system

Abstract

The system according to this embodiment aims to suggest the most suitable menu and recipes for each individual user based on their basic information. [Solution] The system according to the embodiment comprises a registration unit, a generation unit, a suggestion unit, and a list creation unit. The registration unit registers the user's basic information. The generation unit generates a profile based on the information registered by the registration unit. The suggestion unit proposes menus and recipes based on the profile generated by the generation unit. The list creation unit creates a shopping list based on online flyer information.

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 carried out to individually propose an optimal menu or recipe based on the basic information of the user, and there is room for improvement.

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

Means for Solving the Problems

[0006] The system according to this embodiment comprises a registration unit, a generation unit, a suggestion unit, and a list creation unit. The registration unit registers the user's basic information. The generation unit generates a profile based on the information registered by the registration unit. The suggestion unit proposes menus and recipes based on the profile generated by the generation unit. The list creation unit creates a shopping list based on online flyer information. [Effects of the Invention]

[0007] The system according to this embodiment can suggest individually optimized menus and recipes based on the user's basic 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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), 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.

[0017] 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] 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 dining table support system according to an embodiment of the present invention is a system that supports the user's dining table using a generative AI. When the user uses the dining table support system for the first time, they register basic information such as their profile (family structure, gender, age, etc.), food preferences, and allergy information. Based on this information, the generative AI proposes healthy and varied menus and recipes. It also creates a shopping list based on information from online flyers, helping the user efficiently gather ingredients and seasonings that meet their desired preferences (prioritizing low prices, prioritizing freshness, etc.). Specifically, the user registers basic information, and the generative AI generates a profile. The generative AI proposes "menus" and "recipes" that match the user's preferences and also presents a "shopping list" utilizing information from online flyers. The user can customize the menu and shopping list through dialogue with the generative AI, and the generative AI uses the feedback after use to improve the quality of suggestions for the next time. In this way, the dining table support system can efficiently support the user's dining table and provide healthy and varied meals.

[0029] The dining table support system according to this embodiment comprises a registration unit, a generation unit, a suggestion unit, and a list creation unit. The registration unit registers the user's basic information. The user's basic information includes, but is not limited to, name, age, gender, and allergy information. The registration unit stores the information entered by the user in a database, for example. The registration unit can also update the user's basic information. The generation unit generates a profile based on the information registered by the registration unit. The profile includes, but is not limited to, the user's preferences, eating history, and health status, for example. The generation unit generates the profile using a generation AI. The generation AI generates the profile using, for example, an AI model that takes the user's basic information as input and outputs a profile. The suggestion unit proposes menus and recipes based on the profile generated by the generation unit. The menus and recipes include, for example, nutritional balance, cooking time, and types of ingredients, for example. The suggestion unit proposes menus and recipes using a generation AI. The generation AI makes suggestions using, for example, an AI model that takes the profile as input and outputs menus and recipes. The list creation unit creates a shopping list based on online flyer information. Online flyer information includes, but is not limited to, store sale information and discount coupons. The list creation unit uses AI to create the shopping list. The AI ​​creates the list using an AI model that takes online flyer information as input and outputs a shopping list. As a result, the dining support system according to this embodiment can generate a profile based on the user's basic information and efficiently suggest menus and recipes and create shopping lists.

[0030] The registration unit registers the user's basic information. This basic information includes, but is not limited to, name, age, gender, and allergy information. The registration unit stores the information entered by the user in a database. Specifically, information entered by the user through a dedicated application or website is stored in a secure database. This protects the user's privacy and ensures the accuracy of the information. The registration unit can also update the user's basic information. For example, if a user discovers a new allergy or their health condition changes, they can easily update their information. The updated information is immediately reflected in the database, and the latest information is used when other departments utilize it. Furthermore, the registration unit has a reminder function that periodically checks the user's basic information and prompts updates as needed. This ensures that the information is always up-to-date, improving the accuracy and reliability of the entire system.

[0031] The generation unit generates profiles based on information registered by the registration unit. These profiles include, but are not limited to, user preferences, eating history, and health status. The generation unit uses a generation AI to generate profiles. For example, the generation AI uses an AI model that takes basic user information as input and outputs a profile. Specifically, the generation AI analyzes the user's past eating history and preferences to create a profile that suggests the optimal meal plan based on their health status. For example, it considers dishes the user has enjoyed eating in the past, ingredients they avoid, and allergy information to generate a profile optimized for each individual user. The generation AI can automatically update profiles in response to changes in the user's preferences and health status using machine learning algorithms. This allows for flexible responses tailored to user needs. Furthermore, the generation unit can collect user feedback and continuously improve the accuracy of the profiles. For example, by having users evaluate suggested menus and recipes, the generation AI learns from these evaluations and incorporates them into future suggestions. This allows the generation unit to provide users with more satisfying profiles.

[0032] The suggestion unit proposes menus and recipes based on profiles generated by the generation unit. These menus and recipes may include, but are not limited to, nutritional balance, cooking time, and types of ingredients. The suggestion unit uses generative AI to propose menus and recipes. For example, the generative AI uses an AI model that takes a profile as input and outputs menus and recipes. Specifically, the generative AI considers the user's health condition and preferences to propose nutritionally balanced menus. For instance, if a user is on a diet, it will suggest low-calorie, highly nutritious recipes; for busy users, it will suggest recipes that can be prepared quickly. The suggestion unit can also propose menus that take seasonal ingredients into consideration. This ensures that users can always enjoy fresh and delicious meals. Furthermore, the suggestion unit can collect user feedback and continuously improve the accuracy of its suggestions. For example, users can evaluate the results of actually making the suggested recipes, and the generative AI learns from this evaluation. This allows the suggestion unit to provide users with more satisfying menus and recipes.

[0033] The list creation unit creates shopping lists based on online flyer information. Online flyer information includes, but is not limited to, store sale information and discount coupons. The list creation unit uses AI to create shopping lists. For example, the AI ​​uses an AI model that takes online flyer information as input and outputs a shopping list to create the list. Specifically, the AI ​​lists the ingredients needed for the menu or recipe suggested by the user and provides the best place to buy them and sale information based on the online flyer information. For example, it suggests the store where you can buy ingredients at the best price based on sale information at nearby supermarkets. In addition, the list creation unit can take into account the user's past purchase history and automatically add necessary ingredients to the list. This prevents users from forgetting things and allows them to shop efficiently. Furthermore, the list creation unit can collect user feedback and continuously improve the accuracy of list creation. For example, it can reflect information on ingredients that the user actually purchased and coupons used in future list creation. In this way, the list creation unit can provide users with more convenient and efficient shopping lists.

[0034] The suggestion function can propose menus and recipes tailored to the user's preferences. For example, the suggestion function makes suggestions based on information such as the user's favorite and disliked ingredients and dietary preferences. The suggestion function uses generative AI to propose menus and recipes that match the user's preferences. For example, the generative AI uses an AI model that takes user preference information as input and outputs menus and recipes. This makes it possible to propose menus and recipes that are tailored to the user's preferences.

[0035] The list creation unit can create shopping lists based on online flyer information. For example, the list creation unit includes necessary ingredients and seasonings in the list based on online flyer information such as store sale information and discount coupons. The list creation unit uses AI to analyze the online flyer information and create the shopping list. For example, the AI ​​uses an AI model that takes online flyer information as input and outputs a shopping list to create the list. This makes it possible to create efficient shopping lists using online flyer information.

[0036] The suggestion function can customize menus and shopping lists through dialogue with the user. For example, the suggestion function interacts with the user using a chatbot or voice assistant, adjusting menus and shopping lists according to the user's requests. The suggestion function uses generative AI to engage in dialogue and customize suggestions based on the user's needs. For example, the generative AI uses an AI model that takes the content of the user dialogue as input and outputs customized menus and shopping lists. This allows for more personalized suggestions through dialogue with the user.

[0037] The generation unit can generate profiles using learning information from users with similar family structures and living areas. For example, the generation unit generates profiles based on the past usage history of users with similar family structures and living areas, as well as data from other users. The generation unit analyzes the learning information using a generation AI and generates profiles. The generation AI generates profiles using, for example, an AI model that takes learning information as input and outputs a profile. This makes it possible to generate more accurate profiles by utilizing the learning information of similar users.

[0038] The proposal department can use post-use feedback to improve the quality of future proposals. For example, the proposal department adjusts future proposals based on feedback information such as user ratings, comments, and usage history. The proposal department analyzes the feedback information using generative AI to improve the quality of proposals. For example, the generative AI uses an AI model that takes feedback information as input and outputs improved proposals. In this way, the quality of proposals is improved by utilizing post-use feedback.

[0039] The registration unit can analyze a user's past registration history and select the optimal registration method. For example, it can suggest the best registration method based on information the user has previously registered. Furthermore, the registration unit can prioritize the registration of frequently changed items based on the user's past registration history. In addition, the registration unit can analyze the user's past registration history and provide an auto-fill function to reduce the effort required for registration. This allows the system to select the optimal registration method by analyzing past registration history.

[0040] The registration unit can filter basic information based on the user's current health status and dietary restrictions. For example, if a user has allergies, the registration unit will prioritize registering allergy information. Furthermore, if a user has a specific health condition, the registration unit can register dietary information appropriate for that condition. Additionally, if a user has dietary restrictions, the registration unit can filter the registered information based on those restrictions. This allows for the registration of more appropriate information by filtering information based on health status and dietary restrictions.

[0041] The registration unit can prioritize registering highly relevant information by considering the user's geographical location when registering basic information. For example, if a user lives in a specific region, the registration unit will prioritize registering information related to that region. Furthermore, if a user is traveling, the registration unit can prioritize registering information related to their travel destination. Additionally, if a user is planning to move, the registration unit can prioritize registering information related to their new place of residence. This allows for the registration of highly relevant information by considering geographical location.

[0042] The registration unit can analyze a user's social media activity when they register their basic information and register relevant information. For example, the registration unit can register relevant information based on food information shared by the user on social media. It can also register relevant information based on information about accounts the user follows on social media. Furthermore, the registration unit can register relevant information based on information about groups the user participates in on social media. This makes it possible to register relevant information by analyzing social media activity.

[0043] The generation unit can adjust the level of detail in the profile generation based on the user's health goals. For example, if the user is aiming to lose weight, the generation unit will generate a profile that includes detailed information on calorie restriction. Similarly, if the user is aiming to increase muscle mass, the generation unit can generate a profile that includes detailed information on protein intake. Furthermore, if the user is aiming to maintain good health, the generation unit can generate a profile that includes detailed information on a balanced diet. This allows for the creation of more appropriate profiles by adjusting the level of detail based on health goals.

[0044] The generation unit can apply different generation algorithms depending on the user's dietary history when generating profiles. For example, the generation unit can generate an optimal profile based on data of meals the user has eaten in the past. It can also generate a profile that prioritizes the user's preferred ingredients and dishes based on their dietary history. Furthermore, the generation unit can analyze the user's dietary history and generate a profile tailored to their health condition. This allows for the generation of more appropriate profiles by applying different generation algorithms based on dietary history.

[0045] The generation unit can determine the priority of profile generation based on the user's lifestyle. For example, if the user is busy, the generation unit will prioritize generating a profile that includes easy-to-prepare recipes. If the user is health-conscious, the generation unit can also prioritize generating a profile that includes healthy ingredients. Furthermore, if the user eats with family, the generation unit can prioritize generating a profile that everyone in the family can enjoy. This allows for the generation of more appropriate profiles by prioritizing generation based on lifestyle.

[0046] The generation unit can improve the accuracy of profile generation by referring to relevant user data. For example, it can refer to the user's health data to generate an optimal profile. It can also refer to the user's dietary history to generate a profile that suits their preferences. Furthermore, it can refer to the user's allergy information to generate a profile that includes safe ingredients. In this way, the accuracy of generation is improved by referring to relevant data.

[0047] The suggestion department can adjust the level of detail in its suggestions based on the importance of the menu or recipe. For example, it can provide detailed suggestions for important menus and recipes, and concise suggestions for less important ones. Furthermore, it can provide detailed suggestions for menus and recipes that may affect the user's health. By adjusting the level of detail in suggestions based on the importance of the menu or recipe, it becomes possible to provide more appropriate suggestions.

[0048] The suggestion function can apply different suggestion algorithms depending on the menu or recipe category. For example, when suggesting desserts, it can apply an algorithm that considers sweetness and calories. Similarly, when suggesting main dishes, it can apply an algorithm that considers nutritional balance. Furthermore, when suggesting snacks, it can apply an algorithm that considers convenience and shelf life. By applying different suggestion algorithms depending on the menu or recipe category, more appropriate suggestions can be made.

[0049] The suggestion team can prioritize suggestions based on when menus and recipes are submitted. For example, the suggestion team might prioritize suggestions related to recent meals. It can also prioritize suggestions related to events the user is preparing for. Furthermore, if the user has specific health goals, the suggestion team can prioritize suggestions related to those goals. This allows for more appropriate suggestions by prioritizing suggestions based on the timing of menu and recipe submissions.

[0050] The suggestion function can adjust the order of suggestions based on the relationships between menus and recipes. For example, it can prioritize suggesting menus and recipes that match the user's preferences. It can also prioritize suggesting menus and recipes that suit the user's health condition. Furthermore, it can prioritize suggesting safe menus and recipes based on the user's allergy information. By adjusting the order of suggestions based on the relationships between menus and recipes, more appropriate suggestions can be made.

[0051] The list creation unit can generate an optimal shopping list by analyzing the user's past shopping history. For example, it can generate an optimal shopping list based on items the user has purchased in the past. Furthermore, the list creation unit can prioritize the inclusion of frequently purchased items based on the user's past shopping history. In addition, the list creation unit can analyze the user's past shopping history and generate a list designed to prevent forgotten items. This allows for the generation of an optimal shopping list by analyzing past shopping history.

[0052] The list creation function can customize the contents of a shopping list based on the user's current meal plan. For example, it can include necessary ingredients in the list based on the user's meal plan. It can also prioritize including items needed for specific recipes based on the user's meal plan. Furthermore, the list creation function can create an efficient shopping list that takes the user's meal plan into consideration. This allows for the creation of more appropriate shopping lists by customizing the list contents based on the current meal plan.

[0053] The list creation unit can generate an optimal shopping list by considering the user's geographical location. For example, if the user lives in a specific area, the list creation unit will generate a list based on store information in that area. Furthermore, if the user is traveling, the list creation unit can generate a list based on store information in their travel destination. Additionally, if the user is planning to move, the list creation unit can generate a list based on store information in their new place of residence. This allows for the generation of an optimal shopping list by considering geographical location.

[0054] The list creation function can analyze a user's social media activity when creating a shopping list and suggest items for that list. For example, it can include relevant items in the list based on food information the user has shared on social media. It can also include relevant items in the list based on information about accounts the user follows on social media. Furthermore, it can include relevant items in the list based on information about groups the user participates in on social media. This allows the system to suggest more appropriate list contents by analyzing social media activity.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The suggestion function can analyze a user's past eating history and propose optimal menus and recipes. For example, it can suggest similar menus and recipes based on dishes the user has enjoyed eating in the past. It can also suggest menus and recipes that do not include ingredients the user has avoided in the past. Furthermore, it can provide suggestions that take nutritional balance into consideration based on the user's past eating history. In this way, analyzing past eating history makes it possible to suggest more appropriate menus and recipes.

[0057] The list creation section can create shopping lists that take into account the user's current health condition. For example, if a user has a specific health condition, the list can include ingredients and seasonings suitable for that condition. If a user has allergies, the list can also include safe ingredients based on allergy information. Furthermore, if a user has specific dietary restrictions, the list can be adjusted based on those restrictions. This allows for the creation of more appropriate shopping lists by considering the user's current health condition.

[0058] The generation unit can adjust how profiles are generated based on the user's lifestyle. For example, if the user is busy, the generation unit can generate a profile that includes easy-to-prepare recipes. If the user is health-conscious, it can also generate a profile that includes healthy ingredients. Furthermore, if the user eats meals with family, it can generate a profile that includes recipes that the whole family can enjoy. By adjusting the profile generation method based on lifestyle, it becomes possible to generate more appropriate profiles.

[0059] The suggestion function can propose menus and recipes while considering the user's geographical location. For example, if a user lives in a specific region, it can suggest menus and recipes using ingredients from that region. If the user is traveling, it can suggest menus and recipes using ingredients from their travel destination. Furthermore, if the user is planning to move, it can suggest menus and recipes using ingredients from their new place of residence. This allows for more appropriate menu and recipe suggestions by considering geographical location.

[0060] The list creation section can analyze a user's social media activity and include relevant items in their shopping list. For example, it can include relevant ingredients and seasonings based on meal information shared by the user on social media. It can also include relevant items based on information about accounts the user follows. Furthermore, it can include relevant items based on information about groups the user belongs to. This allows for the creation of more appropriate shopping lists by analyzing social media activity.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The registration unit registers the user's basic information. This basic information includes name, age, gender, allergy information, etc. The registration unit saves the information entered by the user to the database and also updates the basic information. Step 2: The generation unit generates a profile based on the information registered by the registration unit. The profile includes the user's preferences, eating history, health status, etc. The generation unit uses a generation AI to generate the profile, and uses an AI model that takes the user's basic information as input and outputs the profile. Step 3: The suggestion unit proposes menus and recipes based on the profiles generated by the generation unit. These menus and recipes include nutritional balance, cooking time, and types of ingredients. The suggestion unit uses an AI model that takes the profiles as input and outputs menus and recipes using a generation AI to make its suggestions. Step 4: The list creation unit creates a shopping list based on online flyer information. Online flyer information includes store sale information, discount coupons, etc. The list creation unit uses an AI model that takes online flyer information as input and outputs a shopping list to create the list.

[0063] (Example of form 2) The dining table support system according to an embodiment of the present invention is a system that supports the user's dining table using a generative AI. When the user uses the dining table support system for the first time, they register basic information such as their profile (family structure, gender, age, etc.), food preferences, and allergy information. Based on this information, the generative AI proposes healthy and varied menus and recipes. It also creates a shopping list based on information from online flyers, helping the user efficiently gather ingredients and seasonings that meet their desired preferences (prioritizing low prices, prioritizing freshness, etc.). Specifically, the user registers basic information, and the generative AI generates a profile. The generative AI proposes "menus" and "recipes" that match the user's preferences and also presents a "shopping list" utilizing information from online flyers. The user can customize the menu and shopping list through dialogue with the generative AI, and the generative AI uses the feedback after use to improve the quality of suggestions for the next time. In this way, the dining table support system can efficiently support the user's dining table and provide healthy and varied meals.

[0064] The dining table support system according to this embodiment comprises a registration unit, a generation unit, a suggestion unit, and a list creation unit. The registration unit registers the user's basic information. The user's basic information includes, but is not limited to, name, age, gender, and allergy information. The registration unit stores the information entered by the user in a database, for example. The registration unit can also update the user's basic information. The generation unit generates a profile based on the information registered by the registration unit. The profile includes, but is not limited to, the user's preferences, eating history, and health status, for example. The generation unit generates the profile using a generation AI. The generation AI generates the profile using, for example, an AI model that takes the user's basic information as input and outputs a profile. The suggestion unit proposes menus and recipes based on the profile generated by the generation unit. The menus and recipes include, for example, nutritional balance, cooking time, and types of ingredients, for example. The suggestion unit proposes menus and recipes using a generation AI. The generation AI makes suggestions using, for example, an AI model that takes the profile as input and outputs menus and recipes. The list creation unit creates a shopping list based on online flyer information. Online flyer information includes, but is not limited to, store sale information and discount coupons. The list creation unit uses AI to create the shopping list. The AI ​​creates the list using an AI model that takes online flyer information as input and outputs a shopping list. As a result, the dining support system according to this embodiment can generate a profile based on the user's basic information and efficiently suggest menus and recipes and create shopping lists.

[0065] The registration unit registers the user's basic information. This basic information includes, but is not limited to, name, age, gender, and allergy information. The registration unit stores the information entered by the user in a database. Specifically, information entered by the user through a dedicated application or website is stored in a secure database. This protects the user's privacy and ensures the accuracy of the information. The registration unit can also update the user's basic information. For example, if a user discovers a new allergy or their health condition changes, they can easily update their information. The updated information is immediately reflected in the database, and the latest information is used when other departments utilize it. Furthermore, the registration unit has a reminder function that periodically checks the user's basic information and prompts updates as needed. This ensures that the information is always up-to-date, improving the accuracy and reliability of the entire system.

[0066] The generation unit generates profiles based on information registered by the registration unit. These profiles include, but are not limited to, user preferences, eating history, and health status. The generation unit uses a generation AI to generate profiles. For example, the generation AI uses an AI model that takes basic user information as input and outputs a profile. Specifically, the generation AI analyzes the user's past eating history and preferences to create a profile that suggests the optimal meal plan based on their health status. For example, it considers dishes the user has enjoyed eating in the past, ingredients they avoid, and allergy information to generate a profile optimized for each individual user. The generation AI can automatically update profiles in response to changes in the user's preferences and health status using machine learning algorithms. This allows for flexible responses tailored to user needs. Furthermore, the generation unit can collect user feedback and continuously improve the accuracy of the profiles. For example, by having users evaluate suggested menus and recipes, the generation AI learns from these evaluations and incorporates them into future suggestions. This allows the generation unit to provide users with more satisfying profiles.

[0067] The suggestion unit proposes menus and recipes based on profiles generated by the generation unit. These menus and recipes may include, but are not limited to, nutritional balance, cooking time, and types of ingredients. The suggestion unit uses generative AI to propose menus and recipes. For example, the generative AI uses an AI model that takes a profile as input and outputs menus and recipes. Specifically, the generative AI considers the user's health condition and preferences to propose nutritionally balanced menus. For instance, if a user is on a diet, it will suggest low-calorie, highly nutritious recipes; for busy users, it will suggest recipes that can be prepared quickly. The suggestion unit can also propose menus that take seasonal ingredients into consideration. This ensures that users can always enjoy fresh and delicious meals. Furthermore, the suggestion unit can collect user feedback and continuously improve the accuracy of its suggestions. For example, users can evaluate the results of actually making the suggested recipes, and the generative AI learns from this evaluation. This allows the suggestion unit to provide users with more satisfying menus and recipes.

[0068] The list creation unit creates shopping lists based on online flyer information. Online flyer information includes, but is not limited to, store sale information and discount coupons. The list creation unit uses AI to create shopping lists. For example, the AI ​​uses an AI model that takes online flyer information as input and outputs a shopping list to create the list. Specifically, the AI ​​lists the ingredients needed for the menu or recipe suggested by the user and provides the best place to buy them and sale information based on the online flyer information. For example, it suggests the store where you can buy ingredients at the best price based on sale information at nearby supermarkets. In addition, the list creation unit can take into account the user's past purchase history and automatically add necessary ingredients to the list. This prevents users from forgetting things and allows them to shop efficiently. Furthermore, the list creation unit can collect user feedback and continuously improve the accuracy of list creation. For example, it can reflect information on ingredients that the user actually purchased and coupons used in future list creation. In this way, the list creation unit can provide users with more convenient and efficient shopping lists.

[0069] The suggestion function can propose menus and recipes tailored to the user's preferences. For example, the suggestion function makes suggestions based on information such as the user's favorite and disliked ingredients and dietary preferences. The suggestion function uses generative AI to propose menus and recipes that match the user's preferences. For example, the generative AI uses an AI model that takes user preference information as input and outputs menus and recipes. This makes it possible to propose menus and recipes that are tailored to the user's preferences.

[0070] The list creation unit can create shopping lists based on online flyer information. For example, the list creation unit includes necessary ingredients and seasonings in the list based on online flyer information such as store sale information and discount coupons. The list creation unit uses AI to analyze the online flyer information and create the shopping list. For example, the AI ​​uses an AI model that takes online flyer information as input and outputs a shopping list to create the list. This makes it possible to create efficient shopping lists using online flyer information.

[0071] The suggestion function can customize menus and shopping lists through dialogue with the user. For example, the suggestion function interacts with the user using a chatbot or voice assistant, adjusting menus and shopping lists according to the user's requests. The suggestion function uses generative AI to engage in dialogue and customize suggestions based on the user's needs. For example, the generative AI uses an AI model that takes the content of the user dialogue as input and outputs customized menus and shopping lists. This allows for more personalized suggestions through dialogue with the user.

[0072] The generation unit can generate profiles using learning information from users with similar family structures and living areas. For example, the generation unit generates profiles based on the past usage history of users with similar family structures and living areas, as well as data from other users. The generation unit analyzes the learning information using a generation AI and generates profiles. The generation AI generates profiles using, for example, an AI model that takes learning information as input and outputs a profile. This makes it possible to generate more accurate profiles by utilizing the learning information of similar users.

[0073] The proposal department can use post-use feedback to improve the quality of future proposals. For example, the proposal department adjusts future proposals based on feedback information such as user ratings, comments, and usage history. The proposal department analyzes the feedback information using generative AI to improve the quality of proposals. For example, the generative AI uses an AI model that takes feedback information as input and outputs improved proposals. In this way, the quality of proposals is improved by utilizing post-use feedback.

[0074] The registration unit can estimate the user's emotions and adjust the timing of basic information registration based on the estimated emotions. For example, if the user is stressed, the registration unit will prompt them to register basic information at a time when they can relax. Furthermore, if the user is busy, the registration unit can reduce the number of basic information input fields to allow for quicker registration. Additionally, if the user is relaxed, the registration unit can prompt them to register more detailed information. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows for registration at a more appropriate time by adjusting the timing of basic information registration according to the user's emotions.

[0075] The registration unit can analyze a user's past registration history and select the optimal registration method. For example, it can suggest the best registration method based on information the user has previously registered. Furthermore, the registration unit can prioritize the registration of frequently changed items based on the user's past registration history. In addition, the registration unit can analyze the user's past registration history and provide an auto-fill function to reduce the effort required for registration. This allows the system to select the optimal registration method by analyzing past registration history.

[0076] The registration unit can filter basic information based on the user's current health status and dietary restrictions. For example, if a user has allergies, the registration unit will prioritize registering allergy information. Furthermore, if a user has a specific health condition, the registration unit can register dietary information appropriate for that condition. Additionally, if a user has dietary restrictions, the registration unit can filter the registered information based on those restrictions. This allows for the registration of more appropriate information by filtering information based on health status and dietary restrictions.

[0077] The registration unit can estimate the user's emotions and determine the priority of basic information to register based on the estimated emotions. For example, if the user is stressed, the registration unit will prioritize registering only important information. If the user is relaxed, the registration unit can also prioritize registering detailed information. Furthermore, if the user is in a hurry, the registration unit can prioritize registering minimal information. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows for the registration of more appropriate information by prioritizing basic information according to the user's emotions.

[0078] The registration unit can prioritize registering highly relevant information by considering the user's geographical location when registering basic information. For example, if a user lives in a specific region, the registration unit will prioritize registering information related to that region. Furthermore, if a user is traveling, the registration unit can prioritize registering information related to their travel destination. Additionally, if a user is planning to move, the registration unit can prioritize registering information related to their new place of residence. This allows for the registration of highly relevant information by considering geographical location.

[0079] The registration unit can analyze a user's social media activity when they register their basic information and register relevant information. For example, the registration unit can register relevant information based on food information shared by the user on social media. It can also register relevant information based on information about accounts the user follows on social media. Furthermore, the registration unit can register relevant information based on information about groups the user participates in on social media. This makes it possible to register relevant information by analyzing social media activity.

[0080] The generation unit can estimate the user's emotions and adjust the profile generation method based on the estimated emotions. For example, if the user is relaxed, the generation unit will generate a detailed profile. If the user is in a hurry, the generation unit can also generate a simplified profile. Furthermore, if the user is stressed, the generation unit can generate a profile that includes information to help reduce stress. Emotion estimation is achieved using emotion estimation functions, 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. This allows for the generation of more appropriate profiles by adjusting the profile generation method according to the user's emotions.

[0081] The generation unit can adjust the level of detail in the profile generation based on the user's health goals. For example, if the user is aiming to lose weight, the generation unit will generate a profile that includes detailed information on calorie restriction. Similarly, if the user is aiming to increase muscle mass, the generation unit can generate a profile that includes detailed information on protein intake. Furthermore, if the user is aiming to maintain good health, the generation unit can generate a profile that includes detailed information on a balanced diet. This allows for the creation of more appropriate profiles by adjusting the level of detail based on health goals.

[0082] The generation unit can apply different generation algorithms depending on the user's dietary history when generating profiles. For example, the generation unit can generate an optimal profile based on data of meals the user has eaten in the past. It can also generate a profile that prioritizes the user's preferred ingredients and dishes based on their dietary history. Furthermore, the generation unit can analyze the user's dietary history and generate a profile tailored to their health condition. This allows for the generation of more appropriate profiles by applying different generation algorithms based on dietary history.

[0083] The generation unit can estimate the user's emotions and adjust the frequency of profile generation based on the estimated emotions. For example, if the user is relaxed, the generation unit will update the profile more frequently. Conversely, if the user is busy, the generation unit can reduce the frequency of profile updates. Furthermore, if the user is stressed, the generation unit can adjust the frequency of generating profiles that include information helpful for stress reduction. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows for more appropriate profile generation at the right time by adjusting the frequency of profile generation according to the user's emotions.

[0084] The generation unit can determine the priority of profile generation based on the user's lifestyle. For example, if the user is busy, the generation unit will prioritize generating a profile that includes easy-to-prepare recipes. If the user is health-conscious, the generation unit can also prioritize generating a profile that includes healthy ingredients. Furthermore, if the user eats with family, the generation unit can prioritize generating a profile that everyone in the family can enjoy. This allows for the generation of more appropriate profiles by prioritizing generation based on lifestyle.

[0085] The generation unit can improve the accuracy of profile generation by referring to relevant user data. For example, it can refer to the user's health data to generate an optimal profile. It can also refer to the user's dietary history to generate a profile that suits their preferences. Furthermore, it can refer to the user's allergy information to generate a profile that includes safe ingredients. In this way, the accuracy of generation is improved by referring to relevant data.

[0086] The suggestion function can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is relaxed, the suggestion function will provide detailed suggestions. If the user is in a hurry, it can provide concise suggestions. Furthermore, if the user is stressed, it can provide suggestions that help reduce stress. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows for more appropriate suggestions by adjusting the way they are presented according to the user's emotions.

[0087] The suggestion department can adjust the level of detail in its suggestions based on the importance of the menu or recipe. For example, it can provide detailed suggestions for important menus and recipes, and concise suggestions for less important ones. Furthermore, it can provide detailed suggestions for menus and recipes that may affect the user's health. By adjusting the level of detail in suggestions based on the importance of the menu or recipe, it becomes possible to provide more appropriate suggestions.

[0088] The suggestion function can apply different suggestion algorithms depending on the menu or recipe category. For example, when suggesting desserts, it can apply an algorithm that considers sweetness and calories. Similarly, when suggesting main dishes, it can apply an algorithm that considers nutritional balance. Furthermore, when suggesting snacks, it can apply an algorithm that considers convenience and shelf life. By applying different suggestion algorithms depending on the menu or recipe category, more appropriate suggestions can be made.

[0089] The suggestion function can estimate the user's emotions and adjust the length of its suggestions based on those emotions. For example, if the user is relaxed, the suggestion function will provide detailed suggestions. If the user is in a hurry, it can provide concise suggestions. Furthermore, if the user is stressed, it can provide suggestions that help reduce stress. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows for more appropriate suggestions by adjusting the length of suggestions according to the user's emotions.

[0090] The suggestion team can prioritize suggestions based on when menus and recipes are submitted. For example, the suggestion team might prioritize suggestions related to recent meals. It can also prioritize suggestions related to events the user is preparing for. Furthermore, if the user has specific health goals, the suggestion team can prioritize suggestions related to those goals. This allows for more appropriate suggestions by prioritizing suggestions based on the timing of menu and recipe submissions.

[0091] The suggestion function can adjust the order of suggestions based on the relationships between menus and recipes. For example, it can prioritize suggesting menus and recipes that match the user's preferences. It can also prioritize suggesting menus and recipes that suit the user's health condition. Furthermore, it can prioritize suggesting safe menus and recipes based on the user's allergy information. By adjusting the order of suggestions based on the relationships between menus and recipes, more appropriate suggestions can be made.

[0092] The list creation unit can estimate the user's emotions and adjust how the shopping list is created based on those emotions. For example, if the user is relaxed, the list creation unit can create a detailed shopping list. If the user is in a hurry, the list creation unit can create a minimal shopping list. Furthermore, if the user is stressed, the list creation unit can create a shopping list that includes items that help reduce stress. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows for the creation of more appropriate shopping lists by adjusting how the shopping list is created according to the user's emotions.

[0093] The list creation unit can generate an optimal shopping list by analyzing the user's past shopping history. For example, it can generate an optimal shopping list based on items the user has purchased in the past. Furthermore, the list creation unit can prioritize the inclusion of frequently purchased items based on the user's past shopping history. In addition, the list creation unit can analyze the user's past shopping history and generate a list designed to prevent forgotten items. This allows for the generation of an optimal shopping list by analyzing past shopping history.

[0094] The list creation function can customize the contents of a shopping list based on the user's current meal plan. For example, it can include necessary ingredients in the list based on the user's meal plan. It can also prioritize including items needed for specific recipes based on the user's meal plan. Furthermore, the list creation function can create an efficient shopping list that takes the user's meal plan into consideration. This allows for the creation of more appropriate shopping lists by customizing the list contents based on the current meal plan.

[0095] The list creation unit can estimate the user's emotions and determine the priority of the shopping list based on those emotions. For example, if the user is relaxed, the list creation unit can set detailed priorities. If the user is in a hurry, the list creation unit can also prioritize including only the bare minimum items on the list. Furthermore, if the user is stressed, the list creation unit can also prioritize including items that help reduce stress. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This makes it possible to create a more appropriate shopping list by determining the priority of the shopping list according to the user's emotions.

[0096] The list creation unit can generate an optimal shopping list by considering the user's geographical location. For example, if the user lives in a specific area, the list creation unit will generate a list based on store information in that area. Furthermore, if the user is traveling, the list creation unit can generate a list based on store information in their travel destination. Additionally, if the user is planning to move, the list creation unit can generate a list based on store information in their new place of residence. This allows for the generation of an optimal shopping list by considering geographical location.

[0097] The list creation function can analyze a user's social media activity when creating a shopping list and suggest items for that list. For example, it can include relevant items in the list based on food information the user has shared on social media. It can also include relevant items in the list based on information about accounts the user follows on social media. Furthermore, it can include relevant items in the list based on information about groups the user participates in on social media. This allows the system to suggest more appropriate list contents by analyzing social media activity.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] The suggestion function can estimate the user's emotions and adjust the timing of suggestions based on those emotions. For example, if the user is relaxed, the suggestion function can offer detailed menus and recipes. If the user is busy, the suggestion function can offer concise suggestions. Furthermore, if the user is stressed, the suggestion function can suggest menus and recipes that help reduce stress. By adjusting the timing of suggestions according to the user's emotions, more appropriate suggestions can be made.

[0100] The list creation unit can estimate the user's emotions and adjust the contents of the shopping list based on those emotions. For example, if the user is relaxed, the list creation unit can create a detailed shopping list. If the user is in a hurry, the list creation unit can create a shopping list containing only the essential items. Furthermore, if the user is stressed, the list creation unit can create a shopping list that includes items that help reduce stress. In this way, by adjusting the contents of the shopping list according to the user's emotions, it becomes possible to create a more appropriate shopping list.

[0101] The generation unit can estimate the user's emotions and adjust the profile generation method based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate a detailed profile. If the user is in a hurry, the generation unit can generate a simplified profile. Furthermore, if the user is stressed, the generation unit can generate a profile that includes information that helps reduce stress. This allows for the generation of more appropriate profiles by adjusting the profile generation method according to the user's emotions.

[0102] The suggestion function can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is relaxed, the suggestion function can offer detailed suggestions. If the user is in a hurry, it can offer concise suggestions. Furthermore, if the user is stressed, the suggestion function can offer suggestions that help reduce stress. By adjusting the way suggestions are presented according to the user's emotions, more appropriate suggestions can be made.

[0103] The list creation function can estimate the user's emotions and determine the priority of items on the shopping list based on those emotions. For example, if the user is relaxed, the list creation function can set detailed priorities. If the user is in a hurry, the list creation function can prioritize including only the bare minimum items on the list. Furthermore, if the user is stressed, the list creation function can prioritize including items that help reduce stress. This allows for the creation of more appropriate shopping lists by determining the priority of items on the list according to the user's emotions.

[0104] The suggestion function can analyze a user's past eating history and propose optimal menus and recipes. For example, it can suggest similar menus and recipes based on dishes the user has enjoyed eating in the past. It can also suggest menus and recipes that do not include ingredients the user has avoided in the past. Furthermore, it can provide suggestions that take nutritional balance into consideration based on the user's past eating history. In this way, analyzing past eating history makes it possible to suggest more appropriate menus and recipes.

[0105] The list creation section can create shopping lists that take into account the user's current health condition. For example, if a user has a specific health condition, the list can include ingredients and seasonings suitable for that condition. If a user has allergies, the list can also include safe ingredients based on allergy information. Furthermore, if a user has specific dietary restrictions, the list can be adjusted based on those restrictions. This allows for the creation of more appropriate shopping lists by considering the user's current health condition.

[0106] The generation unit can adjust how profiles are generated based on the user's lifestyle. For example, if the user is busy, the generation unit can generate a profile that includes easy-to-prepare recipes. If the user is health-conscious, it can also generate a profile that includes healthy ingredients. Furthermore, if the user eats meals with family, it can generate a profile that includes recipes that the whole family can enjoy. By adjusting the profile generation method based on lifestyle, it becomes possible to generate more appropriate profiles.

[0107] The suggestion function can propose menus and recipes while considering the user's geographical location. For example, if a user lives in a specific region, it can suggest menus and recipes using ingredients from that region. If the user is traveling, it can suggest menus and recipes using ingredients from their travel destination. Furthermore, if the user is planning to move, it can suggest menus and recipes using ingredients from their new place of residence. This allows for more appropriate menu and recipe suggestions by considering geographical location.

[0108] The list creation section can analyze a user's social media activity and include relevant items in their shopping list. For example, it can include relevant ingredients and seasonings based on meal information shared by the user on social media. It can also include relevant items based on information about accounts the user follows. Furthermore, it can include relevant items based on information about groups the user belongs to. This allows for the creation of more appropriate shopping lists by analyzing social media activity.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The registration unit registers the user's basic information. This basic information includes name, age, gender, allergy information, etc. The registration unit saves the information entered by the user to the database and also updates the basic information. Step 2: The generation unit generates a profile based on the information registered by the registration unit. The profile includes the user's preferences, eating history, health status, etc. The generation unit uses a generation AI to generate the profile, and uses an AI model that takes the user's basic information as input and outputs the profile. Step 3: The suggestion unit proposes menus and recipes based on the profiles generated by the generation unit. These menus and recipes include nutritional balance, cooking time, and types of ingredients. The suggestion unit uses an AI model that takes the profiles as input and outputs menus and recipes using a generation AI to make its suggestions. Step 4: The list creation unit creates a shopping list based on online flyer information. Online flyer information includes store sale information, discount coupons, etc. The list creation unit uses an AI model that takes online flyer information as input and outputs a shopping list to create the list.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] Each of the multiple elements described above, including the registration unit, generation unit, suggestion 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 registration unit is implemented by the control unit 46A of the smart device 14, which inputs the user's basic information and stores it in the database 24. The generation unit is implemented by the specific processing unit 290 of the data processing device 12, which generates a profile using generation AI. The suggestion unit is implemented by the specific processing unit 290 of the data processing device 12, which suggests menus and recipes using generation AI. The list creation unit is implemented by the control unit 46A of the smart device 14, which creates a shopping list based on online flyer information. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] Each of the multiple elements described above, including the registration unit, generation unit, suggestion unit, and list creation unit, is implemented in at least one of the smart glasses 214 and the data processing device 12. For example, the registration unit is implemented by the control unit 46A of the smart glasses 214, which inputs the user's basic information and stores it in the database 24. The generation unit is implemented by the specific processing unit 290 of the data processing device 12, which generates a profile using generation AI. The suggestion unit is implemented by the specific processing unit 290 of the data processing device 12, which suggests menus and recipes using generation AI. The list creation unit is implemented by the control unit 46A of the smart glasses 214, which creates a shopping list based on online flyer information. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] Each of the multiple elements described above, including the registration unit, generation unit, suggestion 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 registration unit is implemented by the control unit 46A of the headset terminal 314, which inputs the user's basic information and stores it in the database 24. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which generates a profile using generation AI. The suggestion unit is implemented by the specific processing unit 290 of the data processing unit 12, which suggests menus and recipes using generation AI. The list creation unit is implemented by the control unit 46A of the headset terminal 314, which creates a shopping list based on online flyer information. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] Each of the multiple elements described above, including the registration unit, generation unit, suggestion 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 registration unit is implemented by the control unit 46A of the robot 414, which inputs the user's basic information and stores it in the database 24. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which generates a profile using generation AI. The suggestion unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which suggests menus and recipes using generation AI. The list creation unit is implemented by, for example, the control unit 46A of the robot 414, which creates a shopping list based on online flyer information. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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."

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] (Note 1) A registration section for registering the user's basic information, A generation unit generates a profile based on the information registered by the registration unit, A suggestion unit that proposes menus and recipes based on the profile generated by the generation unit, It includes a list creation unit that creates a shopping list based on online flyer information. A system characterized by the following features. (Note 2) The aforementioned proposal section is, We suggest menus and recipes that match the user's preferences. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned list creation unit, Create a shopping list based on online flyer information. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, Customize menus and shopping lists through interaction with the user. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is A profile is generated by using learning information from users with similar family structures and living areas. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, We will use the feedback received after use to improve the quality of future proposals. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned registration unit is The system estimates the user's emotions and adjusts the timing of basic information registration based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned registration unit is Analyze the user's past registration history and select the optimal registration method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned registration unit is When registering basic information, filtering is performed based on the user's current health status and dietary restrictions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned registration unit is The system estimates the user's emotions and determines the priority of basic information to register based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned registration unit is When registering basic information, the system prioritizes registering highly relevant information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned registration unit is When registering basic information, the system analyzes the user's social media activity and registers relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is It estimates the user's emotions and adjusts the profile generation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When generating a profile, adjust the level of detail based on the user's health goals. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating a profile, different generation algorithms are applied depending on the user's dietary history. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is It estimates the user's emotions and adjusts the frequency of profile generation based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is When generating profiles, the generation priority is determined based on the user's lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is When generating profiles, we improve the accuracy of the generation by referencing relevant user data. 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 way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the menu and recipes. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making suggestions, different suggestion algorithms are applied depending on the menu or recipe category. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When submitting proposals, prioritize them based on the timing of menu and recipe submissions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When making suggestions, adjust the order of suggestions based on the relevance of the menu and recipes. The system described in Appendix 1, characterized by the features described herein. (Note 25) 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 26) The aforementioned list creation unit, When creating a shopping list, the system analyzes the user's past shopping history to generate the most optimal list. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned list creation unit, When creating a shopping list, customize the list contents based on the user's current meal plan. 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 prioritizes items on the shopping list 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 shopping list, the system generates an optimal list considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned list creation unit, When creating a shopping list, the system analyzes the user's social media activity and suggests list contents. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0183] 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 registration section for registering the user's basic information, A generation unit generates a profile based on the information registered by the registration unit, A suggestion unit that proposes menus and recipes based on the profile generated by the generation unit, It includes a list creation unit that creates a shopping list based on online flyer information. A system characterized by the following features.

2. The aforementioned proposal section is, We suggest menus and recipes that match the user's preferences. The system according to feature 1.

3. The aforementioned list creation unit, Create a shopping list based on online flyer information. The system according to feature 1.

4. The aforementioned proposal section is, Customize menus and shopping lists through interaction with the user. The system according to feature 1.

5. The generating unit is A profile is generated by using learning information from users with similar family structures and living areas. The system according to feature 1.

6. The aforementioned proposal section is, We will use the feedback received after use to improve the quality of future proposals. The system according to feature 1.

7. The aforementioned registration unit is The system estimates the user's emotions and adjusts the timing of basic information registration based on those emotions. The system according to feature 1.

8. The aforementioned registration unit is Analyze the user's past registration history and select the optimal registration method. The system according to feature 1.

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