system

Abstract

The system according to this embodiment aims to suggest optimal menus and recipes based on the user's profile information. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a suggestion unit, a recipe presentation unit, and a list creation unit. The collection unit collects profile information. The analysis unit analyzes the information collected by the collection unit. The suggestion unit suggests a menu based on the information analyzed by the analysis unit. The recipe presentation unit presents recipes based on the menu suggested by the suggestion unit. The list creation unit creates a shopping list based on the recipes presented by the recipe presentation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it has not been fully done to propose an optimal menu or recipe based on the individual profile information of the user, and there is room for improvement.

[0005] The system according to the embodiment aims to propose an optimal menu or recipe based on the profile information of the user.

Means for Solving the Problems

[0007] The system according to this embodiment can suggest optimal menus and recipes based on the user's profile information. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10]This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the labeled communication I / F (Interface) 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 such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment. [[ID=1,2,3]]

[0017] [[ID=1,2,3]] As shown in FIG. 1, the 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] <00000,96>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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The AI ​​generation service according to an embodiment of the present invention is a system that provides total support for the user's dining table. When the user first uses the AI ​​generation service, they register basic information such as their profile (family structure, gender, age, etc.), dietary preferences, and allergy information. Based on this information, the AI ​​generation suggests healthy and varied menus and recipes. The AI ​​generation service also creates a shopping list based on information from online flyers, helping the user efficiently gather ingredients and seasonings that meet their preferences (prioritizing low prices, prioritizing freshness, etc.). For example, when the user first uses the AI ​​generation service, they register their profile. For example, they input family structure, gender, age, dietary preferences, and allergy information. This information is input into the AI ​​generation system. Next, the AI ​​generation system analyzes the input information and suggests the most suitable menu for the user. For example, for a user who desires healthy meals, it suggests a nutritionally balanced menu. For a user who desires a variety of meals, it suggests menus from different culinary genres. Furthermore, the AI ​​generation system presents recipes based on the suggested menus. For example, it details the ingredients, seasonings, and cooking procedures required for the suggested menu. This allows the user to easily prepare meals. Furthermore, the generating AI creates shopping lists based on information from online flyers. For example, if a user prioritizes low prices, the generating AI adds the cheapest ingredients and seasonings to the list. On the other hand, if freshness is prioritized, the generating AI adds the freshest ingredients and seasonings to the list. This allows users to shop efficiently. This system enables users to enjoy healthy and varied meals while also making shopping more efficient. For example, even busy families can easily prepare nutritionally balanced meals and reduce food waste. In addition, menu suggestions that take allergy information into consideration allow users to enjoy meals with peace of mind. In this way, the generating AI service can provide total support for users' dining tables and offer healthy and varied meals.

[0029] The generation AI service according to the embodiment comprises a collection unit, an analysis unit, a suggestion unit, a recipe presentation unit, and a list creation unit. The collection unit collects profile information. Profile information includes, but is not limited to, family structure, gender, age, dietary preferences, and allergy information. The collection unit stores, for example, information entered by the user in a database. The collection unit can also collect information on the user's past eating history and health status. For example, the collection unit collects information on dishes and ingredients the user has eaten in the past and stores it in a database. The analysis unit analyzes the information collected by the collection unit. For example, data mining techniques and statistical analysis methods are used for the analysis, but is not limited to, such examples. For example, the analysis unit analyzes the user's dietary preferences and allergy information based on the collected profile information. The analysis unit also performs analysis to suggest appropriate meals based on the user's health status and lifestyle. The suggestion unit suggests menus based on the information analyzed by the analysis unit. For example, the suggestions consider nutritional balance, calorie restrictions, and types of ingredients, but is not limited to, such examples. For example, the suggestion unit suggests a nutritionally balanced menu to users who desire healthy meals. The suggestion unit also suggests menus from different culinary genres to users who desire a variety of meals. The recipe presentation unit presents recipes based on the menus suggested by the suggestion unit. Recipes may include, but are not limited to, cooking procedures, necessary ingredients, and cooking time. For example, the recipe presentation unit may detail the ingredients, seasonings, and cooking procedures required for the suggested menu. The recipe presentation unit can also present recipes tailored to the user's cooking skills. The list creation unit creates a shopping list based on the recipes presented by the recipe presentation unit. The shopping list may include, but is not limited to, the types and quantities of ingredients and where to purchase them. For example, the list creation unit may add the cheapest ingredients and seasonings to the list based on information from online flyers. The list creation unit can also add ingredients and seasonings to the list according to the user's preferences (prioritizing low prices, freshness, etc.).As a result, the generation AI service according to this embodiment can provide total support for the user's dining table and offer healthy and varied meals.

[0030] The data collection unit collects profile information. This profile information includes, but is not limited to, family structure, gender, age, dietary preferences, and allergy information. The data collection unit stores information entered by users in a database. Specifically, it stores information entered by users through applications and websites in a secure database and uses it for subsequent analysis and recommendations. The data collection unit can also collect information about users' past eating history and health status. For example, the data collection unit collects information about dishes and ingredients that users have eaten in the past and stores it in a database. This includes data entered by users using applications to record their meals, and health data obtained from smartwatches and fitness trackers. Furthermore, the data collection unit also collects feedback and evaluations about users' meals and stores this in a database. This allows the data collection unit to centrally manage detailed user profile information and eating history, making it available to the analysis and recommendation units. By adjusting the frequency and accuracy of data collection, the data collection unit can flexibly respond to users' lifestyles and needs. For example, if a user tries a new food ingredient or their health condition changes, the data collection unit quickly updates this information to provide the latest data. This allows the data collection unit to efficiently and effectively collect information about the user's diet and health, improving the overall performance of the system.

[0031] The analysis unit analyzes the information collected by the collection unit. This analysis may utilize, but is not limited to, data mining techniques and statistical analysis methods. Specifically, it analyzes users' dietary preferences and allergy information based on collected profile information. For example, it uses data mining techniques to extract patterns from users' past eating history and identify their preference trends. It also uses statistical analysis methods to perform analyses to suggest appropriate meals based on the user's health status and lifestyle. Furthermore, the analysis unit uses AI to analyze collected data in real time and provide optimal meal suggestions tailored to user needs. For example, it uses machine learning algorithms to analyze users' eating history and health data to suggest meals that consider nutritional balance and calorie restrictions. It also uses natural language processing techniques to analyze user feedback and evaluations to improve the accuracy of the suggestions. This allows the analysis unit to quickly and accurately analyze collected data and understand users' dietary and health needs in real time. Additionally, the analysis unit can utilize historical data and statistical information to perform long-term health management and dietary trend analysis. For example, based on past meal data, the system can predict eating trends during specific seasons or events and provide appropriate meal suggestions to users. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns or abnormal data, issuing early warnings. This allows the analysis unit to not only monitor the situation in real time but also to handle long-term health management and anomaly detection, improving the overall reliability and safety of the system.

[0032] The Proposal Department proposes menus based on information analyzed by the Analysis Department. These proposals consider, but are not limited to, nutritional balance, calorie restrictions, and ingredient types. Specifically, based on data provided by the Analysis Department, the Proposal Department proposes optimal menus tailored to the user's health condition and dietary preferences. For example, it proposes nutritionally balanced menus for users who desire healthy eating, and low-calorie menus for users who need calorie restriction. The Proposal Department also proposes menus from different culinary genres for users who desire a variety of meals. This includes various culinary genres such as Japanese, Western, and Chinese cuisine. Furthermore, the Proposal Department can propose special menus tailored to seasons and events. For example, it can propose menus for special events such as Christmas and New Year's, adding color to the user's dining table. The Proposal Department can also propose easy-to-prepare menus and time-saving recipes, taking into account the user's cooking skills and time constraints. This allows the Proposal Department to provide flexible menu suggestions that meet diverse user needs, offering healthy and varied meals. Additionally, the Proposal Department can collect user feedback to continuously improve the accuracy and effectiveness of its suggestions. For example, the system collects evaluations and feedback from users after they actually try the suggested menus and incorporates them into future suggestions. This allows the suggestion department to consistently propose the most suitable menus to users, thereby improving their satisfaction.

[0033] The recipe presentation section presents recipes based on the menu proposed by the suggestion section. Recipes include, but are not limited to, cooking procedures, necessary ingredients, and cooking time. Specifically, they detail the ingredients, seasonings, and cooking procedures required for the proposed menu. For example, the recipe presentation section explains the cooking procedure for each dish step-by-step, making it easy for users to cook. It also clearly indicates the cooking time and difficulty level, allowing users to choose according to their schedule and skill level. Furthermore, the recipe presentation section can present recipes tailored to the user's cooking skill level. For example, it can offer simple recipes for beginners and more elaborate recipes for advanced cooks, supporting skill development. The recipe presentation section can also provide visually easy-to-understand recipes using videos and images. This makes it easier for users to intuitively understand the cooking procedure, improving the success rate of cooking. Additionally, the recipe presentation section can collect user feedback and continuously improve the accuracy and effectiveness of the recipes. For example, it can collect evaluations and comments from users after they have actually cooked the dishes and incorporate them into future recipe presentations. This allows the recipe presentation section to consistently provide users with the best possible recipes, increasing their satisfaction.

[0034] The list creation unit creates a shopping list based on the recipes presented by the recipe presentation unit. The shopping list may include, but is not limited to, the types and quantities of ingredients and where to buy them. Specifically, it lists the ingredients and seasonings needed for the suggested menu, enabling users to shop efficiently. For example, the list creation unit adds the cheapest ingredients and seasonings to the list based on information from online flyers. The list creation unit can also add ingredients and seasonings that align with the user's preferences (prioritizing low price, prioritizing freshness, etc.). Furthermore, the list creation unit can customize the list content by considering the user's past purchase history and preferences. For example, for users who prefer specific brands or organic ingredients, those ingredients will be added to the list preferentially. The list creation unit also provides the shopping list through a smartphone app or website, allowing users to access it anytime, anywhere. This enables the list creation unit to efficiently and effectively support users' shopping and reduce food waste. In addition, the list creation unit can collect user feedback and continuously improve the accuracy and effectiveness of the list content. For example, the system collects user reviews and feedback after they've actually made a purchase and incorporates them into future list creation. This allows the list creation team to consistently provide users with the most optimal shopping lists, thereby improving their satisfaction.

[0035] The list creation unit can create a shopping list based on information from online flyers. For example, the list creation unit can analyze price information from online flyers and add the cheapest ingredients to the list. The list creation unit can also analyze freshness information from online flyers and add the freshest ingredients to the list. The list creation unit can also analyze sale information from online flyers and add sale items to the list. This makes it possible to create an efficient shopping list by utilizing information from online flyers. Some or all of the above processes in the list creation unit may be performed using AI, for example, or without AI. For example, the list creation unit can input information from online flyers into a generating AI and have the generating AI create an optimal shopping list.

[0036] The list creation unit can add ingredients and seasonings that match the user's preferences to the list. For example, if the user prioritizes low prices, the list creation unit will add the cheapest ingredients and seasonings to the list. If the user prioritizes freshness, the list creation unit can also add the freshest ingredients and seasonings to the list. If the user has a preference for a particular ingredient, the list creation unit can also add that ingredient to the list. This makes it possible to create a shopping list tailored to the user's preferences. Some or all of the above processing in the list creation unit may be performed using AI, for example, or without AI. For example, the list creation unit can input user preference information into a generating AI and have the generating AI create an optimal shopping list.

[0037] The data collection unit can analyze the user's past eating history and select the optimal data collection method. For example, the data collection unit can ask relevant questions based on the dishes the user has enjoyed eating in the past to collect detailed dietary preferences. The data collection unit can also identify frequently eaten ingredients and dishes from the user's past eating history and adjust the questions accordingly. The data collection unit can also analyze the user's past eating history and prioritize questions regarding allergy information and dietary restrictions. This allows for efficient collection of profile information based on past eating history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past eating history data into a generating AI and have the generating AI select the optimal data collection method.

[0038] The data collection unit can filter profile information based on the user's current health status and lifestyle. For example, if a user provides the results of a health checkup, the data collection unit can filter dietary preferences and restrictions based on that information. The data collection unit can also ask appropriate dietary questions based on the user's lifestyle (e.g., exercise frequency and sleep patterns). If a user has a specific health goal, the data collection unit can collect profile information tailored to that goal. This enables the collection of appropriate profile information according to the user's health status and lifestyle. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's health checkup data into a generating AI and have the generating AI perform the filtering.

[0039] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location when collecting profile information. For example, the data collection unit can ask relevant questions considering the food culture and availability of ingredients in the area where the user lives. For example, the data collection unit can also collect region-specific allergy information and dietary restrictions based on the user's geographical location. For example, if the user is traveling, the data collection unit can prioritize the collection of information about the food culture and ingredients of that region. This enables the collection of highly relevant profile information based on the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant information.

[0040] The data collection unit can analyze the user's social media activity and collect relevant information when collecting profile information. For example, the data collection unit can analyze photos and posts of meals shared by the user on social media to identify their food preferences. The data collection unit can also collect information about allergies and dietary restrictions from the user's social media activity. For example, the data collection unit can analyze accounts related to food and ingredients that the user follows and ask relevant questions. This makes it possible to collect relevant profile information based on the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media data into a generating AI and have the generating AI collect relevant information.

[0041] The analysis unit can adjust the level of detail of the analysis based on the importance of the profile information during the analysis. For example, if allergy information is important, the analysis unit can perform a detailed analysis and make suggestions to avoid allergies. For example, if dietary preferences are important, the analysis unit can perform a detailed analysis and make suggestions for menus that suit those preferences. For example, if health status is important, the analysis unit can perform a detailed analysis and make suggestions for menus that take health into consideration. This enables detailed analysis according to the importance of the profile information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input profile information into a generating AI and have the generating AI perform adjustments to the level of detail of the analysis based on importance.

[0042] The analysis unit can apply different analysis algorithms depending on the category of profile information during analysis. For example, the analysis unit can apply a specific algorithm to avoid allergens to allergy information. For example, the analysis unit can apply an algorithm to suggest dishes that suit the user's preferences to food preferences. For example, the analysis unit can apply an algorithm to suggest menus that consider nutritional balance to health status. This enables appropriate analysis according to the category of profile information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input profile information into a generating AI and have the generating AI execute the application of analysis algorithms according to the category.

[0043] The analysis unit can determine the priority of analysis based on when the profile information was collected during the analysis. For example, the analysis unit may prioritize the analysis of recently collected information to reflect the latest dietary preferences and allergy information. The analysis unit may also prioritize the analysis of information that is updated regularly (e.g., health status). The analysis unit may also postpone the analysis of information that does not change over a long period (e.g., family structure). This enables analysis with priorities based on the collection period. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input profile information into a generating AI and have the generating AI perform the determination of analysis priorities based on the collection period.

[0044] The analysis unit can adjust the order of analysis based on the relevance of profile information during the analysis process. For example, if allergy information is highly relevant, the analysis unit can perform the analysis first and make suggestions to avoid allergies. For example, if dietary preferences are highly relevant, the analysis unit can perform the analysis next and suggest menus that suit those preferences. For example, if health status is highly relevant, the analysis unit can perform the analysis last and suggest menus that take health into consideration. This enables analysis in an order based on relevance. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input profile information into a generating AI and have the generating AI perform the adjustment of the analysis order based on relevance.

[0045] The suggestion unit can adjust the level of detail in its suggestions based on the importance of the menu. For example, if healthy eating is important, the suggestion unit can provide detailed nutritional information and make suggestions. For example, if variety in meals is important, the suggestion unit can also suggest different culinary genres. For example, if allergy information is important, the suggestion unit can also provide detailed suggestions to avoid allergens. This enables detailed suggestions tailored to the importance of the menu. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input menu information into a generating AI and have the generating AI adjust the level of detail in its suggestions based on importance.

[0046] The suggestion unit can apply different suggestion algorithms depending on the menu category when making suggestions. For example, for healthy meals, the suggestion unit can apply a suggestion algorithm that takes nutritional balance into consideration. For varied meals, the suggestion unit can also apply an algorithm that suggests different culinary genres. For allergy information, the suggestion unit can also apply a suggestion algorithm that avoids allergens. This enables appropriate suggestions according to the menu category. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input menu information into a generating AI and have the generating AI execute the application of a suggestion algorithm according to the category.

[0047] The proposal department can determine the priority of proposals based on the timing of menu submission. For example, the proposal department may prioritize proposals related to the most recent meal. The proposal department may also prioritize proposals for menus that are updated regularly (e.g., weekly menus). The proposal department may also postpone proposals for menus that remain unchanged for a long period (e.g., menus for specific events). This allows for proposals to be made with a priority based on the submission timing. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input menu information into a generating AI and have the generating AI determine the priority of proposals based on the submission timing.

[0048] The suggestion unit can adjust the order of suggestions based on the relevance of the menu items. For example, if healthy eating is highly relevant, the suggestion unit will suggest it first. If a variety of meals is highly relevant, the suggestion unit may suggest it next. If allergy information is highly relevant, the suggestion unit may suggest it last. This allows for suggestions to be made in an order based on relevance. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input menu information into a generating AI and have the generating AI adjust the order of suggestions based on relevance.

[0049] The recipe presentation unit can select the most suitable recipe by referring to the user's past cooking history when presenting a recipe. For example, the recipe presentation unit can suggest relevant recipes based on dishes the user has previously enjoyed making. For example, the recipe presentation unit can identify frequently used ingredients from the user's past cooking history and select a recipe based on that. For example, the recipe presentation unit can analyze the user's past cooking history and select a recipe that takes into account allergy information and dietary restrictions. This allows the system to provide the most suitable recipe based on the user's past cooking history. Some or all of the above-described processes in the recipe presentation unit may be performed using AI, for example, or without AI. For example, the recipe presentation unit can input the user's cooking history data into a generating AI and have the generating AI select the most suitable recipe.

[0050] The recipe presentation unit can adjust the difficulty level of a recipe based on the user's current cooking skill level. For example, if the user is a beginner, the recipe presentation unit may present a simple recipe with basic cooking steps. If the user is an intermediate cook, the recipe presentation unit may present a slightly more difficult recipe to support the improvement of their cooking skills. If the user is an advanced cook, the recipe presentation unit may present a complex and challenging recipe to provide the enjoyment of cooking. This ensures that recipes of appropriate difficulty levels are provided according to the user's cooking skill level. Some or all of the above processing in the recipe presentation unit may be performed using AI, for example, or without AI. For example, the recipe presentation unit can input the user's cooking skill information into a generating AI and have the generating AI perform the difficulty level adjustment.

[0051] The recipe presentation unit can select the most suitable recipe by considering the user's geographical location when presenting recipes. For example, the recipe presentation unit can suggest relevant recipes by considering the food culture and availability of ingredients in the area where the user lives. For example, the recipe presentation unit can also select recipes that use ingredients specific to a particular region based on the user's geographical location. For example, if the user is traveling, the recipe presentation unit can prioritize suggesting recipes related to the food culture and ingredients of that region. This allows for the provision of the most suitable recipe based on geographical location information. Some or all of the above processing in the recipe presentation unit may be performed using AI, for example, or without AI. For example, the recipe presentation unit can input the user's geographical location information into a generating AI and have the generating AI select the most suitable recipe.

[0052] The recipe presentation unit can analyze the user's social media activity and suggest relevant recipes when presenting recipes. For example, the recipe presentation unit can analyze photos and posts of dishes shared by the user on social media and suggest relevant recipes. For example, the recipe presentation unit can identify the user's preferred cooking genre from their social media activity and suggest recipes based on that. For example, the recipe presentation unit can analyze accounts related to cooking and ingredients that the user follows and suggest relevant recipes. This allows the system to provide relevant recipes based on social media activity. Some or all of the above processing in the recipe presentation unit may be performed using AI, for example, or without AI. For example, the recipe presentation unit can input the user's social media data into a generating AI and have the generating AI perform the task of suggesting relevant recipes.

[0053] The list creation unit can create an optimal shopping list by analyzing information from online flyers during list creation. For example, the list creation unit can analyze price information from online flyers and add the cheapest ingredients to the list. The list creation unit can also analyze freshness information from online flyers and add the freshest ingredients to the list. The list creation unit can also analyze sale information from online flyers and add sale items to the list. This allows for the creation of an optimal shopping list based on information from online flyers. Some or all of the above processing in the list creation unit may be performed using AI, for example, or without AI. For example, the list creation unit can input information from online flyers into a generating AI and have the generating AI create an optimal shopping list.

[0054] The list creation unit can improve the accuracy of the list by referring to the user's past shopping history when creating the list. For example, the list creation unit can add related ingredients to the list based on ingredients the user has purchased in the past. The list creation unit can also identify frequently purchased ingredients from the user's past shopping history and create a list based on that. The list creation unit can also analyze the user's past shopping history and create a list that takes into account allergy information and dietary restrictions. This makes it possible to create a highly accurate shopping list based on past shopping history. Some or all of the above processes in the list creation unit may be performed using AI, for example, or without AI. For example, the list creation unit can input the user's shopping history data into a generating AI and have the generating AI perform list accuracy improvements.

[0055] The list creation unit can create an optimal shopping list by considering the user's geographical location information when creating a list. For example, the list creation unit can add relevant ingredients to the list by considering the availability of ingredients in the area where the user lives. The list creation unit can also add region-specific ingredients to the list based on the user's geographical location information. For example, if the user is traveling, the list creation unit can prioritize adding information about ingredients in that region to the list. This makes it possible to create an optimal shopping list based on geographical location information. Some or all of the above processing in the list creation unit may be performed using AI, for example, or without AI. For example, the list creation unit can input the user's geographical location information into a generating AI and have the generating AI create an optimal shopping list.

[0056] The list creation unit can analyze the user's social media activity and create a relevant shopping list when creating a list. For example, the list creation unit can analyze photos and posts of dishes shared by the user on social media and add relevant ingredients to the list. The list creation unit can also identify the user's preferred ingredients from their social media activity and create a list based on that. For example, the list creation unit can analyze accounts related to dishes and ingredients that the user follows and add relevant ingredients to the list. This allows for the creation of relevant shopping lists based on social media activity. Some or all of the above processes in the list creation unit may be performed using AI, for example, or without AI. For example, the list creation unit can input the user's social media data into a generating AI and have the generating AI create a relevant shopping list.

[0057] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0058] The generative AI service can suggest substitutes for specific ingredients based on the user's dietary preferences and allergy information. For example, if a user has a dairy allergy, the generative AI will suggest recipes that do not use dairy products. It can also provide alternative recipes that do not use ingredients that the user dislikes. Furthermore, the generative AI can suggest substitutes for specific ingredients based on the user's dietary preferences and allergy information. This allows users to enjoy meals tailored to their preferences and allergies.

[0059] The generative AI service can analyze the nutritional value of specific ingredients based on the user's dietary preferences and allergy information, and suggest the optimal meal. For example, if a user wants to consume a specific nutrient, it can suggest recipes using ingredients rich in that nutrient. Conversely, if a user wants to avoid a specific nutrient, it can provide recipes using ingredients that do not contain that nutrient. Furthermore, the generative AI can suggest substitutes for ingredients containing specific nutrients based on the user's dietary preferences and allergy information. This allows users to enjoy meals tailored to their nutritional needs.

[0060] The generative AI service can suggest storage methods for specific ingredients based on the user's dietary preferences and allergy information. For example, if a user wants to store a particular ingredient for a long period, it can suggest the optimal storage method. It can also provide storage methods if the user wants to keep a particular ingredient fresh. Furthermore, the generative AI can suggest storage methods for specific ingredients based on the user's dietary preferences and allergy information. This allows users to learn about food storage methods tailored to their preferences and allergies.

[0061] The generative AI service can suggest cooking methods for specific ingredients based on the user's dietary preferences and allergy information. For example, if a user wants to use a particular ingredient, it can suggest the best cooking method for that ingredient. It can also provide alternative recipes that do not use a particular ingredient if the user wants to avoid it. Furthermore, the generative AI can suggest cooking methods for specific ingredients based on the user's dietary preferences and allergy information. This allows users to learn about cooking methods that suit their preferences and allergies.

[0062] The generative AI service can analyze the nutritional value of specific ingredients based on the user's dietary preferences and allergy information, and suggest the optimal meal. For example, if a user wants to consume a specific nutrient, it can suggest recipes using ingredients rich in that nutrient. Conversely, if a user wants to avoid a specific nutrient, it can provide recipes using ingredients that do not contain that nutrient. Furthermore, the generative AI can suggest substitutes for ingredients containing specific nutrients based on the user's dietary preferences and allergy information. This allows users to enjoy meals tailored to their nutritional needs.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The data collection unit collects profile information. This profile information includes family structure, gender, age, dietary preferences, allergy information, past dietary history, and health status. The data collection unit stores the information entered by the user in a database. Step 2: The analysis unit analyzes the information collected by the collection unit. Data mining techniques and statistical analysis methods are used in the analysis to provide appropriate dietary suggestions based on the user's dietary preferences, allergy information, health status, and lifestyle. Step 3: The proposal unit proposes a menu based on the information analyzed by the analysis unit. The proposal takes into account nutritional balance, calorie restrictions, and types of ingredients, and a menu tailored to the user's preferences is suggested. Step 4: The recipe presentation unit presents recipes based on the menu proposed by the suggestion unit. The recipes include cooking procedures, necessary ingredients, and cooking time, and are tailored to the user's cooking skills. Step 5: The list creation unit creates a shopping list based on the recipe provided by the recipe presentation unit. The shopping list includes the types and quantities of ingredients, where to buy them, etc., and the cheapest ingredients and seasonings are added to the list based on information from online flyers.

[0065] (Example of form 2) The AI ​​generation service according to an embodiment of the present invention is a system that provides total support for the user's dining table. When the user first uses the AI ​​generation service, they register basic information such as their profile (family structure, gender, age, etc.), dietary preferences, and allergy information. Based on this information, the AI ​​generation suggests healthy and varied menus and recipes. The AI ​​generation service also creates a shopping list based on information from online flyers, helping the user efficiently gather ingredients and seasonings that meet their preferences (prioritizing low prices, prioritizing freshness, etc.). For example, when the user first uses the AI ​​generation service, they register their profile. For example, they input family structure, gender, age, dietary preferences, and allergy information. This information is input into the AI ​​generation system. Next, the AI ​​generation system analyzes the input information and suggests the most suitable menu for the user. For example, for a user who desires healthy meals, it suggests a nutritionally balanced menu. For a user who desires a variety of meals, it suggests menus from different culinary genres. Furthermore, the AI ​​generation system presents recipes based on the suggested menus. For example, it details the ingredients, seasonings, and cooking procedures required for the suggested menu. This allows the user to easily prepare meals. Furthermore, the generating AI creates shopping lists based on information from online flyers. For example, if a user prioritizes low prices, the generating AI adds the cheapest ingredients and seasonings to the list. On the other hand, if freshness is prioritized, the generating AI adds the freshest ingredients and seasonings to the list. This allows users to shop efficiently. This system enables users to enjoy healthy and varied meals while also making shopping more efficient. For example, even busy families can easily prepare nutritionally balanced meals and reduce food waste. In addition, menu suggestions that take allergy information into consideration allow users to enjoy meals with peace of mind. In this way, the generating AI service can provide total support for users' dining tables and offer healthy and varied meals.

[0066] The generation AI service according to the embodiment comprises a collection unit, an analysis unit, a suggestion unit, a recipe presentation unit, and a list creation unit. The collection unit collects profile information. Profile information includes, but is not limited to, family structure, gender, age, dietary preferences, and allergy information. The collection unit stores, for example, information entered by the user in a database. The collection unit can also collect information on the user's past eating history and health status. For example, the collection unit collects information on dishes and ingredients the user has eaten in the past and stores it in a database. The analysis unit analyzes the information collected by the collection unit. For example, data mining techniques and statistical analysis methods are used for the analysis, but is not limited to, such examples. For example, the analysis unit analyzes the user's dietary preferences and allergy information based on the collected profile information. The analysis unit also performs analysis to suggest appropriate meals based on the user's health status and lifestyle. The suggestion unit suggests menus based on the information analyzed by the analysis unit. For example, the suggestions consider nutritional balance, calorie restrictions, and types of ingredients, but is not limited to, such examples. For example, the suggestion unit suggests a nutritionally balanced menu to users who desire healthy meals. The suggestion unit also suggests menus from different culinary genres to users who desire a variety of meals. The recipe presentation unit presents recipes based on the menus suggested by the suggestion unit. Recipes may include, but are not limited to, cooking procedures, necessary ingredients, and cooking time. For example, the recipe presentation unit may detail the ingredients, seasonings, and cooking procedures required for the suggested menu. The recipe presentation unit can also present recipes tailored to the user's cooking skills. The list creation unit creates a shopping list based on the recipes presented by the recipe presentation unit. The shopping list may include, but is not limited to, the types and quantities of ingredients and where to purchase them. For example, the list creation unit may add the cheapest ingredients and seasonings to the list based on information from online flyers. The list creation unit can also add ingredients and seasonings to the list according to the user's preferences (prioritizing low prices, freshness, etc.).As a result, the generation AI service according to this embodiment can provide total support for the user's dining table and offer healthy and varied meals.

[0067] The data collection unit collects profile information. This profile information includes, but is not limited to, family structure, gender, age, dietary preferences, and allergy information. The data collection unit stores information entered by users in a database. Specifically, it stores information entered by users through applications and websites in a secure database and uses it for subsequent analysis and recommendations. The data collection unit can also collect information about users' past eating history and health status. For example, the data collection unit collects information about dishes and ingredients that users have eaten in the past and stores it in a database. This includes data entered by users using applications to record their meals, and health data obtained from smartwatches and fitness trackers. Furthermore, the data collection unit also collects feedback and evaluations about users' meals and stores this in a database. This allows the data collection unit to centrally manage detailed user profile information and eating history, making it available to the analysis and recommendation units. By adjusting the frequency and accuracy of data collection, the data collection unit can flexibly respond to users' lifestyles and needs. For example, if a user tries a new food ingredient or their health condition changes, the data collection unit quickly updates this information to provide the latest data. This allows the data collection unit to efficiently and effectively collect information about the user's diet and health, improving the overall performance of the system.

[0068] The analysis unit analyzes the information collected by the collection unit. This analysis may utilize, but is not limited to, data mining techniques and statistical analysis methods. Specifically, it analyzes users' dietary preferences and allergy information based on collected profile information. For example, it uses data mining techniques to extract patterns from users' past eating history and identify their preference trends. It also uses statistical analysis methods to perform analyses to suggest appropriate meals based on the user's health status and lifestyle. Furthermore, the analysis unit uses AI to analyze collected data in real time and provide optimal meal suggestions tailored to user needs. For example, it uses machine learning algorithms to analyze users' eating history and health data to suggest meals that consider nutritional balance and calorie restrictions. It also uses natural language processing techniques to analyze user feedback and evaluations to improve the accuracy of the suggestions. This allows the analysis unit to quickly and accurately analyze collected data and understand users' dietary and health needs in real time. Additionally, the analysis unit can utilize historical data and statistical information to perform long-term health management and dietary trend analysis. For example, based on past meal data, the system can predict eating trends during specific seasons or events and provide appropriate meal suggestions to users. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns or abnormal data, issuing early warnings. This allows the analysis unit to not only monitor the situation in real time but also to handle long-term health management and anomaly detection, improving the overall reliability and safety of the system.

[0069] The Proposal Department proposes menus based on information analyzed by the Analysis Department. These proposals consider, but are not limited to, nutritional balance, calorie restrictions, and ingredient types. Specifically, based on data provided by the Analysis Department, the Proposal Department proposes optimal menus tailored to the user's health condition and dietary preferences. For example, it proposes nutritionally balanced menus for users who desire healthy eating, and low-calorie menus for users who need calorie restriction. The Proposal Department also proposes menus from different culinary genres for users who desire a variety of meals. This includes various culinary genres such as Japanese, Western, and Chinese cuisine. Furthermore, the Proposal Department can propose special menus tailored to seasons and events. For example, it can propose menus for special events such as Christmas and New Year's, adding color to the user's dining table. The Proposal Department can also propose easy-to-prepare menus and time-saving recipes, taking into account the user's cooking skills and time constraints. This allows the Proposal Department to provide flexible menu suggestions that meet diverse user needs, offering healthy and varied meals. Additionally, the Proposal Department can collect user feedback to continuously improve the accuracy and effectiveness of its suggestions. For example, the system collects evaluations and feedback from users after they actually try the suggested menus and incorporates them into future suggestions. This allows the suggestion department to consistently propose the most suitable menus to users, thereby improving their satisfaction.

[0070] The recipe presentation section presents recipes based on the menu proposed by the suggestion section. Recipes include, but are not limited to, cooking procedures, necessary ingredients, and cooking time. Specifically, they detail the ingredients, seasonings, and cooking procedures required for the proposed menu. For example, the recipe presentation section explains the cooking procedure for each dish step-by-step, making it easy for users to cook. It also clearly indicates the cooking time and difficulty level, allowing users to choose according to their schedule and skill level. Furthermore, the recipe presentation section can present recipes tailored to the user's cooking skill level. For example, it can offer simple recipes for beginners and more elaborate recipes for advanced cooks, supporting skill development. The recipe presentation section can also provide visually easy-to-understand recipes using videos and images. This makes it easier for users to intuitively understand the cooking procedure, improving the success rate of cooking. Additionally, the recipe presentation section can collect user feedback and continuously improve the accuracy and effectiveness of the recipes. For example, it can collect evaluations and comments from users after they have actually cooked the dishes and incorporate them into future recipe presentations. This allows the recipe presentation section to consistently provide users with the best possible recipes, increasing their satisfaction.

[0071] The list creation unit creates a shopping list based on the recipes presented by the recipe presentation unit. The shopping list may include, but is not limited to, the types and quantities of ingredients and where to buy them. Specifically, it lists the ingredients and seasonings needed for the suggested menu, enabling users to shop efficiently. For example, the list creation unit adds the cheapest ingredients and seasonings to the list based on information from online flyers. The list creation unit can also add ingredients and seasonings that align with the user's preferences (prioritizing low price, prioritizing freshness, etc.). Furthermore, the list creation unit can customize the list content by considering the user's past purchase history and preferences. For example, for users who prefer specific brands or organic ingredients, those ingredients will be added to the list preferentially. The list creation unit also provides the shopping list through a smartphone app or website, allowing users to access it anytime, anywhere. This enables the list creation unit to efficiently and effectively support users' shopping and reduce food waste. In addition, the list creation unit can collect user feedback and continuously improve the accuracy and effectiveness of the list content. For example, the system collects user reviews and feedback after they've actually made a purchase and incorporates them into future list creation. This allows the list creation team to consistently provide users with the most optimal shopping lists, thereby improving their satisfaction.

[0072] The list creation unit can create a shopping list based on information from online flyers. For example, the list creation unit can analyze price information from online flyers and add the cheapest ingredients to the list. The list creation unit can also analyze freshness information from online flyers and add the freshest ingredients to the list. The list creation unit can also analyze sale information from online flyers and add sale items to the list. This makes it possible to create an efficient shopping list by utilizing information from online flyers. Some or all of the above processes in the list creation unit may be performed using AI, for example, or without AI. For example, the list creation unit can input information from online flyers into a generating AI and have the generating AI create an optimal shopping list.

[0073] The list creation unit can add ingredients and seasonings that match the user's preferences to the list. For example, if the user prioritizes low prices, the list creation unit will add the cheapest ingredients and seasonings to the list. If the user prioritizes freshness, the list creation unit can also add the freshest ingredients and seasonings to the list. If the user has a preference for a particular ingredient, the list creation unit can also add that ingredient to the list. This makes it possible to create a shopping list tailored to the user's preferences. Some or all of the above processing in the list creation unit may be performed using AI, for example, or without AI. For example, the list creation unit can input user preference information into a generating AI and have the generating AI create an optimal shopping list.

[0074] The data collection unit can estimate the user's emotions and adjust the timing of profile information collection based on the estimated emotions. For example, if the user is relaxed, the data collection unit can take its time to ask questions to collect detailed profile information. If the user is in a hurry, the data collection unit can quickly collect only basic information and allow for the addition of more detailed information later. If the user is stressed, the data collection unit can start with simple questions and gradually collect more detailed information. This allows for the collection of profile information at the appropriate time according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0075] The data collection unit can analyze the user's past eating history and select the optimal data collection method. For example, the data collection unit can ask relevant questions based on the dishes the user has enjoyed eating in the past to collect detailed dietary preferences. The data collection unit can also identify frequently eaten ingredients and dishes from the user's past eating history and adjust the questions accordingly. The data collection unit can also analyze the user's past eating history and prioritize questions regarding allergy information and dietary restrictions. This allows for efficient collection of profile information based on past eating history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past eating history data into a generating AI and have the generating AI select the optimal data collection method.

[0076] The data collection unit can filter profile information based on the user's current health status and lifestyle. For example, if a user provides the results of a health checkup, the data collection unit can filter dietary preferences and restrictions based on that information. The data collection unit can also ask appropriate dietary questions based on the user's lifestyle (e.g., exercise frequency and sleep patterns). If a user has a specific health goal, the data collection unit can collect profile information tailored to that goal. This enables the collection of appropriate profile information according to the user's health status and lifestyle. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's health checkup data into a generating AI and have the generating AI perform the filtering.

[0077] The data collection unit can estimate the user's emotions and determine the priority of profile information to collect based on the estimated emotions. For example, if the user is relaxed, the data collection unit may prioritize collecting detailed allergy information and dietary preferences. If the user is in a hurry, the data collection unit may also prioritize collecting basic profile information (family structure, gender, age, etc.). If the user is stressed, the data collection unit may start with simple questions and gradually collect more detailed information. This allows for the collection of profile information with priorities corresponding to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0078] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location when collecting profile information. For example, the data collection unit can ask relevant questions considering the food culture and availability of ingredients in the area where the user lives. For example, the data collection unit can also collect region-specific allergy information and dietary restrictions based on the user's geographical location. For example, if the user is traveling, the data collection unit can prioritize the collection of information about the food culture and ingredients of that region. This enables the collection of highly relevant profile information based on the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant information.

[0079] The data collection unit can analyze the user's social media activity and collect relevant information when collecting profile information. For example, the data collection unit can analyze photos and posts of meals shared by the user on social media to identify their food preferences. The data collection unit can also collect information about allergies and dietary restrictions from the user's social media activity. For example, the data collection unit can analyze accounts related to food and ingredients that the user follows and ask relevant questions. This makes it possible to collect relevant profile information based on the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media data into a generating AI and have the generating AI collect relevant information.

[0080] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results and explain the reasons for the meal suggestion. If the user is in a hurry, the analysis unit can also provide concise analysis results and make suggestions that can be implemented immediately. If the user is stressed, the analysis unit can also provide analysis results using positive language. This allows the analysis results to be presented in an appropriate way according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0081] The analysis unit can adjust the level of detail of the analysis based on the importance of the profile information during the analysis. For example, if allergy information is important, the analysis unit can perform a detailed analysis and make suggestions to avoid allergies. For example, if dietary preferences are important, the analysis unit can perform a detailed analysis and make suggestions for menus that suit those preferences. For example, if health status is important, the analysis unit can perform a detailed analysis and make suggestions for menus that take health into consideration. This enables detailed analysis according to the importance of the profile information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input profile information into a generating AI and have the generating AI perform adjustments to the level of detail of the analysis based on importance.

[0082] The analysis unit can apply different analysis algorithms depending on the category of profile information during analysis. For example, the analysis unit can apply a specific algorithm to avoid allergens to allergy information. For example, the analysis unit can apply an algorithm to suggest dishes that suit the user's preferences to food preferences. For example, the analysis unit can apply an algorithm to suggest menus that consider nutritional balance to health status. This enables appropriate analysis according to the category of profile information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input profile information into a generating AI and have the generating AI execute the application of analysis algorithms according to the category.

[0083] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results and explain the reasons for the meal suggestion. If the user is in a hurry, the analysis unit can also provide concise analysis results and make suggestions that can be implemented immediately. If the user is stressed, the analysis unit can also provide analysis results using positive language. This allows the analysis results to be provided at an appropriate length according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not using AI. For example, the analysis unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0084] The analysis unit can determine the priority of analysis based on when the profile information was collected during the analysis. For example, the analysis unit may prioritize the analysis of recently collected information to reflect the latest dietary preferences and allergy information. The analysis unit may also prioritize the analysis of information that is updated regularly (e.g., health status). The analysis unit may also postpone the analysis of information that does not change over a long period (e.g., family structure). This enables analysis with priorities based on the collection period. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input profile information into a generating AI and have the generating AI perform the determination of analysis priorities based on the collection period.

[0085] The analysis unit can adjust the order of analysis based on the relevance of profile information during the analysis process. For example, if allergy information is highly relevant, the analysis unit can perform the analysis first and make suggestions to avoid allergies. For example, if dietary preferences are highly relevant, the analysis unit can perform the analysis next and suggest menus that suit those preferences. For example, if health status is highly relevant, the analysis unit can perform the analysis last and suggest menus that take health into consideration. This enables analysis in an order based on relevance. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input profile information into a generating AI and have the generating AI perform the adjustment of the analysis order based on relevance.

[0086] The suggestion unit can estimate the user's emotions and adjust the way it expresses its suggestions based on those emotions. For example, if the user is relaxed, the suggestion unit can explain the detailed reasons for the suggestion and suggest a meal. If the user is in a hurry, the suggestion unit can offer a concise suggestion and propose a menu that can be implemented immediately. If the user is stressed, the suggestion unit can use positive language in its suggestions. This allows for suggestions to be expressed in a way that is appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0087] The suggestion unit can adjust the level of detail in its suggestions based on the importance of the menu. For example, if healthy eating is important, the suggestion unit can provide detailed nutritional information and make suggestions. For example, if variety in meals is important, the suggestion unit can also suggest different culinary genres. For example, if allergy information is important, the suggestion unit can also provide detailed suggestions to avoid allergens. This enables detailed suggestions tailored to the importance of the menu. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input menu information into a generating AI and have the generating AI adjust the level of detail in its suggestions based on importance.

[0088] The suggestion unit can apply different suggestion algorithms depending on the menu category when making suggestions. For example, for healthy meals, the suggestion unit can apply a suggestion algorithm that takes nutritional balance into consideration. For varied meals, the suggestion unit can also apply an algorithm that suggests different culinary genres. For allergy information, the suggestion unit can also apply a suggestion algorithm that avoids allergens. This enables appropriate suggestions according to the menu category. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input menu information into a generating AI and have the generating AI execute the application of a suggestion algorithm according to the category.

[0089] The suggestion unit can estimate the user's emotions and adjust the length of the suggestion based on the estimated emotions. For example, if the user is relaxed, the suggestion unit can explain the detailed reasons for the suggestion and suggest a meal. If the user is in a hurry, the suggestion unit can also make a concise suggestion and propose a menu that can be implemented immediately. If the user is stressed, the suggestion unit can also make a suggestion using positive language. This allows for suggestions of an appropriate length according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0090] The proposal department can determine the priority of proposals based on the timing of menu submission. For example, the proposal department may prioritize proposals related to the most recent meal. The proposal department may also prioritize proposals for menus that are updated regularly (e.g., weekly menus). The proposal department may also postpone proposals for menus that remain unchanged for a long period (e.g., menus for specific events). This allows for proposals to be made with a priority based on the submission timing. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input menu information into a generating AI and have the generating AI determine the priority of proposals based on the submission timing.

[0091] The suggestion unit can adjust the order of suggestions based on the relevance of the menu items. For example, if healthy eating is highly relevant, the suggestion unit will suggest it first. If a variety of meals is highly relevant, the suggestion unit may suggest it next. If allergy information is highly relevant, the suggestion unit may suggest it last. This allows for suggestions to be made in an order based on relevance. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input menu information into a generating AI and have the generating AI adjust the order of suggestions based on relevance.

[0092] The recipe presentation unit can estimate the user's emotions and adjust how the recipe is displayed based on the estimated emotions. For example, if the user is relaxed, the recipe presentation unit may display a detailed recipe and carefully explain the cooking steps. If the user is in a hurry, for example, the recipe presentation unit may display a concise recipe and provide steps for quick cooking. If the user is stressed, for example, the recipe presentation unit may display the recipe using positive language. This allows the recipe to be presented in an appropriate way according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recipe presentation unit may be performed using AI or not. For example, the recipe presentation unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0093] The recipe presentation unit can select the most suitable recipe by referring to the user's past cooking history when presenting a recipe. For example, the recipe presentation unit can suggest relevant recipes based on dishes the user has previously enjoyed making. For example, the recipe presentation unit can identify frequently used ingredients from the user's past cooking history and select a recipe based on that. For example, the recipe presentation unit can analyze the user's past cooking history and select a recipe that takes into account allergy information and dietary restrictions. This allows the system to provide the most suitable recipe based on the user's past cooking history. Some or all of the above-described processes in the recipe presentation unit may be performed using AI, for example, or without AI. For example, the recipe presentation unit can input the user's cooking history data into a generating AI and have the generating AI select the most suitable recipe.

[0094] The recipe presentation unit can adjust the difficulty level of a recipe based on the user's current cooking skill level. For example, if the user is a beginner, the recipe presentation unit may present a simple recipe with basic cooking steps. If the user is an intermediate cook, the recipe presentation unit may present a slightly more difficult recipe to support the improvement of their cooking skills. If the user is an advanced cook, the recipe presentation unit may present a complex and challenging recipe to provide the enjoyment of cooking. This ensures that recipes of appropriate difficulty levels are provided according to the user's cooking skill level. Some or all of the above processing in the recipe presentation unit may be performed using AI, for example, or without AI. For example, the recipe presentation unit can input the user's cooking skill information into a generating AI and have the generating AI perform the difficulty level adjustment.

[0095] The recipe presentation unit can estimate the user's emotions and determine the priority of recipes based on the estimated emotions. For example, if the user is relaxed, the recipe presentation unit may prioritize displaying detailed recipes. For example, if the user is in a hurry, the recipe presentation unit may prioritize displaying concise recipes. For example, if the user is stressed, the recipe presentation unit may prioritize displaying recipes that use positive language. This allows recipes to be provided with priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recipe presentation unit may be performed using AI, or not using AI. For example, the recipe presentation unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0096] The recipe presentation unit can select the most suitable recipe by considering the user's geographical location when presenting recipes. For example, the recipe presentation unit can suggest relevant recipes by considering the food culture and availability of ingredients in the area where the user lives. For example, the recipe presentation unit can also select recipes that use ingredients specific to a particular region based on the user's geographical location. For example, if the user is traveling, the recipe presentation unit can prioritize suggesting recipes related to the food culture and ingredients of that region. This allows for the provision of the most suitable recipe based on geographical location information. Some or all of the above processing in the recipe presentation unit may be performed using AI, for example, or without AI. For example, the recipe presentation unit can input the user's geographical location information into a generating AI and have the generating AI select the most suitable recipe.

[0097] The recipe presentation unit can analyze the user's social media activity and suggest relevant recipes when presenting recipes. For example, the recipe presentation unit can analyze photos and posts of dishes shared by the user on social media and suggest relevant recipes. For example, the recipe presentation unit can identify the user's preferred cooking genre from their social media activity and suggest recipes based on that. For example, the recipe presentation unit can analyze accounts related to cooking and ingredients that the user follows and suggest relevant recipes. This allows the system to provide relevant recipes based on social media activity. Some or all of the above processing in the recipe presentation unit may be performed using AI, for example, or without AI. For example, the recipe presentation unit can input the user's social media data into a generating AI and have the generating AI perform the task of suggesting relevant recipes.

[0098] The list creation unit can estimate the user's emotions and adjust how it creates the shopping list based on the estimated emotions. For example, if the user is relaxed, the list creation unit can create a detailed shopping list and carefully explain how to choose ingredients. If the user is in a hurry, the list creation unit can also create a concise shopping list to allow for quick shopping. If the user is stressed, the list creation unit can also create a shopping list using positive language. This allows for the creation of a shopping list in an appropriate manner according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the list creation unit may be performed using AI or not. For example, the list creation unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0099] The list creation unit can create an optimal shopping list by analyzing information from online flyers during list creation. For example, the list creation unit can analyze price information from online flyers and add the cheapest ingredients to the list. The list creation unit can also analyze freshness information from online flyers and add the freshest ingredients to the list. The list creation unit can also analyze sale information from online flyers and add sale items to the list. This allows for the creation of an optimal shopping list based on information from online flyers. Some or all of the above processing in the list creation unit may be performed using AI, for example, or without AI. For example, the list creation unit can input information from online flyers into a generating AI and have the generating AI create an optimal shopping list.

[0100] The list creation unit can improve the accuracy of the list by referring to the user's past shopping history when creating the list. For example, the list creation unit can add related ingredients to the list based on ingredients the user has purchased in the past. The list creation unit can also identify frequently purchased ingredients from the user's past shopping history and create a list based on that. The list creation unit can also analyze the user's past shopping history and create a list that takes into account allergy information and dietary restrictions. This makes it possible to create a highly accurate shopping list based on past shopping history. Some or all of the above processes in the list creation unit may be performed using AI, for example, or without AI. For example, the list creation unit can input the user's shopping history data into a generating AI and have the generating AI perform list accuracy improvements.

[0101] The list creation unit can estimate the user's emotions and determine the priority of the shopping list based on the estimated emotions. For example, if the user is relaxed, the list creation unit may prioritize creating a detailed shopping list. For example, if the user is in a hurry, the list creation unit may prioritize creating a concise shopping list. For example, if the user is stressed, the list creation unit may prioritize creating a shopping list using positive language. This allows for the creation of a shopping list with priorities that correspond to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the list creation unit may be performed using AI or not using AI. For example, the list creation unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0102] The list creation unit can create an optimal shopping list by considering the user's geographical location information when creating a list. For example, the list creation unit can add relevant ingredients to the list by considering the availability of ingredients in the area where the user lives. The list creation unit can also add region-specific ingredients to the list based on the user's geographical location information. For example, if the user is traveling, the list creation unit can prioritize adding information about ingredients in that region to the list. This makes it possible to create an optimal shopping list based on geographical location information. Some or all of the above processing in the list creation unit may be performed using AI, for example, or without AI. For example, the list creation unit can input the user's geographical location information into a generating AI and have the generating AI create an optimal shopping list.

[0103] The list creation unit can analyze the user's social media activity and create a relevant shopping list when creating a list. For example, the list creation unit can analyze photos and posts of dishes shared by the user on social media and add relevant ingredients to the list. The list creation unit can also identify the user's preferred ingredients from their social media activity and create a list based on that. For example, the list creation unit can analyze accounts related to dishes and ingredients that the user follows and add relevant ingredients to the list. This allows for the creation of relevant shopping lists based on social media activity. Some or all of the above processes in the list creation unit may be performed using AI, for example, or without AI. For example, the list creation unit can input the user's social media data into a generating AI and have the generating AI create a relevant shopping list.

[0104] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0105] The generative AI service can suggest substitutes for specific ingredients based on the user's dietary preferences and allergy information. For example, if a user has a dairy allergy, the generative AI will suggest recipes that do not use dairy products. It can also provide alternative recipes that do not use ingredients that the user dislikes. Furthermore, the generative AI can suggest substitutes for specific ingredients based on the user's dietary preferences and allergy information. This allows users to enjoy meals tailored to their preferences and allergies.

[0106] The generative AI service can estimate a user's emotions and suggest meals based on those emotions. For example, if a user is feeling stressed, it can suggest recipes using ingredients that have a relaxing effect. If a user is tired, it can suggest nutritious meals to replenish energy. Furthermore, if a user is experiencing a specific emotion, it can suggest meals tailored to that emotion. This allows users to enjoy meals that match their emotional state.

[0107] The generative AI service can analyze the nutritional value of specific ingredients based on the user's dietary preferences and allergy information, and suggest the optimal meal. For example, if a user wants to consume a specific nutrient, it can suggest recipes using ingredients rich in that nutrient. Conversely, if a user wants to avoid a specific nutrient, it can provide recipes using ingredients that do not contain that nutrient. Furthermore, the generative AI can suggest substitutes for ingredients containing specific nutrients based on the user's dietary preferences and allergy information. This allows users to enjoy meals tailored to their nutritional needs.

[0108] The generative AI service can estimate a user's emotions and suggest meals based on those emotions. For example, if the user is relaxed, it can suggest recipes using ingredients that have a relaxing effect. If the user is in a hurry, it can suggest recipes that can be prepared quickly. Furthermore, if the user is experiencing a specific emotion, it can suggest meals that match that emotion. This allows users to enjoy meals that are tailored to their mood.

[0109] The generative AI service can suggest storage methods for specific ingredients based on the user's dietary preferences and allergy information. For example, if a user wants to store a particular ingredient for a long period, it can suggest the optimal storage method. It can also provide storage methods if the user wants to keep a particular ingredient fresh. Furthermore, the generative AI can suggest storage methods for specific ingredients based on the user's dietary preferences and allergy information. This allows users to learn about food storage methods tailored to their preferences and allergies.

[0110] The generative AI service can estimate a user's emotions and suggest meals based on those emotions. For example, if a user is feeling stressed, it can suggest recipes using ingredients that have a relaxing effect. If a user is tired, it can suggest nutritious meals to replenish energy. Furthermore, if a user is experiencing a specific emotion, it can suggest meals tailored to that emotion. This allows users to enjoy meals that match their emotional state.

[0111] The generative AI service can suggest cooking methods for specific ingredients based on the user's dietary preferences and allergy information. For example, if a user wants to use a particular ingredient, it can suggest the best cooking method for that ingredient. It can also provide alternative recipes that do not use a particular ingredient if the user wants to avoid it. Furthermore, the generative AI can suggest cooking methods for specific ingredients based on the user's dietary preferences and allergy information. This allows users to learn about cooking methods that suit their preferences and allergies.

[0112] The generative AI service can estimate a user's emotions and suggest meals based on those emotions. For example, if the user is relaxed, it can suggest recipes using ingredients that have a relaxing effect. If the user is in a hurry, it can suggest recipes that can be prepared quickly. Furthermore, if the user is experiencing a specific emotion, it can suggest meals that match that emotion. This allows users to enjoy meals that are tailored to their mood.

[0113] The generative AI service can analyze the nutritional value of specific ingredients based on the user's dietary preferences and allergy information, and suggest the optimal meal. For example, if a user wants to consume a specific nutrient, it can suggest recipes using ingredients rich in that nutrient. Conversely, if a user wants to avoid a specific nutrient, it can provide recipes using ingredients that do not contain that nutrient. Furthermore, the generative AI can suggest substitutes for ingredients containing specific nutrients based on the user's dietary preferences and allergy information. This allows users to enjoy meals tailored to their nutritional needs.

[0114] The generative AI service can estimate a user's emotions and suggest meals based on those emotions. For example, if a user is feeling stressed, it can suggest recipes using ingredients that have a relaxing effect. If a user is tired, it can suggest nutritious meals to replenish energy. Furthermore, if a user is experiencing a specific emotion, it can suggest meals tailored to that emotion. This allows users to enjoy meals that match their emotional state.

[0115] The following briefly describes the processing flow for example form 2.

[0116] Step 1: The data collection unit collects profile information. This profile information includes family structure, gender, age, dietary preferences, allergy information, past dietary history, and health status. The data collection unit stores the information entered by the user in a database. Step 2: The analysis unit analyzes the information collected by the collection unit. Data mining techniques and statistical analysis methods are used in the analysis to provide appropriate dietary suggestions based on the user's dietary preferences, allergy information, health status, and lifestyle. Step 3: The proposal unit proposes a menu based on the information analyzed by the analysis unit. The proposal takes into account nutritional balance, calorie restrictions, and types of ingredients, and a menu tailored to the user's preferences is suggested. Step 4: The recipe presentation unit presents recipes based on the menu proposed by the suggestion unit. The recipes include cooking procedures, necessary ingredients, and cooking time, and are tailored to the user's cooking skills. Step 5: The list creation unit creates a shopping list based on the recipe provided by the recipe presentation unit. The shopping list includes the types and quantities of ingredients, where to buy them, etc., and the cheapest ingredients and seasonings are added to the list based on information from online flyers.

[0117] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0118] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0119] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0120] Each of the multiple elements described above, including the data collection unit, analysis unit, suggestion unit, recipe presentation unit, and list creation unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the data collection unit is implemented by the control unit 46A of the smart device 14 and stores the user's entered profile information in the database 24. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the collected information. The suggestion unit is implemented by the specific processing unit 290 of the data processing device 12 and suggests a menu based on the analysis results. The recipe presentation unit is implemented by the control unit 46A of the smart device 14 and presents a recipe based on the suggested menu. The list creation unit is implemented by the specific processing unit 290 of the data processing device 12 and creates a shopping list based on information from online flyers. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0121] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0122] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

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

[0124] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0125] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the 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 image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0127] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0128] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0129] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0130] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0131] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0132] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0133] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 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 a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0135] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0136] Each of the multiple elements described above, including the data collection unit, analysis unit, suggestion unit, recipe presentation unit, and list creation unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the smart glasses 214 and stores the user's entered profile information in the database 24. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected information. The suggestion unit is implemented by the specific processing unit 290 of the data processing unit 12 and suggests a menu based on the analysis results. The recipe presentation unit is implemented by the control unit 46A of the smart glasses 214 and presents a recipe based on the suggested menu. The list creation unit is implemented by the specific processing unit 290 of the data processing unit 12 and creates a shopping list based on information from online flyers. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0137] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0138] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0139] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0140] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0141] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0143] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0144] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0145] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0146] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0147] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0148] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0149] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0150] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0151] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0152] Each of the multiple elements described above, including the data collection unit, analysis unit, suggestion unit, recipe presentation unit, and list creation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the headset terminal 314 and stores the user's entered profile information in the database 24. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected information. The suggestion unit is implemented by the specific processing unit 290 of the data processing unit 12 and suggests a menu based on the analysis results. The recipe presentation unit is implemented by the control unit 46A of the headset terminal 314 and presents a recipe based on the suggested menu. The list creation unit is implemented by the specific processing unit 290 of the data processing unit 12 and creates a shopping list based on information from online flyers. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0153] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0154] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0155] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0156] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0157] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0159] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0160] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0161] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0162] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0163] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0164] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0165] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0166] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0167] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0168] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0169] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, recipe presentation unit, and list creation unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the robot 414 and stores user-entered profile information in the database 24. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the collected information. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes a menu based on the analysis results. The recipe presentation unit is implemented by, for example, the control unit 46A of the robot 414 and presents a recipe based on the proposed menu. The list creation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and creates a shopping list based on information from online flyers. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0170] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0171] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0172] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0173] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0174] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0175] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0176] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0177] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0178] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0179] 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.

[0180] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0181] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0182] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0183] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0184] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0185] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0186] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0187] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0188] (Note 1) A collection department that collects profile information, An analysis unit analyzes the information collected by the aforementioned collection unit, A proposal unit that proposes a menu based on the information analyzed by the aforementioned analysis unit, A recipe presentation unit presents recipes based on the menu proposed by the aforementioned proposal unit, The system includes a list creation unit that creates a shopping list based on the recipe presented by the recipe presentation unit. A system characterized by the following features. (Note 2) The aforementioned list creation unit, Create a shopping list based on information from online flyers. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned list creation unit, Add ingredients and seasonings that match the user's preferences to the list. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of profile information collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is Analyze the user's past meal history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is When collecting profile information, filtering is performed based on the user's current health status and lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is It estimates the user's emotions and determines the priority of profile information to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting profile information, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting profile information, we analyze the user's social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, During analysis, the level of detail of the analysis is adjusted based on the importance of the profile information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of profile information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the analysis priority is determined based on when the profile information was collected. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the profile information. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of each menu item. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, When making suggestions, different suggestion algorithms are applied depending on the menu category. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When submitting proposals, prioritize them based on when the menus are submitted. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making suggestions, adjust the order of suggestions based on the relevance of the menu items. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned recipe display unit is, It estimates the user's emotions and adjusts how recipes are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned recipe display unit is, When presenting a recipe, the system selects the most suitable recipe by referring to the user's past cooking history. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned recipe display unit is, When presenting a recipe, adjust the difficulty level based on the user's current cooking skill. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned recipe display unit is, It estimates the user's emotions and prioritizes recipes based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned recipe display unit is, When presenting recipes, the system selects the most suitable recipe by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned recipe display unit is, When presenting recipes, the system analyzes the user's social media activity to suggest relevant recipes. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned list creation unit, It estimates the user's emotions and adjusts how shopping lists are created based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned list creation unit, When creating a list, the system analyzes information from online flyers to create the optimal shopping list. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned list creation unit, When creating lists, we improve the accuracy of the lists by referencing the user's past purchase history. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned list creation unit, It estimates the user's emotions and prioritizes items on the shopping list based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned list creation unit, When creating a list, the system takes the user's geographical location into consideration to create the optimal shopping list. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned list creation unit, When creating a list, the system analyzes the user's social media activity to generate relevant shopping lists. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0189] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection department that collects profile information, An analysis unit analyzes the information collected by the aforementioned collection unit, A proposal unit that proposes a menu based on the information analyzed by the aforementioned analysis unit, A recipe presentation unit presents recipes based on the menu proposed by the aforementioned proposal unit, The system includes a list creation unit that creates a shopping list based on the recipe presented by the recipe presentation unit. A system characterized by the following features.

2. The aforementioned list creation unit, Create a shopping list based on information from online flyers. The system according to feature 1.

3. The aforementioned list creation unit, Add ingredients and seasonings that match the user's preferences to the list. The system according to feature 1.

4. The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of profile information collection based on those estimated emotions. The system according to feature 1.

5. The aforementioned collection unit is Analyze the user's past meal history and select the optimal data collection method. The system according to feature 1.

6. The aforementioned collection unit is When collecting profile information, filtering is performed based on the user's current health status and lifestyle. The system according to feature 1.

7. The aforementioned collection unit is It estimates the user's emotions and determines the priority of profile information to collect based on the estimated user emotions. The system according to feature 1.

8. The aforementioned collection unit is When collecting profile information, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system according to feature 1.

9. The aforementioned collection unit is When collecting profile information, we analyze the user's social media activity and collect relevant information. The system according to feature 1.

10. The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system according to feature 1.

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