjoy local flavors. The dish suggestion unit, for example, builds a system that incorporates regional ingredients and dishes into the dishes suggested by the generation AI. For example, it suggests dishes using local ingredients based on the user's location. The dish suggestion unit also improves the accuracy of the dishes suggested by the generation AI based on regional ingredient and dish data. For example, it suggests traditional regional dishes. The dish suggestion unit also develops a system that incorporates regional ingredients and dishes into the dishes suggested by the generation AI, allowing users to enjoy local flavors. For example, it suggests local dishes at travel destinations. This allows users to incorporate regional ingredients and dishes, allowing users to enjoy local flavors.

[0038] The ingredient learning unit can also take into account the origin and cultivation method of ingredients when the generation AI learns the umami components of food ingredients. For example, when the generation AI learns the umami components of food ingredients, the ingredient learning unit retrieves information about the origin of the ingredients from a database and learns based on that information. For example, it analyzes the umami components of ingredients grown in a specific region. The ingredient learning unit also collects data on the cultivation method of ingredients and builds a system in which the generation AI learns umami components based on that information. For example, it learns the differences in umami components depending on cultivation methods such as organic cultivation and hydroponic cultivation. The ingredient learning unit also develops a system that takes into account the origin and cultivation method when the generation AI learns the umami components of food ingredients. For example, it analyzes the umami components of ingredients grown using a specific cultivation method and reflects this in the learning. This allows the generation AI to learn umami components taking into account the origin and cultivation method of the ingredients.

[0039] The ingredient learning unit can also take cooking methods and cooking times into account when the generation AI learns the umami components of food ingredients. For example, when the generation AI learns the umami components of food ingredients, the ingredient learning unit collects data on cooking methods and learns based on that information. For example, it analyzes how umami components change depending on cooking methods such as baking, boiling, and steaming. The ingredient learning unit also collects data on cooking times and builds a system in which the generation AI learns umami components based on that information. For example, it learns the characteristics of ingredients whose umami components increase when simmered for a long time. The ingredient learning unit also develops a system that takes cooking methods and cooking times into account when the generation AI learns the umami components of food ingredients. For example, it analyzes how umami components change depending on the combination of a specific cooking method and time and reflects this in the learning. This allows the generation AI to learn umami components taking cooking methods and cooking times into account.

[0040] The ingredient learning unit allows the generation AI to learn from a global perspective, incorporating ingredients from different cultures, when learning about the umami components of food ingredients. For example, when the generation AI learns about the umami components of food ingredients, the ingredient learning unit collects food data from different cultures and learns based on that information. For example, it analyzes ingredients from Asia, Europe, America, etc. The ingredient learning unit also incorporates ingredients from different cultures to build a system that allows the generation AI to learn about umami components from a global perspective. For example, it analyzes the umami components of ingredients used in traditional dishes from each country. The ingredient learning unit also develops a system that incorporates ingredients from different cultures when the generation AI learns about the umami components of food ingredients. For example, it learns about the umami components of dishes that combine ingredients from different cultures. This allows the generation AI to incorporate ingredients from different cultures and learn about umami components from a global perspective.

[0041] The ingredient learning unit can refer to winning recipes from past cooking contests when the generation AI learns the umami components of food ingredients. For example, when the generation AI learns the umami components of food ingredients, the ingredient learning unit collects data on winning recipes from past cooking contests and learns based on that information. For example, it analyzes the umami components of ingredients used in winning recipes. The ingredient learning unit also builds a system in which the generation AI learns the umami components of food ingredients by referring to winning recipes from past cooking contests. For example, it identifies the optimal ingredients based on the ingredient data of winning recipes. The ingredient learning unit also develops a system in which the generation AI learns the umami components of food ingredients by referring to winning recipes from past cooking contests. For example, it identifies the optimal ingredients based on the ingredient data of winning recipes. This allows the generation AI to learn umami components by referring to winning recipes from past cooking contests.

[0042] The image analysis unit can also evaluate the freshness and quality of ingredients when the generation AI analyzes ingredients using image judgment. For example, the image analysis unit develops a system to evaluate the freshness of ingredients when the generation AI analyzes ingredients using image judgment. For example, it analyzes the color and shape of vegetables and fruits to determine freshness. The image analysis unit also builds a system in which the generation AI uses image judgment to evaluate the quality of ingredients. For example, it analyzes the color and fat distribution of meat and fish to determine quality. The image analysis unit also develops a system to evaluate the freshness and quality when the generation AI analyzes ingredients using image judgment. For example, it analyzes the condition of the surface of food to determine freshness and quality. This makes it possible to evaluate the freshness and quality of ingredients.

[0043] The image analysis unit will also analyze images of the cooking process when the generative AI analyzes ingredients using image judgment, making it possible to evaluate cooking methods. For example, the image analysis unit will develop a system that analyzes images of the cooking process when the generative AI analyzes ingredients using image judgment. For example, it will analyze changes in temperature and color during cooking and evaluate cooking methods. The image analysis unit will also build a system that allows the generative AI to evaluate cooking methods based on images of the cooking process. For example, it will analyze the degree of doneness or simmering and identify the optimal cooking method. The image analysis unit will also develop a system that analyzes images of the cooking process when the generative AI analyzes ingredients using image judgment, making it possible to analyze changes in ingredients during cooking and identify the optimal cooking method. This will make it possible to analyze images of the cooking process and evaluate cooking methods.

[0044] The image analysis unit can also analyze images taken at different angles and under different lighting conditions when the generative AI analyzes materials through image judgment. For example, the image analysis unit develops a system that analyzes images taken at different angles when the generative AI analyzes materials through image judgment. For example, it analyzes images from multiple angles to grasp the overall picture of the material. The image analysis unit also builds a system that allows the generative AI to analyze materials based on images taken under different lighting conditions. For example, it analyzes the characteristics of the material by taking into account the effects of brightness and shadows. The image analysis unit also develops a system that analyzes images taken at different angles and under different lighting conditions when the generative AI analyzes materials through image judgment. For example, it analyzes images taken under multiple conditions to grasp the detailed characteristics of the material. This makes it possible to analyze images taken at different angles and under different lighting conditions.

[0045] The image analysis unit can also analyze video data and incorporate dynamic information when the generative AI analyzes ingredients through image judgment. For example, the image analysis unit develops a system that analyzes video data when the generative AI analyzes ingredients through image judgment. For example, it analyzes videos of the cooking process and evaluates changes in the ingredients. The image analysis unit also builds a system that allows the generative AI to analyze ingredients based on video data. For example, it analyzes the movement of ingredients during cooking and identifies the optimal cooking method. The image analysis unit also develops a system that analyzes video data and incorporates dynamic information when the generative AI analyzes ingredients through image judgment. For example, it evaluates the characteristics of ingredients based on videos of the cooking process. This makes it possible to analyze video data and incorporate dynamic information.

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

[0047] The dish recommendation system can further include an allergy management unit that registers a user's dietary restrictions and allergy information. For example, if a user is allergic to a specific ingredient, that information can be registered and that ingredient can be excluded from the suggested dishes. The allergy management unit can also suggest appropriate dishes taking into account the user's dietary restrictions based on their health condition. For example, it can suggest low-carbohydrate dishes to a diabetic user. Furthermore, the allergy management unit can suggest alternative ingredients based on the user's dietary restrictions and allergy information. For example, it can suggest dairy-free substitutes to a user who is allergic to dairy products. This makes it possible to suggest dishes that take into consideration the user's health condition and allergies.

[0048] The recipe recommendation system can further include a meal recording unit that records the user's meal frequency and time periods. For example, the user can register the time periods for breakfast, lunch, and dinner, and the system can recommend dishes suitable for those time periods. The meal recording unit can also record the user's meal frequency and recommend the next meal based on the user's past meal history. For example, the system can recommend a balanced meal taking into account the dishes the user ate the previous day. Furthermore, the meal recording unit can make suggestions to regulate the user's meal rhythm based on the time periods and frequency of the user's meals. For example, the system can send reminders at appropriate times to encourage regular eating. This can regulate the user's meal rhythm and support a healthy diet.

[0049] The recipe recommendation system can further include an activity recording unit that records the user's amount of exercise and activity level. For example, the system can record data on the user's daily exercise and activity and suggest an appropriate calorie intake based on that data. The activity recording unit can also suggest nutritionally balanced dishes according to the user's amount of exercise. For example, it can suggest dishes high in protein after exercise. Furthermore, the activity recording unit can also suggest dishes suitable for replenishing energy based on the user's activity level. For example, it can suggest high-calorie dishes suitable for replenishing energy after a long period of exercise. This makes it possible to suggest appropriate meals according to the user's amount of exercise and activity level.

[0050] The recipe suggestion system can further include a purchase history management unit that records the user's ingredient purchase history. For example, it can record data on ingredients purchased by the user in the past and suggest recipes based on that data. The purchase history management unit can also manage inventory based on the ingredients purchased by the user and suggest recipes that use ingredients at the appropriate time. For example, it can suggest recipes that prioritize ingredients that are close to their expiration date. Furthermore, the purchase history management unit can automatically generate a shopping list for the next purchase based on the user's purchase history. For example, it can list ingredients that are purchased regularly to prevent forgetting to buy them. This makes it possible to make efficient recipe suggestions that utilize the user's ingredient purchase history.

[0051] The dish recommendation system can further include a satisfaction evaluation unit that evaluates the user's satisfaction with the meal. For example, the user can evaluate their satisfaction after a meal, and the next dish recommendation can be improved based on that data. The satisfaction evaluation unit can also improve the accuracy of dish recommendations based on the user's evaluation data. For example, it can analyze the ingredients of highly rated dishes and recommend similar dishes. Furthermore, the satisfaction evaluation unit can also recommend dishes tailored to individual preferences based on the user's satisfaction data. For example, if a particular ingredient is preferred, dishes containing a large amount of that ingredient can be recommended. This enables highly accurate dish recommendations that reflect the user's satisfaction.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The preferred component registration unit registers the user's preferred umami components. For example, the user can select and register specific umami components such as glutamic acid or inosinic acid. Step 2: The dish suggestion unit suggests dishes based on the umami components registered by the preferred component registration unit. For example, if the user registers that they like glutamic acid, the dish suggestion unit lists dishes that are high in glutamic acid and suggests them to the user. Step 3: The ingredient learning unit learns the ingredients of the dishes suggested by the dish suggestion unit. For example, the ingredient learning unit acquires ingredient information of food ingredients from a database and learns what umami components each ingredient contains. Step 4: The image analysis unit analyzes the ingredients based on the ingredients learned by the ingredient learning unit. For example, the image analysis unit analyzes an image of a dish, identifies the ingredients contained in it, and analyzes what umami components the ingredients contain.

[0054] (Example 2) The dish recommendation system according to an embodiment of the present invention allows a user to register their favorite umami components, and the system recommends dishes rich in umami components based on the registered umami components. This allows the system to recommend dishes that match the user's preferences, thereby improving the user's satisfaction with the meal.

[0055] A dish suggestion system according to an embodiment includes a preferred ingredient registration unit, a dish suggestion unit, an ingredient learning unit, and an image analysis unit. The preferred ingredient registration unit registers a user's preferred umami ingredients. For example, the user can select and register specific umami ingredients such as glutamic acid or inosinic acid. The dish suggestion unit suggests dishes based on the umami ingredients registered by the preferred ingredient registration unit. For example, if a user registers that they like glutamic acid, the dish suggestion unit lists dishes that are high in glutamic acid and suggests them to the user. The ingredient learning unit learns the ingredients of the dishes suggested by the dish suggestion unit. For example, the ingredient learning unit acquires ingredient information of food ingredients from a database and learns what umami ingredients each ingredient contains. The image analysis unit analyzes the ingredients based on the ingredients learned by the ingredient learning unit. For example, the image analysis unit analyzes an image of a dish, identifies the ingredients contained therein, and analyzes what umami ingredients the ingredients contain. As a result, the dish recommendation system according to the embodiment can suggest dishes based on the user's preferences, learn ingredients, and analyze materials, enabling more accurate suggestions.

[0056] The preferred component registration unit can analyze the user's history of past meals and automatically estimate the user's preferred umami components. For example, the preferred component registration unit inputs the user's history of past meals into the system and automatically estimates the user's preferred umami components based on the data. For example, it analyzes the ingredients of meals frequently ordered by the user and identifies common umami components. The preferred component registration unit also collects reviews and ratings of meals eaten by the user in the past and estimates the user's preferred umami components based on the data. For example, it analyzes the ingredients of meals given high ratings by the user and identifies the user's preferred umami components. The preferred component registration unit also uploads photos of meals eaten by the user in the past and uses image analysis technology to identify the ingredients of the meals and estimate the user's preferred umami components. For example, it extracts major ingredients from the meal images and identifies common umami components. This allows the user's preferred umami components to be automatically estimated based on the user's past meal history.

[0057] The preferred component registration unit creates a taste profile for the user and updates it periodically, thereby responding to changes in preferences. The preferred component registration unit, for example, builds a system that creates a taste profile for the user and updates it periodically. For example, the profile is updated every time the user tries a new dish, thereby responding to changes in preferences. The preferred component registration unit also periodically conducts a survey based on the user's taste profile to understand changes in preferences. For example, the user is asked to rate new dishes and the results are reflected in the profile. The preferred component registration unit also periodically suggests recommended dishes based on the user's taste profile and updates the profile based on that feedback. For example, the user's ratings after trying the suggested dishes are collected and reflected in the profile. In this way, the user's taste profile is created and updated periodically, thereby responding to changes in preferences.

[0058] The preferred component registration unit uses the emotion estimation function to record the emotions of the user when eating a dish, and can estimate the preferred umami components based on the emotion data. The preferred component registration unit develops, for example, an app equipped with the emotion estimation function to record the emotions of the user when eating a dish. For example, the preferred component registration unit analyzes the user's facial expressions and voice and calculates an emotion score. The preferred component registration unit also uses the emotion estimation function to collect emotion data of the user when eating a dish, and estimates the preferred umami components based on the data. For example, it analyzes the components of a dish that evoke strong positive emotions and identifies the preferred umami components. The preferred component registration unit also builds a system that estimates the preferred umami components based on the emotion data of the user when eating a dish. For example, it identifies common umami components based on the user's emotion score. This makes it possible to estimate the preferred umami components based on the user's emotion data.

[0059] The preferred component registration unit can enable an intuitive operation using voice input when a user registers a preferred umami component. For example, a system is developed in which the preferred component registration unit enables an intuitive operation using voice input when a user registers a preferred umami component. For example, a voice recognition technology is used to convert a user's instructions into text data. The preferred component registration unit also provides an interface using voice input that enables a user to easily register a preferred umami component. For example, when a user simply says, "I like glutamic acid," the system registers that umami component. The preferred component registration unit also uses voice input to simplify the operation when a user registers a preferred umami component. For example, when a user simply speaks the name of a dish, the umami components contained in that dish are automatically registered. This allows for intuitive operation using voice input.

[0060] The preferred component registration unit can share a user's preferred umami components with other users and suggest recommended dishes within the community. The preferred component registration unit, for example, develops a community function for sharing a user's preferred umami components with other users. For example, a user publishes his or her preferences and receives recommended dishes from other users. The preferred component registration unit also builds a system for sharing users' preferred umami components within a community and for users with common preferences to suggest recommended dishes to each other. For example, users who like the same umami components exchange recipes. The preferred component registration unit also develops a system for suggesting recommended dishes within a community based on the user's preferred umami components. For example, a list of recommended dishes from other users is created based on the umami components registered by the user. This allows a user's preferred umami components to be shared with other users and for recommended dishes to be suggested within the community.

[0061] The preferred component registration unit uses the emotion estimation function to analyze the emotions of a user when selecting a dish in real time, and can suggest umami components that elicit positive emotions. The preferred component registration unit, for example, uses the emotion estimation function to develop a system that analyzes the emotions of a user when selecting a dish in real time. For example, the preferred component registration unit analyzes the user's facial expressions and voice and calculates an emotion score. The preferred component registration unit also builds a system that suggests umami components that elicit positive emotions based on emotion data when the user selects a dish. For example, common umami components are identified based on the user's emotion score. The preferred component registration unit also uses the emotion estimation function to develop a system that analyzes the emotions of a user when selecting a dish in real time, and suggests umami components that elicit positive emotions. For example, the system suggests optimal umami components based on the user's emotion score. This makes it possible to analyze the user's emotions in real time and suggest umami components that elicit positive emotions.

[0062] The dish suggestion unit reflects users' past evaluation data in the dishes suggested by the generation AI, allowing for more accurate suggestions. For example, the dish suggestion unit builds a system that reflects users' past evaluation data in the dishes suggested by the generation AI. For example, it suggests similar dishes based on data on dishes that users have given high ratings. The dish suggestion unit also improves the accuracy of the dishes suggested by the generation AI based on users' past evaluation data. For example, it avoids ingredients in dishes that users have given low ratings. The dish suggestion unit also reflects users' past evaluation data in the dishes suggested by the generation AI, developing a system that allows for more accurate suggestions. For example, it suggests the optimal dish based on user evaluation data. This allows for more accurate dish suggestions by reflecting users' past evaluation data.

[0063] The dish suggestion unit can include nutritional balance and calorie information in the dishes proposed by the generation AI. For example, the dish suggestion unit will build a system that includes nutritional balance and calorie information in the dishes proposed by the generation AI. For example, nutritional information will be displayed to make it easier for users to choose health-conscious meals. The dish suggestion unit will also improve the accuracy of the dishes proposed by the generation AI based on nutritional balance and calorie information. For example, it will suggest dishes that suit the user's health condition. The dish suggestion unit will also develop a system that includes nutritional balance and calorie information in the dishes proposed by the generation AI. For example, if the user is on a diet, it will suggest low-calorie dishes. This makes it possible to suggest dishes that include nutritional balance and calorie information.

[0064] The dish suggestion unit uses the emotion estimation function to analyze the emotion a user has when selecting a dish, and is able to preferentially suggest dishes that elicit positive emotions. The dish suggestion unit, for example, uses the emotion estimation function to develop a system that analyzes the emotion a user has when selecting a dish. For example, the system analyzes the user's facial expressions and voice and calculates an emotion score. The dish suggestion unit also builds a system that preferentially suggests dishes that elicit positive emotions based on emotion data when the user selected a dish. For example, the system suggests optimal dishes based on the user's emotion score. The dish suggestion unit also uses the emotion estimation function to develop a system that analyzes the emotion a user has when selecting a dish, and preferentially suggests dishes that elicit positive emotions. For example, the system suggests optimal dishes based on the user's emotion score. This makes it possible to analyze the user's emotions and preferentially suggest dishes that elicit positive emotions.

[0065] The dish suggestion unit can add recommended dishes according to the season and weather to the dishes suggested by the generation AI. For example, the dish suggestion unit will build a system that adds recommended dishes according to the season and weather to the dishes suggested by the generation AI. For example, it will suggest cold dishes in the summer and hot dishes in the winter. The dish suggestion unit will also improve the accuracy of the dishes suggested by the generation AI based on season and weather data. For example, it will suggest hot soup on a rainy day. The dish suggestion unit will also develop a system that adds recommended dishes according to the season and weather to the dishes suggested by the generation AI. For example, it will suggest dishes using seasonal ingredients. This will make it possible to add recommended dishes according to the season and weather.

[0066] The dish suggestion unit can incorporate regional ingredients and dishes into the dishes suggested by the generation AI, allowing users to enjoy local flavors. The dish suggestion unit, for example, builds a system that incorporates regional ingredients and dishes into the dishes suggested by the generation AI. For example, it suggests dishes using local ingredients based on the user's location. The dish suggestion unit also improves the accuracy of the dishes suggested by the generation AI based on regional ingredient and dish data. For example, it suggests traditional regional dishes. The dish suggestion unit also develops a system that incorporates regional ingredients and dishes into the dishes suggested by the generation AI, allowing users to enjoy local flavors. For example, it suggests local dishes at travel destinations. This allows users to incorporate regional ingredients and dishes, allowing users to enjoy local flavors.

[0067] The dish suggestion unit uses the emotion estimation function to analyze the emotions of a user when selecting a dish in real time, and can suggest dishes that elicit positive emotions. The dish suggestion unit, for example, uses the emotion estimation function to develop a system that analyzes the emotions of a user when selecting a dish in real time. For example, the emotion estimation function may be used to analyze the user's facial expressions and voice and calculate an emotion score. The dish suggestion unit also builds a system that suggests dishes that elicit positive emotions based on emotion data when the user selects a dish. For example, the system suggests optimal dishes based on the user's emotion score. The dish suggestion unit also uses the emotion estimation function to develop a system that analyzes the emotions of a user when selecting a dish in real time, and suggests dishes that elicit positive emotions. For example, the system suggests optimal dishes based on the user's emotion score. This makes it possible to analyze the user's emotions in real time and suggest dishes that elicit positive emotions.

[0068] The ingredient learning unit can also take into account the origin and cultivation method of ingredients when the generation AI learns the umami components of food ingredients. For example, when the generation AI learns the umami components of food ingredients, the ingredient learning unit retrieves information about the origin of the ingredients from a database and learns based on that information. For example, it analyzes the umami components of ingredients grown in a specific region. The ingredient learning unit also collects data on the cultivation method of ingredients and builds a system in which the generation AI learns umami components based on that information. For example, it learns the differences in umami components depending on cultivation methods such as organic cultivation and hydroponic cultivation. The ingredient learning unit also develops a system that takes into account the origin and cultivation method when the generation AI learns the umami components of food ingredients. For example, it analyzes the umami components of ingredients grown using a specific cultivation method and reflects this in the learning. This allows the generation AI to learn umami components taking into account the origin and cultivation method of the ingredients.

[0069] The ingredient learning unit can also take cooking methods and cooking times into account when the generation AI learns the umami components of food ingredients. For example, when the generation AI learns the umami components of food ingredients, the ingredient learning unit collects data on cooking methods and learns based on that information. For example, it analyzes how umami components change depending on cooking methods such as baking, boiling, and steaming. The ingredient learning unit also collects data on cooking times and builds a system in which the generation AI learns umami components based on that information. For example, it learns the characteristics of ingredients whose umami components increase when simmered for a long time. The ingredient learning unit also develops a system that takes cooking methods and cooking times into account when the generation AI learns the umami components of food ingredients. For example, it analyzes how umami components change depending on the combination of a specific cooking method and time and reflects this in the learning. This allows the generation AI to learn umami components taking cooking methods and cooking times into account.

[0070] The component learning unit uses the emotion estimation function to analyze the emotions felt by the user when selecting ingredients, and can learn ingredients that elicit positive emotions. The component learning unit, for example, uses the emotion estimation function to develop a system that analyzes the emotions felt by the user when selecting ingredients. For example, the component learning unit analyzes the user's facial expressions and voice and calculates an emotion score. The component learning unit also builds a system that learns ingredients that elicit positive emotions based on emotion data felt by the user when selecting ingredients. For example, the component learning unit identifies optimal ingredients based on the user's emotion score. The component learning unit also uses the emotion estimation function to develop a system that analyzes the emotions felt by the user when selecting ingredients, and learns ingredients that elicit positive emotions. For example, the component learning unit identifies optimal ingredients based on the user's emotion score. This makes it possible to analyze the user's emotions and learn ingredients that elicit positive emotions.

[0071] The ingredient learning unit allows the generation AI to learn from a global perspective, incorporating ingredients from different cultures, when learning about the umami components of food ingredients. For example, when the generation AI learns about the umami components of food ingredients, the ingredient learning unit collects food data from different cultures and learns based on that information. For example, it analyzes ingredients from Asia, Europe, America, etc. The ingredient learning unit also incorporates ingredients from different cultures to build a system that allows the generation AI to learn about umami components from a global perspective. For example, it analyzes the umami components of ingredients used in traditional dishes from each country. The ingredient learning unit also develops a system that incorporates ingredients from different cultures when the generation AI learns about the umami components of food ingredients. For example, it learns about the umami components of dishes that combine ingredients from different cultures. This allows the generation AI to incorporate ingredients from different cultures and learn about umami components from a global perspective.

[0072] The ingredient learning unit can refer to winning recipes from past cooking contests when the generation AI learns the umami components of food ingredients. For example, when the generation AI learns the umami components of food ingredients, the ingredient learning unit collects data on winning recipes from past cooking contests and learns based on that information. For example, it analyzes the umami components of ingredients used in winning recipes. The ingredient learning unit also builds a system in which the generation AI learns the umami components of food ingredients by referring to winning recipes from past cooking contests. For example, it identifies the optimal ingredients based on the ingredient data of winning recipes. The ingredient learning unit also develops a system in which the generation AI learns the umami components of food ingredients by referring to winning recipes from past cooking contests. For example, it identifies the optimal ingredients based on the ingredient data of winning recipes. This allows the generation AI to learn umami components by referring to winning recipes from past cooking contests.

[0073] The ingredient learning unit uses the emotion estimation function to analyze the emotions of a user when selecting ingredients in real time, and can learn ingredients that elicit positive emotions. The ingredient learning unit, for example, uses the emotion estimation function to develop a system that analyzes the emotions of a user when selecting ingredients in real time. For example, the component learning unit analyzes the user's facial expressions and voice and calculates an emotion score. The ingredient learning unit also builds a system that learns ingredients that elicit positive emotions based on the emotion data of the user when selecting ingredients. For example, the component learning unit identifies optimal ingredients based on the user's emotion score. The ingredient learning unit also uses the emotion estimation function to develop a system that analyzes the emotions of a user when selecting ingredients in real time, and learns ingredients that elicit positive emotions. For example, the component learning unit identifies optimal ingredients based on the user's emotion score. This makes it possible to analyze the user's emotions in real time and learn ingredients that elicit positive emotions.

[0074] The image analysis unit can also evaluate the freshness and quality of ingredients when the generation AI analyzes ingredients using image judgment. For example, the image analysis unit develops a system to evaluate the freshness of ingredients when the generation AI analyzes ingredients using image judgment. For example, it analyzes the color and shape of vegetables and fruits to determine freshness. The image analysis unit also builds a system in which the generation AI uses image judgment to evaluate the quality of ingredients. For example, it analyzes the color and fat distribution of meat and fish to determine quality. The image analysis unit also develops a system to evaluate the freshness and quality when the generation AI analyzes ingredients using image judgment. For example, it analyzes the surface condition of food to determine freshness and quality. This makes it possible to evaluate the freshness and quality of ingredients.

[0075] The image analysis unit will also analyze images of the cooking process when the generative AI analyzes ingredients using image judgment, making it possible to evaluate cooking methods. For example, the image analysis unit will develop a system that analyzes images of the cooking process when the generative AI analyzes ingredients using image judgment. For example, it will analyze changes in temperature and color during cooking and evaluate cooking methods. The image analysis unit will also build a system that allows the generative AI to evaluate cooking methods based on images of the cooking process. For example, it will analyze the degree of doneness or simmering and identify the optimal cooking method. The image analysis unit will also develop a system that analyzes images of the cooking process when the generative AI analyzes ingredients using image judgment, making it possible to analyze changes in ingredients during cooking and identify the optimal cooking method. This will make it possible to analyze images of the cooking process and evaluate cooking methods.

[0076] The image analysis unit uses the emotion estimation function to analyze the emotions felt by a user when viewing an image of food, and can identify ingredients that elicit positive emotions. The image analysis unit, for example, uses the emotion estimation function to develop a system that analyzes the emotions felt by a user when viewing an image of food. For example, the image analysis unit analyzes the user's facial expressions and voice and calculates an emotion score. The image analysis unit also builds a system that identifies ingredients that elicit positive emotions based on emotion data felt by a user when viewing an image of food. For example, the image analysis unit identifies optimal ingredients based on the user's emotion score. The image analysis unit also uses the emotion estimation function to develop a system that analyzes the emotions felt by a user when viewing an image of food, and can identify ingredients that elicit positive emotions. For example, the image analysis unit identifies optimal ingredients based on the user's emotion score. This makes it possible to analyze the user's emotions and identify ingredients that elicit positive emotions.

[0077] The image analysis unit can also analyze images taken at different angles and under different lighting conditions when the generative AI analyzes materials through image judgment. For example, the image analysis unit develops a system that analyzes images taken at different angles when the generative AI analyzes materials through image judgment. For example, it analyzes images from multiple angles to grasp the overall picture of the material. The image analysis unit also builds a system that allows the generative AI to analyze materials based on images taken under different lighting conditions. For example, it analyzes the characteristics of the material by taking into account the effects of brightness and shadows. The image analysis unit also develops a system that analyzes images taken at different angles and under different lighting conditions when the generative AI analyzes materials through image judgment. For example, it analyzes images taken under multiple conditions to grasp the detailed characteristics of the material. This makes it possible to analyze images taken at different angles and under different lighting conditions.

[0078] The image analysis unit can also analyze video data and incorporate dynamic information when the generative AI analyzes ingredients through image judgment. For example, the image analysis unit develops a system that analyzes video data when the generative AI analyzes ingredients through image judgment. For example, it analyzes videos of the cooking process and evaluates changes in the ingredients. The image analysis unit also builds a system that allows the generative AI to analyze ingredients based on video data. For example, it analyzes the movement of ingredients during cooking and identifies the optimal cooking method. The image analysis unit also develops a system that analyzes video data and incorporates dynamic information when the generative AI analyzes ingredients through image judgment. For example, it evaluates the characteristics of ingredients based on videos of the cooking process. This makes it possible to analyze video data and incorporate dynamic information.

[0079] The image analysis unit uses the emotion estimation function to analyze the emotions of a user when viewing an image of food in real time, and can identify ingredients that elicit positive emotions. The image analysis unit, for example, uses the emotion estimation function to develop a system that analyzes the emotions of a user when viewing an image of food in real time. For example, the image analysis unit analyzes the user's facial expressions and voice and calculates an emotion score. The image analysis unit also builds a system that identifies ingredients that elicit positive emotions based on emotion data when a user views an image of food. For example, the image analysis unit identifies optimal ingredients based on the user's emotion score. The image analysis unit also uses the emotion estimation function to develop a system that analyzes the emotions of a user when viewing an image of food in real time, and can identify ingredients that elicit positive emotions. For example, the image analysis unit identifies optimal ingredients based on the user's emotion score. This makes it possible to analyze the user's emotions in real time and identify ingredients that elicit positive emotions.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] The dish recommendation system can further include an allergy management unit that registers a user's dietary restrictions and allergy information. For example, if a user is allergic to a specific ingredient, that information can be registered and that ingredient can be excluded from the suggested dishes. The allergy management unit can also suggest appropriate dishes taking into account the user's dietary restrictions based on their health condition. For example, it can suggest low-carbohydrate dishes to a diabetic user. Furthermore, the allergy management unit can suggest alternative ingredients based on the user's dietary restrictions and allergy information. For example, it can suggest dairy-free substitutes to a user who is allergic to dairy products. This makes it possible to suggest dishes that take into consideration the user's health condition and allergies.

[0082] The recipe recommendation system can further include a meal recording unit that records the user's meal frequency and time periods. For example, the user can register the time periods for breakfast, lunch, and dinner, and the system can recommend dishes suitable for those time periods. The meal recording unit can also record the user's meal frequency and recommend the next meal based on the user's past meal history. For example, the system can recommend a balanced meal taking into account the dishes the user ate the previous day. Furthermore, the meal recording unit can make suggestions to regulate the user's meal rhythm based on the time periods and frequency of the user's meals. For example, the system can send reminders at appropriate times to encourage regular eating. This can regulate the user's meal rhythm and support a healthy diet.

[0083] The recipe recommendation system can further include an activity recording unit that records the user's amount of exercise and activity level. For example, the system can record data on the user's daily exercise and activity and suggest an appropriate calorie intake based on that data. The activity recording unit can also suggest nutritionally balanced dishes according to the user's amount of exercise. For example, it can suggest dishes high in protein after exercise. Furthermore, the activity recording unit can also suggest dishes suitable for replenishing energy based on the user's activity level. For example, it can suggest high-calorie dishes suitable for replenishing energy after a long period of exercise. This makes it possible to suggest appropriate meals according to the user's amount of exercise and activity level.

[0084] The recipe suggestion system can further include a purchase history management unit that records the user's ingredient purchase history. For example, it can record data on ingredients purchased by the user in the past and suggest recipes based on that data. The purchase history management unit can also manage inventory based on the ingredients purchased by the user and suggest recipes that use ingredients at the appropriate time. For example, it can suggest recipes that prioritize ingredients that are close to their expiration date. Furthermore, the purchase history management unit can automatically generate a shopping list for the next purchase based on the user's purchase history. For example, it can list ingredients that are purchased regularly to prevent forgetting to buy them. This makes it possible to make efficient recipe suggestions that utilize the user's ingredient purchase history.

[0085] The dish recommendation system can further include a satisfaction evaluation unit that evaluates the user's satisfaction with the meal. For example, the user can evaluate their satisfaction after a meal, and the next dish recommendation can be improved based on that data. The satisfaction evaluation unit can also improve the accuracy of dish recommendations based on the user's evaluation data. For example, it can analyze the ingredients of highly rated dishes and recommend similar dishes. Furthermore, the satisfaction evaluation unit can also recommend dishes tailored to individual preferences based on the user's satisfaction data. For example, if a particular ingredient is preferred, dishes containing a large amount of that ingredient can be recommended. This enables highly accurate dish recommendations that reflect the user's satisfaction.

[0086] The dish recommendation system can further include an emotion estimation unit that estimates the user's emotions and suggests dishes based on those emotions. For example, the emotion estimation unit can analyze the user's emotions in real time when selecting dishes and suggest dishes that elicit positive emotions. The emotion estimation unit can also suggest dishes that have stress-reducing or relaxing effects based on the user's emotion data. For example, it can suggest dishes that use herbs that have a relaxing effect. Furthermore, the emotion estimation unit can also suggest dishes that are suitable for changing your mood based on the user's emotion data. For example, when you are feeling down, it can suggest dishes that have a mood-lifting effect. This makes it possible to suggest dishes that take the user's emotions into consideration.

[0087] The cooking recommendation system can further include an emotion estimation unit that estimates the user's emotions and selects ingredients based on those emotions. For example, the emotion estimation unit can analyze the user's emotions in real time when selecting ingredients and suggest ingredients that elicit positive emotions. The emotion estimation unit can also suggest ingredients that have stress-reducing or relaxing effects based on the user's emotion data. For example, it can suggest herbs and spices that have a relaxing effect. Furthermore, the emotion estimation unit can also suggest ingredients that are suitable for changing your mood based on the user's emotion data. For example, when you are feeling down, it can suggest ingredients that have a mood-boosting effect. This makes it possible to select ingredients that take the user's emotions into consideration.

[0088] The cooking recommendation system can further include an emotion estimation unit that estimates the user's emotion and suggests meal times based on the emotion. For example, the emotion estimation unit can analyze in real time when the user feels hungry and suggest appropriate meal times. The emotion estimation unit can also suggest meal times that will reduce stress and have a relaxing effect based on the user's emotion data. For example, it can suggest meals at times that will have a relaxing effect. The emotion estimation unit can also suggest meal times that are suitable for changing your mood based on the user's emotion data. For example, when you are feeling down, it can suggest meals at times that will have a mood-boosting effect. This makes it possible to suggest meal times that take the user's emotions into consideration.

[0089] The cooking recommendation system can further include an emotion estimation unit that estimates the user's emotions and suggests a dining environment based on the emotions. For example, it can analyze an environment in which the user can relax in real time and suggest an appropriate dining environment. The emotion estimation unit can also suggest a dining environment that reduces stress and has a relaxing effect based on the user's emotion data. For example, it can suggest music or lighting that has a relaxing effect. Furthermore, the emotion estimation unit can also suggest a dining environment that is suitable for changing the user's mood based on the user's emotion data. For example, when the user is feeling down, it can suggest an environment that has the effect of lifting the user's mood. This makes it possible to suggest a dining environment that takes the user's emotions into consideration.

[0090] The cooking recommendation system can further include an emotion estimation unit that estimates the user's emotions and suggests meal portions based on those emotions. For example, it can analyze in real time when the user feels full and suggest an appropriate meal portion. The emotion estimation unit can also suggest a meal portion that will reduce stress and have a relaxing effect based on the user's emotion data. For example, it can suggest an appropriate amount of meal that will have a relaxing effect. Furthermore, the emotion estimation unit can also suggest a meal portion that is appropriate for changing your mood based on the user's emotion data. For example, when you are feeling down, it can suggest an appropriate amount of meal that will have a mood-boosting effect. This makes it possible to suggest meal portions that take the user's emotions into consideration.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: The preferred component registration unit registers the user's preferred umami components. For example, the user can select and register specific umami components such as glutamic acid or inosinic acid. Step 2: The dish suggestion unit suggests dishes based on the umami components registered by the preferred component registration unit. For example, if the user registers that they like glutamic acid, the dish suggestion unit lists dishes that are high in glutamic acid and suggests them to the user. Step 3: The ingredient learning unit learns the ingredients of the dishes suggested by the dish suggestion unit. For example, the ingredient learning unit acquires ingredient information of food ingredients from a database and learns what umami components each ingredient contains. Step 4: The image analysis unit analyzes the ingredients based on the ingredients learned by the ingredient learning unit. For example, the image analysis unit analyzes an image of a dish, identifies the ingredients contained in it, and analyzes what umami components the ingredients contain.

[0093] 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.

[0094] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0095] 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.

[0096] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0097] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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).

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0110] 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.

[0111] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0119] 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.

[0120] 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.

[0121] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0122] 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.

[0123] 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.

[0124] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0125] 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.

[0126] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0127] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0137] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0138] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0139] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0140] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0141] The data processing system 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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."

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0159] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0160] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a preferred component registration unit for registering a user's preferred umami component; a dish suggestion unit that suggests dishes based on the umami components registered by the preferred component registration unit; an ingredient learning unit that learns ingredients of the dishes proposed by the dish suggestion unit; an image analysis unit that analyzes materials based on the components learned by the component learning unit; A system characterized by:

2. The preferred component registration unit The user's history of past meals is analyzed, and the user's preferred umami components are automatically estimated.

2. The system of claim 1.

3. The preferred component registration unit Creating and periodically updating a taste profile of the user to accommodate changes in their preferences 2. The system of claim 1.

4. The preferred component registration unit The emotions of the user when eating the dish are recorded, and the preferred umami components are estimated based on the emotion data.

2. The system of claim 1.

5. The preferred component registration unit When the user registers the preferred umami component, the user can intuitively operate the device using voice input.

2. The system of claim 1.

6. The preferred component registration unit The user's favorite umami components are shared with other users, and recommendations for dishes are made within the community.

2. The system of claim 1.

Images