System

Abstract

An object of a system according to an embodiment is to provide a recipe in consideration of individual preferences, meal preferences, and allergy information of a user.SOLUTION: A system includes a user information collection unit, an analysis unit, a recipe generation unit, and a provision unit. The user information collection unit collects user preferences, meal preferences, and allergy information. The analysis unit analyzes the information collected by the user information collection unit. The recipe generation unit generates a recipe based on the information analyzed by the analysis unit. The providing unit provides the user with the recipe generated by the recipe generating unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to provide recipes that take into account a user's individual tastes, dietary preferences, and allergy information.

[0005] The system according to the embodiment aims to provide recipes that take into consideration the individual tastes, dietary preferences, and allergy information of the user. [Means for solving the problem]

[0006] The system according to the embodiment includes a user information collection unit, an analysis unit, a recipe generation unit, and a provision unit. The user information collection unit collects information on a user's preferences, food preferences, and allergies. The analysis unit analyzes the information collected by the user information collection unit. The recipe generation unit generates a recipe based on the information analyzed by the analysis unit. The provision unit provides the recipe generated by the recipe generation unit to the user. [Effects of the Invention]

[0007] The system according to the embodiment can provide recipes that take into consideration the individual tastes, dietary preferences, and allergy information of the user. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The recipe generation system according to the embodiment of the present invention is a system in which a generation AI generates and provides original recipes individually, taking into consideration the user's tastes, dietary preferences, and allergy information. This allows the recipe generation system to provide original recipes that meet the user's requirements.

[0029] A recipe generation system according to an embodiment includes a user information collection unit, an analysis unit, a recipe generation unit, and a provision unit. The user information collection unit collects user preferences, dietary preferences, and allergy information. For example, it collects information entered by the user, such as "I like spicy food and I'm allergic to nuts." The user information collection unit can also collect the user's dietary history and health data. The analysis unit analyzes the information collected by the user information collection unit. For example, it analyzes the user's preferences and allergy information and provides data for generating appropriate recipes. The analysis unit can also analyze the user's dietary history and health data and reflect this in recipe generation. The recipe generation unit generates recipes based on the information analyzed by the analysis unit. For example, the generation AI takes into account the user's preferences and allergy information, and generates recipes that take into account the difficulty, required time, and difficulty of obtaining ingredients. The generation AI can also learn the user's dietary preferences in real time and generate recipes that reflect changes in preferences. The provision unit provides the recipes generated by the recipe generation unit to the user. For example, the generated recipes are displayed in text format. In addition, the recipe steps and ingredient lists are also detailed, which the user can refer to when actually cooking. This allows the recipe creation system according to the embodiment to provide original recipes that meet the user's requirements.

[0030] The user information collection unit collects the user's dietary history and health data, and the analysis unit analyzes the user's dietary history and health data to reflect it in recipe generation. The user information collection unit, for example, collects the user's past dietary history, and the generation AI analyzes that data to reflect it in recipe generation. For example, the user's preference trends are identified based on the types and frequency of dishes the user has eaten in the past. The user information collection unit also collects the user's health data (e.g., blood sugar level and weight), and the generation AI analyzes that data to generate health-conscious recipes. For example, low-carbohydrate recipes are suggested for users with high blood sugar levels. Furthermore, the user information collection unit integrates the user's dietary history and health data, and the generation AI analyzes that data to generate recipes that are optimal for the user's health condition. For example, recipes aimed at weight management are suggested. This makes it possible to generate recipes that take the user's dietary history and health data into consideration.

[0031] The user information collection unit learns the user's food preferences in real time, and the analysis unit can generate recipes based on the user's food preferences. For example, the user information collection unit learns the user's food preferences in real time, and the generation AI generates recipes based on that data. For example, it reflects the user's recent favorite food trends. The user information collection unit also takes into account that the user's preferences change over time, and the generation AI learns those changes and reflects them in the recipes. For example, it incorporates preferences that change with the seasons. Furthermore, the user information collection unit regularly updates the user's food preferences, and the generation AI generates recipes that match the latest preferences based on that data. For example, it incorporates ingredients that the user has recently become interested in. This makes it possible to generate recipes that reflect changes in the user's food preferences in real time.

[0032] The user information collection unit automatically extracts food preferences from the user's social media posts and blog articles, and the analysis unit can generate recipes based on the user's food preferences. The user information collection unit, for example, analyzes the user's social media posts and automatically extracts food preferences and reflects them in the generation AI. For example, preferences are identified based on photos and comments about food shared by the user on social media. The user information collection unit also analyzes the user's blog articles and automatically extracts food preferences and reflects them in the generation AI. For example, preferences are identified based on recipes and ingredients introduced by the user on their blog. Furthermore, the user information collection unit integrates data collected from social media and blogs, and the generation AI analyzes the user's food preferences based on that data to generate recipes. For example, ingredients frequently mentioned by the user are incorporated. This allows food preferences to be automatically extracted from the user's social media posts and blog articles and reflected in recipe generation.

[0033] The user information collection unit collects the food preferences of the user's family and friends, and the analysis unit can generate recipes that are optimal for multiple people based on the food preferences of the family and friends. The user information collection unit, for example, collects the food preferences of the user's family and friends, and the generation AI generates recipes that are optimal for multiple people based on that data. For example, it suggests dishes that the whole family can enjoy. The user information collection unit also collects allergy information and dietary restrictions of the family and friends, and the generation AI generates safe and delicious recipes based on that data. For example, it suggests dishes that family members with allergies can eat safely. Furthermore, the user information collection unit integrates the food preferences of the family and friends, and the generation AI generates recipes that will satisfy everyone based on that data. For example, it suggests dishes that people with different tastes can enjoy together. This makes it possible to generate recipes that take into account the food preferences of the user's family and friends.

[0034] The recipe generation unit generates recipes according to the season and weather, and the providing unit can provide the recipes generated by the recipe generation unit to the user. The recipe generation unit, for example, takes into account seasonal ingredients, and the generation AI generates recipes that incorporate a seasonal feel. For example, it suggests a salad using fresh vegetables in spring and a hot soup in winter. The recipe generation unit also collects weather data, and the generation AI generates recipes appropriate for the weather based on that data. For example, it suggests cold dishes on hot days and hot dishes on cold days. The recipe generation unit also selects ingredients according to the season and weather, and the generation AI generates recipes that incorporate a seasonal feel based on that data. For example, it suggests desserts using seasonal fruits in summer. The providing unit provides the generated recipes to the user. For example, the generated recipes are displayed in text format. In addition, the recipe steps and ingredient lists are also detailed, so the user can refer to them when actually cooking. This makes it possible to generate and provide recipes appropriate for the season and weather.

[0035] The recipe generation unit can learn users' past recipe ratings and generate recipes that incorporate highly rated elements. For example, the recipe generation unit collects users' past recipe ratings, and the generation AI generates recipes that incorporate highly rated elements based on that data. For example, it reflects ingredients and cooking methods that users have given high ratings. The recipe generation unit also analyzes past recipe rating data, and the generation AI generates recipes that combine highly rated elements based on that data. For example, it incorporates seasonings and ingredient combinations that the user prefers. Furthermore, the recipe generation unit learns users' rating data, and the generation AI generates recipes that reflect highly rated elements based on that data. For example, it incorporates features of dishes that the user particularly likes. In this way, it is possible to generate recipes that reflect the user's past recipe ratings.

[0036] The recipe generation unit generates recipes incorporating cuisine from different cultures and regions, and the providing unit can provide the recipes generated by the recipe generation unit to the user. For example, the recipe generation unit generates recipes incorporating cuisine from different cultures and regions to provide the user with a new food experience. For example, it proposes recipes such as Italian cuisine and Indian cuisine. The recipe generation unit also generates recipes incorporating ingredients and cooking methods from different cultures to provide the user with a new taste. For example, it proposes ethnic cuisine and fusion cuisine. Furthermore, the recipe generation unit generates recipes incorporating traditional cuisine from different regions to introduce the user to a new food culture. For example, it proposes Japanese regional cuisine and French regional cuisine. The providing unit provides the generated recipes to the user. For example, the generated recipes are displayed in text format. In addition, the recipe steps and ingredient lists are also described in detail, which the user can refer to when actually cooking the dishes. This makes it possible to generate and provide recipes incorporating cuisine from different cultures and regions.

[0037] The user information collection unit collects the user's ingredient inventory information, and the recipe generation unit can generate recipes that can be made with ingredients the user has on hand. For example, the user information collection unit collects the user's ingredient inventory information, and the generation AI generates recipes that can be made with ingredients on hand based on that data. For example, it suggests dishes using ingredients that are in the refrigerator. The user information collection unit also updates ingredient inventory information in real time, and the generation AI generates recipes based on that data according to the latest inventory status. For example, it suggests dishes that prioritize ingredients that the user wants to use up. Furthermore, the user information collection unit analyzes the user's ingredient inventory data, and the generation AI generates recipes that use ingredients efficiently based on that data. For example, it suggests dishes that use ingredients that tend to be left over. This makes it possible to generate recipes that can be made with ingredients the user has on hand.

[0038] The recipe generation unit can learn the user's cooking skill level and generate recipes that correspond to that skill. For example, the recipe generation unit learns the user's cooking skill level, and the generation AI customizes recipes that correspond to that skill based on that data. For example, it suggests simple recipes for beginners and advanced recipes for advanced cooks. The recipe generation unit also analyzes the user's past cooking experience, and the generation AI customizes recipes that correspond to the skill based on that data. For example, it adjusts recipes based on the difficulty of dishes the user has made in the past. Furthermore, the recipe generation unit updates the user's cooking skill level in real time, and the generation AI customizes recipes that correspond to the latest skill based on that data. For example, it incorporates newly acquired techniques by the user. This makes it possible to generate recipes that correspond to the user's cooking skill level.

[0039] The recipe generation unit can generate recipes according to the user's health goals. For example, the recipe generation unit collects the user's health goals, and the generation AI customizes recipes according to the health goals based on that data. For example, it suggests low-calorie recipes to a user who is on a diet. The recipe generation unit also analyzes the user's health data, and the generation AI customizes recipes according to the health goals based on that data. For example, it suggests high-protein recipes to a user who is aiming to build muscle. Furthermore, the recipe generation unit updates the user's health goals in real time, and the generation AI customizes recipes according to the latest health goals based on that data. For example, it suggests recipes that match new goals set by the user. This makes it possible to generate recipes according to the user's health goals.

[0040] The user information collection unit collects the user's ingredient preferences and allergy information, and the recipe generation unit can provide substitute ingredients based on the user's ingredient preferences and allergy information. The user information collection unit, for example, collects the user's ingredient preferences and allergy information, and the generation AI suggests substitute ingredients based on that data. For example, a user with a nut allergy can be suggested a recipe that uses seeds instead of nuts. The user information collection unit also analyzes the user's ingredient preferences and allergy information, and the generation AI suggests substitute ingredients based on that data. For example, a user with a dairy allergy can be suggested a recipe that uses soy milk instead of dairy products. The user information collection unit also updates the user's ingredient preferences and allergy information in real time, and the generation AI uses that data to suggest substitute ingredients based on the latest information. For example, it suggests recipes that avoid ingredients that have recently been discovered to be allergic. This makes it possible to suggest substitute ingredients based on the user's ingredient preferences and allergy information.

[0041] The user information collection unit collects the type and performance of the user's cooking utensils, and the recipe generation unit can generate recipes that match the type and performance of the user's cooking utensils. For example, the user information collection unit collects the type and performance of the user's cooking utensils, and the generation AI customizes recipes that match the cooking utensils based on that data. For example, it suggests recipes that do not use an oven to a user who does not own an oven. The user information collection unit also analyzes the performance of the user's cooking utensils, and the generation AI customizes recipes that match the cooking utensils based on that data. For example, it suggests recipes that utilize the blender to a user who owns a high-performance blender. Furthermore, the user information collection unit updates the type and performance of the user's cooking utensils in real time, and the generation AI customizes recipes based on the latest information using that data. For example, it suggests recipes that use newly purchased cooking utensils. This makes it possible to generate recipes that match the type and performance of the user's cooking utensils.

[0042] The providing unit can customize the recipe presentation format according to the user's preferences and provide it in text, images, or video. In the providing unit, for example, the generation AI customizes the recipe presentation format according to the user's preferences. For example, for a user who prefers text-format recipes, the generation AI provides detailed steps in text. In addition, for a user who prefers images or videos, the provision unit provides a recipe that is visually easy to understand based on that data. For example, the cooking steps are shown in images or videos. Furthermore, the provision unit allows the generation AI to provide a recipe that combines multiple presentation formats according to the user's preferences. For example, it suggests a recipe that combines text and images. This allows the recipe to be provided in a format that suits the user's preferences.

[0043] The providing unit can provide cooking tips and key points as additional information when providing a recipe. For example, the providing unit provides cooking tips and key points as additional information when the generation AI provides a recipe. For example, it shows how to cut ingredients and an estimate of cooking time. The providing unit also provides information that is useful when the user executes the recipe. For example, it suggests how to use cooking utensils and substitute ingredients. Furthermore, the providing unit allows the generation AI to explain cooking tips and key points in detail when providing a recipe. For example, it introduces techniques to improve the finished dish. This allows cooking tips and key points to be provided as additional information when providing a recipe.

[0044] The providing unit can provide a function that allows recipes to be easily shared on social media and messaging apps. For example, the providing unit provides a function that allows recipes generated by the generation AI to be easily shared on social media and messaging apps. For example, a recipe can be shared with friends and family with one click. The providing unit also enhances the recipe sharing function, allowing users to easily share recipes with other users. For example, it generates and sends a sharing link. Furthermore, the providing unit strengthens collaboration with social media and messaging apps, building a system that allows recipes generated by the generation AI to be easily shared. For example, it provides a function that allows recipes to be sent directly to messaging apps. This allows recipes to be easily shared on social media and messaging apps.

[0045] The providing unit can add links to related cooking videos and articles when providing a recipe. For example, the providing unit adds links to related cooking videos and articles when providing a recipe generated by the generation AI. For example, it provides a link to a video that explains the cooking steps in detail. The providing unit also adds links to related cooking articles and blogs when providing a recipe. For example, it provides a link to an article that introduces the background and history of the dish. Furthermore, the providing unit builds a system that automatically searches for related cooking videos and articles and adds links when providing a recipe generated by the generation AI. For example, it suggests content that the user may be interested in. This makes it possible to add links to related cooking videos and articles when providing a recipe.

[0046] The analysis unit analyzes user feedback, and the recipe generation unit can reflect the user feedback in generating the next recipe. For example, the analysis unit collects user feedback and develops an algorithm that allows the generation AI to reflect that data in generating the next recipe. For example, based on feedback such as "This recipe was delicious, but I would like it a little spicier," the spiciness of the next recipe is adjusted. The analysis unit also analyzes the feedback data, and the generation AI identifies areas for improvement based on that data and reflects these in generating the next recipe. For example, based on feedback such as "The cooking time is too long," the generation AI suggests a recipe that shortens the cooking time. Furthermore, the analysis unit collects user feedback in real time and builds a system in which the generation AI reflects that data in generating the next recipe. For example, the recipe is customized according to the user's preferences and requests. This allows the user's feedback to be reflected in generating the next recipe.

[0047] The analysis unit analyzes user feedback, and the recipe generation unit can automatically suggest improvements to the recipe based on the user feedback. For example, the analysis unit analyzes user feedback, and the generation AI automatically suggests improvements to the recipe based on that data. For example, based on feedback such as "This recipe was delicious, but I would like it to be a little saltier," a suggestion is made to adjust the saltiness. The analysis unit also analyzes the feedback data, and the generation AI identifies improvements to the recipe based on that data and builds a system that automatically makes suggestions. For example, based on feedback such as "The cooking time is too long," a suggestion is made to shorten the cooking time. Furthermore, the analysis unit collects user feedback in real time, and the generation AI automatically suggests improvements to the recipe based on that data. For example, based on feedback such as "I would like to use more vegetables," a suggestion is made to increase the amount of vegetables. This makes it possible to automatically suggest improvements to the recipe based on user feedback.

[0048] The providing unit can share the user's feedback with other users and build a community-based rating system. For example, the providing unit shares the user's feedback with other users, and the generation AI builds a community-based rating system based on that data. For example, the providing unit rates recipes based on the feedback and shares them with other users. The providing unit also analyzes the feedback data, and the generation AI builds a community-based rating system based on that data. For example, the providing unit creates a recipe ranking based on the user's feedback and shares it with other users. Furthermore, the providing unit collects user feedback in real time, and the generation AI builds a community-based rating system based on that data. For example, the providing unit shares improvements to recipes based on the feedback and suggests them to other users. In this way, the user's feedback can be shared with other users and a community-based rating system can be built.

[0049] The analysis unit analyzes user feedback, and the recipe generation unit can automatically update recipe rankings and recommendation levels based on the user feedback. The analysis unit, for example, collects user feedback, and the generation AI automatically updates recipe rankings and recommendation levels based on that data. For example, the analysis unit evaluates recipes based on the feedback and updates the rankings. The analysis unit also builds a system in which the feedback data is analyzed, and the generation AI automatically updates recipe rankings and recommendation levels based on that data. For example, the analysis unit adjusts the recommendation level of a recipe based on user feedback. Furthermore, the analysis unit collects user feedback in real time, and the generation AI automatically updates recipe rankings and recommendation levels based on that data. For example, the analysis unit evaluates recipes based on the feedback and updates the rankings in real time. This makes it possible to automatically update recipe rankings and recommendation levels based on user feedback.

[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] In addition to the user's dietary preferences and allergy information, the user information collection unit can also collect the user's ingredient preferences and the type and performance of cooking utensils. For example, if a user has a preference for a particular ingredient, that information can be collected and the generation AI can generate a recipe based on that data. The type and performance of cooking utensils owned by the user can also be collected and the generation AI can generate recipes that suit the utensils based on that data. For example, if a user does not own an oven, it can suggest recipes that do not require an oven. Furthermore, the user information collection unit collects the user's ingredient inventory information and the generation AI can use that data to generate recipes that can be made with ingredients on hand. For example, it can suggest dishes using ingredients in the refrigerator. This makes it possible to generate recipes that take into account the user's ingredient preferences and the type, performance, and inventory information of cooking utensils.

[0052] The user information collection unit collects the user's food preferences and allergy information, as well as the food preferences of the user's family and friends, and the analysis unit can generate optimal recipes for multiple people based on the food preferences of family and friends. For example, it can suggest dishes that the whole family can enjoy. It also collects allergy information and dietary restrictions of family and friends, and the generation AI uses that data to generate safe and delicious recipes. For example, it can suggest dishes that family members with allergies can eat with peace of mind. It can also integrate the food preferences of family and friends to generate recipes that will satisfy everyone. This makes it possible to generate recipes that take into account the food preferences of the user's family and friends.

[0053] The user information collection unit automatically extracts food preferences from users' social media posts and blog articles, and the analysis unit can also generate recipes based on the user's food preferences. For example, the unit analyzes users' social media posts, automatically extracts food preferences, and reflects them in the generation AI. For example, preferences can be identified based on photos and comments about food shared by users on social media. The unit also analyzes users' blog articles, automatically extracts food preferences, and reflects them in the generation AI. For example, preferences can be identified based on recipes and ingredients introduced by users on their blogs. Furthermore, the unit can integrate data collected from social media and blogs, and the generation AI can analyze the user's food preferences based on that data to generate recipes. This allows food preferences to be automatically extracted from users' social media posts and blog articles and reflected in recipe generation.

[0054] The recipe generation unit generates recipes according to the season and weather, and the provision unit can also provide the recipes generated by the recipe generation unit to the user. For example, the generation AI generates recipes that incorporate a seasonal feel, taking into account seasonal ingredients. For example, it suggests a salad using fresh vegetables in spring and a hot soup in winter. In addition, weather data is collected, and the generation AI generates recipes that suit the weather based on that data. For example, it suggests cold dishes on hot days and hot dishes on cold days. Furthermore, it is possible to select ingredients according to the season and weather, and the generation AI can generate recipes that incorporate a seasonal feel based on that data. This makes it possible to generate and provide recipes that suit the season and weather.

[0055] The recipe generation unit can also learn users' past recipe ratings and generate recipes that incorporate highly rated elements. For example, the generation AI collects users' past recipe ratings and generates recipes that incorporate highly rated elements based on that data. For example, it reflects ingredients and cooking methods that users have given high ratings to. In addition, the generation AI analyzes past recipe rating data and generates recipes that combine highly rated elements based on that data. For example, it incorporates the user's favorite seasonings and ingredient combinations. Furthermore, the generation AI can learn user rating data and generate recipes that reflect highly rated elements based on that data. This makes it possible to generate recipes that reflect users' past recipe ratings.

[0056] The recipe generation unit generates recipes that incorporate cuisine from different cultures and regions, and the provision unit can also provide the user with the recipes generated by the recipe generation unit. For example, recipes that incorporate cuisine from different cultures and regions can be generated to provide the user with a new food experience. For example, recipes such as Italian and Indian cuisine can be proposed. Recipes that incorporate ingredients and cooking methods from different cultures can also be generated to provide the user with a new taste. For example, ethnic cuisine and fusion cuisine can be proposed. Furthermore, recipes that incorporate traditional cuisine from different regions can be generated to introduce the user to a new food culture. In this way, recipes that incorporate cuisine from different cultures and regions can be generated and provided.

[0057] The providing unit can also customize the recipe presentation format according to the user's preferences and provide it in text, images, or video. For example, the generation AI customizes the recipe presentation format according to the user's preferences. For example, for a user who prefers text-format recipes, detailed steps are provided in text. Also, for a user who prefers images or videos, the generation AI provides a recipe that is visually easy to understand based on that data. For example, cooking steps are shown in images or videos. Furthermore, the generation AI can provide a recipe that combines multiple presentation formats according to the user's preferences. This allows recipes to be provided in a format that suits the user's preferences.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The user information collection unit collects the user's preferences, food preferences, and allergy information. For example, it collects information entered by the user, such as "I like spicy food and I have a nut allergy." The user information collection unit can also collect the user's dietary history and health data. Step 2: The analysis unit analyzes the information collected by the user information collection unit. For example, it analyzes the user's preferences and allergy information and provides data for generating appropriate recipes. The analysis unit can also analyze the user's dietary history and health data and reflect this in recipe generation. Step 3: The recipe generation unit generates recipes based on the information analyzed by the analysis unit. For example, the generation AI takes into account the user's preferences and allergy information, and generates recipes that take into account the difficulty, time required, and ease of obtaining ingredients. The generation AI can also learn the user's food preferences in real time and generate recipes that reflect changes in preferences. Step 4: The providing unit provides the recipe generated by the recipe generating unit to the user. For example, the generated recipe is displayed in text format. The recipe also includes detailed instructions and a list of ingredients, which the user can use as a reference when actually cooking the dish.

[0060] (Example 2) The recipe generation system according to the embodiment of the present invention is a system in which a generation AI generates and provides original recipes individually, taking into consideration the user's tastes, dietary preferences, and allergy information. This allows the recipe generation system to provide original recipes that meet the user's requirements.

[0061] A recipe generation system according to an embodiment includes a user information collection unit, an analysis unit, a recipe generation unit, and a provision unit. The user information collection unit collects user preferences, dietary preferences, and allergy information. For example, it collects information entered by the user, such as "I like spicy food and I'm allergic to nuts." The user information collection unit can also collect the user's dietary history and health data. The analysis unit analyzes the information collected by the user information collection unit. For example, it analyzes the user's preferences and allergy information and provides data for generating appropriate recipes. The analysis unit can also analyze the user's dietary history and health data and reflect this in recipe generation. The recipe generation unit generates recipes based on the information analyzed by the analysis unit. For example, the generation AI takes into account the user's preferences and allergy information, and generates recipes that take into account the difficulty, required time, and difficulty of obtaining ingredients. The generation AI can also learn the user's dietary preferences in real time and generate recipes that reflect changes in preferences. The provision unit provides the recipes generated by the recipe generation unit to the user. For example, the generated recipes are displayed in text format. In addition, the recipe steps and ingredient lists are also detailed, which the user can refer to when actually cooking. This allows the recipe creation system according to the embodiment to provide original recipes that meet the user's requirements.

[0062] The user information collection unit collects the user's dietary history and health data, and the analysis unit analyzes the user's dietary history and health data to reflect it in recipe generation. The user information collection unit, for example, collects the user's past dietary history, and the generation AI analyzes that data to reflect it in recipe generation. For example, the user's preference trends are identified based on the types and frequency of dishes the user has eaten in the past. The user information collection unit also collects the user's health data (e.g., blood sugar level and weight), and the generation AI analyzes that data to generate health-conscious recipes. For example, low-carbohydrate recipes are suggested for users with high blood sugar levels. Furthermore, the user information collection unit integrates the user's dietary history and health data, and the generation AI analyzes that data to generate recipes that are optimal for the user's health condition. For example, recipes aimed at weight management are suggested. This makes it possible to generate recipes that take the user's dietary history and health data into consideration.

[0063] The user information collection unit learns the user's food preferences in real time, and the analysis unit can generate recipes based on the user's food preferences. For example, the user information collection unit learns the user's food preferences in real time, and the generation AI generates recipes based on that data. For example, it reflects the user's recent favorite food trends. The user information collection unit also takes into account that the user's preferences change over time, and the generation AI learns those changes and reflects them in the recipes. For example, it incorporates preferences that change with the seasons. Furthermore, the user information collection unit regularly updates the user's food preferences, and the generation AI generates recipes that match the latest preferences based on that data. For example, it incorporates ingredients that the user has recently become interested in. This makes it possible to generate recipes that reflect changes in the user's food preferences in real time.

[0064] The analysis unit analyzes the user's emotions, and the recipe generation unit can generate recipes based on the user's emotions. For example, the analysis unit analyzes the emotions the user expresses when entering a recipe, and the generation AI generates a recipe based on those emotions. For example, if the user is feeling stressed, the analysis unit suggests a recipe with a relaxing effect. The analysis unit also uses an emotion estimation function to analyze the emotions the user expresses in real time when entering a recipe, and generates a recipe that elicits positive emotions. For example, it incorporates ingredients that bring joy to the user. Furthermore, the analysis unit collects user emotional data, and the generation AI generates emotionally satisfying recipes based on that data. For example, it suggests dishes that make the user feel happy. This makes it possible to generate recipes that correspond to the user's emotions.

[0065] The user information collection unit automatically extracts food preferences from the user's social media posts and blog articles, and the analysis unit can generate recipes based on the user's food preferences. The user information collection unit, for example, analyzes the user's social media posts and automatically extracts food preferences and reflects them in the generation AI. For example, preferences are identified based on photos and comments about food shared by the user on social media. The user information collection unit also analyzes the user's blog articles and automatically extracts food preferences and reflects them in the generation AI. For example, preferences are identified based on recipes and ingredients introduced by the user on their blog. Furthermore, the user information collection unit integrates data collected from social media and blogs, and the generation AI analyzes the user's food preferences based on that data to generate recipes. For example, ingredients frequently mentioned by the user are incorporated. This allows food preferences to be automatically extracted from the user's social media posts and blog articles and reflected in recipe generation.

[0066] The user information collection unit collects the food preferences of the user's family and friends, and the analysis unit can generate recipes that are optimal for multiple people based on the food preferences of the family and friends. The user information collection unit, for example, collects the food preferences of the user's family and friends, and the generation AI generates recipes that are optimal for multiple people based on that data. For example, it suggests dishes that the whole family can enjoy. The user information collection unit also collects allergy information and dietary restrictions of the family and friends, and the generation AI generates safe and delicious recipes based on that data. For example, it suggests dishes that family members with allergies can eat safely. Furthermore, the user information collection unit integrates the food preferences of the family and friends, and the generation AI generates recipes that will satisfy everyone based on that data. For example, it suggests dishes that people with different tastes can enjoy together. This makes it possible to generate recipes that take into account the food preferences of the user's family and friends.

[0067] The analysis unit analyzes the user's emotions in real time, and the recipe generation unit can generate recipes based on the user's emotions. The analysis unit, for example, uses an emotion estimation function to analyze the user's emotions when inputting information in real time and make suggestions to adjust the input content. For example, if the user is feeling stressed, the analysis unit suggests ingredients that have a relaxing effect. The analysis unit also analyzes the user's emotions in real time and provides feedback to adjust the input content. For example, it preferentially suggests ingredients that the user feels positive about. Furthermore, the analysis unit uses the emotion estimation function to build a system that suggests adjusting the input content according to the user's emotions. For example, it suggests incorporating ingredients that make the user feel happy. This allows the user's emotions to be analyzed in real time and reflected in recipe generation.

[0068] The recipe generation unit generates recipes according to the season and weather, and the providing unit can provide the recipes generated by the recipe generation unit to the user. The recipe generation unit, for example, takes into account seasonal ingredients, and the generation AI generates recipes that incorporate a seasonal feel. For example, it suggests a salad using fresh vegetables in spring and a hot soup in winter. The recipe generation unit also collects weather data, and the generation AI generates recipes appropriate for the weather based on that data. For example, it suggests cold dishes on hot days and hot dishes on cold days. The recipe generation unit also selects ingredients according to the season and weather, and the generation AI generates recipes that incorporate a seasonal feel based on that data. For example, it suggests desserts using seasonal fruits in summer. The providing unit provides the generated recipes to the user. For example, the generated recipes are displayed in text format. In addition, the recipe steps and ingredient lists are also detailed, so the user can refer to them when actually cooking. This makes it possible to generate and provide recipes appropriate for the season and weather.

[0069] The recipe generation unit can learn users' past recipe ratings and generate recipes that incorporate highly rated elements. For example, the recipe generation unit collects users' past recipe ratings, and the generation AI generates recipes that incorporate highly rated elements based on that data. For example, it reflects ingredients and cooking methods that users have given high ratings. The recipe generation unit also analyzes past recipe rating data, and the generation AI generates recipes that combine highly rated elements based on that data. For example, it incorporates seasonings and ingredient combinations that the user prefers. Furthermore, the recipe generation unit learns users' rating data, and the generation AI generates recipes that reflect highly rated elements based on that data. For example, it incorporates features of dishes that the user particularly likes. In this way, it is possible to generate recipes that reflect the user's past recipe ratings.

[0070] The analysis unit estimates the user's emotions, and the recipe generation unit can generate recipes that increase emotional satisfaction based on the user's emotions. The analysis unit, for example, uses the emotion estimation function to generate recipes that increase the emotional satisfaction desired by the user. For example, if the user feels like relaxing, it suggests recipes that use ingredients that have a relaxing effect. The analysis unit also analyzes the user's emotional data, and the generation AI generates recipes that increase emotional satisfaction based on that data. For example, it incorporates ingredients that make the user feel happy. Furthermore, the analysis unit uses the emotion estimation function to build a system that generates recipes according to the user's emotions. For example, if the user is feeling stressed, it suggests recipes that use ingredients that have a stress-reducing effect. This allows emotional factors to be reflected in the evaluation by generating summaries that capture emotional nuances.

[0071] The recipe generation unit generates recipes incorporating cuisine from different cultures and regions, and the providing unit can provide the recipes generated by the recipe generation unit to the user. For example, the recipe generation unit generates recipes incorporating cuisine from different cultures and regions to provide the user with a new food experience. For example, it proposes recipes such as Italian cuisine and Indian cuisine. The recipe generation unit also generates recipes incorporating ingredients and cooking methods from different cultures to provide the user with a new taste. For example, it proposes ethnic cuisine and fusion cuisine. Furthermore, the recipe generation unit generates recipes incorporating traditional cuisine from different regions to introduce the user to a new food culture. For example, it proposes Japanese regional cuisine and French regional cuisine. The providing unit provides the generated recipes to the user. For example, the generated recipes are displayed in text format. In addition, the recipe steps and ingredient lists are also described in detail, which the user can refer to when actually cooking the dishes. This makes it possible to generate and provide recipes incorporating cuisine from different cultures and regions.

[0072] The user information collection unit collects the user's ingredient inventory information, and the recipe generation unit can generate recipes that can be made with ingredients the user has on hand. For example, the user information collection unit collects the user's ingredient inventory information, and the generation AI generates recipes that can be made with ingredients on hand based on that data. For example, it suggests dishes using ingredients that are in the refrigerator. The user information collection unit also updates ingredient inventory information in real time, and the generation AI generates recipes based on that data according to the latest inventory status. For example, it suggests dishes that prioritize ingredients that the user wants to use up. Furthermore, the user information collection unit analyzes the user's ingredient inventory data, and the generation AI generates recipes that use ingredients efficiently based on that data. For example, it suggests dishes that use ingredients that tend to be left over. This makes it possible to generate recipes that can be made with ingredients the user has on hand.

[0073] The analysis unit estimates the user's emotions, and the recipe generation unit can generate an optimal recipe based on the user's emotions. The analysis unit, for example, uses an emotion estimation function to analyze the user's emotions when selecting a recipe, and the generation AI suggests the optimal recipe based on that data. For example, if the user feels like relaxing, it suggests a recipe with a relaxing effect. The analysis unit also analyzes the user's emotional data in real time, and the generation AI suggests an emotionally satisfying recipe based on that data. For example, it incorporates ingredients that make the user feel happy. Furthermore, the analysis unit uses the emotion estimation function to build a system that suggests recipes based on the user's emotions. For example, if the user is feeling stressed, it suggests a recipe that has a stress-reducing effect. This makes it possible to generate optimal recipes based on the user's emotions.

[0074] The recipe generation unit can learn the user's cooking skill level and generate recipes that correspond to that skill. For example, the recipe generation unit learns the user's cooking skill level, and the generation AI customizes recipes that correspond to that skill based on that data. For example, it suggests simple recipes for beginners and advanced recipes for advanced cooks. The recipe generation unit also analyzes the user's past cooking experience, and the generation AI customizes recipes that correspond to the skill based on that data. For example, it adjusts recipes based on the difficulty of dishes the user has made in the past. Furthermore, the recipe generation unit updates the user's cooking skill level in real time, and the generation AI customizes recipes that correspond to the latest skill based on that data. For example, it incorporates newly acquired techniques by the user. This makes it possible to generate recipes that correspond to the user's cooking skill level.

[0075] The recipe generation unit can generate recipes according to the user's health goals. For example, the recipe generation unit collects the user's health goals, and the generation AI customizes recipes according to the health goals based on that data. For example, it suggests low-calorie recipes to a user who is on a diet. The recipe generation unit also analyzes the user's health data, and the generation AI customizes recipes according to the health goals based on that data. For example, it suggests high-protein recipes to a user who is aiming to build muscle. Furthermore, the recipe generation unit updates the user's health goals in real time, and the generation AI customizes recipes according to the latest health goals based on that data. For example, it suggests recipes that match new goals set by the user. This makes it possible to generate recipes according to the user's health goals.

[0076] The analysis unit estimates the user's emotions, and the recipe generation unit can adjust the recipe based on the user's emotions. The analysis unit, for example, uses an emotion estimation function to customize a recipe according to the user's emotions. For example, if the user feels like relaxing, a recipe using ingredients that have a relaxing effect is suggested. The analysis unit also analyzes the user's emotional data, and the generation AI customizes an emotionally satisfying recipe based on that data. For example, it incorporates ingredients that make the user feel happy. Furthermore, the analysis unit uses the emotion estimation function to build a system that customizes recipes according to the user's emotions. For example, if the user is feeling stressed, a recipe using ingredients that have a stress-reducing effect is suggested. This makes it possible to customize recipes according to the user's emotions.

[0077] The user information collection unit collects the user's ingredient preferences and allergy information, and the recipe generation unit can provide substitute ingredients based on the user's ingredient preferences and allergy information. The user information collection unit, for example, collects the user's ingredient preferences and allergy information, and the generation AI suggests substitute ingredients based on that data. For example, a user with a nut allergy can be suggested a recipe that uses seeds instead of nuts. The user information collection unit also analyzes the user's ingredient preferences and allergy information, and the generation AI suggests substitute ingredients based on that data. For example, a user with a dairy allergy can be suggested a recipe that uses soy milk instead of dairy products. The user information collection unit also updates the user's ingredient preferences and allergy information in real time, and the generation AI uses that data to suggest substitute ingredients based on the latest information. For example, it suggests recipes that avoid ingredients that have recently been discovered to be allergic. This makes it possible to suggest substitute ingredients based on the user's ingredient preferences and allergy information.

[0078] The user information collection unit collects the type and performance of the user's cooking utensils, and the recipe generation unit can generate recipes that match the type and performance of the user's cooking utensils. For example, the user information collection unit collects the type and performance of the user's cooking utensils, and the generation AI customizes recipes that match the cooking utensils based on that data. For example, it suggests recipes that do not use an oven to a user who does not own an oven. The user information collection unit also analyzes the performance of the user's cooking utensils, and the generation AI customizes recipes that match the cooking utensils based on that data. For example, it suggests recipes that utilize the blender to a user who owns a high-performance blender. Furthermore, the user information collection unit updates the type and performance of the user's cooking utensils in real time, and the generation AI customizes recipes based on the latest information using that data. For example, it suggests recipes that use newly purchased cooking utensils. This makes it possible to generate recipes that match the type and performance of the user's cooking utensils.

[0079] The analysis unit estimates the user's emotions, and the recipe generation unit can suggest optimal adjustments based on the user's emotions. The analysis unit, for example, uses an emotion estimation function to analyze the user's emotions when customizing, and the generation AI suggests optimal customizations based on that data. For example, if the user feels like relaxing, the analysis unit suggests customizations using ingredients that have a relaxing effect. The analysis unit also analyzes the user's emotional data in real time, and the generation AI suggests emotionally satisfying customizations based on that data. For example, incorporating ingredients that make the user feel happy. Furthermore, the analysis unit uses the emotion estimation function to build a system that suggests customizations based on the user's emotions. For example, if the user is feeling stressed, the analysis unit suggests customizations using ingredients that have a stress-reducing effect. This makes it possible to suggest optimal customizations based on the user's emotions.

[0080] The providing unit can customize the recipe presentation format according to the user's preferences and provide it in text, images, or video. In the providing unit, for example, the generation AI customizes the recipe presentation format according to the user's preferences. For example, for a user who prefers text-format recipes, the generation AI provides detailed steps in text. In addition, for a user who prefers images or videos, the provision unit provides a recipe that is visually easy to understand based on that data. For example, the cooking steps are shown in images or videos. Furthermore, the provision unit allows the generation AI to provide a recipe that combines multiple presentation formats according to the user's preferences. For example, it suggests a recipe that combines text and images. This allows the recipe to be provided in a format that suits the user's preferences.

[0081] The providing unit can provide cooking tips and key points as additional information when providing a recipe. For example, the providing unit provides cooking tips and key points as additional information when the generation AI provides a recipe. For example, it shows how to cut ingredients and an estimate of cooking time. The providing unit also provides information that is useful when the user executes the recipe. For example, it suggests how to use cooking utensils and substitute ingredients. Furthermore, the providing unit allows the generation AI to explain cooking tips and key points in detail when providing a recipe. For example, it introduces techniques to improve the finished dish. This allows cooking tips and key points to be provided as additional information when providing a recipe.

[0082] The analysis unit estimates the user's emotions, and the provision unit can select a provision method that elicits positive emotions based on the user's emotions. The analysis unit, for example, uses an emotion estimation function to analyze the emotions the user feels when receiving a recipe, and the generation AI selects a provision method that elicits positive emotions based on that data. For example, it proposes a method of introducing a recipe that will make the user feel happy. The analysis unit also analyzes the user's emotion data in real time, and the generation AI selects a recipe provision method that will provide high emotional satisfaction based on that data. For example, it proposes a method of providing a recipe that will help the user relax. Furthermore, the analysis unit uses the emotion estimation function to build a system that selects a recipe provision method that corresponds to the user's emotions. For example, if the user is feeling stressed, it proposes a method of providing a recipe that has a stress-reducing effect. This makes it possible to select a provision method that elicits positive emotions based on the user's emotions.

[0083] The providing unit can provide a function that allows recipes to be easily shared on social media and messaging apps. For example, the providing unit provides a function that allows recipes generated by the generation AI to be easily shared on social media and messaging apps. For example, a recipe can be shared with friends and family with one click. The providing unit also enhances the recipe sharing function, allowing users to easily share recipes with other users. For example, it generates and sends a sharing link. Furthermore, the providing unit strengthens collaboration with social media and messaging apps, building a system that allows recipes generated by the generation AI to be easily shared. For example, it provides a function that allows recipes to be sent directly to messaging apps. This allows recipes to be easily shared on social media and messaging apps.

[0084] The providing unit can add links to related cooking videos and articles when providing a recipe. For example, the providing unit adds links to related cooking videos and articles when providing a recipe generated by the generation AI. For example, it provides a link to a video that explains the cooking steps in detail. The providing unit also adds links to related cooking articles and blogs when providing a recipe. For example, it provides a link to an article that introduces the background and history of the dish. Furthermore, the providing unit builds a system that automatically searches for related cooking videos and articles and adds links when providing a recipe generated by the generation AI. For example, it suggests content that the user may be interested in. This makes it possible to add links to related cooking videos and articles when providing a recipe.

[0085] The analysis unit estimates the user's emotions, and the provision unit can suggest the optimal provision method based on the user's emotions. The analysis unit, for example, uses an emotion estimation function to analyze the user's emotions in real time when receiving a recipe, and the generation AI suggests the optimal provision method based on that data. For example, it proposes a recipe provision method that will help the user relax. The analysis unit also analyzes the user's emotion data in real time, and the generation AI suggests a recipe provision method that will provide emotional satisfaction based on that data. For example, it proposes a method of introducing recipes that will make the user feel happy. Furthermore, the analysis unit uses the emotion estimation function to build a system that suggests a recipe provision method that corresponds to the user's emotions. For example, if the user is feeling stressed, it suggests a recipe provision method that has a stress-reducing effect. This makes it possible to suggest the optimal provision method based on the user's emotions.

[0086] The analysis unit analyzes user feedback, and the recipe generation unit can reflect the user feedback in generating the next recipe. For example, the analysis unit collects user feedback and develops an algorithm that allows the generation AI to reflect that data in generating the next recipe. For example, based on feedback such as "This recipe was delicious, but I would like it a little spicier," the spiciness of the next recipe is adjusted. The analysis unit also analyzes the feedback data, and the generation AI identifies areas for improvement based on that data and reflects these in generating the next recipe. For example, based on feedback such as "The cooking time is too long," the generation AI suggests a recipe that shortens the cooking time. Furthermore, the analysis unit collects user feedback in real time and builds a system in which the generation AI reflects that data in generating the next recipe. For example, the recipe is customized according to the user's preferences and requests. This allows the user's feedback to be reflected in generating the next recipe.

[0087] The analysis unit analyzes user feedback, and the recipe generation unit can automatically suggest improvements to the recipe based on the user feedback. For example, the analysis unit analyzes user feedback, and the generation AI automatically suggests improvements to the recipe based on that data. For example, based on feedback such as "This recipe was delicious, but I would like it to be a little saltier," a suggestion is made to adjust the saltiness. The analysis unit also analyzes the feedback data, and the generation AI identifies improvements to the recipe based on that data and builds a system that automatically makes suggestions. For example, based on feedback such as "The cooking time is too long," a suggestion is made to shorten the cooking time. Furthermore, the analysis unit collects user feedback in real time, and the generation AI automatically suggests improvements to the recipe based on that data. For example, based on feedback such as "I would like to use more vegetables," a suggestion is made to increase the amount of vegetables. This makes it possible to automatically suggest improvements to the recipe based on user feedback.

[0088] The analysis unit estimates the user's emotions, and the provision unit can make suggestions to elicit positive feedback based on the user's emotions. The analysis unit, for example, uses an emotion estimation function to analyze the user's emotions when providing feedback, and the generation AI makes suggestions to elicit positive feedback based on that data. For example, it proposes a feedback method that will make the user feel happy. The analysis unit also analyzes the user's emotion data in real time, and the generation AI makes suggestions to elicit emotionally satisfying feedback based on that data. For example, it proposes a feedback method that will help the user relax. Furthermore, the analysis unit uses the emotion estimation function to build a system that makes suggestions to elicit feedback according to the user's emotions. For example, if the user is feeling stressed, it proposes a feedback method that has a stress-reducing effect. In this way, suggestions are made to elicit positive feedback based on the user's emotions.

[0089] The providing unit can share the user's feedback with other users and build a community-based rating system. For example, the providing unit shares the user's feedback with other users, and the generation AI builds a community-based rating system based on that data. For example, the providing unit rates recipes based on the feedback and shares them with other users. The providing unit also analyzes the feedback data, and the generation AI builds a community-based rating system based on that data. For example, the providing unit creates a recipe ranking based on the user's feedback and shares it with other users. Furthermore, the providing unit collects user feedback in real time, and the generation AI builds a community-based rating system based on that data. For example, the providing unit shares improvements to recipes based on the feedback and suggests them to other users. In this way, the user's feedback can be shared with other users and a community-based rating system can be built.

[0090] The analysis unit analyzes user feedback, and the recipe generation unit can automatically update recipe rankings and recommendation levels based on the user feedback. The analysis unit, for example, collects user feedback, and the generation AI automatically updates recipe rankings and recommendation levels based on that data. For example, the analysis unit evaluates recipes based on the feedback and updates the rankings. The analysis unit also builds a system in which the feedback data is analyzed, and the generation AI automatically updates recipe rankings and recommendation levels based on that data. For example, the analysis unit adjusts the recommendation level of a recipe based on user feedback. Furthermore, the analysis unit collects user feedback in real time, and the generation AI automatically updates recipe rankings and recommendation levels based on that data. For example, the analysis unit evaluates recipes based on the feedback and updates the rankings in real time. This makes it possible to automatically update recipe rankings and recommendation levels based on user feedback.

[0091] The analysis unit estimates the user's emotions, and the provision unit can suggest the optimal feedback method based on the user's emotions. The analysis unit, for example, uses an emotion estimation function to analyze the user's emotions when providing feedback in real time, and the generation AI suggests the optimal feedback method based on that data. For example, it suggests a feedback method that will help the user relax. The analysis unit also analyzes the user's emotion data in real time, and the generation AI suggests a feedback method that will provide emotional satisfaction based on that data. For example, it suggests a feedback method that will make the user feel happy. Furthermore, the analysis unit uses the emotion estimation function to build a system that suggests a feedback method based on the user's emotions. For example, if the user is feeling stressed, it suggests a feedback method that will have a stress-reducing effect. This makes it possible to suggest the optimal feedback method based on the user's emotions.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] In addition to the user's dietary preferences and allergy information, the user information collection unit can also collect the user's ingredient preferences and the type and performance of cooking utensils. For example, if a user has a preference for a particular ingredient, that information can be collected and the generation AI can generate a recipe based on that data. The type and performance of cooking utensils owned by the user can also be collected and the generation AI can generate recipes that suit the utensils based on that data. For example, if a user does not own an oven, it can suggest recipes that do not require an oven. Furthermore, the user information collection unit collects the user's ingredient inventory information and the generation AI can use that data to generate recipes that can be made with ingredients on hand. For example, it can suggest dishes using ingredients in the refrigerator. This makes it possible to generate recipes that take into account the user's ingredient preferences and the type, performance, and inventory information of cooking utensils.

[0094] The analysis unit can estimate the user's emotions in addition to the user's food preferences and allergy information, and the recipe generation unit can generate recipes based on the user's emotions. For example, if the user feels like relaxing, the unit can suggest recipes using ingredients that have a relaxing effect. Also, if the user is feeling stressed, the unit can suggest recipes using ingredients that have a stress-reducing effect. Furthermore, the unit can suggest recipes that incorporate ingredients that make the user feel happy. This makes it possible to generate recipes that match the user's emotions.

[0095] The user information collection unit collects the user's food preferences and allergy information, as well as the food preferences of the user's family and friends, and the analysis unit can generate optimal recipes for multiple people based on the food preferences of family and friends. For example, it can suggest dishes that the whole family can enjoy. It also collects allergy information and dietary restrictions of family and friends, and the generation AI uses that data to generate safe and delicious recipes. For example, it can suggest dishes that family members with allergies can eat with peace of mind. It can also integrate the food preferences of family and friends to generate recipes that will satisfy everyone. This makes it possible to generate recipes that take into account the food preferences of the user's family and friends.

[0096] The analysis unit can analyze the user's emotions in real time, and the recipe generation unit can generate recipes based on the user's emotions. For example, the emotion estimation function can be used to analyze the user's emotions in real time when inputting information and make suggestions to adjust the input content. For example, if the user is feeling stressed, ingredients with a relaxing effect can be suggested. The system can also analyze the user's emotions in real time and provide feedback to adjust the input content. For example, ingredients that the user feels positive about can be suggested preferentially. Furthermore, the emotion estimation function can be used to build a system that suggests adjusting the input content according to the user's emotions. This allows the user's emotions to be analyzed in real time and reflected in recipe generation.

[0097] The user information collection unit automatically extracts food preferences from users' social media posts and blog articles, and the analysis unit can also generate recipes based on the user's food preferences. For example, the unit analyzes users' social media posts, automatically extracts food preferences, and reflects them in the generation AI. For example, preferences can be identified based on photos and comments about food shared by users on social media. The unit also analyzes users' blog articles, automatically extracts food preferences, and reflects them in the generation AI. For example, preferences can be identified based on recipes and ingredients introduced by users on their blogs. Furthermore, the unit can integrate data collected from social media and blogs, and the generation AI can analyze the user's food preferences based on that data to generate recipes. This allows food preferences to be automatically extracted from users' social media posts and blog articles and reflected in recipe generation.

[0098] The recipe generation unit generates recipes according to the season and weather, and the provision unit can also provide the recipes generated by the recipe generation unit to the user. For example, the generation AI generates recipes that incorporate a seasonal feel, taking into account seasonal ingredients. For example, it suggests a salad using fresh vegetables in spring and a hot soup in winter. In addition, weather data is collected, and the generation AI generates recipes that suit the weather based on that data. For example, it suggests cold dishes on hot days and hot dishes on cold days. Furthermore, it is possible to select ingredients according to the season and weather, and the generation AI can generate recipes that incorporate a seasonal feel based on that data. This makes it possible to generate and provide recipes that suit the season and weather.

[0099] The recipe generation unit can also learn users' past recipe ratings and generate recipes that incorporate highly rated elements. For example, the generation AI collects users' past recipe ratings and generates recipes that incorporate highly rated elements based on that data. For example, it reflects ingredients and cooking methods that users have given high ratings to. In addition, the generation AI analyzes past recipe rating data and generates recipes that combine highly rated elements based on that data. For example, it incorporates the user's favorite seasonings and ingredient combinations. Furthermore, the generation AI can learn user rating data and generate recipes that reflect highly rated elements based on that data. This makes it possible to generate recipes that reflect users' past recipe ratings.

[0100] The recipe generation unit generates recipes that incorporate cuisine from different cultures and regions, and the provision unit can also provide the user with the recipes generated by the recipe generation unit. For example, recipes that incorporate cuisine from different cultures and regions can be generated to provide the user with a new food experience. For example, recipes such as Italian and Indian cuisine can be proposed. Recipes that incorporate ingredients and cooking methods from different cultures can also be generated to provide the user with a new taste. For example, ethnic cuisine and fusion cuisine can be proposed. Furthermore, recipes that incorporate traditional cuisine from different regions can be generated to introduce the user to a new food culture. In this way, recipes that incorporate cuisine from different cultures and regions can be generated and provided.

[0101] The analysis unit can estimate the user's emotions, and the recipe generation unit can generate recipes that increase emotional satisfaction based on the user's emotions. For example, the emotion estimation function can be used to generate recipes that increase the emotional satisfaction desired by the user. For example, if the user feels like relaxing, a recipe using ingredients with a relaxing effect can be suggested. In addition, the user's emotional data can be analyzed, and the generation AI can use that data to generate recipes that increase emotional satisfaction. For example, by incorporating ingredients that make the user feel happy. Furthermore, the emotion estimation function can be used to build a system that generates recipes based on the user's emotions. This allows for the generation of summaries that capture emotional nuances, allowing emotional factors to be reflected in the evaluation.

[0102] The providing unit can also customize the recipe presentation format according to the user's preferences and provide it in text, images, or video. For example, the generation AI customizes the recipe presentation format according to the user's preferences. For example, for a user who prefers text-format recipes, detailed steps are provided in text. Also, for a user who prefers images or videos, the generation AI provides a recipe that is visually easy to understand based on that data. For example, cooking steps are shown in images or videos. Furthermore, the generation AI can provide a recipe that combines multiple presentation formats according to the user's preferences. This allows recipes to be provided in a format that suits the user's preferences.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The user information collection unit collects the user's preferences, food preferences, and allergy information. For example, it collects information entered by the user, such as "I like spicy food and I have a nut allergy." The user information collection unit can also collect the user's dietary history and health data. Step 2: The analysis unit analyzes the information collected by the user information collection unit. For example, it analyzes the user's preferences and allergy information and provides data for generating appropriate recipes. The analysis unit can also analyze the user's dietary history and health data and reflect this in recipe generation. Step 3: The recipe generation unit generates recipes based on the information analyzed by the analysis unit. For example, the generation AI takes into account the user's preferences and allergy information, and generates recipes that take into account the difficulty, time required, and ease of obtaining ingredients. The generation AI can also learn the user's food preferences in real time and generate recipes that reflect changes in preferences. Step 4: The providing unit provides the recipe generated by the recipe generating unit to the user. For example, the generated recipe is displayed in text format. The recipe also includes detailed instructions and a list of ingredients, which the user can use as a reference when actually cooking the dish.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0118] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0149] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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."

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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]

[0172] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a user information collection unit that collects user preferences, dietary preferences, and allergy information; an analysis unit that analyzes the information collected by the user information collection unit; a recipe generation unit that generates a recipe based on the information analyzed by the analysis unit; a providing unit that provides the recipe generated by the recipe generating unit to a user. A system characterized by:

2. The user information collection unit Collecting user's dietary history and health data, The analysis unit Analyze the user's diet history and health data and reflect them in recipe generation 2. The system of claim 1.

3. The user information collection unit Learns the user's food preferences in real time, The analysis unit Generate recipes based on the user's food preferences 2. The system of claim 1.

4. The analysis unit Analyzing user emotions, The recipe generation unit Generate a recipe based on the user's emotions 2. The system of claim 1.

5. The user information collection unit Automatically extracts food preferences from users' SNS posts and blog articles, The analysis unit Generate recipes based on the user's food preferences 2. The system of claim 1.

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