System

Abstract

An object of a system according to an embodiment is to propose an unknown dish or ingredient on the basis of a user's preference or eating experience and record and share an adventure of eating.SOLUTION: A system according to an embodiment includes an analysis unit, a proposal unit, a customization unit, a knowledge providing unit, and a record sharing unit. The analysis unit analyzes the user's preference and eating experience. The proposal unit proposes an unknown dish or ingredient on the basis of the user's preference analyzed by the analysis unit. The customization unit customizes the recipe based on the unknown dish or food ingredient proposed by the proposal unit. The knowledge providing unit provides knowledge about the recipe customized by the customization unit. The record sharing unit records and shares the eating adventure of the user based on the knowledge provided by the knowledge providing 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] Previous technologies had the problem of not being able to adequately suggest new dishes and ingredients based on the user's preferences and eating experience, and not being able to record and share food adventures.

[0005] The system according to the embodiment aims to suggest unknown dishes and ingredients based on the user's preferences and eating experience, and to record and share food adventures. [Means for solving the problem]

[0006] The system according to the embodiment includes an analysis unit, a suggestion unit, a customization unit, a knowledge provision unit, and a record sharing unit. The analysis unit analyzes a user's preferences and food experiences. The suggestion unit suggests unknown dishes and ingredients based on the user's preferences analyzed by the analysis unit. The customization unit customizes recipes based on the unknown dishes and ingredients suggested by the suggestion unit. The knowledge provision unit provides knowledge related to the recipes customized by the customization unit. The record sharing unit records and shares the user's food adventures based on the knowledge provided by the knowledge provision unit. [Effects of the Invention]

[0007] The system according to the embodiment suggests unknown dishes and ingredients based on the user's preferences and eating experience, and allows the user to record and share their food adventures. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The CulinaryQuest AI system according to an embodiment of the present invention analyzes a user's preferences and food experiences, suggests unknown dishes and ingredients, customizes recipes, provides food-related knowledge, and records and shares food adventures, thereby expanding the user's taste experience and deepening their food knowledge.

[0029] The CulinaryQuest AI system according to the embodiment includes an analysis unit, a suggestion unit, a customization unit, a knowledge provision unit, and a record sharing unit. The analysis unit analyzes a user's preferences and food experiences. For example, it performs a detailed analysis of the user's preferences and food interests based on information entered by the user and past food experiences. The analysis unit can also input a list of the user's favorite dishes and ingredients, as well as dishes the user has tried in the past. The suggestion unit suggests unknown dishes and ingredients based on the user's preferences analyzed by the analysis unit. For example, if the user likes Italian food, the suggestion unit suggests Italian recipes and ingredients the user has not yet tried. If the user is interested in a particular ingredient, the suggestion unit provides new dish ideas using that ingredient. The customization unit customizes recipes based on the unknown dishes and ingredients suggested by the suggestion unit. For example, if the user is allergic to a particular ingredient, the suggestion unit suggests an alternative recipe that does not use that ingredient. If the user prioritizes specific nutritional value, the customization unit provides recipes that meet those requirements. The knowledge provision unit provides knowledge about the recipes customized by the customization unit. For example, the knowledge provision unit explains the nutritional value, health benefits, and cooking methods of specific ingredients. The knowledge providing unit also provides information on the history and cultural background of dishes that the user is interested in. The record sharing unit records and shares the user's food adventures based on the knowledge provided by the knowledge providing unit. For example, when a user tries a new dish, the user can record their impressions and photos and share them with other users. The record sharing unit can also refer to other users' food adventures to increase the user's motivation to try new dishes and ingredients. In this way, the CulinaryQuest AI system according to the embodiment can expand the user's taste experience and deepen their knowledge about food. For example, the user can discover dishes and ingredients that they have not known before and enjoy new taste experiences. Furthermore, deepening their knowledge about food can further expand the ways in which they can enjoy food.

[0030] The analysis unit can analyze seasonal food preferences based on the user's dietary history and make suggestions according to the season. The analysis unit, for example, analyzes the user's past dietary history and identifies seasonal food preferences. For example, for a user who tends to prefer fresh vegetables in the spring, the analysis unit can suggest recipes using spring vegetables. Furthermore, for a user who prefers cold dishes in the summer, the analysis unit can suggest recipes for cold pasta or salad. In this way, by making suggestions according to seasonal food preferences, the user's taste experience can be optimized to match the season.

[0031] The analysis unit can link the user's diet history with health data and make dietary suggestions based on their health condition. The analysis unit, for example, integrates the user's diet history with health data and makes dietary suggestions based on their health condition. For example, the analysis unit can suggest low-carbohydrate recipes to a user with high blood sugar levels. The analysis unit can also suggest low-salt recipes to a user with high blood pressure. The analysis unit can also suggest recipes using ingredients that do not cause allergies based on allergy information. This makes it possible to support the user's health by suggesting dietary suggestions based on their health condition.

[0032] When analyzing the user's food preferences, the analysis unit can also consider the preferences of family or friends and make suggestions for meals that can be shared. The analysis unit, for example, collects the food preferences of the user's family and friends and makes suggestions for meals that can be shared. For example, it can suggest dishes that the whole family likes. The analysis unit can also suggest dishes that can be enjoyed together with friends. The analysis unit can also consider the food preferences of family and friends and suggest recipes that everyone can enjoy. This can expand the enjoyment of meals by suggesting meals that can be shared with family and friends.

[0033] When analyzing the user's food preferences, the analysis unit can take into consideration the food culture or local ingredients of the travel destination and make food suggestions for the user's trip. The analysis unit, for example, takes into consideration the food culture or local ingredients of the travel destination and makes food suggestions for the user's trip. For example, the analysis unit can suggest recipes using local specialties. The analysis unit can also suggest traditional dishes of the travel destination. The analysis unit can also provide ideas for new dishes using local ingredients. In this way, the analysis unit can enrich the user's travel experience by making food suggestions for the user's trip.

[0034] The suggestion unit can suggest dishes from different cultural spheres based on the user's past eating experiences, thereby expanding the diversity of food choices. The suggestion unit, for example, analyzes the user's past eating experiences and suggests dishes from different cultural spheres. For example, if the user likes Asian cuisine, the suggestion unit can suggest African cuisine or South American cuisine. Furthermore, if the user likes European cuisine, the suggestion unit can suggest Asian cuisine or Middle Eastern cuisine. Furthermore, if the user likes North American cuisine, the suggestion unit can suggest African cuisine or Oceanian cuisine. In this way, the diversity of food choices for the user can be expanded by suggesting dishes from different cultural spheres.

[0035] The suggestion unit can suggest future ingredients or new cooking methods based on the user's food preferences. The suggestion unit can, for example, suggest future ingredients based on the user's food preferences. For example, it can suggest new ingredients such as insect food or cultured meat. The suggestion unit can also suggest new cooking methods. For example, it can suggest new cooking methods such as molecular gastronomy, low-temperature cooking, and vacuum cooking. The suggestion unit can also suggest recipes that combine new ingredients and cooking methods based on the user's food preferences. This can revolutionize the user's food experience by suggesting future ingredients and new cooking methods.

[0036] The suggestion unit can suggest different combinations of ingredients based on the user's food preferences, providing a new taste experience. The suggestion unit, for example, suggests different combinations of ingredients based on the user's food preferences. For example, it can suggest new recipes using ingredients that the user likes. The suggestion unit can also provide ideas for new dishes using ingredients that the user is interested in. The suggestion unit can also suggest combinations of ingredients that the user has not tried. In this way, by suggesting different combinations of ingredients, it is possible to provide the user with a new taste experience.

[0037] The suggestion unit can suggest different cooking methods based on the user's food preferences, thereby increasing the variety of dishes. The suggestion unit, for example, suggests different cooking methods based on the user's food preferences. For example, it suggests recipes using new cooking methods that use ingredients that the user likes. The suggestion unit can also provide new cooking ideas using cooking methods that the user is interested in. The suggestion unit can also suggest cooking methods that the user has not tried. In this way, the suggestion unit can increase the variety of dishes the user can make by suggesting different cooking methods.

[0038] The customization unit can suggest substitute ingredients and customize recipes based on the user's ingredient constraints. The customization unit can, for example, suggest substitute ingredients based on the user's ingredient constraints. For example, the customization unit can suggest recipes that do not use ingredients that the user is allergic to. The customization unit can also suggest recipes that do not use specific ingredients based on religious constraints. The customization unit can also suggest recipes that use low-calorie substitute ingredients based on diet constraints. In this way, by suggesting substitute ingredients and customizing recipes, it is possible to provide a dining experience that meets the user's ingredient constraints.

[0039] The customization unit can optimize recipes based on the user's nutritional requirements. The customization unit can, for example, optimize recipes based on the user's nutritional requirements. For example, the customization unit can suggest low-calorie recipes. The customization unit can also suggest high-protein recipes. The customization unit can also suggest recipes rich in vitamins and minerals. In this way, by optimizing recipes based on the nutritional requirements, it is possible to support the user's health.

[0040] The customization unit can propose new recipes that combine elements of different dishes based on the user's preferences and constraints. The customization unit, for example, proposes new recipes that combine elements of different dishes based on the user's preferences and constraints. For example, the customization unit proposes new recipes that use ingredients that the user likes. The customization unit can also propose new recipes that combine elements of dishes that the user is interested in. The customization unit can also propose new recipes that combine elements of dishes that the user has not tried. This makes it possible to diversify the user's food experience by proposing new recipes that combine elements of different dishes.

[0041] The customization unit can propose new recipes that combine different cooking methods based on the user's preferences and constraints. The customization unit can, for example, propose new recipes that combine different cooking methods based on the user's preferences and constraints. For example, it can propose recipes that use new cooking methods using ingredients that the user likes. The customization unit can also propose new recipes that combine cooking methods that the user is interested in. The customization unit can also propose new recipes that combine cooking methods that the user has not tried. This makes it possible to diversify the user's eating experience by proposing new recipes that combine different cooking methods.

[0042] The knowledge providing unit can provide detailed nutritional values ​​and health benefits of specific ingredients based on the user's interests. The knowledge providing unit, for example, provides detailed nutritional values ​​of specific ingredients based on the user's interests. For example, the nutritional values ​​of ingredients in which the user is interested are explained in detail. The knowledge providing unit can also provide health benefits of specific ingredients. For example, the health benefits of ingredients in which the user is interested are explained in detail. The knowledge providing unit can also provide cooking methods for specific ingredients. For example, the cooking methods for ingredients in which the user is interested are explained in detail. In this way, by providing detailed nutritional values ​​and health benefits of specific ingredients, the user's knowledge about food can be deepened.

[0043] The knowledge providing unit can provide the history and cultural background of a specific dish based on the user's interests. The knowledge providing unit, for example, provides the history of a specific dish based on the user's interests. For example, the history of a dish in which the user is interested is explained in detail. The knowledge providing unit can also provide the cultural background of a specific dish. For example, the cultural background of a dish in which the user is interested is explained in detail. The knowledge providing unit can also provide a cooking method for a specific dish. For example, the cooking method for a dish in which the user is interested is explained in detail. In this way, by providing the history and cultural background of a specific dish, the user's knowledge about food can be deepened.

[0044] The knowledge providing unit can provide knowledge about food in different cultural spheres based on the user's interests. The knowledge providing unit, for example, provides knowledge about food in different cultural spheres based on the user's interests. For example, it provides information about dishes and ingredients in a cultural sphere in which the user is interested. The knowledge providing unit can also provide food culture in different cultural spheres. For example, it can provide detailed explanations about the food culture in a cultural sphere in which the user is interested. The knowledge providing unit can also provide cooking methods for ingredients in different cultural spheres. For example, it can provide detailed explanations about cooking methods for ingredients in a cultural sphere in which the user is interested. In this way, by providing knowledge about food in different cultural spheres, the user's food knowledge can be expanded.

[0045] The knowledge providing unit can provide cooking methods and storage methods for different ingredients based on the user's interests. The knowledge providing unit, for example, provides cooking methods for different ingredients based on the user's interests. For example, it provides detailed explanations of cooking methods for ingredients that the user is interested in. The knowledge providing unit can also provide storage methods for different ingredients. For example, it provides detailed explanations of storage methods for ingredients that the user is interested in. The knowledge providing unit can also provide nutritional values ​​for different ingredients. For example, it provides detailed explanations of nutritional values ​​for ingredients that the user is interested in. In this way, by providing cooking methods and storage methods for different ingredients, the user's knowledge about food can be expanded.

[0046] The record sharing unit can provide a function for recording a user's food adventures in detail and sharing them with other users. The record sharing unit, for example, provides a function for recording a user's food adventures in detail and sharing them with other users. For example, the record sharing unit records and shares photos and impressions of dishes the user has tried. The record sharing unit can also increase a user's motivation to try new dishes and ingredients by referring to the food adventures of other users. The record sharing unit can also make suggestions to other users based on the user's food adventures. This allows the user to share and expand their food experiences by recording their food adventures in detail and sharing them with other users.

[0047] The record sharing unit can make suggestions to other users based on the user's food adventures. The record sharing unit can, for example, make suggestions to other users based on the user's food adventures. For example, the record sharing unit can suggest recipes for dishes that the user has tried to other users. The record sharing unit can also suggest information about restaurants that the user has visited to other users. The record sharing unit can also suggest information about ingredients that the user has tried to other users. In this way, by making suggestions to other users based on the user's food adventures, the user's food experiences can be shared and expanded.

[0048] The record sharing unit can suggest food adventures from different cultures based on the user's food adventures. The record sharing unit, for example, suggests food adventures from different cultures based on the user's food adventures. For example, the record sharing unit can suggest dishes from other cultures based on information about dishes the user has tried. The record sharing unit can also suggest restaurants from other cultures based on information about restaurants the user has visited. The record sharing unit can also suggest ingredients from other cultures based on information about ingredients the user has tried. This makes it possible to broaden the user's food experience by suggesting food adventures from different cultures.

[0049] The record sharing unit can suggest different ingredients and cooking methods based on the user's food adventures. The record sharing unit, for example, suggests different ingredients based on the user's food adventures. For example, it suggests other ingredients based on information about dishes the user has tried. The record sharing unit can also suggest other cooking methods based on information about cooking methods the user has tried. The record sharing unit can also suggest new cooking ideas based on combinations of ingredients and cooking methods the user has tried. This makes it possible to broaden the user's food experience by suggesting different ingredients and cooking methods.

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

[0051] The CulinaryQuest AI system can also be equipped with a time-of-day suggestion module that makes suggestions based on the user's mealtimes. For example, it can suggest high-protein recipes for breakfast to replenish energy, and balanced recipes for lunch to improve concentration. It can also suggest recipes for dinner using ingredients that have a relaxing effect. This allows it to support healthier eating habits by making meal suggestions that fit the user's daily rhythm.

[0052] The CulinaryQuest AI system can also include a prep time suggestion module that makes suggestions based on the user's meal preparation time. For example, it can suggest quick recipes for busy weekday nights and more enjoyable recipes for weekends. The prep time suggestion module can also suggest optimal recipes based on the user's kitchen utensils. This allows for meal suggestions tailored to the user's lifestyle, making meal preparation more efficient and reducing stress.

[0053] The CulinaryQuest AI system can also be equipped with an environment suggestion module that makes suggestions based on the user's dining environment. For example, it can suggest barbecue and camping recipes for outdoor meals, and recipes that can be enjoyed by large groups for home parties. The environment suggestion module can also suggest optimal recipes based on the user's cooking space and equipment. This allows the system to expand the ways in which the user can enjoy meals by making meal suggestions tailored to the user's dining environment.

[0054] The CulinaryQuest AI system can also include a goal suggestion module that makes suggestions based on the user's dietary goals. For example, it can suggest low-calorie recipes to a user on a diet, and high-protein recipes to a user aiming to build muscle. The goal suggestion module can also suggest recipes tailored to specific events or seasons. This allows the system to support the user in achieving their dietary goals by providing meal suggestions tailored to their dietary goals.

[0055] The CulinaryQuest AI system can also include a budget suggestion module that makes suggestions based on the user's meal budget. For example, it can suggest low-cost recipes for budget-conscious users and luxurious recipes for special occasions. The budget suggestion module can also suggest recipes that utilize ingredients the user already has. This reduces the financial burden and waste by making meal suggestions that fit the user's budget.

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

[0057] Step 1: The analysis unit analyzes the user's preferences and food experiences. For example, the analysis unit analyzes the user's preferences and food interests in detail based on the information the user inputs and their past eating experiences. The user can also input a list of their favorite dishes, ingredients, and dishes they have tried in the past. Step 2: The suggestion unit suggests unknown dishes and ingredients based on the user's preferences analyzed by the analysis unit. For example, if the user likes Italian food, the suggestion unit will suggest Italian recipes and ingredients that the user has not tried yet. Also, if the user is interested in a particular ingredient, the suggestion unit will provide ideas for new dishes using that ingredient. Step 3: The customization unit customizes the recipe based on the unknown dishes and ingredients suggested by the suggestion unit. For example, if the user is allergic to a particular ingredient, the customization unit suggests an alternative recipe that does not use that ingredient. Also, if the user prioritizes specific nutritional values, the customization unit provides a recipe that meets those requirements. Step 4: The knowledge provider provides knowledge about the recipe customized by the customization provider. For example, it explains the nutritional value and health benefits of specific ingredients, cooking methods, etc. It also provides information about the history and cultural background of the dish that the user is interested in. Step 5: The record sharing unit records and shares the user's food adventures based on the knowledge provided by the knowledge providing unit. For example, when a user tries a new dish, the user can record their impressions and photos and share them with other users. Also, by referring to other users' food adventures, the user can be motivated to try new dishes and ingredients.

[0058] (Example 2) The CulinaryQuest AI system according to an embodiment of the present invention analyzes a user's preferences and food experiences, suggests unknown dishes and ingredients, customizes recipes, provides food-related knowledge, and records and shares food adventures, thereby expanding the user's taste experience and deepening their food knowledge.

[0059] The CulinaryQuest AI system according to the embodiment includes an analysis unit, a suggestion unit, a customization unit, a knowledge provision unit, and a record sharing unit. The analysis unit analyzes a user's preferences and food experiences. For example, it performs a detailed analysis of the user's preferences and food interests based on information entered by the user and past food experiences. The analysis unit can also input a list of the user's favorite dishes and ingredients, as well as dishes the user has tried in the past. The suggestion unit suggests unknown dishes and ingredients based on the user's preferences analyzed by the analysis unit. For example, if the user likes Italian food, the suggestion unit suggests Italian recipes and ingredients the user has not yet tried. If the user is interested in a particular ingredient, the suggestion unit provides new dish ideas using that ingredient. The customization unit customizes recipes based on the unknown dishes and ingredients suggested by the suggestion unit. For example, if the user is allergic to a particular ingredient, the suggestion unit suggests an alternative recipe that does not use that ingredient. If the user prioritizes specific nutritional value, the customization unit provides recipes that meet those requirements. The knowledge provision unit provides knowledge about the recipes customized by the customization unit. For example, the knowledge provision unit explains the nutritional value, health benefits, and cooking methods of specific ingredients. The knowledge providing unit also provides information on the history and cultural background of dishes that the user is interested in. The record sharing unit records and shares the user's food adventures based on the knowledge provided by the knowledge providing unit. For example, when a user tries a new dish, the user can record their impressions and photos and share them with other users. The record sharing unit can also refer to other users' food adventures to increase the user's motivation to try new dishes and ingredients. In this way, the CulinaryQuest AI system according to the embodiment can expand the user's taste experience and deepen their knowledge about food. For example, the user can discover dishes and ingredients that they have not known before and enjoy new taste experiences. Furthermore, deepening their knowledge about food can further expand the ways in which they can enjoy food.

[0060] The analysis unit can analyze seasonal food preferences based on the user's dietary history and make suggestions according to the season. The analysis unit, for example, analyzes the user's past dietary history and identifies seasonal food preferences. For example, for a user who tends to prefer fresh vegetables in the spring, the analysis unit can suggest recipes using spring vegetables. Furthermore, for a user who prefers cold dishes in the summer, the analysis unit can suggest recipes for cold pasta or salad. In this way, by making suggestions according to seasonal food preferences, the user's taste experience can be optimized to match the season.

[0061] The analysis unit can link the user's diet history with health data and make dietary suggestions based on their health condition. The analysis unit, for example, integrates the user's diet history with health data and makes dietary suggestions based on their health condition. For example, the analysis unit can suggest low-carbohydrate recipes to a user with high blood sugar levels. The analysis unit can also suggest low-salt recipes to a user with high blood pressure. The analysis unit can also suggest recipes using ingredients that do not cause allergies based on allergy information. This makes it possible to support the user's health by suggesting dietary suggestions based on their health condition.

[0062] The analysis unit can use the emotion estimation function to analyze the emotions felt by the user while eating and suggest meals that will elicit positive emotions. The analysis unit, for example, analyzes the user's facial expressions and voice while eating to estimate emotions. For example, the analysis unit can identify meals that make the user smile a lot and suggest recipes related to those meals. The analysis unit can also identify meals that make the user feel relaxed and suggest recipes related to those meals. The analysis unit can also identify meals that the user enjoys and suggest recipes related to those meals. This can improve the user's dining experience by suggesting meals that will elicit positive emotions.

[0063] When analyzing the user's food preferences, the analysis unit can also consider the preferences of family or friends and make suggestions for meals that can be shared. The analysis unit, for example, collects the food preferences of the user's family and friends and makes suggestions for meals that can be shared. For example, it can suggest dishes that the whole family likes. The analysis unit can also suggest dishes that can be enjoyed together with friends. The analysis unit can also consider the food preferences of family and friends and suggest recipes that everyone can enjoy. This can expand the enjoyment of meals by suggesting meals that can be shared with family and friends.

[0064] When analyzing the user's food preferences, the analysis unit can take into consideration the food culture or local ingredients of the travel destination and make food suggestions for the user's trip. The analysis unit, for example, takes into consideration the food culture or local ingredients of the travel destination and makes food suggestions for the user's trip. For example, the analysis unit can suggest recipes using local specialties. The analysis unit can also suggest traditional dishes of the travel destination. The analysis unit can also provide ideas for new dishes using local ingredients. In this way, the analysis unit can enrich the user's travel experience by making food suggestions for the user's trip.

[0065] The analysis unit uses the emotion estimation function to analyze the emotions of the user while enjoying a meal in real time and make suggestions to improve meal satisfaction. The analysis unit, for example, analyzes the user's emotions while eating in real time and makes suggestions to improve satisfaction. For example, the analysis unit can identify a dish that the user enjoys and suggest recipes related to that dish. The analysis unit can also identify a dish that the user finds relaxing and suggest recipes related to that dish. The analysis unit can also identify a dish that the user finds satisfying and suggest recipes related to that dish. This makes it possible to improve the user's dining experience by making suggestions to improve meal satisfaction.

[0066] The suggestion unit can suggest dishes from different cultural spheres based on the user's past eating experiences, thereby expanding the diversity of food choices. The suggestion unit, for example, analyzes the user's past eating experiences and suggests dishes from different cultural spheres. For example, if the user likes Asian cuisine, the suggestion unit can suggest African cuisine or South American cuisine. Furthermore, if the user likes European cuisine, the suggestion unit can suggest Asian cuisine or Middle Eastern cuisine. Furthermore, if the user likes North American cuisine, the suggestion unit can suggest African cuisine or Oceanian cuisine. In this way, the diversity of food choices for the user can be expanded by suggesting dishes from different cultural spheres.

[0067] The suggestion unit can suggest future ingredients or new cooking methods based on the user's food preferences. The suggestion unit can, for example, suggest future ingredients based on the user's food preferences. For example, it can suggest new ingredients such as insect food or cultured meat. The suggestion unit can also suggest new cooking methods. For example, it can suggest new cooking methods such as molecular gastronomy, low-temperature cooking, and vacuum cooking. The suggestion unit can also suggest recipes that combine new ingredients and cooking methods based on the user's food preferences. This can revolutionize the user's food experience by suggesting future ingredients and new cooking methods.

[0068] The suggestion unit can use the emotion estimation function to predict the user's emotions when trying a new dish and provide a positive experience. The suggestion unit, for example, predicts the user's emotions when trying a new dish and provides a positive experience. For example, the suggestion unit can suggest a new dish that the user will enjoy. The suggestion unit can also suggest a new dish that the user can enjoy in a relaxed manner. The suggestion unit can also suggest a new dish that will satisfy the user. In this way, the user's eating experience can be improved by predicting the user's emotions when trying a new dish and providing a positive experience.

[0069] The suggestion unit can suggest different combinations of ingredients based on the user's food preferences, providing a new taste experience. The suggestion unit, for example, suggests different combinations of ingredients based on the user's food preferences. For example, it can suggest new recipes using ingredients that the user likes. The suggestion unit can also provide ideas for new dishes using ingredients that the user is interested in. The suggestion unit can also suggest combinations of ingredients that the user has not tried. In this way, by suggesting different combinations of ingredients, it is possible to provide the user with a new taste experience.

[0070] The suggestion unit can suggest different cooking methods based on the user's food preferences, thereby increasing the variety of dishes. The suggestion unit, for example, suggests different cooking methods based on the user's food preferences. For example, it suggests recipes using new cooking methods that use ingredients that the user likes. The suggestion unit can also provide new cooking ideas using cooking methods that the user is interested in. The suggestion unit can also suggest cooking methods that the user has not tried. In this way, the suggestion unit can increase the variety of dishes the user can make by suggesting different cooking methods.

[0071] The suggestion unit can use the emotion estimation function to analyze the user's emotions in real time when trying a new ingredient and make optimal suggestions. The suggestion unit can, for example, analyze the user's emotions in real time when trying a new ingredient and make optimal suggestions. For example, it can suggest recipes for new ingredients that the user will enjoy. The suggestion unit can also suggest new ingredients that the user can enjoy in a relaxed manner. The suggestion unit can also suggest new ingredients that will satisfy the user. In this way, the user's eating experience can be improved by analyzing the user's emotions in real time when trying a new ingredient and making optimal suggestions.

[0072] The customization unit can suggest substitute ingredients and customize recipes based on the user's ingredient constraints. The customization unit can, for example, suggest substitute ingredients based on the user's ingredient constraints. For example, the customization unit can suggest recipes that do not use ingredients that the user is allergic to. The customization unit can also suggest recipes that do not use specific ingredients based on religious constraints. The customization unit can also suggest recipes that use low-calorie substitute ingredients based on diet constraints. In this way, by suggesting substitute ingredients and customizing recipes, it is possible to provide a dining experience that meets the user's ingredient constraints.

[0073] The customization unit can optimize recipes based on the user's nutritional requirements. The customization unit can, for example, optimize recipes based on the user's nutritional requirements. For example, the customization unit can suggest low-calorie recipes. The customization unit can also suggest high-protein recipes. The customization unit can also suggest recipes rich in vitamins and minerals. In this way, by optimizing recipes based on the nutritional requirements, it is possible to support the user's health.

[0074] The customization unit can use the emotion estimation function to predict the user's emotions when trying a recipe and provide a positive experience. The customization unit, for example, predicts the user's emotions when trying a recipe and provides a positive experience. For example, the customization unit can suggest recipes that the user will enjoy. The customization unit can also suggest recipes that the user can enjoy and relax with. The customization unit can also suggest recipes that the user will be satisfied with. In this way, the user's eating experience can be improved by predicting the user's emotions when trying a recipe and providing a positive experience.

[0075] The customization unit can propose new recipes that combine elements of different dishes based on the user's preferences and constraints. The customization unit, for example, proposes new recipes that combine elements of different dishes based on the user's preferences and constraints. For example, the customization unit proposes new recipes that use ingredients that the user likes. The customization unit can also propose new recipes that combine elements of dishes that the user is interested in. The customization unit can also propose new recipes that combine elements of dishes that the user has not tried. This makes it possible to diversify the user's food experience by proposing new recipes that combine elements of different dishes.

[0076] The customization unit can propose new recipes that combine different cooking methods based on the user's preferences and constraints. The customization unit can, for example, propose new recipes that combine different cooking methods based on the user's preferences and constraints. For example, it can propose recipes that use new cooking methods using ingredients that the user likes. The customization unit can also propose new recipes that combine cooking methods that the user is interested in. The customization unit can also propose new recipes that combine cooking methods that the user has not tried. This makes it possible to diversify the user's eating experience by proposing new recipes that combine different cooking methods.

[0077] The customization unit can use the emotion estimation function to analyze the user's emotions in real time when trying out a recipe and make optimal suggestions. The customization unit, for example, analyzes the user's emotions in real time when trying out a recipe and makes optimal suggestions. For example, it can suggest recipes that the user will enjoy. The customization unit can also suggest recipes that the user can enjoy and relax in. The customization unit can also suggest recipes that will satisfy the user. In this way, the user's eating experience can be improved by analyzing the user's emotions in real time when trying out a recipe and making optimal suggestions.

[0078] The knowledge providing unit can provide detailed nutritional values ​​and health benefits of specific ingredients based on the user's interests. The knowledge providing unit, for example, provides detailed nutritional values ​​of specific ingredients based on the user's interests. For example, the nutritional values ​​of ingredients in which the user is interested are explained in detail. The knowledge providing unit can also provide health benefits of specific ingredients. For example, the health benefits of ingredients in which the user is interested are explained in detail. The knowledge providing unit can also provide cooking methods for specific ingredients. For example, the cooking methods for ingredients in which the user is interested are explained in detail. In this way, by providing detailed nutritional values ​​and health benefits of specific ingredients, the user's knowledge about food can be deepened.

[0079] The knowledge providing unit can provide the history and cultural background of a specific dish based on the user's interests. The knowledge providing unit, for example, provides the history of a specific dish based on the user's interests. For example, the history of a dish in which the user is interested is explained in detail. The knowledge providing unit can also provide the cultural background of a specific dish. For example, the cultural background of a dish in which the user is interested is explained in detail. The knowledge providing unit can also provide a cooking method for a specific dish. For example, the cooking method for a dish in which the user is interested is explained in detail. In this way, by providing the history and cultural background of a specific dish, the user's knowledge about food can be deepened.

[0080] The knowledge providing unit uses the emotion estimation function to provide information about food that the user is interested in, and can provide a positive experience. The knowledge providing unit, for example, uses the emotion estimation function to provide information about food that the user is interested in. For example, it provides information about ingredients and dishes that the user can enjoy. The knowledge providing unit can also provide information about ingredients and dishes that the user can enjoy in a relaxed manner. The knowledge providing unit can also provide information about ingredients and dishes that the user will be satisfied with. In this way, by providing information about food and providing a positive experience, the user's knowledge about food can be deepened.

[0081] The knowledge providing unit can provide knowledge about food in different cultural spheres based on the user's interests. The knowledge providing unit, for example, provides knowledge about food in different cultural spheres based on the user's interests. For example, it provides information about dishes and ingredients in a cultural sphere in which the user is interested. The knowledge providing unit can also provide food culture in different cultural spheres. For example, it can provide detailed explanations about the food culture in a cultural sphere in which the user is interested. The knowledge providing unit can also provide cooking methods for ingredients in different cultural spheres. For example, it can provide detailed explanations about cooking methods for ingredients in a cultural sphere in which the user is interested. In this way, by providing knowledge about food in different cultural spheres, the user's food knowledge can be expanded.

[0082] The knowledge providing unit can provide cooking methods and storage methods for different ingredients based on the user's interests. The knowledge providing unit, for example, provides cooking methods for different ingredients based on the user's interests. For example, it provides detailed explanations of cooking methods for ingredients that the user is interested in. The knowledge providing unit can also provide storage methods for different ingredients. For example, it provides detailed explanations of storage methods for ingredients that the user is interested in. The knowledge providing unit can also provide nutritional values ​​for different ingredients. For example, it provides detailed explanations of nutritional values ​​for ingredients that the user is interested in. In this way, by providing cooking methods and storage methods for different ingredients, the user's knowledge about food can be expanded.

[0083] The knowledge providing unit uses the emotion estimation function to provide information about food that the user is interested in in real time and make optimal suggestions. The knowledge providing unit, for example, uses the emotion estimation function to provide information about food that the user is interested in in real time. For example, it provides information about ingredients and dishes that the user can enjoy in real time. The knowledge providing unit can also provide information about ingredients and dishes that the user can enjoy in a relaxed manner in real time. The knowledge providing unit can also provide information about ingredients and dishes that the user will be satisfied with in real time. In this way, by providing information about food in real time and making optimal suggestions, the user's knowledge about food can be deepened.

[0084] The record sharing unit can provide a function for recording a user's food adventures in detail and sharing them with other users. The record sharing unit, for example, provides a function for recording a user's food adventures in detail and sharing them with other users. For example, the record sharing unit records and shares photos and impressions of dishes the user has tried. The record sharing unit can also increase a user's motivation to try new dishes and ingredients by referring to the food adventures of other users. The record sharing unit can also make suggestions to other users based on the user's food adventures. This allows the user to share and expand their food experiences by recording their food adventures in detail and sharing them with other users.

[0085] The record sharing unit can make suggestions to other users based on the user's food adventures. The record sharing unit can, for example, make suggestions to other users based on the user's food adventures. For example, the record sharing unit can suggest recipes for dishes that the user has tried to other users. The record sharing unit can also suggest information about restaurants that the user has visited to other users. The record sharing unit can also suggest information about ingredients that the user has tried to other users. In this way, by making suggestions to other users based on the user's food adventures, the user's food experiences can be shared and expanded.

[0086] The record sharing unit uses the emotion estimation function to analyze the emotions of the user when recording their food adventures and provide a positive experience. The record sharing unit, for example, analyzes the emotions of the user when recording their food adventures and provides a positive experience. For example, the record sharing unit makes suggestions for recording a food adventure that the user can enjoy. The record sharing unit can also make suggestions for recording a food adventure that the user can enjoy in a relaxed manner. The record sharing unit can also make suggestions for recording a food adventure that the user can be satisfied with. In this way, the user's food experience can be improved by analyzing the emotions of the user when recording their food adventures and providing a positive experience.

[0087] The record sharing unit can suggest food adventures from different cultures based on the user's food adventures. The record sharing unit, for example, suggests food adventures from different cultures based on the user's food adventures. For example, the record sharing unit can suggest dishes from other cultures based on information about dishes the user has tried. The record sharing unit can also suggest restaurants from other cultures based on information about restaurants the user has visited. The record sharing unit can also suggest ingredients from other cultures based on information about ingredients the user has tried. This makes it possible to broaden the user's food experience by suggesting food adventures from different cultures.

[0088] The record sharing unit can suggest different ingredients and cooking methods based on the user's food adventures. The record sharing unit, for example, suggests different ingredients based on the user's food adventures. For example, it suggests other ingredients based on information about dishes the user has tried. The record sharing unit can also suggest other cooking methods based on information about cooking methods the user has tried. The record sharing unit can also suggest new cooking ideas based on combinations of ingredients and cooking methods the user has tried. This makes it possible to broaden the user's food experience by suggesting different ingredients and cooking methods.

[0089] The record sharing unit can use the emotion estimation function to analyze the user's emotions in real time when recording their food adventures and make optimal suggestions. The record sharing unit, for example, analyzes the user's emotions in real time when recording their food adventures and makes optimal suggestions. For example, it can suggest a method for recording a food adventure that the user can enjoy. The record sharing unit can also suggest a method for recording a food adventure that the user can enjoy in a relaxed manner. The record sharing unit can also suggest a method for recording a food adventure that satisfies the user. In this way, the user's food experience can be improved by analyzing the user's emotions in real time when recording their food adventures and making optimal suggestions.

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

[0091] The CulinaryQuest AI system can also be equipped with a time-of-day suggestion module that makes suggestions based on the user's mealtimes. For example, it can suggest high-protein recipes for breakfast to replenish energy, and balanced recipes for lunch to improve concentration. It can also suggest recipes for dinner using ingredients that have a relaxing effect. This allows it to support healthier eating habits by making meal suggestions that fit the user's daily rhythm.

[0092] The CulinaryQuest AI system can also include a prep time suggestion module that makes suggestions based on the user's meal preparation time. For example, it can suggest quick recipes for busy weekday nights and more enjoyable recipes for weekends. The prep time suggestion module can also suggest optimal recipes based on the user's kitchen utensils. This allows for meal suggestions tailored to the user's lifestyle, making meal preparation more efficient and reducing stress.

[0093] The CulinaryQuest AI system can also be equipped with an environment suggestion module that makes suggestions based on the user's dining environment. For example, it can suggest barbecue and camping recipes for outdoor meals, and recipes that can be enjoyed by large groups for home parties. The environment suggestion module can also suggest optimal recipes based on the user's cooking space and equipment. This allows the system to expand the ways in which the user can enjoy meals by making meal suggestions tailored to the user's dining environment.

[0094] The CulinaryQuest AI system can also include a goal suggestion module that makes suggestions based on the user's dietary goals. For example, it can suggest low-calorie recipes to a user on a diet, and high-protein recipes to a user aiming to build muscle. The goal suggestion module can also suggest recipes tailored to specific events or seasons. This allows the system to support the user in achieving their dietary goals by providing meal suggestions tailored to their dietary goals.

[0095] The CulinaryQuest AI system can also include a budget suggestion module that makes suggestions based on the user's meal budget. For example, it can suggest low-cost recipes for budget-conscious users and luxurious recipes for special occasions. The budget suggestion module can also suggest recipes that utilize ingredients the user already has. This reduces the financial burden and waste by making meal suggestions that fit the user's budget.

[0096] The CulinaryQuest AI system can also estimate a user's emotions and suggest meals to reduce stress. For example, if a user is feeling stressed, it can suggest recipes using ingredients that have a relaxing effect. Furthermore, using the emotion estimation function, if the user is feeling relaxed, it can suggest meals to help maintain that state. This allows for support of physical and mental health by suggesting meals based on the user's emotions.

[0097] The CulinaryQuest AI system can also estimate the user's emotions and suggest meals to enhance happiness. For example, if the user is feeling happy, it can suggest ingredients and recipes to maintain that emotion. Furthermore, using the emotion estimation function, it can suggest meals to lift the user's spirits when they are feeling down. This allows for improved quality of daily life by suggesting meals based on the user's emotions.

[0098] The CulinaryQuest AI system can also estimate the user's emotions and suggest meals to elicit specific emotions. For example, if the user wants to relax, it can suggest recipes using ingredients that have a relaxing effect. Also, using the emotion estimation function, if the user needs energy, it can suggest recipes that are suitable for replenishing energy. This allows it to elicit specific emotions by suggesting meals based on the user's emotions.

[0099] The CulinaryQuest AI system can also estimate the user's emotions and select ingredients based on those emotions. For example, if the user is feeling relaxed, the system will suggest ingredients that will help maintain that emotion. Furthermore, using the emotion estimation function, if the user is feeling stressed, the system can suggest ingredients that are effective in reducing stress. This allows the system to maximize the effectiveness of meals by selecting ingredients based on the user's emotions.

[0100] The CulinaryQuest AI system can also estimate a user's emotions and suggest meal times based on their emotions. For example, if a user is feeling relaxed, the system will suggest meal times to maintain that emotion. Furthermore, using the emotion estimation function, if a user needs energy, the system can suggest meal times that are optimal for replenishment. This allows the system to maximize the effectiveness of meals by suggesting meal times based on the user's emotions.

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

[0102] Step 1: The analysis unit analyzes the user's preferences and food experiences. For example, the analysis unit analyzes the user's preferences and food interests in detail based on the information the user inputs and their past eating experiences. The user can also input a list of their favorite dishes, ingredients, and dishes they have tried in the past. Step 2: The suggestion unit suggests unknown dishes and ingredients based on the user's preferences analyzed by the analysis unit. For example, if the user likes Italian food, the suggestion unit will suggest Italian recipes and ingredients that the user has not tried yet. Also, if the user is interested in a particular ingredient, the suggestion unit will provide ideas for new dishes using that ingredient. Step 3: The customization unit customizes the recipe based on the unknown dishes and ingredients suggested by the suggestion unit. For example, if the user is allergic to a particular ingredient, the customization unit suggests an alternative recipe that does not use that ingredient. Also, if the user prioritizes specific nutritional values, the customization unit provides a recipe that meets those requirements. Step 4: The knowledge provider provides knowledge about the recipe customized by the customization provider. For example, it explains the nutritional value and health benefits of specific ingredients, cooking methods, etc. It also provides information about the history and cultural background of the dish that the user is interested in. Step 5: The record sharing unit records and shares the user's food adventures based on the knowledge provided by the knowledge providing unit. For example, when a user tries a new dish, the user can record their impressions and photos and share them with other users. Also, by referring to other users' food adventures, the user can be motivated to try new dishes and ingredients.

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0105] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

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

[0108] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0109] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0110] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0111] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0112] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0113] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0114] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

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

[0117] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0118] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0120] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

[0123] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0128] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0129] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

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

[0132] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0133] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0135] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

[0138] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0139] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0140] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0142] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0143] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0144] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0145] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

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

[0148] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

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

[0151] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0152] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0153] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0154] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0155] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0156] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0157] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0158] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0159] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0160] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0161] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0162] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0163] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0164] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0165] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0166] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0167] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0168] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

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

[0170] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an analysis unit that analyzes the user's preferences and eating experiences; a suggestion unit that suggests unknown dishes and ingredients based on the user's preferences analyzed by the analysis unit; a customization unit that customizes a recipe based on the unknown dishes and ingredients suggested by the suggestion unit; a knowledge providing unit that provides knowledge about the recipe customized by the customization unit; a record sharing unit that records and shares the user's food adventures based on the knowledge provided by the knowledge providing unit. A system characterized by:

2. The analysis unit Linking the user's dietary history with health data to provide dietary suggestions based on their health condition 2. The system of claim 1.

3. The analysis unit When analyzing the user's food preferences, the food culture or local ingredients of the travel destination are taken into consideration to make food suggestions during the trip.

2. The system of claim 1.

4. The customization unit Optimizing the recipe based on the user's nutritional requirements 2. The system of claim 1.

5. The knowledge providing unit Providing the history and cultural context of a particular dish based on the user's interests 2. The system of claim 1.

6. The record sharing unit Analyzing the emotions of the user as they record their food adventures and providing a positive experience 2. The system of claim 1.

7. The analysis unit Analyze the emotions felt by the user while eating and suggest foods that will elicit positive emotions 2. The system of claim 1.

8. The proposal unit Predicting the user's feelings when trying the new dish and providing a positive experience 2. The system of claim 1.

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