system

Abstract

The system according to the embodiment aims to provide personalized recipes based on the individual preferences and health status of the user. [Solution] A system according to an embodiment includes an input unit, a generation unit, a display unit, and a list generation unit. The input unit inputs a user's preferences, allergy information, health conditions, and specific nutritional goals. The generation unit analyzes the information input by the input unit and generates personalized recipes. The display unit displays the recipes generated by the generation unit. The list generation unit generates a shopping list based on the recipes generated by the generation unit.

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 techniques have not adequately addressed providing personalized recipes based on a user's individual preferences and health status, and there is room for improvement.

[0005] The system according to the embodiment aims to provide personalized recipes based on the individual preferences and health status of the user. [Means for solving the problem]

[0006] The system according to the embodiment includes an input unit, a generation unit, a display unit, and a list generation unit. The input unit inputs a user's preferences, allergy information, health conditions, and specific nutritional goals. The generation unit analyzes the information input by the input unit and generates personalized recipes. The display unit displays the recipes generated by the generation unit. The list generation unit generates a shopping list based on the recipes generated by the generation unit. [Effects of the Invention]

[0007] In accordance with an embodiment, the system can provide personalized recipes based on a user's individual preferences and health status. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A recipe generation system according to an embodiment of the present invention analyzes a user's dietary preferences, health status, allergy information, and other information, and generates personalized recipes based on that information. The recipe generation system begins by a user entering their preferences, allergy information, health status, and specific nutritional goals into a profile. The generation AI analyzes this information and generates optimized recipes. The generated recipes display key nutrients and their amounts. The generation AI then provides step-by-step cooking instructions, allowing the user to follow the instructions. A shopping list is also automatically generated based on the generated recipe, making it easy to purchase the necessary ingredients. Furthermore, users can rate the recipes they have cooked and provide specific feedback. Based on this feedback, the generation AI then provides further personalized recipes. Finally, users can share the recipes they have tried with the community. Other users can also try these recipes, and the recipes are shared and rated throughout the community. For example, a recipe that excludes certain ingredients may be generated based on the user's allergy information. Low-calorie or high-protein recipes are also suggested based on the user's health status. Furthermore, the generated recipes also display cooking time and difficulty, allowing users to select recipes that fit their schedule and skill level. This allows the recipe generation system to provide optimal recipes that meet the individual needs of the user, and the recipe generation system can generate personalized recipes based on the user's preferences and health status, and display and generate shopping lists.

[0029] A recipe generation system according to an embodiment includes an input unit, a generation unit, a display unit, and a list generation unit. The input unit inputs a user's preferences, allergy information, health status, and specific nutritional goals. For example, the user can input this information through an application. The input unit stores the information input by the user in a database and provides it to the generation unit. The generation unit uses a generation AI to analyze the information input by the input unit and generate a personalized recipe. The generation AI selects optimal ingredients and cooking methods, taking into account the user's preferences and allergy information, for example. The generation unit provides the recipe generated by the generation AI to the display unit. The display unit displays the generated recipe to the user. For example, the display unit displays the main nutrients in the recipe and their amounts. The display unit can also display cooking methods in specific steps. The list generation unit automatically generates a shopping list based on the generated recipe. For example, the list generation unit lists the necessary ingredients, allowing the user to easily purchase them. This enables the recipe generation system according to an embodiment to generate personalized recipes based on the user's preferences and health status, and to display and generate shopping lists. Some or all of the above-mentioned processes in the generation unit are performed using a generation AI. For example, the generation unit receives user preference and allergy information as input, and the generation AI outputs the optimal recipe. Some or all of the above-mentioned processes in the display unit are performed using a generation AI. For example, the display unit receives a recipe generated by the generation AI as input and displays it to the user. Some or all of the above-mentioned processes in the list generation unit are performed using a generation AI. For example, the list generation unit receives a recipe generated by the generation AI as input and lists the necessary ingredients.

[0030] The generation unit can use the generation AI to analyze the user's preferences, allergy information, health condition, and specific nutritional goals and generate personalized recipes. The generation unit, for example, uses the generation AI to analyze the user's preferences and allergy information. For example, the generation AI selects optimal ingredients and cooking methods based on information entered by the user. The generation unit can also use the generation AI to generate recipes that take the user's health condition into consideration. For example, the generation AI can suggest low-calorie or high-protein recipes based on the user's health condition. Furthermore, the generation unit can use the generation AI to generate recipes based on specific nutritional goals. For example, the generation AI generates optimal recipes based on the user's calorie restriction and protein intake. This makes it possible to generate recipes based on the user's individual information. Some or all of the above-mentioned processes in the generation unit are performed using the generation AI. For example, the generation unit receives the user's preferences and allergy information as input, and the generation AI outputs the optimal recipe. The generation AI can analyze the user's information and generate the optimal recipe using, for example, deep learning or natural language processing technology. The generation AI selects the optimal recipe by, for example, vectorizing the user's input information and performing a similarity calculation. The generation AI can also learn the user's past eating history and generate recipes that match the user's preferences. This allows the generation unit to provide the optimal recipe based on the user's individual information.

[0031] The display unit can display the main nutrients and their amounts of the generated recipe. For example, the display unit displays nutrients such as protein, fat, carbohydrates, vitamins, and minerals. The display unit can also display the amount of each nutrient numerically. For example, the display unit may display 20g of protein, 10g of fat, and 30g of carbohydrates. Furthermore, the display unit can visually display the nutrient balance using a graph or chart. For example, the display unit may display the percentage of each nutrient using a pie chart or bar graph. This allows the user to check the nutritional balance of the generated recipe. Some or all of the above-described processing in the display unit is performed using a generation AI. For example, the display unit receives the recipe generated by the generation AI as input and displays the nutrient information. The generation AI, for example, analyzes the recipe's ingredient information and calculates the amount of each nutrient. The generation AI, for example, references an ingredient database to obtain nutrient information for each ingredient. The display unit can then display the nutrient information for the generated recipe, allowing the user to check the nutritional balance.

[0032] The list generation unit can automatically generate a shopping list based on the generated recipe. For example, the list generation unit lists the ingredients needed based on the generated recipe. For example, the list generation unit automatically extracts ingredients included in the recipe and generates a shopping list. The list generation unit can also include the quantities and purchasing locations of ingredients in the list. For example, the list generation unit displays the quantities of ingredients numerically and provides links to supermarkets and online stores. Furthermore, the list generation unit can refer to the user's past purchasing history and exclude duplicate ingredients. For example, the list generation unit excludes ingredients already purchased by the user from the list and displays only the necessary ingredients. In this way, the list generation unit automatically generates a shopping list based on the generated recipe, thereby improving the efficiency of the user's shopping. Some or all of the above-described processing in the list generation unit is performed using a generation AI. For example, the list generation unit receives the recipe generated by the generation AI as input and lists the necessary ingredients. For example, the generation AI analyzes the ingredient information of the recipe and extracts the necessary ingredients. For example, the generation AI references an ingredient database to obtain the quantity and purchasing location of each ingredient. This allows the list generation unit to automatically generate a shopping list based on the generated recipes.

[0033] The recipe generation system includes a feedback receiving unit that receives user feedback. The feedback receiving unit provides an interface for users to input their evaluations and opinions on the recipes they have cooked. For example, the feedback receiving unit displays a form for users to evaluate the recipe's taste, cooking difficulty, cooking time, etc. The feedback receiving unit can also provide a text box for users to input specific improvements or suggestions. For example, a user can input specific feedback such as "This recipe is too salty." The feedback receiving unit then stores the user's feedback in a database and provides it to the generation AI. This allows the generation AI to further personalize the recipe based on the user's feedback. For example, the generation AI analyzes the user's feedback and takes it into consideration when generating the next recipe. This allows the feedback receiving unit to provide a more personalized recipe by accepting the user's feedback. Some or all of the above-described processing in the feedback receiving unit is performed by the generation AI. For example, the feedback receiving unit allows the generation AI to receive the user's feedback as input and take it into consideration when generating the next recipe. The generation AI, for example, analyzes the user's feedback text and extracts specific improvements. This allows the feedback receiving unit to receive feedback from the user and provide a more personalized recipe.

[0034] The feedback receiving unit enables the generation AI to provide a more personalized recipe based on user feedback. For example, the feedback receiving unit stores the user's input feedback in a database and provides it to the generation AI. The generation AI analyzes the user's feedback and takes it into consideration when generating the next recipe. For example, if the user inputs feedback such as "This recipe is too salty," the generation AI will reduce the salt content the next time it generates a recipe. Also, if the user inputs feedback such as "This recipe is difficult to prepare," the generation AI will suggest a recipe that is easier to prepare the next time it generates a recipe. Furthermore, the generation AI can learn from the user's feedback and provide recipes that suit the user's preferences. For example, the generation AI analyzes the user's past feedback and learns the user's preferred seasonings and cooking methods. This allows the generation AI to further personalize the recipe based on the user's feedback, thereby improving user satisfaction. Some or all of the above-described processing in the feedback receiving unit is performed by the generation AI. For example, the feedback receiving unit allows the generation AI to receive the user's feedback as input and take it into consideration when generating the next recipe. The generation AI, for example, analyzes the text of the user's feedback and extracts specific improvements, allowing the feedback receiving unit to provide more personalized recipes by receiving the user's feedback.

[0035] The recipe generation system includes a community unit that provides community functions. The community unit provides an interface for users to share recipes they have tried with the community. For example, the community unit displays a form for users to post photos and comments about the recipe. The community unit can also provide a function for users to view and rate recipes posted by other users. For example, users can "like" or comment on recipes posted by other users. The community unit also stores recipes posted by users in a database so that other users can search for them. This allows users to share recipes they have tried with the community, thereby enabling information sharing with other users. Some or all of the above-described processing in the community unit is performed using a generation AI. For example, the generation AI in the community unit receives user posts as input and displays them to other users. The generation AI can also analyze the content of the user posts and recommend related recipes. This allows users to share recipes they have tried with the community, allowing other users to try those recipes as well.

[0036] The community unit allows users to share recipes they have tried with the community. For example, the community unit provides an interface for users to post recipes they have tried. For example, the community unit displays a form for users to post photos and comments about the recipe. The community unit can also provide a function for users to view and rate recipes posted by other users. For example, users can "like" or comment on recipes posted by other users. Furthermore, the community unit stores recipes posted by users in a database so that other users can search for them. In this way, the community unit allows users to share recipes they have tried with the community, allowing other users to try the recipes as well. Some or all of the above-mentioned processing in the community unit is performed using a generation AI. For example, the community unit allows the generation AI to receive user posts as input and display them to other users. The generation AI can also analyze the content of the user posts and recommend related recipes, for example. In this way, the community unit allows users to share recipes they have tried with the community, allowing other users to try the recipes as well.

[0037] The input unit can analyze the user's past input history and select the optimal input method. For example, the input unit stores and analyzes the user's past input history in a database. For example, the input unit prioritizes and suggests input methods (such as voice and text) that the user has frequently used in the past. The input unit can also predict and suggest an input method to be used during a specific time period based on the user's past input history. Furthermore, the input unit can automatically complete information previously entered by the user to simplify the input process. For example, the input unit provides an auto-completion function the next time the user enters information based on the information previously entered by the user. This allows the input unit to analyze the user's past input history and provide the optimal input method. Some or all of the above-described processing in the input unit is performed using a generation AI. For example, the generation AI receives the user's past input history as input and selects the optimal input method. For example, the generation AI learns the user's past input history and predicts the user's preferred input method. This allows the input unit to analyze the user's past input history and provide the optimal input method.

[0038] The input unit can filter input information based on the user's current health condition and dietary history. For example, the input unit stores and analyzes the user's current health condition and dietary history in a database. For example, the input unit filters appropriate ingredients and recipes based on the user's current health condition. The input unit can also refer to the user's past dietary history to exclude duplicate ingredients and recipes. Furthermore, the input unit can exclude ingredients and recipes containing allergens based on the user's allergy information. For example, the input unit can exclude ingredients to which the user is allergic from the list and display only appropriate ingredients. This allows the input unit to provide appropriate information by filtering based on the user's health condition and dietary history. Some or all of the above-described processing in the input unit is performed using a generation AI. For example, the input unit uses a generation AI to receive the user's health condition and dietary history as input and perform filtering. The generation AI, for example, learns the user's health condition and dietary history and selects appropriate ingredients and recipes. This allows the input unit to perform filtering based on the user's health condition and dietary history.

[0039] The input unit can prioritize inputting highly relevant information by taking into account the user's geographical location information. The input unit, for example, uses GPS data or location information services to acquire the user's geographical location information. For example, when the user is in a specific region, the input unit prioritizes inputting ingredients available in that region. Also, when the user is traveling, the input unit can prioritize inputting ingredients and recipes available at the user's travel destination. Furthermore, when the user is at home, the input unit can prioritize inputting ingredients available near the user's home. For example, the input unit suggests ingredients and recipes specific to the region based on the user's geographical location information. This allows the input unit to provide more appropriate information by prioritizing highly relevant information by taking into account the user's geographical location information. Some or all of the above-described processing in the input unit is performed using a generation AI. For example, the input unit uses the generation AI to receive the user's geographical location information as input and select highly relevant information. For example, the generation AI learns the user's geographical location information and suggests ingredients and recipes specific to the region. This allows the input unit to prioritize highly relevant information by taking into account the user's geographical location information.

[0040] The input unit can analyze the user's social media activity and input related information at the time of input. The input unit, for example, stores and analyzes the user's social media activity in a database. For example, the input unit can analyze photos of meals shared by the user on social media and input related recipes. The input unit can also reference information about cooking accounts the user follows on social media and input related recipes. The input unit can also input related recipes based on information about meals the user has "liked" on social media. For example, the input unit can suggest recipes that the user is likely to be interested in based on the user's social media activity. In this way, the input unit can provide more appropriate information by analyzing the user's social media activity and inputting related information. Some or all of the above-mentioned processing in the input unit is performed using a generation AI. For example, the input unit receives the user's social media activity as input and selects related information. For example, the generation AI learns the user's social media activity and suggests recipes that the user is likely to be interested in. In this way, the input unit can analyze the user's social media activity and input related information.

[0041] When generating a recipe, the generation unit can adjust the level of detail of the recipe based on the user's health condition. For example, the generation unit stores and analyzes the user's health condition in a database. For example, if the user is in good health, the generation unit generates a recipe that includes detailed nutritional information. Furthermore, if the user is in poor health, the generation unit can generate a concise, to-the-point recipe. Furthermore, if the user has a specific health goal, the generation unit can generate a recipe tailored to that goal. For example, if the user is on a calorie restriction, the generation unit generates a low-calorie recipe. This allows the generation unit to provide a more appropriate recipe by adjusting the level of detail of the recipe based on the user's health condition. Some or all of the above-described processing in the generation unit is performed using a generation AI. For example, the generation AI receives the user's health condition as input and adjusts the level of detail of the recipe. For example, the generation AI learns the user's health condition and suggests optimal recipes for the user. This allows the generation unit to adjust the level of detail of the recipe based on the user's health condition.

[0042] When generating a recipe, the generation unit can apply different generation algorithms depending on the user's dietary history. For example, the generation unit stores and analyzes the user's dietary history in a database. For example, the generation unit generates similar recipes based on dishes the user has previously liked. The generation unit can also generate recipes by excluding ingredients the user has previously avoided. Furthermore, the generation unit can analyze the user's dietary history and generate recipes that include balanced nutrients. For example, the generation unit suggests nutritionally balanced recipes based on the user's dietary history. This allows the generation unit to provide more appropriate recipes by applying different generation algorithms depending on the user's dietary history. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation AI receives the user's dietary history as input and applies different generation algorithms. For example, the generation AI learns the user's dietary history and suggests optimal recipes for the user. This allows the generation unit to apply different generation algorithms depending on the user's dietary history.

[0043] When generating recipes, the generation unit can prioritize recipes based on the user's dietary history. The generation unit, for example, stores and analyzes the user's dietary history in a database. For example, the generation unit prioritizes dishes that the user has previously enjoyed. The generation unit can also prioritize recipes that do not include ingredients that the user has previously avoided. The generation unit can also analyze the user's dietary history and prioritize recipes that include balanced nutrients. For example, the generation unit can recommend nutritionally balanced recipes based on the user's dietary history. This allows the generation unit to provide more appropriate recipes by prioritizing recipes based on the user's dietary history. Some or all of the above-described processing in the generation unit is performed using a generation AI. For example, the generation AI receives the user's dietary history as input and prioritizes recipes. The generation AI, for example, learns the user's dietary history and suggests recipes that are optimal for the user. This allows the generation unit to prioritize recipes based on the user's dietary history.

[0044] When generating recipes, the generation unit can adjust the order of recipes based on the user's eating history. The generation unit, for example, stores and analyzes the user's eating history in a database. For example, the generation unit first suggests dishes that the user has previously enjoyed. The generation unit can also first suggest recipes that do not include ingredients that the user has previously avoided. Furthermore, the generation unit can analyze the user's eating history and first suggest recipes that include balanced nutrients. For example, the generation unit first suggests nutritionally balanced recipes based on the user's eating history. This allows the generation unit to provide more appropriate recipes by adjusting the order of recipes based on the user's eating history. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation AI receives the user's eating history as input and adjusts the order of recipes. For example, the generation AI learns the user's eating history and suggests recipes that are optimal for the user. This allows the generation unit to adjust the order of recipes based on the user's eating history.

[0045] When displaying a recipe, the display unit can select the optimal display method by referring to the user's past operation history. The display unit, for example, stores and analyzes the user's past operation history in a database. For example, the display unit preferentially provides display methods that the user has previously preferred. The display unit can also predict and provide display methods to be used during specific time periods based on the user's past operation history. Furthermore, the display unit can exclude display methods that the user has previously avoided. For example, the display unit suggests a display method that the user prefers based on the user's past operation history. In this way, the display unit can provide the optimal display method by referring to the user's past operation history. Some or all of the above-described processing in the display unit is performed using a generation AI. For example, the generation AI receives the user's past operation history as input and selects the optimal display method. For example, the generation AI learns the user's past operation history and suggests the optimal display method to the user. In this way, the display unit can select the optimal display method by referring to the user's past operation history.

[0046] The display unit can customize the display content based on the user's health condition when displaying recipes. For example, the display unit stores and analyzes the user's health condition in a database. For example, if the user is in good health, the display unit can provide display content including detailed nutritional information. Furthermore, if the user is in poor health, the display unit can provide display content that is concise and to the point. Furthermore, if the user has a specific health goal, the display unit can provide display content tailored to the goal. For example, if the user is on a calorie restriction, the display unit can display low-calorie recipes. This allows the display unit to provide more appropriate information by customizing the display content based on the user's health condition. Some or all of the above-described processing in the display unit is performed using a generation AI. For example, the generation AI receives the user's health condition as input and customizes the display content. For example, the generation AI learns the user's health condition and suggests display content that is optimal for the user. This allows the display unit to customize the display content based on the user's health condition.

[0047] When displaying a recipe, the display unit can select the optimal display method by taking into account the user's device information. The display unit, for example, uses sensors and algorithms to acquire the user's device information. For example, if the user is using a smartphone, the display unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the display unit can provide a display method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the display unit can provide a simple and highly visible display method. For example, the display unit can propose the optimal display method for the device based on the user's device information. This allows the display unit to provide the optimal display method by taking into account the user's device information. Some or all of the above-mentioned processing in the display unit is performed using a generation AI. For example, the generation AI receives the user's device information as input and selects the optimal display method. The generation AI, for example, learns the user's device information and proposes the optimal display method for the user. This allows the display unit to select the optimal display method by taking into account the user's device information.

[0048] When displaying recipes, the display unit can prioritize displaying highly relevant recipes by taking into account the user's geographical location information. The display unit, for example, uses GPS data or location information services to acquire the user's geographical location information. For example, when the user is in a specific region, the display unit can prioritize displaying recipes using ingredients available in that region. Furthermore, when the user is traveling, the display unit can prioritize displaying ingredients and recipes available at the user's travel destination. Furthermore, when the user is at home, the display unit can prioritize displaying recipes using ingredients available near the user's home. For example, the display unit can suggest ingredients and recipes specific to the region based on the user's geographical location information. This allows the display unit to provide more appropriate information by prioritizing highly relevant recipes by taking into account the user's geographical location information. Some or all of the above-described processing in the display unit is performed using a generation AI. For example, the generation AI in the display unit receives the user's geographical location information as input and selects highly relevant recipes. For example, the generation AI learns the user's geographical location information and suggests ingredients and recipes specific to the region. This allows the display unit to prioritize and display highly relevant recipes in consideration of the user's geographical location information.

[0049] When generating a shopping list, the list generation unit can generate an optimal list by referring to the user's past purchase history. The list generation unit, for example, stores and analyzes the user's past purchase history in a database. For example, the list generation unit generates an optimal shopping list based on ingredients the user has purchased in the past. The list generation unit can also predict ingredients that the user will purchase during a specific time period based on the user's past purchase history and add them to the list. Furthermore, the list generation unit can generate a shopping list by excluding ingredients that the user has avoided in the past. For example, the list generation unit adds ingredients that the user prefers to the list based on the user's past purchase history. In this way, the list generation unit can provide an optimal shopping list by referring to the user's past purchase history. Some or all of the above-described processing in the list generation unit is performed using a generation AI. For example, the list generation unit receives the user's past purchase history as input and generates an optimal list. For example, the generation AI learns the user's past purchase history and suggests an optimal shopping list to the user. In this way, the list generation unit can generate an optimal list by referring to the user's past purchase history.

[0050] When generating a shopping list, the list generation unit can customize the list contents based on the user's current health condition. The list generation unit, for example, stores and analyzes the user's health condition in a database. For example, if the user is in good health, the list generation unit can add ingredients with balanced nutrients to the list. Furthermore, if the user is in poor health, the list generation unit can add ingredients that are easy to digest to the list. Furthermore, if the user has a specific health goal, the list generation unit can add ingredients to the list that are tailored to that goal. For example, if the user is on a calorie restriction, the list generation unit can add low-calorie ingredients to the list. This allows the list generation unit to provide a more appropriate shopping list by customizing the list contents based on the user's health condition. Some or all of the above-described processing in the list generation unit is performed using a generation AI. For example, the list generation unit receives the user's health condition as input and customizes the list contents. For example, the generation AI learns the user's health condition and suggests ingredients that are optimal for the user. This allows the list generation unit to customize the list contents based on the user's health condition.

[0051] When generating a shopping list, the list generation unit can generate an optimal list by taking into account the user's geographical location information. The list generation unit, for example, uses GPS data or a location information service to acquire the user's geographical location information. For example, if the user is in a specific area, the list generation unit can add ingredients available in that area to the list. Also, if the user is traveling, the list generation unit can add ingredients available at the travel destination to the list. Furthermore, if the user is at home, the list generation unit can add ingredients available near the user's home to the list. For example, the list generation unit can add ingredients specific to the area to the list based on the user's geographical location information. This allows the list generation unit to generate an optimal list by taking into account the user's geographical location information, thereby providing a more appropriate shopping list. Some or all of the above-described processing in the list generation unit is performed using a generation AI. For example, the list generation unit receives the user's geographical location information as input and generates an optimal list. The generation AI, for example, learns the user's geographical location information and suggests ingredients that are optimal for the user. This allows the list generation unit to generate an optimal list by taking into account the user's geographical location information.

[0052] When generating a shopping list, the list generation unit can analyze the user's social media activity to generate a related list. The list generation unit, for example, stores and analyzes the user's social media activity in a database. For example, the list generation unit analyzes photos of meals shared by the user on social media and adds related ingredients to the list. The list generation unit can also reference information about cooking accounts the user follows on social media and add related ingredients to the list. Furthermore, the list generation unit can add related ingredients to the list based on information about meals the user has "liked" on social media. For example, the list generation unit suggests ingredients that the user is likely to be interested in based on the user's social media activity. This allows the list generation unit to analyze the user's social media activity to generate a related list, thereby providing a more appropriate shopping list. Some or all of the above-described processing in the list generation unit is performed using a generation AI. For example, the list generation unit generates a related list by receiving the user's social media activity as input. The generation AI, for example, learns the user's social media activity and suggests ingredients that are best suited to the user. This allows the list generation unit to analyze the user's social media activity and generate a related list.

[0053] When receiving feedback, the feedback receiving unit can select the optimal feedback method by referring to the user's past feedback history. The feedback receiving unit, for example, stores and analyzes the user's past feedback history in a database. For example, the feedback receiving unit prioritizes and suggests feedback methods (text, voice, etc.) that the user has used in the past. The feedback receiving unit can also predict and suggest a feedback method to be used during a specific time period based on the user's past feedback history. Furthermore, the feedback receiving unit can automatically complement feedback content previously provided by the user to simplify the feedback process. For example, the feedback receiving unit provides an auto-completion function for the next feedback request based on the user's past feedback history. This allows the feedback receiving unit to provide the optimal feedback method by referring to the user's past feedback history. Some or all of the above-described processing in the feedback receiving unit is performed using a generation AI. For example, the feedback receiving unit receives the user's past feedback history as input and selects the optimal feedback method. The generation AI, for example, learns the user's past feedback history and suggests the optimal feedback method to the user. This allows the feedback receiving unit to select the optimal feedback method by referring to the user's past feedback history.

[0054] The feedback receiving unit can customize the feedback content based on the user's current health condition when receiving the feedback. The feedback receiving unit, for example, stores and analyzes the user's health condition in a database. For example, if the user's health condition is good, the feedback receiving unit provides a form requesting detailed feedback. Furthermore, if the user's health condition is poor, the feedback receiving unit can provide a concise and to-the-point feedback form. Furthermore, if the user has a specific health goal, the feedback receiving unit can provide feedback items tailored to the goal. For example, if the user is on a calorie restriction, the feedback receiving unit requests feedback on low-calorie ingredients. This allows the feedback receiving unit to provide more appropriate feedback by customizing the feedback content based on the user's health condition. Some or all of the above-described processing in the feedback receiving unit is performed using a generation AI. For example, the feedback receiving unit uses the generation AI to receive the user's health condition as input and customize the feedback content. The generation AI, for example, learns the user's health condition and suggests feedback items that are optimal for the user. This allows the feedback receiving unit to customize the feedback content based on the user's health condition.

[0055] The feedback receiving unit can accept optimal feedback by taking into account the user's geographical location information when receiving feedback. The feedback receiving unit uses, for example, GPS data or a location information service to acquire the user's geographical location information. For example, when the user is in a specific area, the feedback receiving unit can preferentially accept feedback items related to that area. Also, when the user is traveling, the feedback receiving unit can preferentially accept feedback items related to the travel destination. Furthermore, when the user is at home, the feedback receiving unit can preferentially accept feedback items related to the area around the user's home. For example, the feedback receiving unit suggests region-specific feedback items based on the user's geographical location information. This allows the feedback receiving unit to provide more appropriate feedback by accepting optimal feedback by taking into account the user's geographical location information. Some or all of the above-described processing in the feedback receiving unit is performed using a generation AI. For example, the feedback receiving unit receives the user's geographical location information as input and accepts optimal feedback. The generation AI, for example, learns the user's geographical location information and suggests optimal feedback items to the user. This allows the feedback receiving unit to receive optimal feedback in consideration of the geographical location information of the user.

[0056] The feedback receiving unit can analyze the user's social media activity and accept related feedback when receiving feedback. The feedback receiving unit, for example, stores and analyzes the user's social media activity in a database. For example, the feedback receiving unit analyzes photos of meals shared by the user on social media and accepts related feedback items. The feedback receiving unit can also refer to information about cooking accounts the user follows on social media and accept related feedback items. Furthermore, the feedback receiving unit can accept related feedback items based on information about meals the user has "liked" on social media. For example, the feedback receiving unit suggests feedback items that the user is likely to be interested in based on the user's social media activity. In this way, the feedback receiving unit can provide more appropriate feedback by analyzing the user's social media activity and accepting related feedback. Some or all of the above-described processing in the feedback receiving unit is performed using a generation AI. For example, the feedback receiving unit receives the user's social media activity as input and accepts related feedback. The generation AI, for example, learns the user's social media activity and suggests optimal feedback items for the user. This allows the feedback receiving unit to analyze the user's social media activity and receive related feedback.

[0057] When displaying a community, the community unit can select the optimal display method by referring to the user's past operation history. For example, the community unit stores and analyzes the user's past operation history in a database. For example, the community unit prioritizes and provides display methods that the user has previously preferred. The community unit can also predict and provide display methods to be used during specific time periods based on the user's past operation history. Furthermore, the community unit can exclude and provide display methods that the user has previously avoided. For example, the community unit suggests a display method that the user prefers based on the user's past operation history. This allows the community unit to provide the optimal display method by referring to the user's past operation history. Some or all of the above-described processing in the community unit is performed using a generation AI. For example, the community unit receives the user's past operation history as input and selects the optimal display method. The generation AI, for example, learns the user's past operation history and suggests the optimal display method to the user. This allows the community unit to select the optimal display method by referring to the user's past operation history.

[0058] The community unit can customize the display content based on the user's health condition when displaying the community. For example, the community unit stores and analyzes the user's health condition in a database. For example, if the user's health condition is good, the community unit can provide display content including detailed information. Furthermore, if the user's health condition is poor, the community unit can provide display content that is concise and to the point. Furthermore, if the user has a specific health goal, the community unit can provide display content tailored to the goal. For example, if the user is on a calorie restriction, the community unit can display information about low-calorie recipes. This allows the community unit to provide more appropriate information by customizing the display content based on the user's health condition. Some or all of the above-described processing in the community unit is performed using a generation AI. For example, the generation AI in the community unit receives the user's health condition as input and customizes the display content. For example, the generation AI learns the user's health condition and suggests display content that is optimal for the user. This allows the community unit to customize the display content based on the user's health condition.

[0059] When displaying the community, the community unit can select the optimal display method by taking into account the user's device information. The community unit, for example, uses sensors and algorithms to acquire the user's device information. For example, if the user is using a smartphone, the community unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the community unit can provide a display method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the community unit can provide a simple and highly visible display method. For example, the community unit proposes the optimal display method for the device based on the user's device information. This allows the community unit to provide the optimal display method by taking into account the user's device information. Some or all of the above-mentioned processing in the community unit is performed using a generation AI. For example, in the community unit, the generation AI receives the user's device information as input and selects the optimal display method. For example, the generation AI learns the user's device information and proposes the optimal display method for the user. This allows the community unit to select the optimal display method by taking into account the user's device information.

[0060] When displaying a community, the community unit can prioritize displaying highly relevant information by taking into account the user's geographical location information. The community unit, for example, uses GPS data or location information services to acquire the user's geographical location information. For example, if the user is in a specific area, the community unit can prioritize displaying information related to that area. Also, if the user is traveling, the community unit can prioritize displaying information related to the user's travel destination. Furthermore, if the user is at home, the community unit can prioritize displaying information related to the area around the user's home. For example, the community unit displays region-specific information based on the user's geographical location information. This allows the community unit to provide more appropriate information by prioritizing highly relevant information by taking into account the user's geographical location information. Some or all of the above-described processing in the community unit is performed using a generation AI. For example, the generation AI in the community unit receives the user's geographical location information as input and selects highly relevant information. The generation AI, for example, learns the user's geographical location information and suggests optimal information to the user. This allows the community unit to prioritize highly relevant information by taking into account the user's geographical location information.

[0061] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0062] The recipe generation system may further include a history analysis unit that analyzes the user's dietary history. The history analysis unit stores dietary information previously entered by the user in a database and provides it to the generation unit. For example, the history analysis unit may analyze the user's past favorite dishes and avoided ingredients and provide this information to the generation unit, thereby generating more personalized recipes. Furthermore, the history analysis unit may suggest nutritionally balanced recipes based on the user's dietary history. This allows the history analysis unit to utilize the user's past dietary history to provide more appropriate recipes.

[0063] The display unit can select the optimal display method in consideration of the user's device information. For example, if the user is using a smartphone, a display method that matches the screen size can be provided. Also, if the user is using a tablet, a display method optimized for a large screen can be provided. Furthermore, if the user is using a smartwatch, a simple and highly visible display method can be provided. This allows the display unit to provide the optimal display method in consideration of the user's device information.

[0064] The list generation unit can generate an optimal shopping list taking into account the user's geographical location information. For example, if the user is in a specific area, ingredients available in that area can be added to the list. If the user is traveling, ingredients available at the travel destination can be added to the list. Furthermore, if the user is at home, ingredients available around the home can be added to the list. In this way, the list generation unit can provide an optimal shopping list taking into account the user's geographical location information.

[0065] The feedback receiving unit can select the optimal feedback method by referring to the user's past feedback history. For example, it can preferentially suggest feedback methods (text, voice, etc.) that the user has used in the past. It can also predict and suggest a feedback method to be used in a specific time period based on the user's past feedback history. Furthermore, it can automatically complement feedback content provided by the user in the past, simplifying the feedback process. In this way, the feedback receiving unit can provide the optimal feedback method by referring to the user's past feedback history.

[0066] The community unit can select the optimal display method by referring to the user's past operation history. For example, it can provide preferentially the display method that the user has used favorably in the past. It can also predict and provide the display method that will be used in a specific time period from the user's past operation history. It can also exclude and provide display methods that the user has avoided in the past. In this way, the community unit can provide the optimal display method by referring to the user's past operation history.

[0067] The community unit can customize the display content based on the user's health condition. For example, if the user's health condition is good, the community unit can provide display content with detailed information. If the user's health condition is poor, the community unit can provide display content that is concise and to the point. Furthermore, if the user has a specific health goal, the community unit can provide display content that is tailored to that goal. This allows the community unit to provide more appropriate information by customizing the display content based on the user's health condition.

[0068] The processing flow of the first embodiment will be briefly explained below.

[0069] Step 1: The input unit inputs the user's preferences, allergy information, health conditions, and specific nutritional goals. For example, the user can input this information through the application. The input unit stores the information entered by the user in a database and provides it to the generation unit. Step 2: The generation unit uses the generation AI to analyze the information input by the input unit and generate a personalized recipe. The generation AI selects the optimal ingredients and cooking method, taking into account, for example, the user's preferences and allergy information. The generation unit provides the recipe generated by the generation AI to the display unit. Step 3: The display unit displays the generated recipe to the user. For example, the display unit may display the main nutrients in the recipe and their amounts. The display unit may also display cooking instructions in specific steps. Step 4: The list generator automatically generates a shopping list based on the generated recipe. For example, the list generator may list the ingredients needed to make it easy for the user to purchase them.

[0070] (Example 2) A recipe generation system according to an embodiment of the present invention analyzes a user's dietary preferences, health status, allergy information, and other information, and generates personalized recipes based on that information. The recipe generation system begins by a user entering their preferences, allergy information, health status, and specific nutritional goals into a profile. The generation AI analyzes this information and generates optimized recipes. The generated recipes display key nutrients and their amounts. The generation AI then provides step-by-step cooking instructions, allowing the user to follow the instructions. A shopping list is also automatically generated based on the generated recipe, making it easy to purchase the necessary ingredients. Furthermore, users can rate the recipes they have cooked and provide specific feedback. Based on this feedback, the generation AI then provides further personalized recipes. Finally, users can share the recipes they have tried with the community. Other users can also try these recipes, and the recipes are shared and rated throughout the community. For example, a recipe that excludes certain ingredients may be generated based on the user's allergy information. Low-calorie or high-protein recipes are also suggested based on the user's health status. Furthermore, the generated recipes also display cooking time and difficulty, allowing users to select recipes that fit their schedule and skill level. This allows the recipe generation system to provide optimal recipes that meet the individual needs of the user, and the recipe generation system can generate personalized recipes based on the user's preferences and health status, and display and generate shopping lists.

[0071] A recipe generation system according to an embodiment includes an input unit, a generation unit, a display unit, and a list generation unit. The input unit inputs a user's preferences, allergy information, health status, and specific nutritional goals. For example, the user can input this information through an application. The input unit stores the information input by the user in a database and provides it to the generation unit. The generation unit uses a generation AI to analyze the information input by the input unit and generate a personalized recipe. The generation AI selects optimal ingredients and cooking methods, taking into account the user's preferences and allergy information, for example. The generation unit provides the recipe generated by the generation AI to the display unit. The display unit displays the generated recipe to the user. For example, the display unit displays the main nutrients in the recipe and their amounts. The display unit can also display cooking methods in specific steps. The list generation unit automatically generates a shopping list based on the generated recipe. For example, the list generation unit lists the necessary ingredients, allowing the user to easily purchase them. This enables the recipe generation system according to an embodiment to generate personalized recipes based on the user's preferences and health status, and to display and generate shopping lists. Some or all of the above-mentioned processes in the generation unit are performed using a generation AI. For example, the generation unit receives user preference and allergy information as input, and the generation AI outputs the optimal recipe. Some or all of the above-mentioned processes in the display unit are performed using a generation AI. For example, the display unit receives a recipe generated by the generation AI as input and displays it to the user. Some or all of the above-mentioned processes in the list generation unit are performed using a generation AI. For example, the list generation unit receives a recipe generated by the generation AI as input and lists the necessary ingredients.

[0072] The generation unit can use the generation AI to analyze the user's preferences, allergy information, health condition, and specific nutritional goals and generate personalized recipes. The generation unit, for example, uses the generation AI to analyze the user's preferences and allergy information. For example, the generation AI selects optimal ingredients and cooking methods based on information entered by the user. The generation unit can also use the generation AI to generate recipes that take the user's health condition into consideration. For example, the generation AI can suggest low-calorie or high-protein recipes based on the user's health condition. Furthermore, the generation unit can use the generation AI to generate recipes based on specific nutritional goals. For example, the generation AI generates optimal recipes based on the user's calorie restriction and protein intake. This makes it possible to generate recipes based on the user's individual information. Some or all of the above-mentioned processes in the generation unit are performed using the generation AI. For example, the generation unit receives the user's preferences and allergy information as input, and the generation AI outputs the optimal recipe. The generation AI can analyze the user's information and generate the optimal recipe using, for example, deep learning or natural language processing technology. The generation AI selects the optimal recipe by, for example, vectorizing the user's input information and performing a similarity calculation. The generation AI can also learn the user's past eating history and generate recipes that match the user's preferences. This allows the generation unit to provide the optimal recipe based on the user's individual information.

[0073] The display unit can display the main nutrients and their amounts of the generated recipe. For example, the display unit displays nutrients such as protein, fat, carbohydrates, vitamins, and minerals. The display unit can also display the amount of each nutrient numerically. For example, the display unit may display 20g of protein, 10g of fat, and 30g of carbohydrates. Furthermore, the display unit can visually display the nutrient balance using a graph or chart. For example, the display unit may display the percentage of each nutrient using a pie chart or bar graph. This allows the user to check the nutritional balance of the generated recipe. Some or all of the above-described processing in the display unit is performed using a generation AI. For example, the display unit receives the recipe generated by the generation AI as input and displays the nutrient information. The generation AI, for example, analyzes the recipe's ingredient information and calculates the amount of each nutrient. The generation AI, for example, references an ingredient database to obtain nutrient information for each ingredient. The display unit can then display the nutrient information for the generated recipe, allowing the user to check the nutritional balance.

[0074] The list generation unit can automatically generate a shopping list based on the generated recipe. For example, the list generation unit lists the ingredients needed based on the generated recipe. For example, the list generation unit automatically extracts ingredients included in the recipe and generates a shopping list. The list generation unit can also include the quantities and purchasing locations of ingredients in the list. For example, the list generation unit displays the quantities of ingredients numerically and provides links to supermarkets and online stores. Furthermore, the list generation unit can refer to the user's past purchasing history and exclude duplicate ingredients. For example, the list generation unit excludes ingredients already purchased by the user from the list and displays only the necessary ingredients. In this way, the list generation unit automatically generates a shopping list based on the generated recipe, thereby improving the efficiency of the user's shopping. Some or all of the above-described processing in the list generation unit is performed using a generation AI. For example, the list generation unit receives the recipe generated by the generation AI as input and lists the necessary ingredients. For example, the generation AI analyzes the ingredient information of the recipe and extracts the necessary ingredients. For example, the generation AI references an ingredient database to obtain the quantity and purchasing location of each ingredient. This allows the list generation unit to automatically generate a shopping list based on the generated recipes.

[0075] The recipe generation system includes a feedback receiving unit that receives user feedback. The feedback receiving unit provides an interface for users to input their evaluations and opinions on the recipes they have cooked. For example, the feedback receiving unit displays a form for users to evaluate the recipe's taste, cooking difficulty, cooking time, etc. The feedback receiving unit can also provide a text box for users to input specific improvements or suggestions. For example, a user can input specific feedback such as "This recipe is too salty." The feedback receiving unit then stores the user's feedback in a database and provides it to the generation AI. This allows the generation AI to further personalize the recipe based on the user's feedback. For example, the generation AI analyzes the user's feedback and takes it into consideration when generating the next recipe. This allows the feedback receiving unit to provide a more personalized recipe by accepting the user's feedback. Some or all of the above-described processing in the feedback receiving unit is performed by the generation AI. For example, the feedback receiving unit allows the generation AI to receive the user's feedback as input and take it into consideration when generating the next recipe. The generation AI, for example, analyzes the user's feedback text and extracts specific improvements. This allows the feedback receiving unit to receive feedback from the user and provide a more personalized recipe.

[0076] The feedback receiving unit enables the generation AI to provide a more personalized recipe based on user feedback. For example, the feedback receiving unit stores the user's input feedback in a database and provides it to the generation AI. The generation AI analyzes the user's feedback and takes it into consideration when generating the next recipe. For example, if the user inputs feedback such as "This recipe is too salty," the generation AI will reduce the salt content the next time it generates a recipe. Also, if the user inputs feedback such as "This recipe is difficult to prepare," the generation AI will suggest a recipe that is easier to prepare the next time it generates a recipe. Furthermore, the generation AI can learn from the user's feedback and provide recipes that suit the user's preferences. For example, the generation AI analyzes the user's past feedback and learns the user's preferred seasonings and cooking methods. This allows the generation AI to further personalize the recipe based on the user's feedback, thereby improving user satisfaction. Some or all of the above-described processing in the feedback receiving unit is performed by the generation AI. For example, the feedback receiving unit allows the generation AI to receive the user's feedback as input and take it into consideration when generating the next recipe. The generation AI, for example, analyzes the text of the user's feedback and extracts specific improvements, allowing the feedback receiving unit to provide more personalized recipes by receiving the user's feedback.

[0077] The recipe generation system includes a community unit that provides community functions. The community unit provides an interface for users to share recipes they have tried with the community. For example, the community unit displays a form for users to post photos and comments about the recipe. The community unit can also provide a function for users to view and rate recipes posted by other users. For example, users can "like" or comment on recipes posted by other users. The community unit also stores recipes posted by users in a database so that other users can search for them. This allows users to share recipes they have tried with the community, thereby enabling information sharing with other users. Some or all of the above-described processing in the community unit is performed using a generation AI. For example, the generation AI in the community unit receives user posts as input and displays them to other users. The generation AI can also analyze the content of the user posts and recommend related recipes. This allows users to share recipes they have tried with the community, allowing other users to try those recipes as well.

[0078] The community unit allows users to share recipes they have tried with the community. For example, the community unit provides an interface for users to post recipes they have tried. For example, the community unit displays a form for users to post photos and comments about the recipe. The community unit can also provide a function for users to view and rate recipes posted by other users. For example, users can "like" or comment on recipes posted by other users. Furthermore, the community unit stores recipes posted by users in a database so that other users can search for them. In this way, the community unit allows users to share recipes they have tried with the community, allowing other users to try the recipes as well. Some or all of the above-mentioned processing in the community unit is performed using a generation AI. For example, the community unit allows the generation AI to receive user posts as input and display them to other users. The generation AI can also analyze the content of the user posts and recommend related recipes, for example. In this way, the community unit allows users to share recipes they have tried with the community, allowing other users to try the recipes as well.

[0079] The recipe generation system includes an input unit that estimates a user's emotion and adjusts input timing based on the estimated user emotion. The input unit uses sensors and algorithms to estimate the user's emotion. For example, the input unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The input unit can also record the user's voice and estimate the emotion using voice analysis technology. The input unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the input unit calculates an emotion score based on heart rate fluctuations. This allows the input unit to estimate the user's emotion and adjust the input timing based on the estimated user emotion. For example, if the user is feeling stressed, the input timing can be delayed to provide a relaxing environment. If the user is relaxed, the input timing can be accelerated to promote smooth operation. Furthermore, if the user is in a hurry, the input timing can be optimized to allow the user to input information quickly. This allows the input unit to adjust the input timing according to the user's emotion, enabling more appropriate input. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the input unit is performed using the generation AI. For example, the input unit receives the user's emotion as input and adjusts the timing of the input. This allows the input unit to adjust the timing of the input based on the user's emotion.

[0080] The input unit can analyze the user's past input history and select the optimal input method. For example, the input unit stores and analyzes the user's past input history in a database. For example, the input unit prioritizes and suggests input methods (such as voice and text) that the user has frequently used in the past. The input unit can also predict and suggest an input method to be used during a specific time period based on the user's past input history. Furthermore, the input unit can automatically complete information previously entered by the user to simplify the input process. For example, the input unit provides an auto-completion function the next time the user enters information based on the information previously entered by the user. This allows the input unit to analyze the user's past input history and provide the optimal input method. Some or all of the above-described processing in the input unit is performed using a generation AI. For example, the generation AI receives the user's past input history as input and selects the optimal input method. For example, the generation AI learns the user's past input history and predicts the user's preferred input method. This allows the input unit to analyze the user's past input history and provide the optimal input method.

[0081] The input unit can filter input information based on the user's current health condition and dietary history. For example, the input unit stores and analyzes the user's current health condition and dietary history in a database. For example, the input unit filters appropriate ingredients and recipes based on the user's current health condition. The input unit can also refer to the user's past dietary history to exclude duplicate ingredients and recipes. Furthermore, the input unit can exclude ingredients and recipes containing allergens based on the user's allergy information. For example, the input unit can exclude ingredients to which the user is allergic from the list and display only appropriate ingredients. This allows the input unit to provide appropriate information by filtering based on the user's health condition and dietary history. Some or all of the above-described processing in the input unit is performed using a generation AI. For example, the input unit uses a generation AI to receive the user's health condition and dietary history as input and perform filtering. The generation AI, for example, learns the user's health condition and dietary history and selects appropriate ingredients and recipes. This allows the input unit to perform filtering based on the user's health condition and dietary history.

[0082] The input unit can estimate the user's emotion and determine the priority of information to be input based on the estimated user's emotion. The input unit uses, for example, a sensor and an algorithm for estimating the user's emotion. For example, the input unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The input unit can also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the input unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the input unit calculates an emotion score based on heart rate fluctuations. This allows the input unit to estimate the user's emotion and determine the priority of information to be input based on the estimated user's emotion. For example, if the user is feeling stressed, important information is prioritized. Also, if the user is relaxed, detailed information is prioritized. Furthermore, if the user is in a hurry, the minimum necessary information is prioritized. As a result, the input unit can provide more appropriate information by determining the priority of information to be input based on the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the input unit is performed using the generation AI. For example, the input unit receives the user's emotion as input and determines the priority of the information. This allows the input unit to determine the priority of the information to be input based on the user's emotion.

[0083] The input unit can prioritize inputting highly relevant information by taking into account the user's geographical location information. The input unit, for example, uses GPS data or location information services to acquire the user's geographical location information. For example, when the user is in a specific region, the input unit prioritizes inputting ingredients available in that region. Also, when the user is traveling, the input unit can prioritize inputting ingredients and recipes available at the user's travel destination. Furthermore, when the user is at home, the input unit can prioritize inputting ingredients available near the user's home. For example, the input unit suggests ingredients and recipes specific to the region based on the user's geographical location information. This allows the input unit to provide more appropriate information by prioritizing highly relevant information by taking into account the user's geographical location information. Some or all of the above-described processing in the input unit is performed using a generation AI. For example, the input unit uses the generation AI to receive the user's geographical location information as input and select highly relevant information. For example, the generation AI learns the user's geographical location information and suggests ingredients and recipes specific to the region. This allows the input unit to prioritize highly relevant information by taking into account the user's geographical location information.

[0084] The input unit can analyze the user's social media activity and input related information at the time of input. The input unit, for example, stores and analyzes the user's social media activity in a database. For example, the input unit can analyze photos of meals shared by the user on social media and input related recipes. The input unit can also reference information about cooking accounts the user follows on social media and input related recipes. The input unit can also input related recipes based on information about meals the user has "liked" on social media. For example, the input unit can suggest recipes that the user is likely to be interested in based on the user's social media activity. In this way, the input unit can provide more appropriate information by analyzing the user's social media activity and inputting related information. Some or all of the above-mentioned processing in the input unit is performed using a generation AI. For example, the input unit receives the user's social media activity as input and selects related information. For example, the generation AI learns the user's social media activity and suggests recipes that the user is likely to be interested in. In this way, the input unit can analyze the user's social media activity and input related information.

[0085] The generation unit can estimate the user's emotions and adjust the presentation of the recipe based on the estimated user's emotions. The generation unit uses, for example, a sensor and an algorithm for estimating the user's emotions. For example, the generation unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. The generation unit can also record the user's voice and estimate the emotions using voice analysis technology. Furthermore, the generation unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the generation unit calculates an emotion score based on heart rate fluctuations. This allows the generation unit to estimate the user's emotions and adjust the presentation of the recipe based on the estimated user's emotions. For example, if the user is relaxed, the generation unit generates a recipe with detailed instructions. If the user is in a hurry, the generation unit generates a concise and to-the-point recipe. Furthermore, if the user is excited, the generation unit generates a visually appealing recipe. In this way, the generation unit can provide a more appropriate recipe by adjusting the presentation of the recipe according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit is performed using the generation AI. For example, the generation unit receives the user's emotion as input and adjusts the way the recipe is presented. This allows the generation unit to adjust the way the recipe is presented based on the user's emotion.

[0086] When generating a recipe, the generation unit can adjust the level of detail of the recipe based on the user's health condition. For example, the generation unit stores and analyzes the user's health condition in a database. For example, if the user is in good health, the generation unit generates a recipe that includes detailed nutritional information. Furthermore, if the user is in poor health, the generation unit can generate a concise, to-the-point recipe. Furthermore, if the user has a specific health goal, the generation unit can generate a recipe tailored to that goal. For example, if the user is on a calorie restriction, the generation unit generates a low-calorie recipe. This allows the generation unit to provide a more appropriate recipe by adjusting the level of detail of the recipe based on the user's health condition. Some or all of the above-described processing in the generation unit is performed using a generation AI. For example, the generation AI receives the user's health condition as input and adjusts the level of detail of the recipe. For example, the generation AI learns the user's health condition and suggests optimal recipes for the user. This allows the generation unit to adjust the level of detail of the recipe based on the user's health condition.

[0087] When generating a recipe, the generation unit can apply different generation algorithms depending on the user's dietary history. For example, the generation unit stores and analyzes the user's dietary history in a database. For example, the generation unit generates similar recipes based on dishes the user has previously liked. The generation unit can also generate recipes by excluding ingredients the user has previously avoided. Furthermore, the generation unit can analyze the user's dietary history and generate recipes that include balanced nutrients. For example, the generation unit suggests nutritionally balanced recipes based on the user's dietary history. This allows the generation unit to provide more appropriate recipes by applying different generation algorithms depending on the user's dietary history. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation AI receives the user's dietary history as input and applies different generation algorithms. For example, the generation AI learns the user's dietary history and suggests optimal recipes for the user. This allows the generation unit to apply different generation algorithms depending on the user's dietary history.

[0088] The generation unit can estimate the user's emotions and adjust the length of the recipe based on the estimated user emotions. The generation unit uses, for example, a sensor and an algorithm to estimate the user's emotions. For example, the generation unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. The generation unit can also record the user's voice and estimate the emotions using voice analysis technology. Furthermore, the generation unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the generation unit calculates an emotion score based on heart rate fluctuations. This allows the generation unit to estimate the user's emotions and adjust the length of the recipe based on the estimated user emotions. For example, if the user is in a hurry, the generation unit generates a short, concise recipe. If the user is relaxed, the generation unit generates a longer recipe with detailed instructions. If the user is excited, the generation unit generates a recipe with visually stimulating effects. In this way, the generation unit can provide more appropriate recipes by adjusting the length of the recipe according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit is performed using the generation AI. For example, the generation unit receives the user's emotion as input and adjusts the length of the recipe. This allows the generation unit to adjust the length of the recipe based on the user's emotion.

[0089] When generating recipes, the generation unit can prioritize recipes based on the user's dietary history. The generation unit, for example, stores and analyzes the user's dietary history in a database. For example, the generation unit prioritizes dishes that the user has previously enjoyed. The generation unit can also prioritize recipes that do not include ingredients that the user has previously avoided. The generation unit can also analyze the user's dietary history and prioritize recipes that include balanced nutrients. For example, the generation unit can recommend nutritionally balanced recipes based on the user's dietary history. This allows the generation unit to provide more appropriate recipes by prioritizing recipes based on the user's dietary history. Some or all of the above-described processing in the generation unit is performed using a generation AI. For example, the generation AI receives the user's dietary history as input and prioritizes recipes. The generation AI, for example, learns the user's dietary history and suggests recipes that are optimal for the user. This allows the generation unit to prioritize recipes based on the user's dietary history.

[0090] When generating recipes, the generation unit can adjust the order of recipes based on the user's eating history. The generation unit, for example, stores and analyzes the user's eating history in a database. For example, the generation unit first suggests dishes that the user has previously enjoyed. The generation unit can also first suggest recipes that do not include ingredients that the user has previously avoided. Furthermore, the generation unit can analyze the user's eating history and first suggest recipes that include balanced nutrients. For example, the generation unit first suggests nutritionally balanced recipes based on the user's eating history. This allows the generation unit to provide more appropriate recipes by adjusting the order of recipes based on the user's eating history. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation AI receives the user's eating history as input and adjusts the order of recipes. For example, the generation AI learns the user's eating history and suggests recipes that are optimal for the user. This allows the generation unit to adjust the order of recipes based on the user's eating history.

[0091] The display unit can estimate the user's emotions and adjust the recipe display method based on the estimated user emotions. The display unit uses, for example, a sensor and an algorithm to estimate the user's emotions. For example, the display unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The display unit can also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the display unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the display unit calculates an emotion score based on heart rate fluctuations. This allows the display unit to estimate the user's emotions and adjust the recipe display method based on the estimated user emotions. For example, if the user is nervous, a simple, highly visible display method is provided. If the user is relaxed, a display method including detailed information is provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points is provided. This allows the display unit to adjust the recipe display method according to the user's emotions, thereby enabling a more appropriate display. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the display unit is performed using the generative AI. For example, the generative AI receives the user's emotion as input and adjusts the display method in the display unit. This allows the display unit to adjust the recipe display method based on the user's emotion.

[0092] When displaying a recipe, the display unit can select the optimal display method by referring to the user's past operation history. The display unit, for example, stores and analyzes the user's past operation history in a database. For example, the display unit preferentially provides display methods that the user has previously preferred. The display unit can also predict and provide display methods to be used during specific time periods based on the user's past operation history. Furthermore, the display unit can exclude display methods that the user has previously avoided. For example, the display unit suggests a display method that the user prefers based on the user's past operation history. In this way, the display unit can provide the optimal display method by referring to the user's past operation history. Some or all of the above-described processing in the display unit is performed using a generation AI. For example, the generation AI receives the user's past operation history as input and selects the optimal display method. For example, the generation AI learns the user's past operation history and suggests the optimal display method to the user. In this way, the display unit can select the optimal display method by referring to the user's past operation history.

[0093] The display unit can customize the display content based on the user's health condition when displaying recipes. For example, the display unit stores and analyzes the user's health condition in a database. For example, if the user is in good health, the display unit can provide display content including detailed nutritional information. Furthermore, if the user is in poor health, the display unit can provide display content that is concise and to the point. Furthermore, if the user has a specific health goal, the display unit can provide display content tailored to the goal. For example, if the user is on a calorie restriction, the display unit can display low-calorie recipes. This allows the display unit to provide more appropriate information by customizing the display content based on the user's health condition. Some or all of the above-described processing in the display unit is performed using a generation AI. For example, the generation AI receives the user's health condition as input and customizes the display content. For example, the generation AI learns the user's health condition and suggests display content that is optimal for the user. This allows the display unit to customize the display content based on the user's health condition.

[0094] The display unit can estimate the user's emotions and adjust the display order of recipes based on the estimated user emotions. The display unit uses, for example, sensors and algorithms to estimate the user's emotions. For example, the display unit captures the user's facial expressions with a camera and estimates the user's emotions using an emotion estimation algorithm. The display unit can also record the user's voice and estimate the user's emotions using voice analysis technology. Furthermore, the display unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the user's emotions using an emotion estimation algorithm. For example, the display unit calculates an emotion score based on heart rate fluctuations. This allows the display unit to estimate the user's emotions and adjust the display order of recipes based on the estimated user emotions. For example, if the user is nervous, important information can be displayed first. If the user is relaxed, detailed information can be displayed first. Furthermore, if the user is in a hurry, the minimum necessary information can be displayed first. This allows the display unit to provide more appropriate information by adjusting the display order of recipes according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the display unit is performed using the generation AI. For example, the display unit receives the user's emotions as input and adjusts the display order. This allows the display unit to adjust the display order of recipes based on the user's emotions.

[0095] When displaying a recipe, the display unit can select the optimal display method by taking into account the user's device information. The display unit, for example, uses sensors and algorithms to acquire the user's device information. For example, if the user is using a smartphone, the display unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the display unit can provide a display method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the display unit can provide a simple and highly visible display method. For example, the display unit can propose the optimal display method for the device based on the user's device information. This allows the display unit to provide the optimal display method by taking into account the user's device information. Some or all of the above-mentioned processing in the display unit is performed using a generation AI. For example, the generation AI receives the user's device information as input and selects the optimal display method. The generation AI, for example, learns the user's device information and proposes the optimal display method for the user. This allows the display unit to select the optimal display method by taking into account the user's device information.

[0096] When displaying recipes, the display unit can prioritize displaying highly relevant recipes by taking into account the user's geographical location information. The display unit, for example, uses GPS data or location information services to acquire the user's geographical location information. For example, when the user is in a specific region, the display unit can prioritize displaying recipes using ingredients available in that region. Furthermore, when the user is traveling, the display unit can prioritize displaying ingredients and recipes available at the user's travel destination. Furthermore, when the user is at home, the display unit can prioritize displaying recipes using ingredients available near the user's home. For example, the display unit can suggest ingredients and recipes specific to the region based on the user's geographical location information. This allows the display unit to provide more appropriate information by prioritizing highly relevant recipes by taking into account the user's geographical location information. Some or all of the above-described processing in the display unit is performed using a generation AI. For example, the display unit uses the generation AI to receive the user's geographical location information as input and select highly relevant recipes. For example, the generation AI learns the user's geographical location information and suggests ingredients and recipes specific to the region. This allows the display unit to prioritize and display highly relevant recipes in consideration of the user's geographical location information.

[0097] The list generation unit can estimate the user's emotions and adjust the method for generating a shopping list based on the estimated user's emotions. The list generation unit uses, for example, a sensor and an algorithm for estimating the user's emotions. For example, the list generation unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. The list generation unit can also record the user's voice and estimate the emotions using voice analysis technology. Furthermore, the list generation unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the list generation unit calculates an emotion score based on heart rate fluctuations. This allows the list generation unit to estimate the user's emotions and adjust the method for generating a shopping list based on the estimated user's emotions. For example, if the user is feeling stressed, the list generation unit generates a simple, highly visible shopping list. If the user is relaxed, the list generation unit generates a shopping list with detailed information. Furthermore, if the user is in a hurry, the list generation unit generates a shopping list with the minimum necessary information. This allows the list generation unit to adjust the method for generating a shopping list according to the user's emotions, thereby providing a more appropriate list. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the list generation unit is performed using the generation AI. For example, the list generation unit receives the user's emotion as input and adjusts the generation method. This allows the list generation unit to adjust the shopping list generation method based on the user's emotion.

[0098] When generating a shopping list, the list generation unit can generate an optimal list by referring to the user's past purchase history. The list generation unit, for example, stores and analyzes the user's past purchase history in a database. For example, the list generation unit generates an optimal shopping list based on ingredients the user has purchased in the past. The list generation unit can also predict ingredients that the user will purchase during a specific time period based on the user's past purchase history and add them to the list. Furthermore, the list generation unit can generate a shopping list by excluding ingredients that the user has avoided in the past. For example, the list generation unit adds ingredients that the user prefers to the list based on the user's past purchase history. In this way, the list generation unit can provide an optimal shopping list by referring to the user's past purchase history. Some or all of the above-described processing in the list generation unit is performed using a generation AI. For example, the list generation unit receives the user's past purchase history as input and generates an optimal list. For example, the generation AI learns the user's past purchase history and suggests an optimal shopping list to the user. In this way, the list generation unit can generate an optimal list by referring to the user's past purchase history.

[0099] When generating a shopping list, the list generation unit can customize the list contents based on the user's current health condition. The list generation unit, for example, stores and analyzes the user's health condition in a database. For example, if the user is in good health, the list generation unit can add ingredients with balanced nutrients to the list. Furthermore, if the user is in poor health, the list generation unit can add ingredients that are easy to digest to the list. Furthermore, if the user has a specific health goal, the list generation unit can add ingredients to the list that are tailored to that goal. For example, if the user is on a calorie restriction, the list generation unit can add low-calorie ingredients to the list. This allows the list generation unit to provide a more appropriate shopping list by customizing the list contents based on the user's health condition. Some or all of the above-described processing in the list generation unit is performed using a generation AI. For example, the list generation unit receives the user's health condition as input and customizes the list contents. For example, the generation AI learns the user's health condition and suggests ingredients that are optimal for the user. This allows the list generation unit to customize the list contents based on the user's health condition.

[0100] The list generation unit can estimate the user's emotions and prioritize the shopping list based on the estimated user emotions. The list generation unit uses, for example, a sensor and an algorithm for estimating the user's emotions. For example, the list generation unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. The list generation unit can also record the user's voice and estimate the emotions using voice analysis technology. Furthermore, the list generation unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the list generation unit calculates an emotion score based on heart rate fluctuations. This allows the list generation unit to estimate the user's emotions and prioritize the shopping list based on the estimated user emotions. For example, if the user is feeling stressed, important ingredients are prioritized to be added to the list. Also, if the user is relaxed, ingredients with detailed information are prioritized to be added to the list. Furthermore, if the user is in a hurry, ingredients with the minimum necessary are prioritized to be added to the list. This allows the list generation unit to prioritize the shopping list according to the user's emotions, thereby providing a more appropriate list. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the list generation unit is performed using the generation AI. For example, the list generation unit receives the user's emotions as input and determines priorities. This allows the list generation unit to determine the priorities of the shopping list based on the user's emotions.

[0101] When generating a shopping list, the list generation unit can generate an optimal list by taking into account the user's geographical location information. The list generation unit, for example, uses GPS data or a location information service to acquire the user's geographical location information. For example, if the user is in a specific area, the list generation unit can add ingredients available in that area to the list. Also, if the user is traveling, the list generation unit can add ingredients available at the travel destination to the list. Furthermore, if the user is at home, the list generation unit can add ingredients available near the user's home to the list. For example, the list generation unit can add ingredients specific to the area to the list based on the user's geographical location information. This allows the list generation unit to generate an optimal list by taking into account the user's geographical location information, thereby providing a more appropriate shopping list. Some or all of the above-described processing in the list generation unit is performed using a generation AI. For example, the list generation unit receives the user's geographical location information as input and generates an optimal list. The generation AI, for example, learns the user's geographical location information and suggests ingredients that are optimal for the user. This allows the list generation unit to generate an optimal list by taking into account the user's geographical location information.

[0102] When generating a shopping list, the list generation unit can analyze the user's social media activity to generate a related list. The list generation unit, for example, stores and analyzes the user's social media activity in a database. For example, the list generation unit analyzes photos of meals shared by the user on social media and adds related ingredients to the list. The list generation unit can also reference information about cooking accounts the user follows on social media and add related ingredients to the list. Furthermore, the list generation unit can add related ingredients to the list based on information about meals the user has "liked" on social media. For example, the list generation unit suggests ingredients that the user is likely to be interested in based on the user's social media activity. This allows the list generation unit to analyze the user's social media activity to generate a related list, thereby providing a more appropriate shopping list. Some or all of the above-described processing in the list generation unit is performed using a generation AI. For example, the list generation unit generates a related list by receiving the user's social media activity as input. The generation AI, for example, learns the user's social media activity and suggests ingredients that are best suited to the user. This allows the list generation unit to analyze the user's social media activity and generate a related list.

[0103] The feedback receiving unit can estimate the user's emotion and adjust the feedback receiving method based on the estimated user's emotion. The feedback receiving unit uses, for example, a sensor and an algorithm for estimating the user's emotion. For example, the feedback receiving unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The feedback receiving unit can also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the feedback receiving unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the feedback receiving unit calculates an emotion score based on heart rate fluctuations. This allows the feedback receiving unit to estimate the user's emotion and adjust the feedback receiving method based on the estimated user's emotion. For example, if the user is feeling stressed, a feedback form that is concise and to the point is provided. On the other hand, if the user is relaxed, a form that requests detailed feedback is provided. Furthermore, if the user is in a hurry, voice input is prioritized and feedback is quickly received. As a result, the feedback receiving unit can adjust the feedback receiving method according to the user's emotion and provide more appropriate feedback. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the feedback receiving unit is performed using the generation AI. For example, the feedback receiving unit receives the user's emotion as input and adjusts the reception method. This allows the feedback receiving unit to adjust the feedback reception method based on the user's emotion.

[0104] When receiving feedback, the feedback receiving unit can select the optimal feedback method by referring to the user's past feedback history. The feedback receiving unit, for example, stores and analyzes the user's past feedback history in a database. For example, the feedback receiving unit prioritizes and suggests feedback methods (text, voice, etc.) that the user has used in the past. The feedback receiving unit can also predict and suggest a feedback method to be used during a specific time period based on the user's past feedback history. Furthermore, the feedback receiving unit can automatically complement feedback content previously provided by the user to simplify the feedback process. For example, the feedback receiving unit provides an auto-completion function for the next feedback request based on the user's past feedback history. This allows the feedback receiving unit to provide the optimal feedback method by referring to the user's past feedback history. Some or all of the above-described processing in the feedback receiving unit is performed using a generation AI. For example, the feedback receiving unit receives the user's past feedback history as input and selects the optimal feedback method. The generation AI, for example, learns the user's past feedback history and suggests the optimal feedback method to the user. This allows the feedback receiving unit to select the optimal feedback method by referring to the user's past feedback history.

[0105] The feedback receiving unit can customize the feedback content based on the user's current health condition when receiving the feedback. The feedback receiving unit, for example, stores and analyzes the user's health condition in a database. For example, if the user's health condition is good, the feedback receiving unit provides a form requesting detailed feedback. Furthermore, if the user's health condition is poor, the feedback receiving unit can provide a concise and to-the-point feedback form. Furthermore, if the user has a specific health goal, the feedback receiving unit can provide feedback items tailored to the goal. For example, if the user is on a calorie restriction, the feedback receiving unit requests feedback on low-calorie ingredients. This allows the feedback receiving unit to provide more appropriate feedback by customizing the feedback content based on the user's health condition. Some or all of the above-described processing in the feedback receiving unit is performed using a generation AI. For example, the feedback receiving unit uses the generation AI to receive the user's health condition as input and customize the feedback content. The generation AI, for example, learns the user's health condition and suggests feedback items that are optimal for the user. This allows the feedback receiving unit to customize the feedback content based on the user's health condition.

[0106] The feedback receiving unit can estimate the user's emotions and determine the priority of feedback based on the estimated user's emotions. The feedback receiving unit uses, for example, a sensor and an algorithm for estimating the user's emotions. For example, the feedback receiving unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The feedback receiving unit can also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the feedback receiving unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the feedback receiving unit calculates an emotion score based on heart rate fluctuations. This allows the feedback receiving unit to estimate the user's emotions and determine the priority of feedback based on the estimated user's emotions. For example, if the user is feeling stressed, important feedback items are given priority. Also, if the user is relaxed, detailed feedback items are given priority. Furthermore, if the user is in a hurry, the minimum necessary feedback items are given priority. As a result, the feedback receiving unit can provide more appropriate feedback by determining the priority of feedback according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the feedback receiving unit is performed using the generation AI. For example, the feedback receiving unit receives the user's emotions as input and determines the priority. This allows the feedback receiving unit to determine the priority of feedback based on the user's emotions.

[0107] The feedback receiving unit can accept optimal feedback by taking into account the user's geographical location information when receiving feedback. The feedback receiving unit uses, for example, GPS data or a location information service to acquire the user's geographical location information. For example, when the user is in a specific area, the feedback receiving unit can preferentially accept feedback items related to that area. Also, when the user is traveling, the feedback receiving unit can preferentially accept feedback items related to the travel destination. Furthermore, when the user is at home, the feedback receiving unit can preferentially accept feedback items related to the area around the user's home. For example, the feedback receiving unit suggests region-specific feedback items based on the user's geographical location information. This allows the feedback receiving unit to provide more appropriate feedback by accepting optimal feedback by taking into account the user's geographical location information. Some or all of the above-described processing in the feedback receiving unit is performed using a generation AI. For example, the feedback receiving unit receives the user's geographical location information as input and accepts optimal feedback. The generation AI, for example, learns the user's geographical location information and suggests optimal feedback items to the user. This allows the feedback receiving unit to receive optimal feedback in consideration of the geographical location information of the user.

[0108] The feedback receiving unit can analyze the user's social media activity and accept related feedback when receiving feedback. The feedback receiving unit, for example, stores and analyzes the user's social media activity in a database. For example, the feedback receiving unit analyzes photos of meals shared by the user on social media and accepts related feedback items. The feedback receiving unit can also refer to information about cooking accounts the user follows on social media and accept related feedback items. Furthermore, the feedback receiving unit can accept related feedback items based on information about meals the user has "liked" on social media. For example, the feedback receiving unit suggests feedback items that the user is likely to be interested in based on the user's social media activity. In this way, the feedback receiving unit can provide more appropriate feedback by analyzing the user's social media activity and accepting related feedback. Some or all of the above-described processing in the feedback receiving unit is performed using a generation AI. For example, the feedback receiving unit receives the user's social media activity as input and accepts related feedback. The generation AI, for example, learns the user's social media activity and suggests optimal feedback items for the user. This allows the feedback receiving unit to analyze the user's social media activity and receive related feedback.

[0109] The community unit can estimate the user's emotions and adjust the display method of the community based on the estimated user's emotions. The community unit uses, for example, a sensor and an algorithm to estimate the user's emotions. For example, the community unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. The community unit can also record the user's voice and estimate the emotions using voice analysis technology. Furthermore, the community unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the community unit calculates an emotion score based on heart rate fluctuations. This allows the community unit to estimate the user's emotions and adjust the display method of the community based on the estimated user's emotions. For example, if the user is nervous, a simple and highly visible display method is provided. If the user is relaxed, a display method including detailed information is provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points is provided. This allows the community unit to adjust the display method of the community according to the user's emotions, thereby enabling a more appropriate display. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the community unit is performed using the generation AI. For example, in the community unit, the generation AI receives the user's emotion as input and adjusts the display method. This allows the community unit to adjust the display method of the community based on the user's emotion.

[0110] When displaying a community, the community unit can select the optimal display method by referring to the user's past operation history. For example, the community unit stores and analyzes the user's past operation history in a database. For example, the community unit prioritizes and provides display methods that the user has previously preferred. The community unit can also predict and provide display methods to be used during specific time periods based on the user's past operation history. Furthermore, the community unit can exclude and provide display methods that the user has previously avoided. For example, the community unit suggests a display method that the user prefers based on the user's past operation history. This allows the community unit to provide the optimal display method by referring to the user's past operation history. Some or all of the above-described processing in the community unit is performed using a generation AI. For example, the community unit receives the user's past operation history as input and selects the optimal display method. The generation AI, for example, learns the user's past operation history and suggests the optimal display method to the user. This allows the community unit to select the optimal display method by referring to the user's past operation history.

[0111] The community unit can customize the display content based on the user's health condition when displaying the community. For example, the community unit stores and analyzes the user's health condition in a database. For example, if the user's health condition is good, the community unit can provide display content including detailed information. Furthermore, if the user's health condition is poor, the community unit can provide display content that is concise and to the point. Furthermore, if the user has a specific health goal, the community unit can provide display content tailored to the goal. For example, if the user is on a calorie restriction, the community unit can display information about low-calorie recipes. This allows the community unit to provide more appropriate information by customizing the display content based on the user's health condition. Some or all of the above-described processing in the community unit is performed using a generation AI. For example, the generation AI in the community unit receives the user's health condition as input and customizes the display content. For example, the generation AI learns the user's health condition and suggests display content that is optimal for the user. This allows the community unit to customize the display content based on the user's health condition.

[0112] The community unit can estimate the user's emotions and adjust the display order of communities based on the estimated user emotions. The community unit, for example, uses sensors and algorithms to estimate the user's emotions. For example, the community unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. The community unit can also record the user's voice and estimate the emotions using voice analysis technology. Furthermore, the community unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the community unit calculates an emotion score based on heart rate fluctuations. This allows the community unit to estimate the user's emotions and adjust the display order of communities based on the estimated user emotions. For example, if the user is nervous, important information can be displayed first. Also, if the user is relaxed, detailed information can be displayed first. Furthermore, if the user is in a hurry, the minimum necessary information can be displayed first. This allows the community unit to provide more appropriate information by adjusting the display order of communities according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the community unit are performed using the generation AI. For example, in the community unit, the generation AI receives a user's emotions as input and adjusts the display order. This allows the community unit to adjust the display order of the communities based on the user's emotions.

[0113] When displaying the community, the community unit can select the optimal display method by taking into account the user's device information. The community unit, for example, uses sensors and algorithms to acquire the user's device information. For example, if the user is using a smartphone, the community unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the community unit can provide a display method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the community unit can provide a simple and highly visible display method. For example, the community unit proposes the optimal display method for the device based on the user's device information. This allows the community unit to provide the optimal display method by taking into account the user's device information. Some or all of the above-mentioned processing in the community unit is performed using a generation AI. For example, in the community unit, the generation AI receives the user's device information as input and selects the optimal display method. For example, the generation AI learns the user's device information and proposes the optimal display method for the user. This allows the community unit to select the optimal display method by taking into account the user's device information.

[0114] When displaying a community, the community unit can prioritize displaying highly relevant information by taking into account the user's geographical location information. The community unit, for example, uses GPS data or location information services to acquire the user's geographical location information. For example, if the user is in a specific area, the community unit can prioritize displaying information related to that area. Also, if the user is traveling, the community unit can prioritize displaying information related to the user's travel destination. Furthermore, if the user is at home, the community unit can prioritize displaying information related to the area around the user's home. For example, the community unit displays region-specific information based on the user's geographical location information. This allows the community unit to provide more appropriate information by prioritizing highly relevant information by taking into account the user's geographical location information. Some or all of the above-described processing in the community unit is performed using a generation AI. For example, the generation AI in the community unit receives the user's geographical location information as input and selects highly relevant information. The generation AI, for example, learns the user's geographical location information and suggests optimal information to the user. This allows the community unit to prioritize highly relevant information by taking into account the user's geographical location information. === Hard Collateral 1-1 === Each of the multiple elements, including the input unit, generation unit, display unit, list generation unit, feedback reception unit, community unit, and emotion estimation unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the input unit inputs user preference and allergy information using the reception device 38 of the smart device 14 and provides the input to the specific processing unit 290 of the data processing device 12. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates personalized recipes using a generation AI. The display unit displays the generated recipes to the user using the output device 40 of the smart device 14. The list generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a shopping list based on the generated recipes. The feedback reception unit receives user feedback using the reception device 38 of the smart device 14 and provides the feedback to the specific processing unit 290 of the data processing device 12. The community unit provides an interface for users to share recipes they have tried with the community using the output device 40 of the smart device 14. The emotion estimation unit estimates the user's emotion using the camera 42 and microphone 38B of the smart device 14, and adjusts the input timing using the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned input unit, generation unit, display unit, list generation unit, feedback reception unit, community unit, and emotion estimation unit is realized, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the input unit inputs a user's preference or allergy information using the microphone 238 of the smart glasses 214 and provides it to the specific processing unit 290 of the data processing device 12. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates personalized recipes using a generation AI. The display unit displays the generated recipes to the user using the speaker 240 of the smart glasses 214. The list generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a shopping list based on the generated recipes. The feedback reception unit receives user feedback using the microphone 238 of the smart glasses 214 and provides it to the specific processing unit 290 of the data processing device 12. The community unit provides an interface for users to share recipes they have tried with the community using the speaker 240 of the smart glasses 214. The emotion estimation unit estimates the user's emotion using the camera 42 and microphone 238 of the smart glasses 214, and adjusts the input timing using the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned input unit, generation unit, display unit, list generation unit, feedback reception unit, community unit, and emotion estimation unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the input unit inputs user preference and allergy information using the microphone 238 of the headset-type terminal 314 and provides the information to the specific processing unit 290 of the data processing device 12. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates personalized recipes using a generation AI. The display unit displays the generated recipes to the user using the display 343 of the headset-type terminal 314. The list generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a shopping list based on the generated recipes. The feedback reception unit receives user feedback using the microphone 238 of the headset-type terminal 314 and provides the feedback to the specific processing unit 290 of the data processing device 12. The community unit provides an interface for users to share recipes they have tried with the community using the display 343 of the headset-type terminal 314. The emotion estimation unit estimates the user's emotion using the camera 42 and microphone 238 of the headset terminal 314, and adjusts the input timing using the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned input unit, generation unit, display unit, list generation unit, feedback reception unit, community unit, and emotion estimation unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the input unit inputs user preference and allergy information using the microphone 238 of the robot 414 and provides the input to the specific processing unit 290 of the data processing device 12. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates personalized recipes using a generation AI. The display unit displays the generated recipes to the user using the speaker 240 of the robot 414. The list generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a shopping list based on the generated recipes. The feedback reception unit receives user feedback using the microphone 238 of the robot 414 and provides the feedback to the specific processing unit 290 of the data processing device 12. The community unit provides an interface for users to share recipes they have tried with the community using the speaker 240 of the robot 414. The emotion estimation unit estimates the user's emotion using the camera 42 and microphone 238 of the robot 414, and adjusts the input timing by the specific processing unit 290 of the data processing device 12.

[0115] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0116] The recipe generation system may further include a history analysis unit that analyzes the user's dietary history. The history analysis unit stores dietary information previously entered by the user in a database and provides it to the generation unit. For example, the history analysis unit may analyze the user's past favorite dishes and avoided ingredients and provide this information to the generation unit, thereby generating more personalized recipes. Furthermore, the history analysis unit may suggest nutritionally balanced recipes based on the user's dietary history. This allows the history analysis unit to utilize the user's past dietary history to provide more appropriate recipes.

[0117] The generation unit can estimate the user's emotions and adjust the difficulty of the recipe based on the estimated user's emotions. For example, if the user is feeling stressed, it can suggest a simple and easy recipe. If the user is relaxed, it can suggest a slightly more time-consuming recipe. Furthermore, if the user is excited, it can suggest a more challenging recipe. In this way, the generation unit can provide more appropriate recipes by adjusting the difficulty of the recipe according to the user's emotions.

[0118] The display unit can select the optimal display method in consideration of the user's device information. For example, if the user is using a smartphone, a display method that matches the screen size can be provided. Also, if the user is using a tablet, a display method optimized for a large screen can be provided. Furthermore, if the user is using a smartwatch, a simple and highly visible display method can be provided. This allows the display unit to provide the optimal display method in consideration of the user's device information.

[0119] The list generation unit can generate an optimal shopping list taking into account the user's geographical location information. For example, if the user is in a specific area, ingredients available in that area can be added to the list. If the user is traveling, ingredients available at the travel destination can be added to the list. Furthermore, if the user is at home, ingredients available around the home can be added to the list. In this way, the list generation unit can provide an optimal shopping list taking into account the user's geographical location information.

[0120] The feedback receiving unit can estimate the user's emotions and adjust the feedback receiving method based on the estimated user's emotions. For example, if the user is feeling stressed, a concise and to-the-point feedback form is provided. Alternatively, if the user is relaxed, a form requesting detailed feedback is provided. Furthermore, if the user is in a hurry, voice input is prioritized and feedback is received quickly. In this way, the feedback receiving unit can provide more appropriate feedback by adjusting the feedback receiving method according to the user's emotions.

[0121] The feedback receiving unit can select the optimal feedback method by referring to the user's past feedback history. For example, it can preferentially suggest feedback methods (text, voice, etc.) that the user has used in the past. It can also predict and suggest a feedback method to be used in a specific time period based on the user's past feedback history. Furthermore, it can automatically complement feedback content provided by the user in the past, simplifying the feedback process. In this way, the feedback receiving unit can provide the optimal feedback method by referring to the user's past feedback history.

[0122] The community unit can estimate the user's emotions and adjust the display method of the community based on the estimated user's emotions. For example, if the user is nervous, a simple, highly visible display method is provided. If the user is relaxed, a display method including detailed information is provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points is provided. In this way, the community unit can adjust the display method of the community according to the user's emotions, thereby enabling more appropriate display.

[0123] The community unit can select the optimal display method by referring to the user's past operation history. For example, it can provide preferentially the display method that the user has used favorably in the past. It can also predict and provide the display method that will be used in a specific time period from the user's past operation history. It can also exclude and provide display methods that the user has avoided in the past. In this way, the community unit can provide the optimal display method by referring to the user's past operation history.

[0124] The community unit can customize the display content based on the user's health condition. For example, if the user's health condition is good, the community unit can provide display content with detailed information. If the user's health condition is poor, the community unit can provide display content that is concise and to the point. Furthermore, if the user has a specific health goal, the community unit can provide display content that is tailored to that goal. This allows the community unit to provide more appropriate information by customizing the display content based on the user's health condition.

[0125] The community unit can estimate the user's emotions and adjust the display order of communities based on the estimated user's emotions. For example, if the user is nervous, important information is displayed first. If the user is relaxed, detailed information is displayed first. If the user is in a hurry, the minimum necessary information is displayed first. In this way, the community unit can provide more appropriate information by adjusting the display order of communities according to the user's emotions.

[0126] The processing flow of the second embodiment will be briefly explained below.

[0127] Step 1: The input unit inputs the user's preferences, allergy information, health conditions, and specific nutritional goals. For example, the user can input this information through the application. The input unit stores the information entered by the user in a database and provides it to the generation unit. Step 2: The generation unit uses the generation AI to analyze the information input by the input unit and generate a personalized recipe. The generation AI selects the optimal ingredients and cooking method, taking into account, for example, the user's preferences and allergy information. The generation unit provides the recipe generated by the generation AI to the display unit. Step 3: The display unit displays the generated recipe to the user. For example, the display unit may display the main nutrients in the recipe and their amounts. The display unit may also display cooking instructions in specific steps. Step 4: The list generator automatically generates a shopping list based on the generated recipe. For example, the list generator may list the ingredients needed to make it easy for the user to purchase them.

[0128] 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.

[0129] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0130] 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.

[0131] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0132] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0133] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0146] 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.

[0147] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0148] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0159] 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.

[0160] 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.

[0161] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0162] 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.

[0163] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0164] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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).

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0176] 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.

[0177] 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.

[0178] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0179] 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.

[0180] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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).

[0185] 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.

[0186] 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."

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] [Explanation of symbols]

[0200] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an input section for inputting the user's preferences, allergy information, health status, and specific nutritional goals; a generation unit that analyzes the information input by the input unit and generates a personalized recipe; a display unit that displays the recipe generated by the generation unit; a list generation unit that generates a shopping list based on the recipes generated by the generation unit; A system characterized by:

2. The generation unit Generative AI analyzes user preferences, allergy information, health conditions, and specific nutritional goals to generate personalized recipes. The system of claim 1 .

3. The system according to claim 1 , wherein the display unit displays main nutrients and their amounts for the generated recipe.

4. The list generation unit Automatically generate a shopping list based on the generated recipes The system of claim 1 .

5. A system comprising a feedback receiving unit that receives feedback from a user.

6. The feedback receiving unit Generative AI provides more personalized recipes based on user feedback 6. The system of claim 5.

7. Equipped with a community department that provides community functions The system of claim 1 .

8. The community section Users share tried and tested recipes with the community 8. The system of claim 7.

9. The input unit Estimate the user's emotions and adjust the timing of input based on the estimated user emotions. The system of claim 1 .

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