System

Abstract

An object of the system according to the embodiment is to explain sensuous expressions and technical terms included in a cooking recipe specifically and in detail.SOLUTION: A system according to an embodiment includes a sensuous expression concretizing unit and a procedure detailing unit. The sensuous expression concretizing unit replaces the sensuous expression included in the cooking recipe with a concrete numerical value or image using the generation AI. The procedure detailing unit describes technical terms and procedures included in the cooking recipe in detail.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem that the sensory expressions and technical terms contained in cooking recipes are difficult to understand, making cooking difficult for beginners.

[0005] The system according to the embodiment aims to provide specific and detailed explanations of sensory expressions and technical terms contained in cooking recipes. [Means for solving the problem]

[0006] The system according to the embodiment includes a sensory expression concretization unit and a procedure detailing unit. The sensory expression concretization unit uses a generation AI to replace sensory expressions included in a cooking recipe with specific numerical values ​​and images. The procedure detailing unit provides detailed explanations of technical terms and procedures included in the cooking recipe. [Effects of the Invention]

[0007] The system according to the embodiment can provide specific and detailed explanations of sensory expressions and technical terms contained in cooking recipes. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The cooking recipe assistance system according to the embodiment of the present invention is a system that eliminates ambiguous expressions and unclear steps in cooking recipes and provides explanations using specific indicators and images / videos, thereby enabling users to cook safely and accurately.

[0029] A cooking recipe assistance system according to an embodiment includes a sensory expression concretization unit and a procedure refining unit. The sensory expression concretization unit uses a generation AI to replace sensory expressions included in a cooking recipe with specific numerical values ​​and images. For example, if the generation AI describes "appropriate amount of salt," it presents the specific amount as "one teaspoon of salt." Also, if the generation AI describes "fry until golden brown," it displays a specific image of golden brown. The generation AI converts sensory expressions into specific numerical values ​​and images using a text generation AI (e.g., LLM). The generation AI can also convert sensory expressions into specific images using a multimodal generation AI. For example, if a user inputs "appropriate amount of salt," the generation AI presents the specific amount as "one teaspoon" based on past data. The procedure refining unit uses the generation AI to provide detailed explanations of technical terms and procedures included in a cooking recipe. For example, if the generation AI describes "julienne," it presents an image or video of the specific cutting method as "how to cut vegetables into strips." The generation AI uses text generation AI to provide detailed explanations of technical terms and procedures. The generation AI can also use multimodal generation AI to convert technical terms and procedures into specific images and videos. For example, if a user inputs "julienne," the generation AI provides images and videos of specific cutting methods for "how to cut vegetables into strips" based on past data. This allows the cooking recipe assistance system according to the embodiment to eliminate ambiguous expressions and unclear steps in cooking recipes, enabling users to cook safely and accurately. For example, if a user inputs "appropriate amount of salt," the generation AI presents the specific amount as "one teaspoon." If the user inputs "julienne," the generation AI provides images and videos of specific cutting methods for "how to cut vegetables into strips."

[0030] The sensory expression materialization unit can refer to the user's past cooking history and present individually optimized values. For example, if the generation AI analyzes the user's past cooking history and the text reads "appropriate amount of salt," the sensory expression materialization unit presents a specific amount, such as "one teaspoon," based on the amount of salt the user has used in the past. The generation AI presents individually optimized values ​​based on the user's past cooking history. For example, the generation AI analyzes data on dishes the user has made in the past and presents optimal values ​​based on the user's preferences. This allows for more accurate cooking by presenting individually optimized values ​​based on the user's past cooking history.

[0031] The sensory expression materialization unit can automatically adjust optimal values ​​according to the type and freshness of ingredients. For example, if the generation AI analyzes the type and freshness of ingredients and the description says "appropriate amount of salt," the sensory expression materialization unit will suggest a specific amount, such as "1 teaspoon" for fresh ingredients and "1 / 2 teaspoon" for ingredients that cannot be preserved. The generation AI automatically adjusts optimal values ​​according to the type and freshness of ingredients. For example, the generation AI suggests optimal cooking times and temperatures based on the type and freshness of ingredients. This improves the quality of cooking by automatically adjusting optimal values ​​according to the type and freshness of ingredients.

[0032] The sensory expression materialization unit can provide customization options according to the user's preferences, allowing the user to adjust the values ​​themselves. For example, the generation AI provides customization options according to the user's preferences, and when "appropriate amount of salt" is described, the user can adjust the value to "1 teaspoon" or "1 / 2 teaspoon" themselves. The generation AI provides options to customize values ​​according to the user's preferences. For example, the generation AI allows the user to select the desired flavor strength or cooking time. This allows the user to adjust the values ​​according to their preferences, enabling more personalized cooking.

[0033] The sensory representation instantiation unit can provide numerical values ​​and images corresponding to cuisines of different cultural spheres and regions. For example, if the generation AI provides numerical values ​​corresponding to cuisines of different cultural spheres and regions, and the sensory representation instantiation unit describes "appropriate amount of salt," it will present a specific amount such as "1 teaspoon" for Japanese cuisine and "1 / 2 teaspoon" for Western cuisine. The generation AI provides numerical values ​​and images corresponding to cuisines of different cultural spheres and regions. For example, the generation AI provides numerical values ​​and images corresponding to cuisines of different cultural spheres and regions, such as Asian cuisine, European cuisine, and American cuisine. This makes it possible to respond to the diversity of global cuisine by providing numerical values ​​and images corresponding to cuisines of different cultural spheres and regions.

[0034] The step detailing unit can automatically adjust the level of detail according to the user's skill level. For example, the generation AI analyzes the user's skill level and provides a detailed explanation of "julienne" as "how to cut vegetables into thin strips" to beginners, and a concise explanation to advanced users. The generation AI automatically adjusts the level of detail of the steps according to the user's skill level. For example, the generation AI provides detailed steps to beginners and concise steps to advanced users. In this way, by automatically adjusting the level of detail according to the user's skill level, it can accommodate a wide range of users, from beginners to advanced users.

[0035] The procedure detailing unit can provide optimal procedures according to the characteristics of ingredients and the type of cooking utensils. For example, if the generation AI analyzes the characteristics of ingredients and the instruction to "stir-fry" is entered, the procedure detailing unit will provide specific instructions such as "stir-fry over medium heat for 5 minutes" depending on the type of vegetable. The generation AI provides optimal procedures according to the characteristics of ingredients and the type of cooking utensils. For example, the generation AI provides optimal cooking times and temperatures according to the hardness and moisture content of ingredients and the type of cooking utensils. This improves the quality of cooking by providing optimal procedures according to the characteristics of ingredients and the type of cooking utensils.

[0036] The step detailing unit can simultaneously display other recipes related to the recipe selected by the user and suggest variations of the dish. For example, if the generation AI displays other recipes related to the recipe selected by the user and "stir-fry" is written, the step detailing unit will suggest another recipe using the same stir-frying technique. The generation AI displays other recipes related to the recipe selected by the user. For example, the generation AI displays recipes that use the same ingredients or the same cooking method. This simultaneously displays other recipes related to the recipe selected by the user, thereby expanding the variety of dishes available.

[0037] The procedure detailing unit can refer to the user's past cooking history and customize the optimal procedure. For example, the generation AI analyzes the user's past cooking history, and if "stir-fry" is entered, the procedure detailing unit customizes the optimal stir-fry time based on the past cooking data. The generation AI customizes the optimal procedure based on the user's past cooking history. For example, the generation AI analyzes data on dishes the user has made in the past and provides the optimal procedure based on the user's preferences. This enables more personalized cooking by customizing the optimal procedure based on the user's past cooking history.

[0038] The safe heating method presentation unit can provide the optimal heating method according to the user's cooking environment. For example, if the generation AI analyzes the type and performance of the user's oven and the instruction says "heat at 180 degrees for 20 minutes," the safe heating method presentation unit will provide the optimal heating time and temperature according to the oven's performance. The generation AI provides the optimal heating method according to the user's cooking environment. For example, the generation AI provides the optimal heating method based on the type and performance of the oven, the size of the kitchen, the cooking utensils used, etc. This allows for safe and efficient cooking by providing the optimal heating method according to the user's cooking environment.

[0039] The safe heating method suggestion unit can automatically adjust the optimal heating time and temperature according to the type and amount of ingredients. For example, if the generation AI analyzes the type of ingredients and the instructions say "heat for 20 minutes at 180 degrees," the safe heating method suggestion unit will suggest the optimal heating method for the type of meat, such as "heat for 18 minutes at 190 degrees." The generation AI automatically adjusts the optimal heating time and temperature according to the type and amount of ingredients. For example, the generation AI will suggest the optimal heating time and temperature according to the type and amount of vegetables, and the cut and amount of meat. This improves the quality of cooking by automatically adjusting the optimal heating time and temperature according to the type and amount of ingredients.

[0040] The safe heating method suggestion unit can also simultaneously suggest other cooking methods related to the recipe selected by the user. For example, if the generation AI suggests other cooking methods related to the recipe selected by the user and the recipe says "heat at 180 degrees for 20 minutes," the safe heating method suggestion unit will also simultaneously suggest heating in a microwave. The generation AI suggests other cooking methods related to the recipe selected by the user. For example, the generation AI suggests different cooking methods such as microwave, grill, and steamer. This simultaneously suggests other cooking methods related to the recipe selected by the user, thereby expanding the variety of cooking options.

[0041] The safe heating method presentation unit can refer to the user's past cooking history and customize the optimal heating method. For example, if the generation AI analyzes the user's past cooking history and the user enters "heat at 180 degrees for 20 minutes," the safe heating method presentation unit customizes the optimal heating time and temperature based on the past cooking data. The generation AI customizes the optimal heating method based on the user's past cooking history. For example, the generation AI analyzes data on dishes the user has made in the past and provides the optimal heating method based on the user's preferences. This enables more personalized cooking by customizing the optimal heating method based on the user's past cooking history.

[0042] The image and video providing unit can automatically adjust the optimal resolution and viewpoint according to the user's visual comprehension. For example, if the generation AI analyzes the user's visual comprehension and the word "stir-fry" is entered, the image and video providing unit provides a video of the stir-frying steps at the optimal resolution and viewpoint. The generation AI automatically adjusts the optimal resolution and viewpoint according to the user's visual comprehension. For example, the generation AI provides images and videos at the optimal resolution and viewpoint based on the user's visual comprehension. This improves visual comprehension by automatically adjusting the optimal resolution and viewpoint according to the user's visual comprehension.

[0043] The image and video providing unit can provide optimal visual information according to the characteristics of ingredients and the type of cooking utensil. For example, if the generation AI analyzes the characteristics of ingredients and the instruction says "stir-fry," the image and video providing unit provides optimal visual information according to the type of vegetable. The generation AI provides optimal visual information according to the characteristics of ingredients and the type of cooking utensil. For example, the generation AI provides optimal visual information according to the hardness and moisture content of ingredients and the type of cooking utensil. This improves the quality of cooking by providing optimal visual information according to the characteristics of ingredients and the type of cooking utensil.

[0044] The image and video providing unit can simultaneously display other visual information related to the recipe selected by the user. For example, if the generation AI displays other visual information related to the recipe selected by the user and the recipe includes the word "stir-fry," the image and video providing unit simultaneously provides videos of tips and tricks for the stir-frying procedure. The generation AI displays other visual information related to the recipe selected by the user. For example, the generation AI displays visual information such as cooking tips, tricks, and points to note. This simultaneously displays other visual information related to the recipe selected by the user, thereby expanding the variety of dishes that can be made.

[0045] The image and video providing unit can refer to the user's past cooking history and customize the optimal visual information. For example, if the generation AI analyzes the user's past cooking history and the command "stir-fry" is entered, the image and video providing unit customizes the optimal visual information based on the past cooking data. The generation AI customizes the optimal visual information based on the user's past cooking history. For example, the generation AI analyzes data on dishes the user has made in the past and provides the optimal visual information according to the user's preferences. This enables more personalized cooking by customizing the optimal visual information based on the user's past cooking history.

[0046] The knowledge providing unit can customize optimal knowledge according to the user's past cooking history and skill level. For example, if the generation AI analyzes the user's past cooking history and the question "how to choose vegetables" is entered, the knowledge providing unit customizes the optimal way to choose vegetables based on the past cooking data. The generation AI customizes optimal knowledge according to the user's past cooking history and skill level. For example, the generation AI analyzes data on dishes the user has made in the past and the skill level, and provides optimal knowledge according to the user's preferences. This enables more personalized cooking by customizing optimal knowledge based on the user's past cooking history and skill level.

[0047] The knowledge provision unit can provide the most appropriate knowledge according to the characteristics of ingredients and the type of cooking utensil. For example, if the generation AI analyzes the characteristics of ingredients and the description is "how to select vegetables," the knowledge provision unit will provide the most appropriate way to select vegetables according to the type of vegetable. The generation AI will provide the most appropriate knowledge according to the characteristics of ingredients and the type of cooking utensil. For example, the generation AI will provide the most appropriate knowledge according to the hardness and moisture content of ingredients and the type of cooking utensil. This will improve the quality of cooking by providing the most appropriate knowledge according to the characteristics of ingredients and the type of cooking utensil.

[0048] The knowledge providing unit can simultaneously display other knowledge related to the recipe selected by the user. For example, if the generation AI displays other knowledge related to the recipe selected by the user and the knowledge providing unit describes "how to choose vegetables," it simultaneously provides nutritional information and substitutes for ingredients. The generation AI displays other knowledge related to the recipe selected by the user. For example, the generation AI displays knowledge such as nutritional information, substitutes for ingredients, and cooking tips. This simultaneously displays other knowledge related to the recipe selected by the user, thereby expanding the variety of dishes that can be made.

[0049] The knowledge provision unit can refer to the user's past cooking history and customize the most appropriate knowledge. For example, if the generation AI analyzes the user's past cooking history and the question "how to choose vegetables" is entered, the knowledge provision unit customizes the most appropriate knowledge based on the past cooking data. The generation AI customizes the most appropriate knowledge based on the user's past cooking history. For example, the generation AI analyzes data on dishes the user has made in the past and provides the most appropriate knowledge based on the user's preferences. This enables more personalized cooking by customizing the most appropriate knowledge based on the user's past cooking history.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The cooking recipe support system can register a user's food allergy information, automatically detect allergic ingredients in a recipe, and suggest alternative ingredients. For example, if a user has a nut allergy, the system can detect the nuts in the recipe and suggest sunflower seeds or other safe ingredients as substitutes. In addition, for users with dairy allergies, the system can suggest almond milk or coconut milk instead of cow's milk. This allows users to enjoy cooking safely.

[0052] The cooking recipe support system manages the user's ingredient inventory information and can suggest recipes based on the inventory. For example, if you input the ingredients in your refrigerator, it will suggest recipes using those ingredients. It can also take into account the expiration dates of ingredients and suggest recipes that prioritize ingredients that should be used up quickly. This reduces food waste and enables more efficient cooking.

[0053] The recipe support system can suggest recipes that suit the user's health condition and diet goals. For example, if the user is on a low-calorie diet, it can suggest recipes that use low-calorie ingredients. It can also suggest low-carbohydrate recipes to a user with diabetes. It can also suggest high-protein recipes to a user who is trying to build muscle. This makes it possible to create optimal meals that suit the user's health goals.

[0054] The cooking recipe assistance system can reference a user's ingredient purchasing history and automatically generate a shopping list for the next purchase. For example, it can add ingredients that the user frequently purchases to the list and suggest the necessary quantities. It can also add ingredients needed for specific recipes to the list. This allows users to shop efficiently and ensure that they do not miss out on any ingredients they need.

[0055] The cooking recipe assistance system can refer to the user's cooking utensil usage history and suggest the most suitable cooking utensil. For example, it can suggest the most suitable cooking utensil based on the frying pans and pots that the user uses frequently. It can also provide instructions on how to use and maintain new cooking utensils. This allows users to use the most suitable cooking utensils and cook efficiently.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The sensory expression materialization unit uses a generation AI to replace sensory expressions contained in a cooking recipe with concrete numerical values ​​and images. For example, if the generation AI describes "just the right amount of salt," it will present the specific amount as "one teaspoon of salt." Also, if the generation AI describes "fry until golden brown," it will display a concrete image of golden brown food. The generation AI uses a text generation AI (e.g., LLM) to convert sensory expressions into concrete numerical values ​​and images. The generation AI can also use a multimodal generation AI to convert sensory expressions into concrete images. For example, if a user enters "just the right amount of salt," the generation AI will present the specific amount as "one teaspoon" based on past data. Step 2: The step detailing unit uses the generation AI to provide detailed explanations of the technical terms and steps included in the recipe. For example, if the generation AI describes "julienne," it will provide images and videos of specific cutting methods as "how to cut vegetables into strips." The generation AI uses text generation AI to provide detailed explanations of the technical terms and steps. The generation AI can also use multimodal generation AI to convert the technical terms and steps into specific images and videos. For example, if a user inputs "julienne," the generation AI will provide images and videos of specific cutting methods as "how to cut vegetables into strips" based on past data.

[0058] (Example 2) The cooking recipe assistance system according to the embodiment of the present invention is a system that eliminates ambiguous expressions and unclear steps in cooking recipes and provides explanations using specific indicators and images / videos, thereby enabling users to cook safely and accurately.

[0059] A cooking recipe assistance system according to an embodiment includes a sensory expression concretization unit and a procedure refining unit. The sensory expression concretization unit uses a generation AI to replace sensory expressions included in a cooking recipe with specific numerical values ​​and images. For example, if the generation AI describes "appropriate amount of salt," it presents the specific amount as "one teaspoon of salt." Also, if the generation AI describes "fry until golden brown," it displays a specific image of golden brown. The generation AI converts sensory expressions into specific numerical values ​​and images using a text generation AI (e.g., LLM). The generation AI can also convert sensory expressions into specific images using a multimodal generation AI. For example, if a user inputs "appropriate amount of salt," the generation AI presents the specific amount as "one teaspoon" based on past data. The procedure refining unit uses the generation AI to provide detailed explanations of technical terms and procedures included in a cooking recipe. For example, if the generation AI describes "julienne," it presents an image or video of the specific cutting method as "how to cut vegetables into strips." The generation AI uses text generation AI to provide detailed explanations of technical terms and procedures. The generation AI can also use multimodal generation AI to convert technical terms and procedures into specific images and videos. For example, if a user inputs "julienne," the generation AI provides images and videos of specific cutting methods for "how to cut vegetables into strips" based on past data. This allows the cooking recipe assistance system according to the embodiment to eliminate ambiguous expressions and unclear steps in cooking recipes, enabling users to cook safely and accurately. For example, if a user inputs "appropriate amount of salt," the generation AI presents the specific amount as "one teaspoon." If the user inputs "julienne," the generation AI provides images and videos of specific cutting methods for "how to cut vegetables into strips."

[0060] The sensory expression materialization unit can refer to the user's past cooking history and present individually optimized values. For example, if the generation AI analyzes the user's past cooking history and the text reads "appropriate amount of salt," the sensory expression materialization unit presents a specific amount, such as "one teaspoon," based on the amount of salt the user has used in the past. The generation AI presents individually optimized values ​​based on the user's past cooking history. For example, the generation AI analyzes data on dishes the user has made in the past and presents optimal values ​​based on the user's preferences. This allows for more accurate cooking by presenting individually optimized values ​​based on the user's past cooking history.

[0061] The sensory expression materialization unit can automatically adjust optimal values ​​according to the type and freshness of ingredients. For example, if the generation AI analyzes the type and freshness of ingredients and the description says "appropriate amount of salt," the sensory expression materialization unit will suggest a specific amount, such as "1 teaspoon" for fresh ingredients and "1 / 2 teaspoon" for ingredients that cannot be preserved. The generation AI automatically adjusts optimal values ​​according to the type and freshness of ingredients. For example, the generation AI suggests optimal cooking times and temperatures based on the type and freshness of ingredients. This improves the quality of cooking by automatically adjusting optimal values ​​according to the type and freshness of ingredients.

[0062] The sensory expression instantiation unit can use the emotion estimation function to analyze the user's emotions regarding a sensory expression and present specific numerical values ​​or images that elicit positive emotions. For example, the sensory expression instantiation unit can use the emotion estimation function to analyze the user's emotions when a description of "appropriate amount of salt" is displayed and present a specific amount such as "one teaspoon" to elicit positive emotions. The emotion estimation function analyzes the user's emotions using technologies such as facial expression recognition, voice analysis, and text analysis. For example, the emotion estimation function can capture the user's facial expressions with a camera and analyze emotions based on changes in facial expressions. The emotion estimation function can also record the user's voice and analyze the tone and speed of the voice to infer emotions. Furthermore, the emotion estimation function can analyze text entered by the user and infer emotions from the content of the text. This improves the enjoyment of cooking by analyzing the user's emotions and presenting specific numerical values ​​or images that elicit positive emotions.

[0063] The sensory expression materialization unit can provide customization options according to the user's preferences, allowing the user to adjust the values ​​themselves. For example, the generation AI provides customization options according to the user's preferences, and when "appropriate amount of salt" is described, the user can adjust the value to "1 teaspoon" or "1 / 2 teaspoon" themselves. The generation AI provides options to customize values ​​according to the user's preferences. For example, the generation AI allows the user to select the desired flavor strength or cooking time. This allows the user to adjust the values ​​according to their preferences, enabling more personalized cooking.

[0064] The sensory representation instantiation unit can provide numerical values ​​and images corresponding to cuisines of different cultural spheres and regions. For example, if the generation AI provides numerical values ​​corresponding to cuisines of different cultural spheres and regions, and the sensory representation instantiation unit describes "appropriate amount of salt," it will present a specific amount such as "1 teaspoon" for Japanese cuisine and "1 / 2 teaspoon" for Western cuisine. The generation AI provides numerical values ​​and images corresponding to cuisines of different cultural spheres and regions. For example, the generation AI provides numerical values ​​and images corresponding to cuisines of different cultural spheres and regions, such as Asian cuisine, European cuisine, and American cuisine. This makes it possible to respond to the diversity of global cuisine by providing numerical values ​​and images corresponding to cuisines of different cultural spheres and regions.

[0065] The sensory expression instantiation unit can use the emotion estimation function to analyze in real time what emotions a user feels in response to a sensory expression and suggest an optimal instantiation method. For example, the sensory expression instantiation unit can use the emotion estimation function to analyze in real time the emotions a user feels when the phrase "appropriate amount of salt" is written and suggest "one teaspoon" as the optimal instantiation method. The emotion estimation function analyzes the user's emotions in real time using technologies such as facial expression recognition, voice analysis, and text analysis. For example, the emotion estimation function can capture the user's facial expressions with a camera and analyze the emotions in real time based on changes in the facial expressions. The emotion estimation function can also record the user's voice and analyze the tone and speed of the voice in real time to infer emotions. Furthermore, the emotion estimation function can analyze text entered by the user in real time and infer emotions from the content of the text. This improves user satisfaction by analyzing the user's emotions in real time and suggesting the optimal instantiation method.

[0066] The step detailing unit can automatically adjust the level of detail according to the user's skill level. For example, the generation AI analyzes the user's skill level and provides a detailed explanation of "julienne" as "how to cut vegetables into thin strips" to beginners, and a concise explanation to advanced users. The generation AI automatically adjusts the level of detail of the steps according to the user's skill level. For example, the generation AI provides detailed steps to beginners and concise steps to advanced users. In this way, by automatically adjusting the level of detail according to the user's skill level, it can accommodate a wide range of users, from beginners to advanced users.

[0067] The procedure detailing unit can provide optimal procedures according to the characteristics of ingredients and the type of cooking utensils. For example, if the generation AI analyzes the characteristics of ingredients and the instruction to "stir-fry" is entered, the procedure detailing unit will provide specific instructions such as "stir-fry over medium heat for 5 minutes" depending on the type of vegetable. The generation AI provides optimal procedures according to the characteristics of ingredients and the type of cooking utensils. For example, the generation AI provides optimal cooking times and temperatures according to the hardness and moisture content of ingredients and the type of cooking utensils. This improves the quality of cooking by providing optimal procedures according to the characteristics of ingredients and the type of cooking utensils.

[0068] The step detailing unit can use the emotion estimation function to analyze the user's emotions regarding the steps and provide detailed steps to reduce stress. For example, the step detailing unit can use the emotion estimation function to analyze the user's emotions when a step is written as "stir-fry" and provide detailed steps such as "stir-fry over medium heat for 5 minutes" to reduce stress. The emotion estimation function analyzes the user's emotions using technologies such as facial expression recognition, voice analysis, and text analysis. For example, the emotion estimation function can capture the user's facial expressions with a camera and analyze emotions based on changes in facial expressions. The emotion estimation function can also record the user's voice and analyze the tone and speed of the voice to estimate emotions. Furthermore, the emotion estimation function can analyze text entered by the user and estimate emotions from the content of the text. This improves the enjoyment of cooking by analyzing the user's emotions and providing detailed steps to reduce stress.

[0069] The step detailing unit can simultaneously display other recipes related to the recipe selected by the user and suggest variations of the dish. For example, if the generation AI displays other recipes related to the recipe selected by the user and "stir-fry" is written, the step detailing unit will suggest another recipe using the same stir-frying technique. The generation AI displays other recipes related to the recipe selected by the user. For example, the generation AI displays recipes that use the same ingredients or the same cooking method. This simultaneously displays other recipes related to the recipe selected by the user, thereby expanding the variety of dishes available.

[0070] The procedure detailing unit can refer to the user's past cooking history and customize the optimal procedure. For example, the generation AI analyzes the user's past cooking history, and if "stir-fry" is entered, the procedure detailing unit customizes the optimal stir-fry time based on the past cooking data. The generation AI customizes the optimal procedure based on the user's past cooking history. For example, the generation AI analyzes data on dishes the user has made in the past and provides the optimal procedure based on the user's preferences. This enables more personalized cooking by customizing the optimal procedure based on the user's past cooking history.

[0071] The step detailing unit can use the emotion estimation function to analyze in real time how the user feels about the steps and suggest an optimal step detailing method. For example, the step detailing unit can use the emotion estimation function to analyze in real time how the user feels when the step detailing unit sees the word "stir-fry" and suggest "stir-fry over medium heat for 5 minutes" as the optimal step detailing method. The emotion estimation function analyzes the user's emotions in real time using technologies such as facial expression recognition, voice analysis, and text analysis. For example, the emotion estimation function can capture the user's facial expressions with a camera and analyze the emotions in real time based on changes in the facial expressions. The emotion estimation function can also record the user's voice and analyze the tone and speed of the voice in real time to estimate the emotions. Furthermore, the emotion estimation function can analyze text entered by the user in real time and estimate the emotions from the content of the text. This improves user satisfaction by analyzing the user's emotions in real time and suggesting the optimal step detailing method.

[0072] The safe heating method presentation unit can provide the optimal heating method according to the user's cooking environment. For example, if the generation AI analyzes the type and performance of the user's oven and the instruction says "heat at 180 degrees for 20 minutes," the safe heating method presentation unit will provide the optimal heating time and temperature according to the oven's performance. The generation AI provides the optimal heating method according to the user's cooking environment. For example, the generation AI provides the optimal heating method based on the type and performance of the oven, the size of the kitchen, the cooking utensils used, etc. This allows for safe and efficient cooking by providing the optimal heating method according to the user's cooking environment.

[0073] The safe heating method suggestion unit can automatically adjust the optimal heating time and temperature according to the type and amount of ingredients. For example, if the generation AI analyzes the type of ingredients and the instructions say "heat for 20 minutes at 180 degrees," the safe heating method suggestion unit will suggest the optimal heating method for the type of meat, such as "heat for 18 minutes at 190 degrees." The generation AI automatically adjusts the optimal heating time and temperature according to the type and amount of ingredients. For example, the generation AI will suggest the optimal heating time and temperature according to the type and amount of vegetables, and the cut and amount of meat. This improves the quality of cooking by automatically adjusting the optimal heating time and temperature according to the type and amount of ingredients.

[0074] The safe heating method presentation unit can use the emotion estimation function to analyze the user's emotions regarding the heating method and provide specific instructions to give a sense of security. For example, the safe heating method presentation unit can use the emotion estimation function to analyze the user's emotions when a menu item says "heat at 180 degrees for 20 minutes" and provide specific instructions such as "preheat the oven to 180 degrees and heat for 20 minutes" to give a sense of security. The emotion estimation function analyzes the user's emotions using technologies such as facial expression recognition, voice analysis, and text analysis. For example, the emotion estimation function can capture the user's facial expressions with a camera and analyze emotions based on changes in facial expressions. The emotion estimation function can also record the user's voice and analyze the tone and speed of the voice to infer emotions. Furthermore, the emotion estimation function can analyze text entered by the user and infer emotions from the content of the text. This enables safe and efficient cooking by analyzing the user's emotions and providing specific instructions to give a sense of security.

[0075] The safe heating method suggestion unit can also simultaneously suggest other cooking methods related to the recipe selected by the user. For example, if the generation AI suggests other cooking methods related to the recipe selected by the user and the recipe says "heat at 180 degrees for 20 minutes," the safe heating method suggestion unit will also simultaneously suggest heating in a microwave. The generation AI suggests other cooking methods related to the recipe selected by the user. For example, the generation AI suggests different cooking methods such as microwave, grill, and steamer. This simultaneously suggests other cooking methods related to the recipe selected by the user, thereby expanding the variety of cooking options.

[0076] The safe heating method presentation unit can refer to the user's past cooking history and customize the optimal heating method. For example, if the generation AI analyzes the user's past cooking history and the user enters "heat at 180 degrees for 20 minutes," the safe heating method presentation unit customizes the optimal heating time and temperature based on the past cooking data. The generation AI customizes the optimal heating method based on the user's past cooking history. For example, the generation AI analyzes data on dishes the user has made in the past and provides the optimal heating method based on the user's preferences. This enables more personalized cooking by customizing the optimal heating method based on the user's past cooking history.

[0077] The safe heating method suggestion unit can use the emotion estimation function to analyze in real time how the user feels about the heating method and suggest the optimal heating method. For example, the safe heating method suggestion unit can use the emotion estimation function to analyze in real time the emotions the user feels when a recipe says "heat at 180 degrees for 20 minutes" and suggest the optimal heating method as "preheat the oven to 180 degrees and heat for 20 minutes." The emotion estimation function analyzes the user's emotions in real time using technologies such as facial expression recognition, voice analysis, and text analysis. For example, the emotion estimation function can capture the user's facial expressions with a camera and analyze the emotions in real time based on changes in facial expressions. The emotion estimation function can also record the user's voice and analyze the tone and speed of the voice in real time to suggest emotions. Furthermore, the emotion estimation function can analyze text entered by the user in real time and suggest emotions from the content of the text. This improves user satisfaction by analyzing the user's emotions in real time and suggesting the optimal heating method.

[0078] The image and video providing unit can automatically adjust the optimal resolution and viewpoint according to the user's visual comprehension. For example, if the generation AI analyzes the user's visual comprehension and the word "stir-fry" is entered, the image and video providing unit provides a video of the stir-frying steps at the optimal resolution and viewpoint. The generation AI automatically adjusts the optimal resolution and viewpoint according to the user's visual comprehension. For example, the generation AI provides images and videos at the optimal resolution and viewpoint based on the user's visual comprehension. This improves visual comprehension by automatically adjusting the optimal resolution and viewpoint according to the user's visual comprehension.

[0079] The image and video providing unit can provide optimal visual information according to the characteristics of ingredients and the type of cooking utensil. For example, if the generation AI analyzes the characteristics of ingredients and the instruction says "stir-fry," the image and video providing unit provides optimal visual information according to the type of vegetable. The generation AI provides optimal visual information according to the characteristics of ingredients and the type of cooking utensil. For example, the generation AI provides optimal visual information according to the hardness and moisture content of ingredients and the type of cooking utensil. This improves the quality of cooking by providing optimal visual information according to the characteristics of ingredients and the type of cooking utensil.

[0080] The image and video providing unit can use the emotion estimation function to analyze the emotions a user feels toward an image or video and provide visual information to elicit positive emotions. For example, the image and video providing unit can use the emotion estimation function to analyze the emotions a user feels when the word "stir-fry" is written, and provide optimal visual information to elicit positive emotions. The emotion estimation function analyzes the user's emotions using technologies such as facial expression recognition, voice analysis, and text analysis. For example, the emotion estimation function can capture the user's facial expressions with a camera and analyze emotions based on changes in facial expressions. The emotion estimation function can also record the user's voice and analyze the tone and speed of the voice to infer emotions. Furthermore, the emotion estimation function can analyze text entered by the user and infer emotions from the content of the text. This improves the enjoyment of cooking by analyzing the user's emotions and providing visual information to elicit positive emotions.

[0081] The image and video providing unit can simultaneously display other visual information related to the recipe selected by the user. For example, if the generation AI displays other visual information related to the recipe selected by the user and the recipe includes the word "stir-fry," the image and video providing unit simultaneously provides videos of tips and tricks for the stir-frying procedure. The generation AI displays other visual information related to the recipe selected by the user. For example, the generation AI displays visual information such as cooking tips, tricks, and points to note. This simultaneously displays other visual information related to the recipe selected by the user, thereby expanding the variety of dishes that can be made.

[0082] The image and video providing unit can refer to the user's past cooking history and customize the optimal visual information. For example, if the generation AI analyzes the user's past cooking history and the command "stir-fry" is entered, the image and video providing unit customizes the optimal visual information based on the past cooking data. The generation AI customizes the optimal visual information based on the user's past cooking history. For example, the generation AI analyzes data on dishes the user has made in the past and provides the optimal visual information according to the user's preferences. This enables more personalized cooking by customizing the optimal visual information based on the user's past cooking history.

[0083] The image and video providing unit can use the emotion estimation function to analyze in real time what emotions a user feels about an image or video and suggest the most appropriate visual information. For example, the image and video providing unit can use the emotion estimation function to analyze in real time the emotions a user feels when the word "stir-fry" is written, and suggest a video of the stir-frying procedure as the most appropriate visual information. The emotion estimation function analyzes the user's emotions in real time using technologies such as facial expression recognition, voice analysis, and text analysis. For example, the emotion estimation function can capture the user's facial expressions with a camera and analyze emotions in real time based on changes in facial expressions. The emotion estimation function can also record the user's voice and analyze the tone and speed of the voice in real time to infer emotions. Furthermore, the emotion estimation function can analyze text entered by the user in real time and infer emotions from the content of the text. This improves user satisfaction by analyzing the user's emotions in real time and suggesting the most appropriate visual information.

[0084] The knowledge providing unit can customize optimal knowledge according to the user's past cooking history and skill level. For example, if the generation AI analyzes the user's past cooking history and the question "how to choose vegetables" is entered, the knowledge providing unit customizes the optimal way to choose vegetables based on the past cooking data. The generation AI customizes optimal knowledge according to the user's past cooking history and skill level. For example, the generation AI analyzes data on dishes the user has made in the past and the skill level, and provides optimal knowledge according to the user's preferences. This enables more personalized cooking by customizing optimal knowledge based on the user's past cooking history and skill level.

[0085] The knowledge provision unit can provide the most appropriate knowledge according to the characteristics of ingredients and the type of cooking utensil. For example, if the generation AI analyzes the characteristics of ingredients and the description is "how to select vegetables," the knowledge provision unit will provide the most appropriate way to select vegetables according to the type of vegetable. The generation AI will provide the most appropriate knowledge according to the characteristics of ingredients and the type of cooking utensil. For example, the generation AI will provide the most appropriate knowledge according to the hardness and moisture content of ingredients and the type of cooking utensil. This will improve the quality of cooking by providing the most appropriate knowledge according to the characteristics of ingredients and the type of cooking utensil.

[0086] The knowledge providing unit can use the emotion estimation function to analyze the emotions a user has toward knowledge necessary for cooking and provide knowledge for eliciting positive emotions. For example, the knowledge providing unit can use the emotion estimation function to analyze the emotions a user has when a phrase "how to choose vegetables" is written, and provide optimal knowledge for eliciting positive emotions. The emotion estimation function analyzes the user's emotions using technologies such as facial expression recognition, voice analysis, and text analysis. For example, the emotion estimation function captures the user's facial expressions with a camera and analyzes emotions based on changes in facial expressions. The emotion estimation function can also record the user's voice and analyze the tone and speed of the voice to estimate emotions. Furthermore, the emotion estimation function can analyze text entered by the user and estimate emotions from the content of the text. This improves the enjoyment of cooking by analyzing the user's emotions and providing knowledge for eliciting positive emotions.

[0087] The knowledge providing unit can simultaneously display other knowledge related to the recipe selected by the user. For example, if the generation AI displays other knowledge related to the recipe selected by the user and the knowledge providing unit describes "how to choose vegetables," it simultaneously provides nutritional information and substitutes for ingredients. The generation AI displays other knowledge related to the recipe selected by the user. For example, the generation AI displays knowledge such as nutritional information, substitutes for ingredients, and cooking tips. This simultaneously displays other knowledge related to the recipe selected by the user, thereby expanding the variety of dishes that can be made.

[0088] The knowledge provision unit can refer to the user's past cooking history and customize the most appropriate knowledge. For example, if the generation AI analyzes the user's past cooking history and the question "how to choose vegetables" is entered, the knowledge provision unit customizes the most appropriate knowledge based on the past cooking data. The generation AI customizes the most appropriate knowledge based on the user's past cooking history. For example, the generation AI analyzes data on dishes the user has made in the past and provides the most appropriate knowledge based on the user's preferences. This enables more personalized cooking by customizing the most appropriate knowledge based on the user's past cooking history.

[0089] The knowledge providing unit can use the emotion estimation function to analyze in real time how the user feels about cooking knowledge and suggest the most appropriate knowledge. For example, the knowledge providing unit can use the emotion estimation function to analyze in real time the emotions the user feels when the phrase "how to choose vegetables" is written, and suggest how to choose vegetables as the most appropriate knowledge. The emotion estimation function analyzes the user's emotions in real time using technologies such as facial expression recognition, voice analysis, and text analysis. For example, the emotion estimation function captures the user's facial expressions with a camera and analyzes emotions in real time based on changes in facial expressions. The emotion estimation function can also record the user's voice and analyze the tone and speed of the voice in real time to guess emotions. Furthermore, the emotion estimation function can analyze text entered by the user in real time and guess emotions from the content of the text. In this way, the user's emotions are analyzed in real time and the most appropriate knowledge is suggested, thereby improving user satisfaction.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The cooking recipe support system can register a user's food allergy information, automatically detect allergic ingredients in a recipe, and suggest alternative ingredients. For example, if a user has a nut allergy, the system can detect the nuts in the recipe and suggest sunflower seeds or other safe ingredients as substitutes. In addition, for users with dairy allergies, the system can suggest almond milk or coconut milk instead of cow's milk. This allows users to enjoy cooking safely.

[0092] The cooking recipe support system manages the user's ingredient inventory information and can suggest recipes based on the inventory. For example, if you input the ingredients in your refrigerator, it will suggest recipes using those ingredients. It can also take into account the expiration dates of ingredients and suggest recipes that prioritize ingredients that should be used up quickly. This reduces food waste and enables more efficient cooking.

[0093] The recipe support system can suggest recipes that suit the user's health condition and diet goals. For example, if the user is on a low-calorie diet, it can suggest recipes that use low-calorie ingredients. It can also suggest low-carbohydrate recipes to a user with diabetes. It can also suggest high-protein recipes to a user who is trying to build muscle. This makes it possible to create optimal meals that suit the user's health goals.

[0094] The cooking recipe assistance system can estimate the user's emotions and adjust the difficulty of the cooking. For example, if the user is feeling stressed, the system can suggest easy and relaxing recipes. On the other hand, if the user is feeling challenging, the system can suggest more difficult recipes. This allows the system to provide an optimal cooking experience according to the user's emotions.

[0095] The cooking recipe assistance system can estimate the user's emotions and provide encouragement and advice while cooking. For example, if the user is confused, the system can display an encouraging message such as "Don't worry, it's almost done." If the user is confident, the system can provide advice such as "Great progress, keep it up." This makes the user's cooking experience more enjoyable and improves their satisfaction.

[0096] A cooking recipe assistance system can estimate a user's emotions and suggest ways to present food. For example, if a user is cooking for a special event, the system can provide beautiful presentation and decoration ideas. If a user is cooking an everyday meal, the system can suggest simple and efficient presentation methods. This allows for optimal presentation based on the user's emotions.

[0097] The cooking recipe assistance system can estimate the user's emotions and play music that corresponds to the progress of cooking. For example, if the user wants to relax, the system can play calm music. If the user is feeling energetic, the system can play up-tempo music. This improves the enjoyment of cooking by providing optimal music according to the user's emotions.

[0098] The cooking recipe assistance system can estimate the user's emotions and suggest break times according to the progress of cooking. For example, if the user is tired, the system can suggest, "Let's take a short break here." If the user is concentrating, the system can advise, "Let's keep going." This improves cooking efficiency and satisfaction by providing optimal break times according to the user's emotions.

[0099] The cooking recipe assistance system can reference a user's ingredient purchasing history and automatically generate a shopping list for the next purchase. For example, it can add ingredients that the user frequently purchases to the list and suggest the necessary quantities. It can also add ingredients needed for specific recipes to the list. This allows users to shop efficiently and ensure that they do not miss out on any ingredients they need.

[0100] The cooking recipe assistance system can refer to the user's cooking utensil usage history and suggest the most suitable cooking utensil. For example, it can suggest the most suitable cooking utensil based on the frying pans and pots that the user uses frequently. It can also provide instructions on how to use and maintain new cooking utensils. This allows users to use the most suitable cooking utensils and cook efficiently.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The sensory expression materialization unit uses a generation AI to replace sensory expressions contained in a cooking recipe with concrete numerical values ​​and images. For example, if the generation AI describes "just the right amount of salt," it will present the specific amount as "one teaspoon of salt." Also, if the generation AI describes "fry until golden brown," it will display a concrete image of golden brown food. The generation AI uses a text generation AI (e.g., LLM) to convert sensory expressions into concrete numerical values ​​and images. The generation AI can also use a multimodal generation AI to convert sensory expressions into concrete images. For example, if a user enters "just the right amount of salt," the generation AI will present the specific amount as "one teaspoon" based on past data. Step 2: The step detailing unit uses the generation AI to provide detailed explanations of the technical terms and steps included in the recipe. For example, if the generation AI describes "julienne," it will provide images and videos of specific cutting methods as "how to cut vegetables into strips." The generation AI uses text generation AI to provide detailed explanations of the technical terms and steps. The generation AI can also use multimodal generation AI to convert the technical terms and steps into specific images and videos. For example, if a user inputs "julienne," the generation AI will provide images and videos of specific cutting methods as "how to cut vegetables into strips" based on past data.

[0103] 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.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] 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.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0116] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0117] 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.

[0118] 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.

[0119] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] 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.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0131] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0132] 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.

[0133] 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.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] 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.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0147] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0148] 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.

[0149] 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.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0170] 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. Using generative AI, a sensory expression materialization unit that converts sensory expressions contained in cooking recipes into concrete numerical values ​​and images; and a procedure detailing unit that provides detailed explanations of technical terms and procedures included in cooking recipes. A system characterized by:

2. The sensory expression realization unit Automatically adjusts the optimal values ​​according to the type and freshness of ingredients 2. The system of claim 1.

3. The procedure detailing unit Automatically adjusts level of detail based on the user's skill level 2. The system of claim 1.

4. The safe heating method display section Automatically adjusts the optimal heating time and temperature according to the type and amount of food.

2. The system of claim 1.

5. Image and video provided by: Providing optimal visual information according to the characteristics of ingredients and the type of cooking equipment 2. The system of claim 1.

6. The Knowledge Department Analyzing the feelings that users have about the knowledge necessary for cooking and providing the knowledge to elicit positive feelings 2. The system of claim 1.

7. The sensory expression realization unit Analyze the emotions that users have toward sensory expressions and present specific numerical values ​​and images that elicit positive emotions.

2. The system of claim 1.

8. The procedure detailing unit Analyze the user's feelings about the procedure and provide detailed instructions to reduce stress 2. The system of claim 1.

Images