System

Abstract

An object of a system according to an embodiment is to efficiently propose a recipe in accordance with individual eating habits and nutrition balance of a user.SOLUTION: A system includes a user information input unit, a recipe generation unit, and a recipe proposal unit. The user information input unit inputs information related to eating habits, food material preferences, and nutrition balance of the user. The recipe generation unit analyzes the information input by the user information input unit and generates a recipe. The recipe proposing unit proposes the recipe generated by the recipe generating unit to the user.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 to efficiently suggest recipes that match a user's individual eating habits and nutritional balance.

[0005] The system according to the embodiment aims to efficiently propose recipes that are suited to the individual eating habits and nutritional balance of a user. [Means for solving the problem]

[0006] The system according to the embodiment includes a user information input unit, a recipe generation unit, and a recipe suggestion unit. The user information input unit inputs information about the user's eating habits, ingredient preferences, and nutritional balance. The recipe generation unit analyzes the information input by the user information input unit and generates a recipe. The recipe suggestion unit suggests the recipe generated by the recipe generation unit to the user. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently suggest recipes that are suited to the individual eating habits and nutritional balance 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 CookAI system according to an embodiment of the present invention is a system that generates and suggests customized recipes tailored to a user's preferences and nutritional needs. As a result, the CookAI system can provide customized recipes tailored to a user's preferences and nutritional needs.

[0029] The CookAI system according to the embodiment includes a user information input unit, a recipe generation unit, and a recipe suggestion unit. The user information input unit inputs information about the user's eating habits, ingredient preferences, and nutritional balance. For example, the user can input information such as favorite and disliked ingredients, allergy information, diet goals, and daily calorie intake. The recipe generation unit analyzes the information input by the user information input unit and generates a recipe. For example, the generation AI generates a low-calorie chicken recipe based on the user's input information, such as "I like chicken and want to reduce calories." The generation AI generates recipes using a text generation AI (e.g., LLM). The generation AI can also generate recipe content using a multimodal generation AI. The recipe suggestion unit suggests recipes generated by the recipe generation unit to the user. For example, specific recipes such as "Healthy Stir-fried Chicken and Vegetables" or "Low-calorie Chicken Salad" are displayed. This allows the CookAI system to provide customized recipes tailored to the user's preferences and nutritional needs.

[0030] The user information input unit automatically analyzes the user's past meal history, eliminating the need for input. The user information input unit automatically analyzes, for example, the meal history previously entered by the user, and learns preferences and habits. For example, frequently eaten ingredients and dishes can be identified from past meal data, and automatically completed the next time the information is entered. This eliminates the need for input by the user.

[0031] The user information input unit can display the nutritional value and health benefits of ingredients in real time based on the information entered by the user. For example, the user information input unit uses a generation AI to display nutritional values ​​in real time based on the ingredient information entered by the user. For example, the vitamin and mineral content is displayed in a graph. The user information input unit also displays the health benefits of ingredients in real time. For example, it displays health benefits such as improved immunity, lowered blood pressure, and antioxidant effects. This allows the user to optimize their selection.

[0032] The user information input unit uses voice recognition technology to allow the user to input information simply by speaking. For example, the user information input unit uses voice recognition technology to build a system that allows the user to input information about eating habits and preferences simply by speaking. For example, information is input simply by saying, "I like chicken and want to keep the calories low." The voice recognition technology uses technologies such as deep learning and voice command analysis. This allows the user to input information without using their hands.

[0033] The user information input unit allows the generation AI to automatically generate a grocery shopping list based on the information entered by the user, and can be linked to online shopping. The user information input unit allows the generation AI to automatically generate a shopping list based on the grocery information entered by the user. For example, it may list the necessary ingredients and link to an online shopping site. The generation AI uses technologies such as neural networks and generative models. The shopping list includes the type and quantity of ingredients, where to purchase them, etc. This allows the user to purchase ingredients efficiently.

[0034] The recipe generation unit can generate recipes that are appropriate for the season and weather, as well as considering the user's ingredient preferences and nutritional balance. For example, the generation AI of the recipe generation unit considers seasonal ingredients and generates recipes using seasonal ingredients. For example, in summer it will suggest recipes using tomatoes and cucumbers. The generation AI considers the user's ingredient preferences and nutritional balance and generates recipes that are appropriate for the season and weather. Seasons include spring, summer, autumn, and winter, and weather includes sunny, rainy, snowy, and temperature. This makes it possible to provide recipes that are appropriate for the season and weather.

[0035] The recipe generation unit can learn the user's past recipe selection history and generate more personalized recipes. For example, the recipe generation unit uses a generation AI to learn the user's past recipe selection history and generate recipes that reflect their preferences and habits. For example, it can suggest new recipes based on the trends of recipes selected in the past. The recipe selection history includes recipes selected in the past and their frequency of selection. This makes it possible to provide recipes that match the user's preferences.

[0036] The recipe generation unit can generate recipes that are rooted in the region, taking into account the user's cultural background and local specialties. For example, the recipe generation unit generates recipes using traditional dishes and local specialties by taking into account the user's cultural background using a generation AI. For example, it can suggest local dishes from the user's hometown. Cultural background includes regional food culture and traditional dishes. Regional specialties include local agricultural products and specialty goods. This makes it possible to provide recipes that take into account the user's cultural background and local specialties.

[0037] The recipe generation unit can generate allergen-free recipes by taking into account the user's ingredient allergy information. For example, the recipe generation unit generates allergen-free recipes by using a generation AI that takes into account the user's allergy information. For example, a user with a nut allergy can be suggested a nut-free recipe. Allergy information includes specific allergens and types of allergies. Allergens include nuts, dairy products, and gluten. This allows the system to provide recipes that take into account the user's allergy information.

[0038] The recipe suggestion unit takes into account the user's past feedback and can prioritize suggesting recipes that are more popular. For example, the recipe suggestion unit uses a generation AI to analyze the user's past feedback and prioritize suggesting recipes that are more popular. For example, it re-suggests recipes that have received high ratings in the past. Feedback includes user ratings, comments, usage history, etc. This allows it to prioritize suggesting recipes that match the user's preferences.

[0039] The recipe suggestion unit can display the cooking time and difficulty level for the generated recipes to make it easier for the user to select. The recipe suggestion unit, for example, builds a system that displays the cooking time and difficulty level for recipes generated by the generation AI. For example, the cooking time and difficulty level are clearly displayed on the recipe details page. Cooking time includes preparation time, cooking time, total time, etc. Difficulty level includes evaluation criteria such as for beginners, intermediate, and advanced. This makes it possible to display the cooking time and difficulty level to make it easier for the user to select.

[0040] The recipe suggestion unit can take into account the user's ingredient inventory information and make suggestions to reduce waste. For example, the recipe suggestion unit uses a generation AI to analyze the user's ingredient inventory information and suggest recipes that make effective use of inventory. For example, recipes that use ingredients in the refrigerator are displayed preferentially. Inventory information includes the ingredients in the refrigerator, their expiration date, and quantities. This allows suggestions to be made to reduce the user's ingredient waste.

[0041] The recipe suggestion unit can suggest recipes that match the user's dietary goals. For example, the recipe suggestion unit uses a generation AI to analyze the user's dietary goals and suggest recipes that match those goals. For example, low-calorie recipes can be suggested to a user on a diet. Dietary goals include dieting, muscle building, and maintaining health. This makes it possible to provide recipes that match the user's dietary goals.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The CookAI system can further include a goal section that suggests recipes according to the user's dietary goals. For example, the goal section allows the user to input goals such as dieting, muscle building, or maintaining health. Based on the user's input, the generation AI generates recipes according to the goals. For example, low-calorie recipes are suggested for a user on a diet, and high-protein recipes are suggested for a user aiming to build muscle. This makes it possible to provide recipes customized to the user's dietary goals.

[0044] The CookAI system can also be equipped with an inventory management unit that takes into account the user's ingredient inventory information. For example, the inventory management unit allows the user to input information about ingredients in the refrigerator or pantry. Based on the input information, the inventory management unit's generation AI generates recipes that make effective use of inventory. For example, it can prioritize recipes that use ingredients in the refrigerator, reducing ingredient waste. This allows the user to use ingredients efficiently.

[0045] The CookAI system can further include a cultural background section that takes into account the user's cultural background and local specialties. For example, the user can input information about their hometown and cultural background into the cultural background section. Based on the input information, the generation AI in the cultural background section generates recipes using traditional dishes and local specialties. For example, it can suggest local dishes from the user's hometown. This makes it possible to provide recipes that take into account the user's cultural background and local specialties.

[0046] The CookAI system can further include a feedback analysis unit that takes into account the user's past feedback. For example, the feedback analysis unit allows the user to input ratings and comments on recipes previously provided. Based on the input information, the feedback analysis unit allows the generation AI to preferentially suggest recipes that match the user's preferences. For example, it can re-suggest recipes that have received high ratings in the past. This makes it possible to provide recipes that match the user's preferences.

[0047] The CookAI system can also be equipped with an allergy management unit that takes into account the user's allergy information. For example, the allergy management unit allows the user to input information about specific allergens or types of allergies. Based on the input information, the allergy management unit uses the generation AI to generate recipes that do not contain allergens. For example, a user with a nut allergy can be suggested a recipe that does not contain nuts. This allows the system to provide recipes that take into account the user's allergy information.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The user information input unit inputs information about the user's eating habits, food preferences, and nutritional balance. For example, the user can input information about their favorite and least favorite foods, allergy information, diet goals, and daily calorie intake. Step 2: The recipe generation unit analyzes the information input by the user information input unit and generates a recipe. For example, the generation AI generates a low-calorie recipe using chicken based on the information input by the user, such as "I like chicken and want to reduce the calories." The generation AI generates the recipe using a text generation AI (e.g., LLM). The generation AI can also generate the contents of the recipe using a multimodal generation AI. Step 3: The recipe suggestion unit suggests the recipes generated by the recipe generation unit to the user. For example, specific recipes such as "Healthy Stir-fried Chicken and Vegetables" or "Low-Calorie Chicken Salad" are displayed.

[0050] (Example 2) The CookAI system according to an embodiment of the present invention is a system that generates and suggests customized recipes tailored to a user's preferences and nutritional needs. As a result, the CookAI system can provide customized recipes tailored to a user's preferences and nutritional needs.

[0051] The CookAI system according to the embodiment includes a user information input unit, a recipe generation unit, and a recipe suggestion unit. The user information input unit inputs information about the user's eating habits, ingredient preferences, and nutritional balance. For example, the user can input information such as favorite and disliked ingredients, allergy information, diet goals, and daily calorie intake. The recipe generation unit analyzes the information input by the user information input unit and generates a recipe. For example, the generation AI generates a low-calorie chicken recipe based on the user's input information, such as "I like chicken and want to reduce calories." The generation AI generates recipes using a text generation AI (e.g., LLM). The generation AI can also generate recipe content using a multimodal generation AI. The recipe suggestion unit suggests recipes generated by the recipe generation unit to the user. For example, specific recipes such as "Healthy Stir-fried Chicken and Vegetables" or "Low-calorie Chicken Salad" are displayed. This allows the CookAI system to provide customized recipes tailored to the user's preferences and nutritional needs.

[0052] The user information input unit automatically analyzes the user's past meal history, eliminating the need for input. The user information input unit automatically analyzes, for example, the meal history previously entered by the user, and learns preferences and habits. For example, frequently eaten ingredients and dishes can be identified from past meal data, and automatically completed the next time the information is entered. This eliminates the need for input by the user.

[0053] The user information input unit can display the nutritional value and health benefits of ingredients in real time based on the information entered by the user. For example, the user information input unit uses a generation AI to display nutritional values ​​in real time based on the ingredient information entered by the user. For example, the vitamin and mineral content is displayed in a graph. The user information input unit also displays the health benefits of ingredients in real time. For example, it displays health benefits such as improved immunity, lowered blood pressure, and antioxidant effects. This allows the user to optimize their selection.

[0054] The user information input unit uses an emotion estimation function to analyze the emotions of the user when inputting information, and can suggest ingredients and recipes that will help relax the user if the user is feeling stressed. The user information input unit, for example, analyzes the user's facial expressions and voice when inputting information to estimate the user's emotional state. For example, if the user is feeling stressed, it suggests ingredients that have a relaxing effect. The emotion estimation function uses technologies such as facial expression recognition, voice analysis, and text analysis. Examples of ingredients that can help relax include herbal tea, chocolate, and nuts. This can help reduce the user's stress.

[0055] The user information input unit uses voice recognition technology to allow the user to input information simply by speaking. For example, the user information input unit uses voice recognition technology to build a system that allows the user to input information about eating habits and preferences simply by speaking. For example, information is input simply by saying, "I like chicken and want to keep the calories low." The voice recognition technology uses technologies such as deep learning and voice command analysis. This allows the user to input information without using their hands.

[0056] The user information input unit allows the generation AI to automatically generate a grocery shopping list based on the information entered by the user, and can be linked to online shopping. The user information input unit allows the generation AI to automatically generate a shopping list based on the grocery information entered by the user. For example, it may list the necessary ingredients and link to an online shopping site. The generation AI uses technologies such as neural networks and generative models. The shopping list includes the type and quantity of ingredients, where to purchase them, etc. This allows the user to purchase ingredients efficiently.

[0057] The user information input unit can use the emotion estimation function to analyze the emotions of the user when entering information and provide an interface design that elicits positive emotions. The user information input unit, for example, uses the emotion estimation function to analyze the emotions of the user when entering information in real time and provide an interface design that elicits positive emotions. For example, bright colors or encouraging messages can be displayed. The emotion estimation function uses technologies such as facial expression recognition, voice analysis, and text analysis. Positive emotions include happiness, satisfaction, and a sense of security. The interface design includes color, layout, usability, and the like. This makes it possible to elicit positive emotions from the user.

[0058] The recipe generation unit can generate recipes that are appropriate for the season and weather, as well as considering the user's ingredient preferences and nutritional balance. For example, the generation AI of the recipe generation unit considers seasonal ingredients and generates recipes using seasonal ingredients. For example, in summer it will suggest recipes using tomatoes and cucumbers. The generation AI considers the user's ingredient preferences and nutritional balance and generates recipes that are appropriate for the season and weather. Seasons include spring, summer, autumn, and winter, and weather includes sunny, rainy, snowy, and temperature. This makes it possible to provide recipes that are appropriate for the season and weather.

[0059] The recipe generation unit can learn the user's past recipe selection history and generate more personalized recipes. For example, the recipe generation unit uses a generation AI to learn the user's past recipe selection history and generate recipes that reflect their preferences and habits. For example, it can suggest new recipes based on the trends of recipes selected in the past. The recipe selection history includes recipes selected in the past and their frequency of selection. This makes it possible to provide recipes that match the user's preferences.

[0060] The recipe generation unit uses the emotion estimation function to generate recipes according to the user's current emotional state and can use ingredients that reduce stress. The recipe generation unit, for example, uses the emotion estimation function to analyze the user's current emotional state and generate recipes using ingredients that reduce stress. For example, it suggests recipes using chamomile tea and dark chocolate. The emotion estimation function uses technologies such as facial expression recognition, voice analysis, and text analysis. Ingredients that reduce stress include herbal tea, dark chocolate, and foods containing omega-3 fatty acids. This makes it possible to provide recipes that reduce the user's stress.

[0061] The recipe generation unit can generate recipes that are rooted in the region, taking into account the user's cultural background and local specialties. For example, the recipe generation unit generates recipes using traditional dishes and local specialties by taking into account the user's cultural background using a generation AI. For example, it can suggest local dishes from the user's hometown. Cultural background includes regional food culture and traditional dishes. Regional specialties include local agricultural products and specialty goods. This makes it possible to provide recipes that take into account the user's cultural background and local specialties.

[0062] The recipe generation unit can generate allergen-free recipes by taking into account the user's ingredient allergy information. For example, the recipe generation unit generates allergen-free recipes by using a generation AI that takes into account the user's allergy information. For example, a user with a nut allergy can be suggested a nut-free recipe. Allergy information includes specific allergens and types of allergies. Allergens include nuts, dairy products, and gluten. This allows the system to provide recipes that take into account the user's allergy information.

[0063] The recipe generation unit uses the emotion estimation function to generate recipes according to the user's emotional state and can use ingredients that enhance happiness. The recipe generation unit, for example, uses the emotion estimation function to analyze the user's emotional state and generate recipes using ingredients that enhance happiness. For example, it suggests recipes using dark chocolate and bananas. The emotion estimation function uses technologies such as facial recognition, voice analysis, and text analysis. Ingredients that enhance happiness include dark chocolate, bananas, and foods containing omega-3 fatty acids. This makes it possible to provide recipes that enhance the user's happiness.

[0064] The recipe suggestion unit takes into account the user's past feedback and can prioritize suggesting recipes that are more popular. For example, the recipe suggestion unit uses a generation AI to analyze the user's past feedback and prioritize suggesting recipes that are more popular. For example, it re-suggests recipes that have received high ratings in the past. Feedback includes user ratings, comments, usage history, etc. This allows it to prioritize suggesting recipes that match the user's preferences.

[0065] The recipe suggestion unit can display the cooking time and difficulty level for the generated recipes to make it easier for the user to select. The recipe suggestion unit, for example, builds a system that displays the cooking time and difficulty level for recipes generated by the generation AI. For example, the cooking time and difficulty level are clearly displayed on the recipe details page. Cooking time includes preparation time, cooking time, total time, etc. Difficulty level includes evaluation criteria such as for beginners, intermediate, and advanced. This makes it possible to display the cooking time and difficulty level to make it easier for the user to select.

[0066] The recipe suggestion unit uses the emotion estimation function to suggest recipes that correspond to the user's current emotional state and use ingredients that are relaxing. For example, the recipe suggestion unit uses the emotion estimation function to analyze the user's current emotional state and suggest recipes that use ingredients that are relaxing. For example, it displays recipes that use chamomile tea and avocado. The emotion estimation function uses technologies such as facial expression recognition, voice analysis, and text analysis. Examples of ingredients that are relaxing include herbal tea, chocolate, and nuts. This makes it possible to provide relaxing recipes that correspond to the user's emotional state.

[0067] The recipe suggestion unit can take into account the user's ingredient inventory information and make suggestions to reduce waste. For example, the recipe suggestion unit uses a generation AI to analyze the user's ingredient inventory information and suggest recipes that make effective use of inventory. For example, recipes that use ingredients in the refrigerator are displayed preferentially. Inventory information includes the ingredients in the refrigerator, their expiration date, and quantities. This allows suggestions to be made to reduce the user's ingredient waste.

[0068] The recipe suggestion unit can suggest recipes that match the user's dietary goals. For example, the recipe suggestion unit uses a generation AI to analyze the user's dietary goals and suggest recipes that match those goals. For example, low-calorie recipes can be suggested to a user on a diet. Dietary goals include dieting, muscle building, and maintaining health. This makes it possible to provide recipes that match the user's dietary goals.

[0069] The recipe suggestion unit can use the emotion estimation function to suggest recipes that correspond to the user's emotional state and use ingredients that increase energy. For example, the recipe suggestion unit uses the emotion estimation function to analyze the user's emotional state and suggest recipes that use ingredients that increase energy. For example, recipes that use bananas and nuts are displayed. The emotion estimation function uses technologies such as facial expression recognition, voice analysis, and text analysis. Ingredients that increase energy include bananas, nuts, and whole grains. This makes it possible to provide recipes that increase the user's energy.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The CookAI system can further include a goal section that suggests recipes according to the user's dietary goals. For example, the goal section allows the user to input goals such as dieting, muscle building, or maintaining health. Based on the user's input, the generation AI generates recipes according to the goals. For example, low-calorie recipes are suggested for a user on a diet, and high-protein recipes are suggested for a user aiming to build muscle. This makes it possible to provide recipes customized to the user's dietary goals.

[0072] The CookAI system can also be equipped with an inventory management unit that takes into account the user's ingredient inventory information. For example, the inventory management unit allows the user to input information about ingredients in the refrigerator or pantry. Based on the input information, the inventory management unit's generation AI generates recipes that make effective use of inventory. For example, it can prioritize recipes that use ingredients in the refrigerator, reducing ingredient waste. This allows the user to use ingredients efficiently.

[0073] The CookAI system can further include a cultural background section that takes into account the user's cultural background and local specialties. For example, the user can input information about their hometown and cultural background into the cultural background section. Based on the input information, the generation AI in the cultural background section generates recipes using traditional dishes and local specialties. For example, it can suggest local dishes from the user's hometown. This makes it possible to provide recipes that take into account the user's cultural background and local specialties.

[0074] The CookAI system can further include a feedback analysis unit that takes into account the user's past feedback. For example, the feedback analysis unit allows the user to input ratings and comments on recipes previously provided. Based on the input information, the feedback analysis unit allows the generation AI to preferentially suggest recipes that match the user's preferences. For example, it can re-suggest recipes that have received high ratings in the past. This makes it possible to provide recipes that match the user's preferences.

[0075] The CookAI system can also be equipped with an allergy management unit that takes into account the user's allergy information. For example, the allergy management unit allows the user to input information about specific allergens or types of allergies. Based on the input information, the allergy management unit uses the generation AI to generate recipes that do not contain allergens. For example, a user with a nut allergy can be suggested a recipe that does not contain nuts. This allows the system to provide recipes that take into account the user's allergy information.

[0076] The CookAI system can further include an emotion analysis unit that suggests recipes according to the user's emotional state. The emotion analysis unit, for example, analyzes the user's facial expressions and voice when inputting information to estimate the user's emotional state. Based on the estimated emotional state, the emotion analysis unit allows the generation AI to suggest recipes using ingredients that have a relaxing effect. For example, if the user is feeling stressed, the system can suggest recipes using chamomile tea or chocolate. This makes it possible to provide recipes that match the user's emotional state.

[0077] The CookAI system can further include an emotional interface unit that provides an interface design according to the user's emotional state. The emotional interface unit, for example, analyzes the user's facial expressions and voice when inputting information to estimate the user's emotional state. Based on the estimated emotional state, the emotional interface unit provides an interface design to elicit positive emotions. For example, it can display bright colors or encouraging messages. This can elicit positive emotions from the user.

[0078] The CookAI system can further include an emotional recipe unit that generates recipes according to the user's emotional state. The emotional recipe unit, for example, analyzes the user's facial expressions and voice when inputting information to estimate the user's emotional state. Based on the estimated emotional state, the emotional recipe unit generates recipes using ingredients that enhance happiness. For example, it can suggest recipes using dark chocolate and bananas. This makes it possible to provide recipes that enhance the user's happiness.

[0079] The CookAI system can further include an emotion suggestion unit that suggests recipes according to the user's emotional state. The emotion suggestion unit, for example, analyzes the user's facial expressions and voice when inputting information to estimate the user's emotional state. Based on the estimated emotional state, the emotion suggestion unit suggests recipes using ingredients that increase energy. For example, it can suggest recipes using bananas and nuts. This makes it possible to provide recipes that increase the user's energy.

[0080] The CookAI system can further include an emotion generation unit that generates recipes according to the user's emotional state. The emotion generation unit, for example, analyzes the user's facial expressions and voice when inputting information to estimate the user's emotional state. Based on the estimated emotional state, the emotion generation unit generates recipes using ingredients that reduce stress. For example, it can suggest recipes using chamomile tea or dark chocolate. This makes it possible to provide recipes that reduce the user's stress.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The user information input unit inputs information about the user's eating habits, food preferences, and nutritional balance. For example, the user can input information about their favorite and least favorite foods, allergy information, diet goals, and daily calorie intake. Step 2: The recipe generation unit analyzes the information input by the user information input unit and generates a recipe. For example, the generation AI generates a low-calorie recipe using chicken based on the information input by the user, such as "I like chicken and want to reduce the calories." The generation AI generates the recipe using a text generation AI (e.g., LLM). The generation AI can also generate the contents of the recipe using a multimodal generation AI. Step 3: The recipe suggestion unit suggests the recipes generated by the recipe generation unit to the user. For example, specific recipes such as "Healthy Stir-fried Chicken and Vegetables" or "Low-Calorie Chicken Salad" are displayed.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0096] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0111] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0127] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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]

[0150] 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 user information input unit for inputting information regarding the user's eating habits, food preferences, and nutritional balance; a recipe generation unit that analyzes the information input by the user information input unit and generates a recipe; a recipe suggestion unit that suggests the recipe generated by the recipe generation unit to a user. A system characterized by:

2. The user information input unit Automatically analyzes the user's past meal history, eliminating the need to enter information 2. The system of claim 1.

3. The user information input unit Using voice recognition technology, users can input information simply by speaking.

2. The system of claim 1.

4. The recipe generation unit It generates recipes that take into account not only the user's ingredient preferences and nutritional balance, but also the season and weather.

2. The system of claim 1.

5. The recipe suggestion unit Prioritize recipe suggestions based on user feedback 2. The system of claim 1.

6. The user information input unit Analyzes the emotions users input and suggests ingredients and recipes that will help them relax if they are feeling stressed.

2. The system of claim 1.

7. The recipe generation unit Generate recipes based on the user's current emotional state and use ingredients that reduce stress 2. The system of claim 1.

8. The recipe suggestion unit Suggest recipes based on the user's current emotional state and use relaxing ingredients 2. The system of claim 1.

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